WO2018214588A1 - Brake energy recovery method and electric vehicle - Google Patents

Brake energy recovery method and electric vehicle Download PDF

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Publication number
WO2018214588A1
WO2018214588A1 PCT/CN2018/073971 CN2018073971W WO2018214588A1 WO 2018214588 A1 WO2018214588 A1 WO 2018214588A1 CN 2018073971 W CN2018073971 W CN 2018073971W WO 2018214588 A1 WO2018214588 A1 WO 2018214588A1
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WO
WIPO (PCT)
Prior art keywords
vehicle
feedback torque
parameter information
state
braking
Prior art date
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PCT/CN2018/073971
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French (fr)
Chinese (zh)
Inventor
郑建锋
曾小华
王广义
朱星宇
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华为技术有限公司
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Publication of WO2018214588A1 publication Critical patent/WO2018214588A1/en

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    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60L—PROPULSION OF ELECTRICALLY-PROPELLED VEHICLES; SUPPLYING ELECTRIC POWER FOR AUXILIARY EQUIPMENT OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRODYNAMIC BRAKE SYSTEMS FOR VEHICLES IN GENERAL; MAGNETIC SUSPENSION OR LEVITATION FOR VEHICLES; MONITORING OPERATING VARIABLES OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRIC SAFETY DEVICES FOR ELECTRICALLY-PROPELLED VEHICLES
    • B60L7/00—Electrodynamic brake systems for vehicles in general
    • B60L7/10—Dynamic electric regenerative braking
    • B60L7/18—Controlling the braking effect
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60L—PROPULSION OF ELECTRICALLY-PROPELLED VEHICLES; SUPPLYING ELECTRIC POWER FOR AUXILIARY EQUIPMENT OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRODYNAMIC BRAKE SYSTEMS FOR VEHICLES IN GENERAL; MAGNETIC SUSPENSION OR LEVITATION FOR VEHICLES; MONITORING OPERATING VARIABLES OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRIC SAFETY DEVICES FOR ELECTRICALLY-PROPELLED VEHICLES
    • B60L7/00—Electrodynamic brake systems for vehicles in general
    • B60L7/10—Dynamic electric regenerative braking
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60L—PROPULSION OF ELECTRICALLY-PROPELLED VEHICLES; SUPPLYING ELECTRIC POWER FOR AUXILIARY EQUIPMENT OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRODYNAMIC BRAKE SYSTEMS FOR VEHICLES IN GENERAL; MAGNETIC SUSPENSION OR LEVITATION FOR VEHICLES; MONITORING OPERATING VARIABLES OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRIC SAFETY DEVICES FOR ELECTRICALLY-PROPELLED VEHICLES
    • B60L7/00—Electrodynamic brake systems for vehicles in general
    • B60L7/10—Dynamic electric regenerative braking
    • B60L7/14—Dynamic electric regenerative braking for vehicles propelled by ac motors
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60L—PROPULSION OF ELECTRICALLY-PROPELLED VEHICLES; SUPPLYING ELECTRIC POWER FOR AUXILIARY EQUIPMENT OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRODYNAMIC BRAKE SYSTEMS FOR VEHICLES IN GENERAL; MAGNETIC SUSPENSION OR LEVITATION FOR VEHICLES; MONITORING OPERATING VARIABLES OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRIC SAFETY DEVICES FOR ELECTRICALLY-PROPELLED VEHICLES
    • B60L2240/00—Control parameters of input or output; Target parameters
    • B60L2240/10—Vehicle control parameters
    • B60L2240/12—Speed

Definitions

  • Embodiments of the present invention relate to the field of electric vehicle technologies, and in particular, to a braking energy recovery method and an electric vehicle.
  • Electric Vehicles have received widespread attention because they use electricity that does not produce pollutants as a driving source and are more environmentally friendly. Since the EV is powered by the energy of the battery, and the most common battery in the EV can only travel more than 100 kilometers, the short driving distance of the EV is the biggest problem that hinders the promotion of the EV.
  • Brake energy recovery includes both the release feedback energy recovery of the accelerator pedal and the brake feedback energy recovery of the brake pedal.
  • the excess energy released by the EV during braking or coasting is converted into electrical energy, which is then stored in the EV battery, thereby recycling the excess energy released to provide driving energy for the subsequent driving of the EV. . It can be seen that braking energy recovery is very important for improving the energy utilization of EV.
  • braking energy recovery is mainly realized by changing the working state of the motor and determining the magnitude of the feedback torque of the motor for braking energy recovery in combination with the real-time running speed.
  • the motor control unit adjusts the operating state of the motor from the driving state to the generating braking state, and then, in combination with the current vehicle speed and the motor speed.
  • the feedback torque of the motor is determined, and the braking energy is converted into the electric energy of the EV battery by the feedback torque to the motor, so as to realize the braking energy recovery.
  • the driver of the EV needs to adjust the feedback torque of the motor by operating the accelerator pedal or the brake pedal to adapt to the current driving.
  • the driver can increase the pedaling depth of the brake pedal, thereby increasing the feedback torque of the motor during the coasting energy recovery to achieve the effect of deceleration in a shorter time;
  • the driver can reduce the stepping depth of the brake pedal, thereby reducing the feedback torque of the motor when the coasting energy is recovered, so as to achieve the effect of extending the coasting distance.
  • the adjustment of the feedback torque of the motor in the prior art is only subjectively judged by the driver by personal experience, which may result in lower accuracy of adjusting the magnitude of the feedback torque of the motor.
  • the embodiment of the invention provides a braking energy recovery method and an electric vehicle for improving the accuracy of adjusting the magnitude of the feedback torque of the motor during braking energy recovery.
  • a braking energy recovery method includes: a braking energy recovery system of a vehicle first collecting speed information of the vehicle within a preset time period, and acquiring the vehicle within a preset time period based on the speed information.
  • the operating parameter information wherein the preset duration is greater than a duration of the vehicle being in any braking state, the braking state is a state in which the vehicle is actively decelerating or not actively accelerating; and then, based on the operating parameter information, predicting The traffic state of the running road on which the vehicle is located.
  • the feedback torque mapping relationship information matching the current traffic state is pre-stored in the braking energy recovery system of the vehicle, so that after determining the current traffic state of the vehicle, the vehicle is calculated according to the feedback torque mapping relationship information.
  • a feedback torque when entering a braking state in a traffic state wherein the feedback torque mapping relationship information includes a mapping relationship between a speed, a braking degree, and a feedback torque of at least one group of vehicles, the feedback torque being used to indicate the vehicle a moment for braking the vehicle when in the braking state, the braking degree characterizing a speed of the vehicle before the braking state and a speed of the vehicle after the braking state; and finally,
  • the braking energy of the vehicle is converted into electrical energy based on the feedback torque, and the electrical energy is stored in the battery of the vehicle.
  • the operating parameter of the vehicle itself when braking energy recovery is performed, can be automatically obtained according to the speed information of the vehicle within a preset time period, and the traffic state of the running road where the vehicle is located is predicted based on the acquired operating parameters.
  • the magnitude of the feedback torque can be adaptively adjusted according to the traffic state. For example, when the traffic state is urban congestion state, the feedback torque can be appropriately increased. When the traffic state is high-speed unblocked state, the feedback torque can be appropriately reduced, and the accuracy of the determined feedback torque can be improved, which can better The recovery of braking energy is performed.
  • predicting a current traffic state of the running road where the vehicle is located based on the operating parameter information including: acquiring at least one characteristic parameter information of the at least one preset traffic state; determining the operating parameter The preset traffic state corresponding to the feature parameter information matched by the information is the current traffic state.
  • a plurality of preset traffic states and feature parameter information corresponding to each preset traffic state are pre-stored in the braking energy recovery system, so that the current operating parameter information of the vehicle and each pre- By setting the characteristic parameter information of the traffic state to perform the matching operation, it can be determined which preset traffic state is most similar to the feature of the current operating parameter information, thereby predicting the running path of the vehicle according to the current operating parameter information of the vehicle. Traffic status, the method is simple.
  • determining the preset traffic state corresponding to the feature parameter information that matches the operation parameter information is the current traffic state, including: acquiring each feature parameter information of the at least one feature parameter information and a membership function between the value ranges of each feature parameter information; determining feature parameter information matching the operation parameter information according to the membership function.
  • the characteristic parameter information of each preset traffic state is represented by a membership function, so that when the braking energy recovery system acquires the operating parameter information of the vehicle, the operating parameter information and each preset traffic are The membership function of the state is compared to predict the traffic state of the running road described by the vehicle, that is, the operating parameter information of the vehicle is blurred by the membership function, and then the current traffic state of the vehicle is predicted by the fuzzy inference method.
  • the characteristic parameter information includes an average vehicle speed of the vehicle in an interval length from the start of one deceleration to the start of the next deceleration, and the total duration of the vehicle at which the speed is zero during the interval duration and the interval.
  • Obtaining a ratio of the duration and an average acceleration of the vehicle in a state where the speed is not zero during the interval duration acquiring each feature parameter information of the at least one feature parameter information and a value range of each feature parameter information a membership function between: obtaining a first membership function between the average vehicle speed and a range of values of the average vehicle speed, a second membership function between the ratio and a range of values of the ratio, and the average acceleration and a third membership function between the range of values of the average acceleration; wherein the first membership function, the second membership function, and the third membership function are both piecewise functions.
  • the form of the membership function of each feature parameter information in the preset traffic state is given.
  • the form of the membership function of each feature parameter information is not limited to the above enumerated ones.
  • determining, according to the membership function, the feature parameter information that matches the operation parameter information includes: determining a first segment function corresponding to an average vehicle speed in the first membership function in the operation parameter information. a second piecewise function corresponding to the ratio in the second membership function, and a third piecewise function corresponding to the average acceleration in the running parameter information in the third membership function; determining and The first piecewise function, the second piecewise function, and the feature parameter information matched by the third piecewise function.
  • the running parameter information of the vehicle is compared with a plurality of piecewise functions of the established membership function, and a piecewise function matching the running parameter information is determined, thereby predicting the current state of the vehicle according to the piecewise function. Traffic status.
  • determining the preset traffic state corresponding to the feature parameter information that matches the operation parameter information is the current traffic state, including: calculating at least one of the operation parameter information and the at least one feature parameter information a difference value; determining a minimum value of the at least one difference value; determining a preset traffic state corresponding to the minimum value as the current traffic state.
  • calculating a difference value between the operation parameter information and the first feature parameter information of the at least one feature parameter information includes: calculating a difference between the operation parameter information and the first feature parameter information;
  • the first feature parameter information is any one of the at least one feature parameter information;
  • the difference is normalized to obtain a normalized difference;
  • the normalized difference is A weight vector set by the feature parameter information is multiplied to obtain a difference vector; and a modulus value of the difference vector is determined as a difference value between the operation parameter information and the first feature parameter information.
  • a difference between the operating parameter information of the vehicle and the characteristic parameter information in each preset traffic state is calculated, and then the calculated difference is normalized and multiplied by the weight vector.
  • Processing, using the finally obtained processing result as the difference value between the operating parameter information of the vehicle and the characteristic parameter information in each preset traffic state simplifies the calculation amount of the subsequent minimum value of the plurality of difference values.
  • determining a feedback torque of the vehicle in the braking state in the current traffic state including: from at least one feedback torque And obtaining, in the mapping relationship information, the feedback torque mapping relationship information that matches the current traffic state; wherein the at least one preset traffic state and the at least one feedback torque mapping relationship information are in one-to-one correspondence; based on the speed of the vehicle and the vehicle The degree of braking determines the feedback torque of the vehicle from the feedback torque mapping relationship information.
  • each preset traffic state is set with matching feedback torque mapping relationship information, for example, the high-speed unblocked state matches the feedback torque mapping relationship 1, and the urban congestion state and the feedback torque mapping relationship are 2 phases. Matching, so as to determine the feedback torque of the vehicle under different traffic states according to different feedback torque mapping relationships, so that the feedback torque is more in line with the current traffic state.
  • determining a feedback torque of the vehicle in the braking state in the current traffic state including: from at least one feedback torque And obtaining, in the mapping relationship information, the feedback torque mapping relationship information that matches the current traffic state; wherein the at least one preset traffic state and the at least one feedback torque mapping relationship information are in one-to-one correspondence; determining from the feedback torque mapping relationship information a feedback torque corresponding to the speed of the vehicle and the degree of braking of the vehicle; determining a first maximum feedback torque supported by the motor when the vehicle is in the braking state; determining a second maximum feedback torque supported by the battery at the current time Determining a minimum of a feedback torque corresponding to a speed of the vehicle and a degree of braking of the vehicle, the first maximum feedback torque, and the second maximum feedback torque is the feedback torque of the vehicle.
  • the feedback torque mapping relationship information matched with the current traffic state, the maximum braking torque that the battery can support, and the maximum braking torque supported by the motor are combined to determine the current vehicle.
  • the feedback torque in the traffic state makes the determined feedback torque more in line with the performance index of the vehicle.
  • determining a feedback torque of the vehicle in the braking state in the current traffic state including: from at least one feedback torque And obtaining, in the mapping relationship information, the feedback torque mapping relationship information that matches the current traffic state; wherein the at least one preset traffic state and the at least one feedback torque mapping relationship information are in one-to-one correspondence; determining from the feedback torque mapping relationship information a feedback torque corresponding to the speed of the vehicle and the degree of braking of the vehicle; determining a first maximum feedback torque supported by the motor when the vehicle is in the braking state; determining a second maximum feedback torque supported by the battery at the current time Determining a braking intention of a driver of the vehicle based on at least one of a gear state of the vehicle, an active deceleration state, an active acceleration state, and an anti-lock braking state; based on the driver's braking intention, a feedback torque corresponding to the speed of the vehicle and the degree of braking of the vehicle
  • the feedback torque mapping relationship information matched with the current traffic state, the maximum braking torque that the battery can support, the maximum braking torque supported by the motor, and the braking intention of the driver of the vehicle are combined.
  • Four factors are used to determine the feedback torque of the vehicle in the current traffic state, so that the determined feedback torque can meet the driving needs of the driver of the vehicle, making the braking energy recovery system more intelligent.
  • an electric vehicle including: a controller, an electric motor, and a battery, the module included in the electric vehicle for performing the braking energy recovery method described in the first aspect.
  • a computer storage medium in a third aspect, storing instructions that, when executed on a computer, cause the computer to perform the methods described in the above aspects.
  • a computer program product comprising instructions for causing a computer to perform the methods described in the above aspects when the instructions are run on a computer.
  • the braking energy recovery scheme provided by the embodiment of the present invention, when braking energy recovery is performed, it is not necessary to add an additional information detecting device or sensor, and the traffic state obtained by the vehicle according to the vehicle speed is determined, and the traffic state is determined. After the current traffic state, adaptively adjusting the magnitude of the feedback torque according to the current traffic state can avoid the inaccuracy of adjusting the magnitude of the feedback torque only by the driver's experience, and try to avoid excessive feedback torque or The energy utilization rate caused by being too small is low, and the effect of adjusting the accuracy of the feedback torque of the motor and improving the utilization of energy can be achieved with a lower hardware cost.
  • FIG. 1 is a schematic structural view of a brake energy recovery system in the prior art
  • FIG. 2 is a flowchart of a braking energy recovery method according to an embodiment of the present invention.
  • 3A is a schematic diagram of a first triangular membership function of an average running vehicle speed according to an embodiment of the present invention
  • FIG. 3B is a schematic diagram of a second triangular membership function of a parking time ratio according to an embodiment of the present invention
  • 3C is a schematic diagram of a third triangular membership function of an average acceleration according to an embodiment of the present invention.
  • FIG. 4 is a structural diagram of a simplified competitive neural network used in an embodiment of the present invention.
  • FIG. 5 is a schematic diagram of a calculation method for applying a simplified competitive neural network to predicting a current traffic state according to an embodiment of the present invention
  • FIG. 6 is a flow chart of a method for calculating a feedback torque of an EV in a braking state by a braking energy recovery system in combination with a braking intention of a driver of an EV according to an embodiment of the present invention
  • FIG. 7 is a schematic structural diagram of an electric vehicle according to an embodiment of the present invention.
  • FIG. 8 is a schematic diagram of a specific implementation of the method shown in FIG. 2 in the electric vehicle shown in FIG. 7 according to an embodiment of the present invention.
  • FIG. 9 is a block diagram of a possible structure of an electric vehicle according to an embodiment of the present invention.
  • the term "and/or" in this document is merely an association relationship describing an associated object, indicating that there may be three relationships, for example, A and/or B, which may indicate that A exists separately, and A and B exist at the same time. There are three cases of B alone.
  • the character "/” in this article unless otherwise specified, generally indicates that the contextual object is an "or” relationship.
  • FIG. 1 is a schematic structural diagram of a brake energy recovery system in the prior art.
  • the brake energy recovery system includes a vehicle controller 10, a motor controller 20, an electric motor 30, and a battery 40.
  • the braking energy recovery system may be an integral part of the EV, for example, it may be a Battery Electric Vehicle (BEV) or a Plug-in Hybrid Electric Vehicle (PHEV).
  • BEV Battery Electric Vehicle
  • PHEV Plug-in Hybrid Electric Vehicle
  • the component may also be part of another vehicle driven by an electric motor, or the braking energy recovery system may be the vehicle itself, such as the EV itself or the PHEV itself, and is not limited herein.
  • the Vehicle Management System (VMS) 10 is the core control component of the entire vehicle.
  • the vehicle controller collects the signals generated by the accelerator pedal signal, the brake pedal signal and other components of the vehicle, and after making corresponding judgments, controls the actions of other components to realize vehicle drive control, energy optimization control, brake feedback control, etc. Features.
  • a motor controller (MCU) 20 controls the motor 30 to operate in accordance with a set direction, speed, angle, and response time by an integrated circuit connected thereto.
  • the motor 30 generally has two functions during operation of the vehicle: when the vehicle is in a starting state or an acceleration state, the motor 30 provides a driving force for the vehicle; when the vehicle is in a braking energy recovery state, due to the operation of the motor 30, According to the right-hand rule, it is known that the coil generates an electromotive force in a direction that hinders the change of the magnetic flux, so that the motor 30 functions as a generator, thereby realizing recovery of braking energy.
  • a battery 40 is provided for supplying drive energy to the motor controller 20.
  • the electric motor 30 converts the braking energy of the vehicle into electric energy of the battery 40 according to the feedback torque, and stores it in the battery 40.
  • the vehicle controller 10 is used to monitor the operating state of various components in the vehicle and finally determine the feedback torque for braking energy recovery, by which the torque is applied to the motor 30.
  • the anti-drag can convert the braking energy into electrical energy and store the electrical energy in the battery 40 to complete the braking energy recovery.
  • the motor controller 20 is used to control the current operating state of the motor 30, for example, the motor 30 can be controlled to be in a driving state or a power generating braking state.
  • the motor 30 operates in accordance with the motor controller 20.
  • the driving state of the electric motor 30 is a state in which driving energy is supplied to the vehicle, and the power generating braking state of the electric motor is a state in which braking energy recovery is performed according to the feedback torque to convert the braking energy of the vehicle into electric energy.
  • an accelerator pedal is used to increase the traveling speed of the vehicle.
  • the accelerator pedal opening degree is 0; when the accelerator pedal is stepped on the bottom end by the driver, the accelerator pedal opening degree is 100%.
  • the accelerator pedal When the driver steps on the accelerator pedal, the accelerator pedal generates a throttle signal that carries the opening degree of the accelerator pedal and transmits it to the vehicle controller 10.
  • the brake pedal is used to reduce the running speed of the vehicle.
  • the opening degree of the brake pedal is 0, when the brake pedal is stepped on the bottom end by the driver, The brake pedal has an opening of 100%.
  • the brake pedal When the driver steps on the brake pedal, the brake pedal generates a brake signal carrying the opening degree of the brake pedal and transmits it to the vehicle controller 10.
  • the vehicle controller 10 first determines whether the vehicle is in the braking state by collecting the throttle signal and the braking signal. If the vehicle controller 10 does not detect the braking signal, and the detected accelerator signal indicates that the opening of the accelerator pedal is less than If the threshold is set, the driver is considered to have the desire to reduce the vehicle speed or stop. Then, the vehicle controller 10 acquires the current rotational speed of the motor 30, the current state of charge of the battery 40, and the maximum allowable charging current of the battery 40 through the motor controller 20. Further, according to the current rotational speed of the motor 30, the current state of charge of the battery 40, and the maximum allowable charging current of the battery 40, the feedback torque currently used by the vehicle for braking energy recovery is calculated, and the feedback torque command is generated by the vehicle controller 10, and the vehicle is completed.
  • the controller 10 sends the feedback torque command to the motor controller 20, the feedback torque command is used to indicate the calculated feedback torque, the motor controller 20 controls the motor 30 to generate power by using the feedback torque, and the motor 3 transmits the generated electric energy to The battery 40 is used to charge the battery 40.
  • the vehicle controller 10 determines the expected feedback torque of the brake pedal at the current opening degree according to the opening degree of the brake pedal in the brake signal, and acquires the current current of the motor 30 through the motor controller 20.
  • the maximum allowable charging current, calculating the feedback torque currently used by the vehicle for braking energy recovery, and generating a feedback torque command is sent to the motor controller 20, the feedback torque command is used to indicate the calculated feedback torque, and the motor controller 20 controls the motor 30.
  • the feedback torque is used to generate electricity, and the electric motor 30 transmits the generated electric energy to the battery 40 to realize charging of the battery 40.
  • the vehicle controller 10 when the vehicle controller 10 detects that the driver releases the accelerator pedal, that is, the vehicle is in the coasting feedback energy recovery state, the magnitude of the feedback torque is determined mainly according to the vehicle speed.
  • the vehicle controller 10 detects that the driver is stepping on the brake pedal, that is, the vehicle is in the braking energy recovery state, the braking feedback torque is determined according to the opening degree of the brake pedal and the vehicle speed.
  • the driver's operation of the accelerator pedal or the brake pedal only affects part of the brake energy recovery, and the brake energy recovery may be affected by other factors. For example, the feedback torque of the brake energy recovery is also affected by the traffic state.
  • Influence when the vehicle is driving in a congested situation, because the distance between the vehicles is relatively close, if the driver depresses the brake pedal, the driver expects to decelerate in the shortest time. Increase the feedback torque of the motor to shorten the braking time, or when the vehicle is driving on a smooth highway, because the distance between the vehicles is large, at this time, if the driver steps on the brake pedal, the driver The staff expects to extend the sliding distance to avoid deceleration too fast and cause the accelerator pedal to be stepped on again. At this time, it is reasonable to reduce the feedback torque of the motor to increase the braking time.
  • the braking energy recovery method described above is only related to the vehicle speed and the operation of the accelerator pedal or the brake pedal by the driver, and the operation of the accelerator pedal or the brake pedal by the driver is judged only by personal experience, thereby causing the feedback torque.
  • the size of the adjustment is less accurate.
  • the embodiment of the present invention further provides a braking energy recovery method, which can automatically acquire the operating parameters of the vehicle according to the speed information of the vehicle within a preset time period when performing braking energy recovery, and based on the acquired
  • the operating parameter predicts the traffic state of the running road on which the vehicle is located, so that the magnitude of the feedback torque can be adaptively adjusted according to the traffic state. For example, when the traffic state is an urban congestion state, the feedback torque can be appropriately increased, and when the traffic state is high speed When the state is unblocked, the feedback torque can be appropriately reduced, and the inaccuracy of adjusting the magnitude of the feedback torque by the driver's experience alone is avoided, and the accuracy of the determined feedback torque is improved accordingly, which is better.
  • the braking energy is recovered.
  • the magnitude of the feedback torque can be adaptively adjusted according to the traffic state when braking energy recovery is performed, it is avoided as much as possible that the accelerator pedal needs to be depressed again due to excessive feedback torque.
  • the loss of electrical energy is a waste of energy from the driving force provided by the vehicle. It is also possible to avoid the problem that the braking energy recovery is insufficient due to the feedback torque being too small, thereby improving the energy utilization rate of the vehicle.
  • the traffic state obtained by the vehicle is directly determined according to the motion parameter acquired by the vehicle's own vehicle speed, and no additional information detecting device or sensor is needed to detect the external environment parameter, so that the determined traffic state conforms to the current state.
  • the actual situation saves hardware costs.
  • FIG. 2 is a flowchart of a braking energy recovery method according to an embodiment of the present invention.
  • the method may be implemented by using a braking energy recovery system as shown in FIG. 1 , and the method may include the following steps:
  • the braking energy recovery system on the vehicle acquires operating parameter information of the vehicle within a preset time period based on the speed information of the vehicle within a preset time period.
  • the braking energy recovery system of the vehicle When the vehicle performs braking energy recovery, the braking energy recovery system of the vehicle first acquires speed information of the vehicle within a preset time period, and then acquires operating parameter information of the vehicle within the preset time length based on the acquired speed information.
  • the braking state is a state in which the vehicle is actively decelerating or not actively accelerating.
  • the active deceleration state may be a state when the driver of the vehicle steps on the brake pedal of the vehicle.
  • the active deceleration state may be The state of the vehicle when the driver controls the deceleration of the vehicle by voice command is not exemplified here.
  • the inactive acceleration state may be a state in which the accelerator pedal of the vehicle is in a state of being released, that is, a state in which the driver of the vehicle does not step on the accelerator pedal, for example, may be in a state when the vehicle is coasting, and of course, if the vehicle does not have acceleration
  • the pedal for example, an EV controlled by voice
  • the active deceleration state may be a state in which the vehicle does not control the deceleration or acceleration of the vehicle by a voice command, and is not exemplified herein.
  • the vehicle will be described in detail as an EV having a brake pedal and an accelerator pedal.
  • the preset duration may be determined in advance, so that only the pre-acquisition is obtained. Set the speed of the vehicle within the length of time.
  • set t is an integer multiple of the average transit time of a city peak hour from a traffic light intersection to the next traffic light intersection.
  • preset duration is not limited in the embodiment of the present invention.
  • the vehicle speed v After acquiring the vehicle speed v of the EV within the preset time period, the vehicle speed v is used to acquire the operating parameter information of the vehicle.
  • the operating parameter information includes any one or more of the following: average vehicle speed V Avg , average running vehicle speed V1 Avg , parking time ratio ⁇ , average running acceleration A Avg , average acceleration A1 Avg, and average deceleration A2 Avg .
  • the average running speed V1 Avg represents the average speed of the EV in the time when the speed of the EV is not zero within the preset time period
  • the average running acceleration A Avg represents the average EV in the time when the speed of the EV is not zero within the preset time period. Acceleration, of course, the operating parameter information may also include other parameter information, which is not limited in the embodiment of the present invention.
  • Average running acceleration A Avg Where t' is the moment when the speed of the EV is not zero within the preset duration;
  • Average acceleration A1 Avg Where t acc is the moment when the EV enters the acceleration state;
  • Average deceleration A2 Avg Where t dec is the time when the EV is decelerating;
  • the above calculation method is only an example, and the embodiment of the present invention does not limit the manner in which the operation parameter information is obtained. From the above calculation, the operating parameter vector [V Avg , V1 Avg , ⁇ , A Avg , A1 Avg , A2 Avg ] of the EV within the preset duration is obtained, and all parameters in the running parameter vector are not necessarily all used, for example, It is possible to use only A1 Avg , A2 Avg , as to which parameters are selected as part of the operating parameter vector, which is not limited in the embodiment of the present invention.
  • S202 The braking energy recovery system predicts a current traffic state of the running road where the vehicle is located based on the operating parameter information.
  • the braking energy recovery system needs to store a plurality of preset traffic states and operating parameter information corresponding to each preset traffic state. That is, a set of operational parameter information is used to represent a preset traffic state, and the braking energy recovery system can store only the identification information of each preset traffic state.
  • each preset traffic state can be numbered, and the identification information is That is, the number of each preset traffic state, so that the operating parameter information corresponding to each preset traffic state stored in the braking energy recovery system is a set of operating parameter information corresponding to a number, so that the braking energy recovery system is based on The number or a set of operating parameter information can distinguish each preset traffic state.
  • the operation parameter information corresponding to the preset traffic state in the embodiment of the present invention includes, but is not limited to, the operating parameter information corresponding to the preset preset traffic state, which can accurately reflect the running state of the EV in different preset traffic states. Obtained as follows:
  • Collect a large amount of real running data in advance For example, the real-time vehicle speed of the EV at multiple times in one month is collected, or the EV of each vehicle type is used as an acquisition target, and the real-time vehicle speed of each type of EV at multiple times in one month is collected.
  • the real-time vehicle speed of each type of EV at multiple times in one month is collected.
  • the value of the feature parameter information of each kinematic segment is calculated, wherein each kinematic segment is a period of time from the start of one idle speed to the start of the next idle speed.
  • the cluster analysis method is used to analyze the difference value between the values of the characteristic parameter information of each kinematic fragment, and the plurality of sets of characteristic parameter information whose difference value is smaller than the preset threshold is divided into one category, and each category is a vehicle.
  • a preset traffic state For example, the characteristic parameter information of all kinematic segments can be divided into five categories, namely urban congestion, urban smooth flow, suburban low-speed, suburban high-speed and high-speed state.
  • the feature parameter information of the kinematic segments may be further classified into a plurality of categories, which is not limited in the embodiment of the present invention.
  • the embodiment of the present invention further provides another manner of acquiring operating parameter information corresponding to each preset traffic state more efficiently, that is, the following second manner.
  • an application software such as a map or a car network system collects the position information of the EV and the moving speed information to predict the real-time road condition of a certain road segment. Therefore, in the embodiment of the present invention, each of the preset traffic states is acquired.
  • the feature parameters of various road conditions may be acquired by a third party such as a map or a car network system as the feature parameter information of the preset traffic state.
  • the real-time road conditions predicted by third parties such as the Internet of Vehicles or maps may only include road congestion or smooth roads.
  • the manner of acquiring the feature parameter information of the preset traffic state is not limited to the manner described above, and the feature parameter information of each preset traffic state may be the same as the operation parameter information in step S401, for example,
  • the operation parameter information in step S401 includes parameters such as V Avg , V1 Avg , ⁇ , A Avg , A1 Avg , and A2 Avg , and the characteristic parameter information of each preset traffic state also includes V Avg , V1 Avg , ⁇ , A Avg , A1 Avg , and A2 Avg parameters.
  • the parameter parameter information of the preset traffic state may include more parameters than the parameter included in the operation parameter information in step S401.
  • the operation parameter information in step S401 includes two parameters, V Avg and V1 Avg .
  • the characteristic parameter information of the preset traffic state may include parameters such as V Avg , V1 Avg , ⁇ , A Avg , A1 Avg , and A2 Avg .
  • the characteristic parameter information of the preset traffic state may include as many parameters as possible to be able to be compared with various operating parameter information.
  • the parameters of the parameter information of the preset traffic state and the operating parameter information of the preset traffic state are not limited in the embodiment of the present invention.
  • the braking energy recovery system After acquiring the characteristic parameter information of the preset traffic state, the braking energy recovery system stores the feature parameter information corresponding to each preset traffic state.
  • the braking energy recovery system stores an urban congestion state and a high-speed state, wherein the characteristic parameter information corresponding to the urban congestion state is: the parking time is longer than 1/4 of the preset duration, the average vehicle speed is less than 50 kmph, and the average acceleration is less than 0.3m/s 2 ;
  • the characteristic parameter information corresponding to the high speed state is: the parking time is less than 1/10 of the preset duration, the average vehicle speed is greater than 70kmph, and the average acceleration is greater than 0.5m/s 2 .
  • the braking energy recovery system After the braking energy recovery system acquires the operating parameter information of the EV according to the method of S401, the obtained operating parameter information is matched with the characteristic parameter information of various preset traffic states, and the characteristic parameter information matching the operating parameter information corresponds to The default traffic status is the current traffic status of the EV.
  • the braking energy recovery system establishes a membership function between each of the pre-stored feature parameter information and the value range of the feature parameter information, and the membership function may be a triangular membership function or a trapezoidal membership function or a normal state. Type membership function, etc.
  • the characteristic parameter information included in the preset traffic state is the average running speed, the parking time ratio, and the average acceleration introduced in step S401, so that the triangular energy membership function of the average running vehicle speed can be stored in the braking energy recovery system. It is called a first triangular membership function.
  • the brake energy recovery system also stores a triangular membership function of the parking time ratio, which is referred to herein as a second triangular membership function, as shown in FIG. 3B.
  • the triangular membership function of the average acceleration is also stored in the dynamic energy recovery system, which is referred to herein as the third triangular membership function, as shown in FIG. 3C.
  • the membership functions of each feature parameter information are composed of a plurality of different segmentation functions.
  • the first triangular membership function in FIG. 3A is composed of a first partial membership function corresponding to a low speed with a vehicle speed of less than 50 kmph, a second partial membership function corresponding to a medium speed of a vehicle speed greater than 30 kmph and less than or equal to 80 kmph, and a high speed corresponding to a vehicle speed greater than 70 kmph.
  • the three-part membership function is composed; the second triangular membership function in FIG.
  • 3B is composed of a low-parking ratio with a parking ratio of less than 0.12 corresponding to the first partial membership function, a parking ratio of more than 0.11 and less than 0.24, and a second partial membership function corresponding to the parking ratio and parking
  • the third part membership function corresponding to the high parking ratio of the ratio greater than 0.23; the third triangular membership function in FIG.
  • 3C corresponds to the first partial membership function corresponding to the low acceleration of the absolute value of the acceleration less than 0.3, and the absolute value of the acceleration is greater than 0.25 and less than
  • the function is symmetrically symmetrical with the acceleration 0
  • the two membership functions are composed.
  • the operating parameter information is compared with the established membership function to determine a segmentation function corresponding to the operating parameter information. For example, determining that the average vehicle speed in the operating parameter information corresponds to a segmentation function in the first triangular membership function, a corresponding segmentation function of the parking time ratio in the second triangular membership function, and an average acceleration corresponding to the third triangular membership function Segmentation function.
  • the vehicle speed corresponds to the second partial membership function corresponding to the medium speed of the first triangular membership function in FIG. 3A.
  • the current traffic state of the EV is predicted according to the segmentation function.
  • the segmentation function corresponding to the operating parameter of the EV is the first partial membership function corresponding to the low speed in the first triangular membership function, the third partial membership function corresponding to the high parking ratio in the second triangular membership function, and the third triangular membership function.
  • the low acceleration corresponds to the first part of the membership function; and the pre-stored urban congestion state corresponds to the characteristic parameter information: the parking time is longer than 1/4 of the preset duration, the average vehicle speed is less than 50kmph, and the average acceleration is less than 0.3m/s 2 .
  • the corresponding piecewise function is a third partial membership function corresponding to the high parking ratio in the second triangular membership function, a low-speed corresponding first partial membership function in the first triangular membership function, and a low acceleration corresponding in the third triangular membership function.
  • the first part of the membership function; the characteristic parameter information corresponding to the pre-stored high-speed state is: the parking time is less than 1/10 of the preset duration, the average vehicle speed is greater than 70kmph, and the average acceleration is greater than 0.5m/s 2 , and the corresponding segmentation function is The first part of the membership function corresponding to the low parking ratio in the two triangular membership functions, the first three a high-speed corresponding third-part membership function in the shape membership function and a third-part membership function corresponding to the high acceleration in the first triangular membership function, so that the pre-stored urban congestion state matches the current operational parameter information of the EV, thereby The current traffic state of the EV is predicted by the piecewise function.
  • the braking energy recovery system can further establish a fuzzy rule base for predicting the current traffic state according to the preset traffic state and the corresponding feature parameter information, in addition to the membership function.
  • the mapping rule in the fuzzy rule base may be a two-dimensional mapping or a three-dimensional mapping, which is not limited herein. The following is an example in which the mapping rule in the fuzzy rule base is a two-dimensional mapping.
  • the piecewise function of the average running speed V1 Avg is obtained as low speed, medium speed and high speed respectively, and the average acceleration A Avg is a piecewise function of small acceleration, medium acceleration and large acceleration, and the parking time ratio ⁇ It is a piecewise function for low parking ratio, medium parking ratio and high parking ratio. Therefore, the fuzzy rule base is also divided into low speed, medium speed and high speed with average running speed V1 Avg .
  • the average acceleration A Avg is divided into small acceleration and medium acceleration.
  • the large acceleration, the parking time ratio ⁇ is divided into a low parking ratio, a medium parking ratio and a high parking ratio to establish a fuzzy rule, as shown in Table 1 - Table 3.
  • Table 1 is the fuzzy inference rule for predicting the current traffic state based on the average running vehicle speed V1 Avg and the parking time ratio ⁇ when the average acceleration A Avg is a small acceleration.
  • the fuzzy inference rule 1 is shown, and the average acceleration A Avg is when the acceleration, the average running speed V1 Avg parking time and the ratio ⁇ prediction fuzzy inference rules of the current traffic state, hereinafter referred to as a fuzzy inference rule two
  • table 3 is the average acceleration a Avg large acceleration, based on the average operating speed V1 Avg and the parking time ratio ⁇ predict the fuzzy inference rules of the current traffic state, hereinafter referred to as fuzzy inference rule 3.
  • a person skilled in the art can also establish an average acceleration A Avg and an average running speed V1 Avg similar to those of Tables 1 to 3 according to the difference of the parking time ratio ⁇ , which is not limited in the embodiment of the present invention.
  • the current traffic state of the EV can be predicted according to the fuzzy rule corresponding to the different accelerations.
  • Table 1 when the average acceleration of the EV is a small acceleration, if the average running speed of the EV is low speed and the parking time ratio is the medium parking ratio, the current traffic state of the EV is predicted to be a suburban low-speed state; in Table 2, When the average acceleration of the EV is medium acceleration, if the average running speed of the EV is high speed and the parking time ratio is a low parking ratio, the current traffic state of the EV is predicted to be a high speed state; in Table 3, when the average acceleration of the EV is In the case of large acceleration, if the average running speed of the EV is medium speed and the parking time ratio is a high parking ratio, it is predicted that the current traffic state of the EV is an urban unblocked state.
  • the operating parameters of the EV are fuzzified by the membership function, and then the current traffic state of the EV is predicted by the fuzzy rule base and the fuzzy inference method.
  • the embodiment of the present invention may also use a competitive neural network method to compare and determine the operating parameter information of the EV with the characteristic parameter information of the preset traffic state.
  • FIG. 4 it is a structural diagram of a simplified competitive neural network used in an embodiment of the present invention.
  • a plurality of operating parameter information of the braking energy recovery system is first acquired as the input vector of the neural network competition, and then multiplies the input vector and the respective values of the weights of each neuron in a neural network Competitive vector W i, When there are multiple input vectors, each of the plurality of input vectors is multiplied by the weight vector of each neuron, respectively, and the multiplied result is used as the input to the competition layer transfer function.
  • the output of the competition layer transfer function corresponding to the weight vector is 1 to win the competition.
  • the operating parameter information of the vehicle acquired by the braking energy recovery system is used as an input vector of the competitive neural network.
  • the acquired operating parameter information includes the average vehicle speed V Avg , the average running speed V1 Avg , the parking time ratio ⁇ , the average acceleration A1 Avg , and the average deceleration A2 Avg
  • the input vector composed of the operating parameter information is [V Avg , V1 Avg , ⁇ , A1 Avg , A2 Avg ].
  • the neuron in the competitive neural network combines the input vector with a feature vector [V Avg_prei , V1 Avg_prei , ⁇ _prei , A1 Avg_prei , which is composed of characteristic parameter information of each preset traffic state in the known preset traffic state.
  • A2 Avg_prei compares, obtains a difference between the input vector and the feature vector of each preset traffic state, determines a minimum value among the plurality of differences, and the preset traffic state corresponding to the minimum value is the current EV Traffic status.
  • FIG. 5 is a schematic diagram of a calculation method for applying the competitive neural network to predicting the current traffic state according to an embodiment of the present invention.
  • the inverse tangent function (actan) of each absolute value is multiplied by 2/pi for each inverse tangent function, respectively, to obtain a normalized result, and then the normalized result is multiplied by the weight vector W, wherein the weight
  • the absolute value is obtained for each multiplied result, and finally the modulus value of each vector after taking the absolute value is obtained, thereby indicating the current traffic state and the preset traffic state.
  • the difference in size, and finally, the preset traffic state corresponding to the minimum value in the modulus value is determined as the current traffic state of the EV.
  • the braking energy recovery system determines the feedback torque when the vehicle is in the braking state in the current traffic state based on the feedback torque mapping relationship information matched with the current traffic state.
  • the feedback torque is used to indicate a moment for braking the vehicle when the vehicle enters the braking state.
  • the braking torque recovery relationship corresponding to each preset traffic state is stored in advance in the braking energy recovery system.
  • the braking torque mapping relationship 1 corresponding to the urban congestion state and the braking torque mapping relationship 2 corresponding to the high speed state correspond to different braking torque mapping relationships.
  • the braking energy recovery system After the braking energy recovery system determines the current traffic state of the EV, the braking energy recovery system needs to calculate the feedback of the EV in the current traffic state when entering the braking state according to the traffic state and the operating parameter information of the EV. Torque.
  • the feedback torque mapping relationship information includes a mapping relationship between a speed, a braking degree, and a feedback torque of at least one group of vehicles, wherein the braking degree represents a speed before the vehicle enters the braking state and the vehicle enters
  • the degree of braking is characterized by the opening degree of the brake pedal of the EV; of course, those skilled in the art should know that when the EV has other factors capable of generating EV
  • the degree of braking can also be characterized by detecting the state of the brake device, which is not limited herein.
  • any one of the braking torque mapping relationships includes at least a mapping relationship between the vehicle speed, the brake pedal opening degree, and the braking torque, the braking torque.
  • the mapping relationship can be stored in the form of a table or a chart, or it can be stored in other forms. As shown in Table 4, an example of the braking torque mapping relationship corresponding to the urban congestion state stored in a tabular form:
  • the braking torque mapping relationship corresponding to the current traffic state is selected from the plurality of braking torque mapping relationships to calculate the entry speed of the EV under the current traffic state.
  • the feedback torque in the moving state is selected from the plurality of braking torque mapping relationships to calculate the entry speed of the EV under the current traffic state.
  • the braking energy recovery system can calculate the feedback torque when entering the braking state in the current traffic state, and can have the following three situations:
  • the braking energy recovery system determines the braking torque mapping relationship corresponding to the current traffic state
  • the speed information of the EV acquired when the EV enters the braking state and the opening degree of the brake pedal of the EV are checked by the system.
  • the dynamic torque mapping relationship directly obtains the feedback torque at the current moment, thereby easily and quickly determining the feedback torque for braking energy recovery.
  • the current traffic state of the EV is an urban congestion state
  • the braking torque mapping relationship corresponding to the urban congestion state is Table 4.
  • the braking energy recovery system acquires a vehicle speed of 50 kmph when the EV enters the braking state, and the brake pedal The opening is 20%, so by querying Table 4, it is determined that the current feedback torque of the EV is 250 Nm.
  • the second embodiment of the present invention provides a second way of calculating the feedback torque. In the second mode, the maximum braking torque that the EV battery can support and the maximum braking torque supported by the EV motor. As a factor in determining the current feedback torque of the EV.
  • the braking energy recovery system determines the braking torque mapping relationship corresponding to the current traffic state, first, according to the speed information of the EV acquired when the EV enters the braking state and the opening degree of the brake pedal of the EV, the inquiry system is adopted.
  • the dynamic torque mapping relationship obtains the initial braking torque at the current moment, and then determines the maximum braking torque that the EV motor can support when entering the braking state and the maximum braking torque that the EV battery can support.
  • the minimum value is selected as the feedback torque of the EV from the initial braking torque, the maximum braking torque that the EV motor can support, and the maximum braking torque that the EV battery can support.
  • the embodiment of the present invention provides a third way of calculating the feedback torque, and in the third mode, the driver of the EV is braked. The intention is to determine the influencing factor of the current feedback torque of the EV.
  • the braking energy recovery system determines the braking torque mapping relationship corresponding to the current traffic state, first, according to the speed information of the EV acquired when the EV enters the braking state and the opening degree of the brake pedal of the EV, the inquiry system is adopted.
  • the dynamic torque mapping relationship obtains the initial braking torque at the current moment.
  • the braking energy recovery system is based on the gear state, the active deceleration state, the active acceleration state, and the anti-lock braking system (Antilock) when the EV enters the braking state.
  • Antilock anti-lock braking system
  • Any one or more factors in the Brake System, ABS determine the braking intention of the driver of the EV, and finally the motor can support the braking intention according to the driver, the initial braking torque, and the EV when entering the braking state.
  • the maximum braking torque and the maximum braking torque that the battery can support when the EV enters the braking state determine the feedback torque of the EV.
  • FIG. 6 is a flow chart of a method for calculating the feedback torque of the EV in the braking state by the braking energy recovery system by combining the braking intention of the driver of the EV.
  • T Bat is the real-time maximum feedback torque allowed by the battery.
  • Absolute value T M is the absolute value of the real-time maximum feedback torque allowed by the motor
  • T is the feedback torque of the EV.
  • the active deceleration state can be characterized by detecting the opening degree of the brake pedal of the EV
  • the active acceleration state can be characterized by detecting the opening degree of the accelerator pedal of the EV.
  • the active deceleration can also be characterized by other means. State and active acceleration state are not limited here.
  • the process of determining the feedback torque is as follows:
  • the braking energy recovery system determines whether the gear position of the EV is reverse (R, R) or forward (Drive, D). If it is not R/D, the driver is considered to have no feedback intention. At this time, it is determined that the feedback torque of the EV is 0;
  • the brake energy recovery system further determines whether the brake pedal is in a depressed state, that is, whether the brake energy recovery system detects the brake signal, and if the brake energy recovery system detects the brake signal , it is determined that the brake pedal is in the depressed state; if the brake energy recovery system does not detect the brake signal, it is determined that the brake pedal is in the relaxed state. If the brake pedal is depressed and the ABS or the Electronic Stability Program (ESP) is not activated, the brake recovery system will support the maximum braking torque from the initial braking torque and the EV when entering the braking state. The dynamic torque and the maximum braking torque that the battery can support when the EV enters the braking state determine the minimum value as the feedback torque of the EV.
  • ESP Electronic Stability Program
  • the braking energy recovery system can also store the mapping relationship between the state of the ABS system and the braking torque or the relationship between the state of the ESP system and the braking torque, so that the state of the ABS system or the state of the ESP system can be determined.
  • a braking torque, the other braking torque and the initial braking torque, the maximum braking torque that the motor can support when the EV enters the braking state, and the maximum braking torque that the battery can support when the EV enters the braking state Comparing, determining the minimum of the four data as the feedback torque of the EV;
  • the braking energy recovery system determines that the EV feedback torque is 0, wherein the braking energy recovery system determines whether the accelerator pedal is in a depressed state, that is, whether the brake energy recovery system detects the throttle signal, and if the brake energy recovery system detects the throttle signal, determining that the accelerator pedal is in the depressed state; If the brake energy recovery system does not detect the throttle signal, it is determined that the accelerator pedal is in a relaxed state;
  • the braking energy recovery system performs the calculation of the feedback torque according to the method described in the embodiment of the present invention. .
  • the braking intention of the driver of the EV is combined into the calculation process of determining the feedback torque, and the intelligent adjustment of the feedback torque by the braking energy recovery system is realized.
  • the braking energy recovery system converts braking energy of the vehicle into electrical energy based on a feedback torque, and stores the electrical energy into a battery of the vehicle.
  • each component in the control braking energy recovery system recovers the braking energy of the EV according to the feedback torque, thereby converting the braking energy of the EV into electrical energy stored in the EV.
  • the process of converting the braking energy of the EV into the electric energy by the braking energy recovery system is the same as the conversion method of converting the braking energy into the electric energy in the prior art, and details are not described herein again.
  • the traffic state obtained by the vehicle according to the operating parameter acquired by the vehicle's own vehicle speed is determined, and after determining the current traffic state.
  • the low rate can achieve an effect of adjusting the accuracy of the feedback torque of the motor and improving the utilization of energy with a lower hardware cost.
  • FIG. 7 is a schematic structural diagram of an electric vehicle according to an embodiment of the present invention.
  • the electric vehicle may be a BEV or a Hybrid Electric Vehicle (HEV) or a PHEV or a fuel cell vehicle. , FCEV), etc., the electric vehicle is used to implement some or all of the steps in the method shown in FIG. 2, and the specific configuration may be determined according to actual needs.
  • HEV Hybrid Electric Vehicle
  • FCEV fuel cell vehicle
  • the controller 701 is configured to: acquire, according to the speed information of the electric vehicle within a preset time period, the electric vehicle at the preset The operating parameter information in the duration; wherein the preset duration is greater than the length of the electric vehicle in any braking state, the braking state is a state in which the electric vehicle is actively decelerating or not actively accelerating; based on the operating parameter information Predicting a current traffic state of the running road on which the electric vehicle is located; determining feedback of the electric vehicle when the braking state is in the current traffic state based on the feedback torque mapping relationship information matching the current traffic state a torque; wherein the feedback torque mapping relationship information includes a mapping relationship between a speed, a braking degree, and a feedback torque of the at least one group of electric vehicles, the feedback torque is used to indicate that the electric vehicle is used when the braking state is in the braking state a torque that produces a braking effect on the electric vehicle, the degree
  • FIG. 8 is a schematic diagram of a specific implementation of the method shown in FIG. 2 for the electric vehicle shown in FIG. 7 .
  • the controller 701 acquires operation parameter information of the electric vehicle within a preset time period based on the speed information of the electric vehicle within a preset time period;
  • the controller 701 predicts a current traffic state of the running road where the electric vehicle is located based on the operation parameter information;
  • the controller 701 determines a feedback torque when the electric vehicle is in a braking state in a current traffic state, based on the feedback torque mapping relationship information matched with the current traffic state.
  • S804 The controller 701 sends the feedback torque to the motor 702, and the motor 702 receives the feedback torque command.
  • a feedback torque command is generated, by which the feedback torque is commanded to the motor 702, and the motor 702 is controlled to enter the feedback power generation state.
  • the motor 702 converts braking energy into electrical energy based on the feedback torque.
  • the motor 702 After receiving the feedback torque command sent by the controller 701, the motor 702 adjusts the operating state of the motor 702 to the generating braking state, and converts the braking energy into electrical energy according to the feedback torque in the command.
  • the electric motor 702 transmits the converted electric energy to the storage battery 703 for storage when converting the braking energy into electric energy, thereby completing the process of braking energy recovery.
  • FIG. 9 is a block diagram showing a possible structure of an electric vehicle according to an embodiment of the present invention.
  • the electric vehicle includes an acquisition unit 901, a determination unit 902, a calculation unit 903, and an execution unit 904.
  • the physical device corresponding to the collecting unit 901, the determining unit 902, and the calculating unit 903 may be the controller 701 in FIG. 7, and the physical device corresponding to the executing unit 904 may be the motor 702 and the battery 703 in FIG.
  • the embodiment of the invention is not limited.
  • the electric vehicle in the embodiment of the present invention can be used to perform the method provided in the embodiment shown in FIG. 2 above.
  • the functions and functions implemented by the modules in the electric vehicle reference may be made to the description of the previous method section, and More details.
  • the disclosed system and method may be implemented in other manners.
  • the system embodiment described above is merely illustrative.
  • the division of the vehicle controller or the motor controller is only a logical function division, and the actual implementation may have another division manner, for example, The vehicle controller and motor controller are integrated into one structure, or some features can be ignored or not executed.
  • the coupling or direct coupling or communication connection between the components shown or discussed may be through some interfaces, indirect coupling or communication connections of the various components, and may be electrical or otherwise.
  • the invention may be implemented in whole or in part by software, hardware, firmware or any combination thereof.
  • software it may be implemented in whole or in part in the form of a computer program product.
  • the computer program product includes one or more computer instructions.
  • the computer program instructions When the computer program instructions are loaded and executed on a computer, the processes or functions described in accordance with embodiments of the present invention are generated in whole or in part.
  • the computer can be a general purpose computer, a special purpose computer, a computer network, or other programmable device.
  • the computer instructions can be stored in a computer readable storage medium or transferred from one computer readable storage medium to another readable storage medium, for example, the computer instructions can be passed from a website site, computer, server or data center Wired (eg, coaxial cable, fiber optic, digital subscriber line (DSL)) or wireless (eg, infrared, wireless, microwave, etc.) to another website site, computer, server, or data center.
  • the computer readable storage medium can be any available media that can be accessed by a computer or a data storage device such as a server, data center, or the like that includes one or more available media.
  • the usable medium may be a magnetic medium (eg, a floppy disk, a hard disk, a magnetic tape), an optical medium (eg, a DVD), or a semiconductor medium (eg, a solid state disk (SSD)) or the like.
  • a magnetic medium eg, a floppy disk, a hard disk, a magnetic tape
  • an optical medium eg, a DVD
  • a semiconductor medium eg, a solid state disk (SSD)

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Abstract

Disclosed is a brake energy recovery method, comprising: a brake energy recovery system of a vehicle acquiring travelling parameter information about the vehicle in a pre-set period, based on speed information about the vehicle in the pre-set period, wherein the pre-set period is longer than any period where the vehicle is in a braking state, the braking state being a state where the vehicle is actively decelerating or not actively accelerating; predicting a current traffic state of a travelling road where the vehicle is located, based on the travelling parameter information; determining a feedback torque when the vehicle is in a braking state in the current traffic state, based on mapping relationship information about the feedback torque matching the current traffic state; and when the vehicle is in a braking state, converting braking energy of the vehicle into electric energy based on the feedback torque, and storing the electric energy in a storage battery (40) of the vehicle. An electric vehicle using the method is also disclosed. According to the braking energy recovery method, it is possible to adaptively adjust the magnitude of the feedback torque according the current traffic state, so as to avoid inaccuracies when the driver adjusts the feedback torque only depending on their experience, and to avoid a low energy utilization ratio caused by the feedback torque being too high or too low. It is possible to improve the accuracy of adjusting the magnitude of the feedback torque of the motor and to improve the utilization ratio of energy at a lower hardware cost.

Description

一种制动能量回收方法及电动汽车Braking energy recovery method and electric vehicle
本申请要求于2017年5月26日提交中国专利局、申请号为201710386963.3、申请名称为“一种制动能量回收方法及电动汽车”的中国专利申请的优先权，其全部内容通过引用结合在本申请中。This application claims the priority of the Chinese Patent Application entitled "A Brake Energy Recovery Method and Electric Vehicle" filed on May 26, 2017 by the Chinese Patent Office, Application No. 201710386963.3, the entire contents of which are incorporated by reference. In this application.
技术领域Technical field
本发明实施例涉及电动汽车技术领域，尤其涉及一种制动能量回收方法及电动汽车。Embodiments of the present invention relate to the field of electric vehicle technologies, and in particular, to a braking energy recovery method and an electric vehicle.
背景技术Background technique
随着环境保护、能源安全等问题的重要性日益显著，电动汽车(Electric Vehicle，EV)因其使用不产生污染物的电能作为驱动能源，较为绿色环保，因而受到了广泛关注。由于EV靠电池的能量提供动力，而EV中最通用的电池充一次电也只能行驶一百多公里，因此，EV的行驶里程短是阻碍EV推广的最大问题。With the increasing importance of environmental protection and energy security issues, Electric Vehicles (EVs) have received widespread attention because they use electricity that does not produce pollutants as a driving source and are more environmentally friendly. Since the EV is powered by the energy of the battery, and the most common battery in the EV can only travel more than 100 kilometers, the short driving distance of the EV is the biggest problem that hinders the promotion of the EV.
可以想到的是，提高EV的能量储备，以及提高EV对于能量的利用率是解决EV行驶里程短的问题的主要手段。然而EV的能量储备是由蓄电池提供，目前，蓄电池技术因受到安全性、经济性等因素的制约，技术进展缓慢，因此，提高EV的能量利用率就变得至关重要。It is conceivable that increasing the energy reserve of the EV and increasing the utilization of energy by the EV are the main means for solving the problem of short EV mileage. However, the energy reserve of the EV is provided by the battery. At present, the battery technology is slowed down due to factors such as safety and economy, so it is crucial to improve the energy utilization rate of the EV.
传统车辆在行驶过程中，当进入制动或者根据惯性滑行时，由于机械制动或者摩擦力，会使车辆的动能转换为热量进而释放，若能将该能量利用起来，则能量利用率自然得到了提高。因此，制动能量回收技术应运而生。制动能量回收包括松开加速踏板的滑行回馈能量回收和踩下制动踏板的制动回馈能量回收两种。通过制动能量回收技术，将EV在制动或惯性滑行中释放出的多余能量转化为电能，再储存在EV的蓄电池中，从而将释放的多余能量回收利用，为EV的后续行驶提供驱动能量。可见，制动能量回收对于提高EV的能量利用率具有非常重要的意义。目前，制动能量回收主要通过改变电动机的工作状态以及结合实时运行车速确定电动机用于制动能量回收的回馈力矩的大小来实现。当EV的整机管理***检测到加速踏板松开或者制动踏板被踩下时，则通过电动机控制单元将电动机的工作状态由驱动状态调整为发电制动状态，然后，结合当前车速以及电动机转速，确定电动机的回馈力矩，通过回馈力矩对电动机的反拖，将制动能量转换为EV的蓄电池的电能，以实现制动能量回收。When the traditional vehicle is driving, when it enters the brake or coasts according to the inertia, the kinetic energy of the vehicle is converted into heat and released due to mechanical braking or friction. If the energy can be utilized, the energy utilization rate is naturally obtained. Improved. Therefore, braking energy recovery technology came into being. Brake energy recovery includes both the release feedback energy recovery of the accelerator pedal and the brake feedback energy recovery of the brake pedal. Through the brake energy recovery technology, the excess energy released by the EV during braking or coasting is converted into electrical energy, which is then stored in the EV battery, thereby recycling the excess energy released to provide driving energy for the subsequent driving of the EV. . It can be seen that braking energy recovery is very important for improving the energy utilization of EV. At present, braking energy recovery is mainly realized by changing the working state of the motor and determining the magnitude of the feedback torque of the motor for braking energy recovery in combination with the real-time running speed. When the EV's overall management system detects that the accelerator pedal is released or the brake pedal is depressed, the motor control unit adjusts the operating state of the motor from the driving state to the generating braking state, and then, in combination with the current vehicle speed and the motor speed. The feedback torque of the motor is determined, and the braking energy is converted into the electric energy of the EV battery by the feedback torque to the motor, so as to realize the braking energy recovery.
目前的技术方案虽然实现了制动能量回收，但在进行制动能量回收时，EV的驾驶员需要通过对加速踏板或者制动踏板进行操作来调整电动机的回馈力矩的大小，以适应当前的行驶情况。比如，如果EV在市区拥堵的情况下行驶，驾驶员可以增加对制动踏板的踩踏深度，从而增加滑行回馈能量回收时的电动机的回馈力矩，以实现在较短的时间内减速的效果；而如果EV在市区畅通的情况下行驶，则驾驶员可以减小对制动踏板的踩踏深度，从而减小滑行回馈能量回收时的电动机的回馈力矩，以实现延长滑行距离的效果。可见，现有技术中对电动机的回馈力矩的调节仅由驾驶员凭借个人经验进行主观判断，这样可能会导致对电动机的回馈力矩的大小进行调节的准确性较低。Although the current technical solution realizes braking energy recovery, when performing braking energy recovery, the driver of the EV needs to adjust the feedback torque of the motor by operating the accelerator pedal or the brake pedal to adapt to the current driving. Happening. For example, if the EV is driven in a city congestion situation, the driver can increase the pedaling depth of the brake pedal, thereby increasing the feedback torque of the motor during the coasting energy recovery to achieve the effect of deceleration in a shorter time; On the other hand, if the EV is driven in a smooth urban area, the driver can reduce the stepping depth of the brake pedal, thereby reducing the feedback torque of the motor when the coasting energy is recovered, so as to achieve the effect of extending the coasting distance. It can be seen that the adjustment of the feedback torque of the motor in the prior art is only subjectively judged by the driver by personal experience, which may result in lower accuracy of adjusting the magnitude of the feedback torque of the motor.
因此，如何提高在制动能量回收时对电动机的回馈力矩的大小进行调节的准确性 是目前亟待解决的技术问题。Therefore, how to improve the accuracy of adjusting the magnitude of the feedback torque of the motor during braking energy recovery is a technical problem to be solved.
发明内容Summary of the invention
本发明实施例提供一种制动能量回收方法及电动汽车，用于提高在制动能量回收时对电动机的回馈力矩的大小进行调节的准确性。The embodiment of the invention provides a braking energy recovery method and an electric vehicle for improving the accuracy of adjusting the magnitude of the feedback torque of the motor during braking energy recovery.
第一方面，提供一种制动能量回收方法，该方法包括：车辆的制动能量回收***首先采集该车辆在预设时长内的速度信息，并基于该速度信息获取该车辆在预设时长内的运行参数信息，其中，该预设时长大于该车辆在任意一次处于制动状态的时长，该制动状态为该车辆处于主动减速或不主动加速的状态；然后，基于该运行参数信息，预测该车辆所处的运行道路的交通状态。在该车辆的制动能量回收***中预先存储与当前的交通状态匹配的回馈力矩映射关系信息，从而在确定该车辆当前的交通状态后，则根据该回馈力矩映射关系信息计算该车辆在当前的交通状态下进入制动状态时的回馈力矩，其中，该回馈力矩映射关系信息中包括至少一组车辆的速度、制动程度以及回馈力矩之间的映射关系，该回馈力矩用于指示在该车辆处于该制动状态时用于对该车辆产生制动作用的力矩，该制动程度表征该车辆处于该制动状态前的速度与该车辆处于该制动状态后的速度的变化量；最后，在该车辆处于该制动状态时，基于该回馈力矩将该车辆的制动能量转化为电能，并将该电能存储至该车辆的蓄电池中。In a first aspect, a braking energy recovery method is provided. The method includes: a braking energy recovery system of a vehicle first collecting speed information of the vehicle within a preset time period, and acquiring the vehicle within a preset time period based on the speed information. The operating parameter information, wherein the preset duration is greater than a duration of the vehicle being in any braking state, the braking state is a state in which the vehicle is actively decelerating or not actively accelerating; and then, based on the operating parameter information, predicting The traffic state of the running road on which the vehicle is located. The feedback torque mapping relationship information matching the current traffic state is pre-stored in the braking energy recovery system of the vehicle, so that after determining the current traffic state of the vehicle, the vehicle is calculated according to the feedback torque mapping relationship information. a feedback torque when entering a braking state in a traffic state, wherein the feedback torque mapping relationship information includes a mapping relationship between a speed, a braking degree, and a feedback torque of at least one group of vehicles, the feedback torque being used to indicate the vehicle a moment for braking the vehicle when in the braking state, the braking degree characterizing a speed of the vehicle before the braking state and a speed of the vehicle after the braking state; and finally, When the vehicle is in the braking state, the braking energy of the vehicle is converted into electrical energy based on the feedback torque, and the electrical energy is stored in the battery of the vehicle.
在本发明实施例中，进行制动能量回收时，能够自动根据车辆在预设时长内的速度信息获取车辆自身的运行参数，并基于获取的运行参数预测车辆所处的运行道路的交通状态，从而可以根据交通状态自适应地调整回馈力矩的大小。比如，在交通状态为市区拥堵状态时，可适当增加回馈力矩，当交通状态为高速畅通状态时，则可以适当减小回馈力矩，可以提高所确定的回馈力矩的准确性，能够更好地进行制动能量的回收。In the embodiment of the present invention, when braking energy recovery is performed, the operating parameter of the vehicle itself can be automatically obtained according to the speed information of the vehicle within a preset time period, and the traffic state of the running road where the vehicle is located is predicted based on the acquired operating parameters. Thereby, the magnitude of the feedback torque can be adaptively adjusted according to the traffic state. For example, when the traffic state is urban congestion state, the feedback torque can be appropriately increased. When the traffic state is high-speed unblocked state, the feedback torque can be appropriately reduced, and the accuracy of the determined feedback torque can be improved, which can better The recovery of braking energy is performed.
在一个可能的设计方式中，基于该运行参数信息，预测该车辆所处的运行道路的当前的交通状态，包括：获取至少一个预设交通状态的至少一个特征参数信息；确定与所述运行参数信息相匹配的特征参数信息对应的预设交通状态为所述当前的交通状态。In a possible design manner, predicting a current traffic state of the running road where the vehicle is located based on the operating parameter information, including: acquiring at least one characteristic parameter information of the at least one preset traffic state; determining the operating parameter The preset traffic state corresponding to the feature parameter information matched by the information is the current traffic state.
在本发明实施例中，制动能量回收***中预先存储有多个预设交通状态及与每个预设交通状态对应的特征参数信息，从而只要将该车辆当前的运行参数信息与每个预设交通状态的特征参数信息进行匹配运算，便能确定出哪个预设交通状态与当前的运行参数信息的特征最为相似，从而根据该车辆当前的运行参数信息预测出该车辆所处的运行道路的交通状态，方法简便。In the embodiment of the present invention, a plurality of preset traffic states and feature parameter information corresponding to each preset traffic state are pre-stored in the braking energy recovery system, so that the current operating parameter information of the vehicle and each pre- By setting the characteristic parameter information of the traffic state to perform the matching operation, it can be determined which preset traffic state is most similar to the feature of the current operating parameter information, thereby predicting the running path of the vehicle according to the current operating parameter information of the vehicle. Traffic status, the method is simple.
在一个可能的设计方式中，确定与该运行参数信息相匹配的特征参数信息对应的预设交通状态为该当前的交通状态，包括：获取该至少一个特征参数信息中的每个特征参数信息与该每个特征参数信息的取值范围之间的隶属函数；根据该隶属函数确定与该运行参数信息相匹配的特征参数信息。In a possible design manner, determining the preset traffic state corresponding to the feature parameter information that matches the operation parameter information is the current traffic state, including: acquiring each feature parameter information of the at least one feature parameter information and a membership function between the value ranges of each feature parameter information; determining feature parameter information matching the operation parameter information according to the membership function.
在本发明实施例中，每个预设交通状态的特征参数信息通过隶属函数来表示，从而当制动能量回收***获取该车辆的运行参数信息后，则将运行参数信息与每个预设交通状态的隶属函数进行比较，预测出该车辆所述的运行道路的交通状态，即，通过隶属函数将车辆的运行参数信息模糊化，然后通过模糊推理法，预测车辆当前的交通 状态。In the embodiment of the present invention, the characteristic parameter information of each preset traffic state is represented by a membership function, so that when the braking energy recovery system acquires the operating parameter information of the vehicle, the operating parameter information and each preset traffic are The membership function of the state is compared to predict the traffic state of the running road described by the vehicle, that is, the operating parameter information of the vehicle is blurred by the membership function, and then the current traffic state of the vehicle is predicted by the fuzzy inference method.
在一个可能的设计方式中，在该特征参数信息包括该车辆在一次减速开始到下一次减速开始的间隔时长内的平均车速、该车辆在该间隔时长内处于速度为零的总时长与该间隔时长的比例、以及该车辆在该间隔时长内处于速度不为零的状态内的平均加速度时，获取该至少一个特征参数信息中的每个特征参数信息与该每个特征参数信息的取值范围之间的隶属函数，包括：获取该平均车速与该平均车速的取值范围之间的第一隶属函数、该比例与该比例的取值范围之间的第二隶属函数、以及该平均加速度与该平均加速度的取值范围之间的第三隶属函数；其中，该第一隶属函数、该第二隶属函数及该第三隶属函数均为分段函数。In a possible design manner, the characteristic parameter information includes an average vehicle speed of the vehicle in an interval length from the start of one deceleration to the start of the next deceleration, and the total duration of the vehicle at which the speed is zero during the interval duration and the interval. Obtaining a ratio of the duration and an average acceleration of the vehicle in a state where the speed is not zero during the interval duration, acquiring each feature parameter information of the at least one feature parameter information and a value range of each feature parameter information a membership function between: obtaining a first membership function between the average vehicle speed and a range of values of the average vehicle speed, a second membership function between the ratio and a range of values of the ratio, and the average acceleration and a third membership function between the range of values of the average acceleration; wherein the first membership function, the second membership function, and the third membership function are both piecewise functions.
给出了预设交通状态中的每个特征参数信息的隶属函数的几种形式，在本发明实施例中，每个特征参数信息的隶属函数的形式不限于以上列举的几种。The form of the membership function of each feature parameter information in the preset traffic state is given. In the embodiment of the present invention, the form of the membership function of each feature parameter information is not limited to the above enumerated ones.
在一个可能的设计方式中，根据该隶属函数确定与该运行参数信息相匹配的特征参数信息，包括：确定该运行参数信息中的平均车速在该第一隶属函数中对应的第一分段函数、该运行参数信息中的比例在该第二隶属函数中对应的第二分段函数、以及该运行参数信息中的平均加速度在该第三隶属函数中对应的第三分段函数；确定与该第一分段函数、该第二分段函数以及该第三分段函数相匹配的特征参数信息。In a possible design manner, determining, according to the membership function, the feature parameter information that matches the operation parameter information includes: determining a first segment function corresponding to an average vehicle speed in the first membership function in the operation parameter information. a second piecewise function corresponding to the ratio in the second membership function, and a third piecewise function corresponding to the average acceleration in the running parameter information in the third membership function; determining and The first piecewise function, the second piecewise function, and the feature parameter information matched by the third piecewise function.
在本发明实施例中，将该车辆的运行参数信息与建立的隶属函数的多个分段函数进行比较，确定与该运行参数信息匹配的分段函数，从而根据分段函数预测该车辆当前的交通状态。In the embodiment of the present invention, the running parameter information of the vehicle is compared with a plurality of piecewise functions of the established membership function, and a piecewise function matching the running parameter information is determined, thereby predicting the current state of the vehicle according to the piecewise function. Traffic status.
在一个可能的设计方式中，确定与该运行参数信息相匹配的特征参数信息对应的预设交通状态为该当前的交通状态，包括：计算该运行参数信息与该至少一个特征参数信息的至少一个差异值；确定该至少一个差异值中的最小值；确定与该最小值对应的预设交通状态为该当前的交通状态。In a possible design manner, determining the preset traffic state corresponding to the feature parameter information that matches the operation parameter information is the current traffic state, including: calculating at least one of the operation parameter information and the at least one feature parameter information a difference value; determining a minimum value of the at least one difference value; determining a preset traffic state corresponding to the minimum value as the current traffic state.
在本发明实施例中，通过计算车辆的运行参数信息与每个预设交通状态中的特征参数信息的差异值，确定差异值最小的预设交通状态为该车辆当前的交通状态，从而能够更为准确地预测车辆当前的交通状态。In the embodiment of the present invention, by calculating a difference value between the operating parameter information of the vehicle and the characteristic parameter information in each preset traffic state, determining that the preset traffic state with the smallest difference value is the current traffic state of the vehicle, thereby enabling To accurately predict the current traffic status of the vehicle.
在一个可能的设计方式中，计算该运行参数信息与该至少一个特征参数信息中的第一特征参数信息的差异值，包括：计算该运行参数信息与该第一特征参数信息的差值；该第一特征参数信息为该至少一个特征参数信息中的任意一个特征参数信息；将该差值进行归一化处理，得到归一化的差值；将该归一化的差值与为该第一特征参数信息设置的权重向量相乘，得到差值向量；确定该差值向量的模值为该运行参数信息与该第一特征参数信息的差异值。In a possible design manner, calculating a difference value between the operation parameter information and the first feature parameter information of the at least one feature parameter information includes: calculating a difference between the operation parameter information and the first feature parameter information; The first feature parameter information is any one of the at least one feature parameter information; the difference is normalized to obtain a normalized difference; the normalized difference is A weight vector set by the feature parameter information is multiplied to obtain a difference vector; and a modulus value of the difference vector is determined as a difference value between the operation parameter information and the first feature parameter information.
在本发明实施例中，首先计算车辆的运行参数信息与每个预设交通状态中的特征参数信息之间的差值，然后将计算的差值进行归一化处理以及与权重向量相乘的处理，将最终得到的处理结果作为该车辆的运行参数信息与每个预设交通状态中的特征参数信息之间的差异值，简化了后续求取多个差异值的最小值的计算量。In the embodiment of the present invention, first, a difference between the operating parameter information of the vehicle and the characteristic parameter information in each preset traffic state is calculated, and then the calculated difference is normalized and multiplied by the weight vector. Processing, using the finally obtained processing result as the difference value between the operating parameter information of the vehicle and the characteristic parameter information in each preset traffic state, simplifies the calculation amount of the subsequent minimum value of the plurality of difference values.
在一个可能的设计方式中，基于与该当前的交通状态匹配的回馈力矩映射关系信息，确定该车辆在该当前的交通状态下处于该制动状态时的回馈力矩，包括：从至少一个回馈力矩映射关系信息中获取与该当前的交通状态匹配的回馈力矩映射关系信息；其中，该至少一个预设交通状态与该至少一个回馈力矩映射关系信息一一对应；基于 该车辆的速度及该车辆的制动程度，从该回馈力矩映射关系信息中确定该车辆的所述回馈力矩。In a possible design manner, based on the feedback torque mapping relationship information matching the current traffic state, determining a feedback torque of the vehicle in the braking state in the current traffic state, including: from at least one feedback torque And obtaining, in the mapping relationship information, the feedback torque mapping relationship information that matches the current traffic state; wherein the at least one preset traffic state and the at least one feedback torque mapping relationship information are in one-to-one correspondence; based on the speed of the vehicle and the vehicle The degree of braking determines the feedback torque of the vehicle from the feedback torque mapping relationship information.
在本发明实施例中，每个预设交通状态设置有相匹配的回馈力矩映射关系信息，比如，,高速畅通状态与回馈力矩映射关系1相匹配，市区拥堵状态与回馈力矩映射关系2相匹配，从而根据不同的回馈力矩映射关系确定在该车辆在不同的交通状态下的回馈力矩，使回馈力矩更加符合当前的交通状态。In the embodiment of the present invention, each preset traffic state is set with matching feedback torque mapping relationship information, for example, the high-speed unblocked state matches the feedback torque mapping relationship 1, and the urban congestion state and the feedback torque mapping relationship are 2 phases. Matching, so as to determine the feedback torque of the vehicle under different traffic states according to different feedback torque mapping relationships, so that the feedback torque is more in line with the current traffic state.
在一个可能的设计方式中，基于与该当前的交通状态匹配的回馈力矩映射关系信息，确定该车辆在该当前的交通状态下处于该制动状态时的回馈力矩，包括：从至少一个回馈力矩映射关系信息中获取与该当前的交通状态匹配的回馈力矩映射关系信息；其中，该至少一个预设交通状态与该至少一个回馈力矩映射关系信息一一对应；从该回馈力矩映射关系信息中确定与该车辆的速度及该车辆的制动程度相应的回馈力矩；确定该车辆在该制动状态时，该电动机支持的第一最大回馈力矩；确定该蓄电池在当前时刻支持的第二最大回馈力矩；确定与该车辆的速度及该车辆的制动程度相应的回馈力矩、该第一最大回馈力矩以及该第二最大回馈力矩中的最小值为该车辆的所述回馈力矩。In a possible design manner, based on the feedback torque mapping relationship information matching the current traffic state, determining a feedback torque of the vehicle in the braking state in the current traffic state, including: from at least one feedback torque And obtaining, in the mapping relationship information, the feedback torque mapping relationship information that matches the current traffic state; wherein the at least one preset traffic state and the at least one feedback torque mapping relationship information are in one-to-one correspondence; determining from the feedback torque mapping relationship information a feedback torque corresponding to the speed of the vehicle and the degree of braking of the vehicle; determining a first maximum feedback torque supported by the motor when the vehicle is in the braking state; determining a second maximum feedback torque supported by the battery at the current time Determining a minimum of a feedback torque corresponding to a speed of the vehicle and a degree of braking of the vehicle, the first maximum feedback torque, and the second maximum feedback torque is the feedback torque of the vehicle.
在本发明实施例中，结合了与当前的交通状态匹配的回馈力矩映射关系信息、蓄电池所能支持的最大制动力矩以及电动机所支持的最大制动力矩三个因素来确定该车辆在当前的交通状态下的回馈力矩，使得确定的回馈力矩更加符合该车辆的性能指标。In the embodiment of the present invention, the feedback torque mapping relationship information matched with the current traffic state, the maximum braking torque that the battery can support, and the maximum braking torque supported by the motor are combined to determine the current vehicle. The feedback torque in the traffic state makes the determined feedback torque more in line with the performance index of the vehicle.
在一个可能的设计方式中，基于与该当前的交通状态匹配的回馈力矩映射关系信息，确定该车辆在该当前的交通状态下处于该制动状态时的回馈力矩，包括：从至少一个回馈力矩映射关系信息中获取与该当前的交通状态匹配的回馈力矩映射关系信息；其中，该至少一个预设交通状态与该至少一个回馈力矩映射关系信息一一对应；从该回馈力矩映射关系信息中确定与该车辆的速度及该车辆的制动程度相应的回馈力矩；确定该车辆在该制动状态时，该电动机支持的第一最大回馈力矩；确定该蓄电池在当前时刻支持的第二最大回馈力矩；基于该车辆的档位状态、主动减速状态、主动加速状态以及防抱死制动状态中的至少一种因素，确定该车辆的驾驶员的制动意图；基于该驾驶员的制动意图、与该车辆的速度及该车辆的制动程度相应的回馈力矩、该第一最大回馈力矩以及该第二最大回馈力矩，确定该车辆的该回馈力矩。In a possible design manner, based on the feedback torque mapping relationship information matching the current traffic state, determining a feedback torque of the vehicle in the braking state in the current traffic state, including: from at least one feedback torque And obtaining, in the mapping relationship information, the feedback torque mapping relationship information that matches the current traffic state; wherein the at least one preset traffic state and the at least one feedback torque mapping relationship information are in one-to-one correspondence; determining from the feedback torque mapping relationship information a feedback torque corresponding to the speed of the vehicle and the degree of braking of the vehicle; determining a first maximum feedback torque supported by the motor when the vehicle is in the braking state; determining a second maximum feedback torque supported by the battery at the current time Determining a braking intention of a driver of the vehicle based on at least one of a gear state of the vehicle, an active deceleration state, an active acceleration state, and an anti-lock braking state; based on the driver's braking intention, a feedback torque corresponding to the speed of the vehicle and the degree of braking of the vehicle, the first most Second maximum torque feedback and torque feedback, the feedback is determined moment of the vehicle.
在本发明实施例中，结合了与当前的交通状态匹配的回馈力矩映射关系信息、蓄电池所能支持的最大制动力矩、电动机所支持的最大制动力矩以及该车辆的驾驶员的制动意图四个因素来确定该车辆在当前的交通状态下的回馈力矩，使得确定的回馈力矩能够符合该车辆的驾驶员的驾驶需求，使制动能量回收***更加智能。In the embodiment of the present invention, the feedback torque mapping relationship information matched with the current traffic state, the maximum braking torque that the battery can support, the maximum braking torque supported by the motor, and the braking intention of the driver of the vehicle are combined. Four factors are used to determine the feedback torque of the vehicle in the current traffic state, so that the determined feedback torque can meet the driving needs of the driver of the vehicle, making the braking energy recovery system more intelligent.
第二方面，提供一种电动汽车，该电动汽车包括：控制器、电动机和蓄电池，该电动汽车所包括的模块用于执行第一方面中所述的制动能量回收方法。In a second aspect, an electric vehicle is provided, the electric vehicle including: a controller, an electric motor, and a battery, the module included in the electric vehicle for performing the braking energy recovery method described in the first aspect.
第三方面，提供一种计算机存储介质，计算机存储介质存储有指令，当指令在计算机上运行时，使得计算机执行上述各方面所述的方法。In a third aspect, a computer storage medium is provided, the computer storage medium storing instructions that, when executed on a computer, cause the computer to perform the methods described in the above aspects.
第四方面，提供一种计算机程序产品，计算机程序产品包含有指令，当指令在计算机上运行时，使得计算机执行上述各方面所述的方法。In a fourth aspect, a computer program product is provided, the computer program product comprising instructions for causing a computer to perform the methods described in the above aspects when the instructions are run on a computer.
在本发明实施例提供的制动能量回收方案，进行制动能量回收时，无需增加额外的信息检测装置或传感器，根据车辆自身的车速获取的运行参数确定车辆所处的交通 状态，在确定出当前的交通状态后，根据当前的交通状态自适应地调整回馈力矩的大小，可以避免由于仅凭驾驶员的经验对回馈力矩的大小进行调节的不准确性，也尽量避免由于回馈力矩过大或过小导致的能量利用率低，可以实现以较低的硬件成本，来提高对电动机的回馈力矩的大小进行调节的准确性、提高能量的利用率的效果。In the braking energy recovery scheme provided by the embodiment of the present invention, when braking energy recovery is performed, it is not necessary to add an additional information detecting device or sensor, and the traffic state obtained by the vehicle according to the vehicle speed is determined, and the traffic state is determined. After the current traffic state, adaptively adjusting the magnitude of the feedback torque according to the current traffic state can avoid the inaccuracy of adjusting the magnitude of the feedback torque only by the driver's experience, and try to avoid excessive feedback torque or The energy utilization rate caused by being too small is low, and the effect of adjusting the accuracy of the feedback torque of the motor and improving the utilization of energy can be achieved with a lower hardware cost.
附图说明DRAWINGS
图1为现有技术中的一种制动能量回收***的结构示意图；1 is a schematic structural view of a brake energy recovery system in the prior art;
图2为本发明实施例提供的一种制动能量回收方法的流程图；2 is a flowchart of a braking energy recovery method according to an embodiment of the present invention;
图3A为本发明实施例提供的平均运行车速的第一三角形隶属函数的一种示意图；3A is a schematic diagram of a first triangular membership function of an average running vehicle speed according to an embodiment of the present invention;
图3B为本发明实施例提供的停车时间比例的第二三角形隶属函数的一种示意图；FIG. 3B is a schematic diagram of a second triangular membership function of a parking time ratio according to an embodiment of the present invention; FIG.
图3C为本发明实施例提供的平均加速度的第三三角形隶属函数的一种示意图；3C is a schematic diagram of a third triangular membership function of an average acceleration according to an embodiment of the present invention;
图4为本发明实施例采用的简化的竞争型神经网络的结构图；4 is a structural diagram of a simplified competitive neural network used in an embodiment of the present invention;
图5为本发明实施例将简化的竞争型神经网络应用到预测当前的交通状态中的一种计算方法示意图；5 is a schematic diagram of a calculation method for applying a simplified competitive neural network to predicting a current traffic state according to an embodiment of the present invention;
图6为本发明实施例中制动能量回收***通过结合EV的驾驶员的制动意图的方式计算EV在制动状态下的回馈力矩的方法流程图；6 is a flow chart of a method for calculating a feedback torque of an EV in a braking state by a braking energy recovery system in combination with a braking intention of a driver of an EV according to an embodiment of the present invention;
图7本发明实施例提供的一种电动汽车的可能的结构示意图；FIG. 7 is a schematic structural diagram of an electric vehicle according to an embodiment of the present invention;
图8为本发明实施例中如图7所示的电动汽车在执行如图2所示的方法的具体实现示意图；FIG. 8 is a schematic diagram of a specific implementation of the method shown in FIG. 2 in the electric vehicle shown in FIG. 7 according to an embodiment of the present invention; FIG.
图9为本发明实施例提供的一种电动汽车的可能的结构框图。FIG. 9 is a block diagram of a possible structure of an electric vehicle according to an embodiment of the present invention.
具体实施方式detailed description
为使本发明实施例的目的、技术方案和优点更加清楚，下面将结合附图，对本发明实施例提供的技术方案进行描述。The technical solutions provided by the embodiments of the present invention will be described below in conjunction with the accompanying drawings.
另外，本文中术语“和/或”，仅仅是一种描述关联对象的关联关系，表示可以存在三种关系，比如，A和/或B，可以表示：单独存在A，同时存在A和B，单独存在B这三种情况。另外，本文中字符“/”，如无特殊说明，一般表示前后关联对象是一种“或”的关系，本文中的字符“、”，如无特殊说明，一般表示前后关联对象是一种“和”的关系。In addition, the term "and/or" in this document is merely an association relationship describing an associated object, indicating that there may be three relationships, for example, A and/or B, which may indicate that A exists separately, and A and B exist at the same time. There are three cases of B alone. In addition, the character "/" in this article, unless otherwise specified, generally indicates that the contextual object is an "or" relationship. The character "," in this article, unless otherwise specified, generally means that the contextual object is a kind of " And the relationship.
下面首先介绍现有技术中的制动能量回收***。The brake energy recovery system of the prior art will first be described below.
请参考图1，为现有技术中的制动能量回收***的结构示意图。该制动能量回收***包括整车控制器10、电动机控制器20、电动机30及蓄电池40。在具体实施过程中，该制动能量回收***可以是EV的组成部分，比如，可以是纯电动汽车(Battery Electric Vehicle，BEV)或者插电式混合动力汽车(Plug-in Hybrid Electric Vehicle，PHEV)的组成部分，当然，也可以是其他通过电动机驱动的车辆的组成部分，或者该制动能量回收***可以是车辆本身，比如是EV本身或PHEV本身等，在此不作限制。Please refer to FIG. 1 , which is a schematic structural diagram of a brake energy recovery system in the prior art. The brake energy recovery system includes a vehicle controller 10, a motor controller 20, an electric motor 30, and a battery 40. In a specific implementation process, the braking energy recovery system may be an integral part of the EV, for example, it may be a Battery Electric Vehicle (BEV) or a Plug-in Hybrid Electric Vehicle (PHEV). The component, of course, may also be part of another vehicle driven by an electric motor, or the braking energy recovery system may be the vehicle itself, such as the EV itself or the PHEV itself, and is not limited herein.
整车控制器(Vehicle Management System，VMS)10，是整个车辆的核心控制部件。整车控制器采集加速踏板信号、制动踏板信号及车辆的其他部件产生的信号，在做出相应判断后，控制其他部件的动作，实现整车驱动控制、能量优化控制、制动回馈控制等功能。The Vehicle Management System (VMS) 10 is the core control component of the entire vehicle. The vehicle controller collects the signals generated by the accelerator pedal signal, the brake pedal signal and other components of the vehicle, and after making corresponding judgments, controls the actions of other components to realize vehicle drive control, energy optimization control, brake feedback control, etc. Features.
电动机控制器(Motor Control Unit，MCU)20，电动机控制器20通过与之连接的集 成电路来控制电动机30按照设定的方向、速度、角度、响应时间进行工作。A motor controller (MCU) 20, the motor controller 20 controls the motor 30 to operate in accordance with a set direction, speed, angle, and response time by an integrated circuit connected thereto.
电动机30，一般来说在车辆的运行过程中有两种功能：当车辆起步状态或加速状态时，电动机30为车辆提供驱动力；当车辆处于制动能量回收状态时，由于电动机30的运转，根据右手定则可知，线圈在阻碍磁通变化的方向上发生电动势，使电动机30起到发电机的作用，从而实现制动能量的回收。The motor 30 generally has two functions during operation of the vehicle: when the vehicle is in a starting state or an acceleration state, the motor 30 provides a driving force for the vehicle; when the vehicle is in a braking energy recovery state, due to the operation of the motor 30, According to the right-hand rule, it is known that the coil generates an electromotive force in a direction that hinders the change of the magnetic flux, so that the motor 30 functions as a generator, thereby realizing recovery of braking energy.
蓄电池40，用于给电动机控制器20提供驱动能量。电动机30根据回馈力矩将车辆的制动能量转化为蓄电池40的电能，存储在蓄电池40中。A battery 40 is provided for supplying drive energy to the motor controller 20. The electric motor 30 converts the braking energy of the vehicle into electric energy of the battery 40 according to the feedback torque, and stores it in the battery 40.
在图1所示的制动能量回收***中，整车控制器10用于监测车辆中各个部件的工作状态，并最终确定用于制动能量回收的回馈力矩，通过该回馈力矩对电动机30的反拖，能够将制动能量转换为电能，并将该电能存储在蓄电池40中，完成制动能量回收。电动机控制器20用于控制电动机30当前的工作状态，比如，可以控制电动机30处于驱动状态或发电制动状态。电动机30则按照电动机控制器20的来运行。其中，电动机30的驱动状态为给车辆提供驱动能量的状态，电动机的发电制动状态为根据回馈力矩进行制动能量回收，以将车辆的制动能量转化为电能的状态。In the braking energy recovery system shown in FIG. 1, the vehicle controller 10 is used to monitor the operating state of various components in the vehicle and finally determine the feedback torque for braking energy recovery, by which the torque is applied to the motor 30. The anti-drag can convert the braking energy into electrical energy and store the electrical energy in the battery 40 to complete the braking energy recovery. The motor controller 20 is used to control the current operating state of the motor 30, for example, the motor 30 can be controlled to be in a driving state or a power generating braking state. The motor 30 operates in accordance with the motor controller 20. The driving state of the electric motor 30 is a state in which driving energy is supplied to the vehicle, and the power generating braking state of the electric motor is a state in which braking energy recovery is performed according to the feedback torque to convert the braking energy of the vehicle into electric energy.
在图1所示的制动能量回收***中，加速踏板用于增加车辆的行驶速度。当加速踏板未被驾驶员踩下时，加速踏板的开度为0；当加速踏板被驾驶员踩到最底端时，加速踏板的开度为100％。当驾驶员对加速踏板进行踩踏时，加速踏板会产生携带加速踏板的开度的油门信号，发送给整车控制器10。相应地，制动踏板用于减小车辆的行驶速度，当制动踏板未被驾驶员踩下时，制动踏板的开度为0，当制动踏板被驾驶员踩到最底端时，制动踏板的开度为100％。当驾驶员对制动踏板进行踩踏时，制动踏板会产生携带制动踏板的开度的制动信号，并发送给整车控制器10。In the brake energy recovery system shown in Fig. 1, an accelerator pedal is used to increase the traveling speed of the vehicle. When the accelerator pedal is not depressed by the driver, the accelerator pedal opening degree is 0; when the accelerator pedal is stepped on the bottom end by the driver, the accelerator pedal opening degree is 100%. When the driver steps on the accelerator pedal, the accelerator pedal generates a throttle signal that carries the opening degree of the accelerator pedal and transmits it to the vehicle controller 10. Correspondingly, the brake pedal is used to reduce the running speed of the vehicle. When the brake pedal is not depressed by the driver, the opening degree of the brake pedal is 0, when the brake pedal is stepped on the bottom end by the driver, The brake pedal has an opening of 100%. When the driver steps on the brake pedal, the brake pedal generates a brake signal carrying the opening degree of the brake pedal and transmits it to the vehicle controller 10.
下面以利用如图1所示的制动能量回收***进行制动能量回收为例，介绍进行制动能量回收的一种可采用的方式：In the following, an example of braking energy recovery using the braking energy recovery system shown in FIG. 1 is described as an example of a braking energy recovery method:
整车控制器10首先通过采集油门信号及制动信号来判断车辆是否处于制动状态，若整车控制器10未检测到制动信号，且检测的油门信号指示的加速踏板的开度小于预设阈值，则认为驾驶员有降低车速或者停车的期望，然后，整车控制器10通过电动机控制器20获取电动机30当前的转速，蓄电池40当前的荷电状态以及蓄电池40的最大允许充电电流，进而根据电动机30当前的转速、蓄电池40当前的荷电状态以及蓄电池40最大允许充电电流计算车辆当前用于进行制动能量回收的回馈力矩，并由整车控制器10生成回馈力矩指令，整车控制器10将该回馈力矩指令发送给电动机控制器20，该回馈力矩指令用于指示计算得到的回馈力矩，电动机控制器20控制电动机30采用该回馈力矩进行发电，电动机3将产生的电能传输给蓄电池40，以实现对蓄电池40的充电。The vehicle controller 10 first determines whether the vehicle is in the braking state by collecting the throttle signal and the braking signal. If the vehicle controller 10 does not detect the braking signal, and the detected accelerator signal indicates that the opening of the accelerator pedal is less than If the threshold is set, the driver is considered to have the desire to reduce the vehicle speed or stop. Then, the vehicle controller 10 acquires the current rotational speed of the motor 30, the current state of charge of the battery 40, and the maximum allowable charging current of the battery 40 through the motor controller 20. Further, according to the current rotational speed of the motor 30, the current state of charge of the battery 40, and the maximum allowable charging current of the battery 40, the feedback torque currently used by the vehicle for braking energy recovery is calculated, and the feedback torque command is generated by the vehicle controller 10, and the vehicle is completed. The controller 10 sends the feedback torque command to the motor controller 20, the feedback torque command is used to indicate the calculated feedback torque, the motor controller 20 controls the motor 30 to generate power by using the feedback torque, and the motor 3 transmits the generated electric energy to The battery 40 is used to charge the battery 40.
此外，若整车控制器10检测到制动信号，则整车控制器10也认为驾驶员有降低车速或停车的期望，且停车期望较大。此时与上述方法类似，整车控制器10根据制动信号中的制动踏板的开度，确定制动踏板在当前开度下的期望回馈力矩，并通过电动机控制器20获取电动机30当前的转速，蓄电池40当前的荷电状态以及蓄电池40的最大允许充电电流，进而根据电动机30当前的转速、制动踏板在当前开度下的期望回馈力矩、蓄电池40当前的荷电状态以及蓄电池40的最大允许充电电流，计算车辆当前用于制动能量回收的回馈力矩，并生成回馈力矩指令发送给电动机控制器20，该回馈力矩指令用于指示计算得到的回馈力矩，电动机控制器20控制电动机30采用该回馈力矩进行发电，电动机30将 产生的电能传输给蓄电池40，以实现对蓄电池40的充电。Further, if the vehicle controller 10 detects the brake signal, the vehicle controller 10 also considers that the driver has a desire to lower the vehicle speed or stop, and the parking expectation is large. At this time, similar to the above method, the vehicle controller 10 determines the expected feedback torque of the brake pedal at the current opening degree according to the opening degree of the brake pedal in the brake signal, and acquires the current current of the motor 30 through the motor controller 20. The rotational speed, the current state of charge of the battery 40, and the maximum allowable charging current of the battery 40, and further, according to the current rotational speed of the motor 30, the expected feedback torque of the brake pedal at the current opening degree, the current state of charge of the battery 40, and the battery 40 The maximum allowable charging current, calculating the feedback torque currently used by the vehicle for braking energy recovery, and generating a feedback torque command is sent to the motor controller 20, the feedback torque command is used to indicate the calculated feedback torque, and the motor controller 20 controls the motor 30. The feedback torque is used to generate electricity, and the electric motor 30 transmits the generated electric energy to the battery 40 to realize charging of the battery 40.
在上述技术方案中，当整车控制器10检测到驾驶员松开加速踏板后，即车辆处于滑行回馈能量回收状态，此时主要是根据车速来确定回馈力矩的大小。当整车控制器10检测到驾驶员踩下制动踏板后，即车辆处于制动能量回收状态，此时主要是根据制动踏板的开度与车速确定制动回馈力矩的大小。而驾驶员对加速踏板或者制动踏板的操作只是影响制动能量回收的一部分因素，而制动能量回收还可能受到其他因素的影响，比如，制动能量回收的回馈力矩还会受到交通状态的影响，当车辆在拥堵的情况下行驶时，由于各车辆之间的间隔距离较近，此时若驾驶员踩下制动踏板，则驾驶员期望能在最短的时间内减速，此时，理应增加电动机的回馈力矩，以缩短制动时长，或者，当车辆在畅通的高速公路上行驶时，由于各车辆之间的间隔距离较大，此时，若驾驶员踩下制动踏板，则驾驶员期望能延长滑行距离以避免减速过快而导致需要再次踩加速踏板，此时，理应减小电动机的回馈力矩，以增加制动时长。但如上介绍的制动能量回收方法只与车速及驾驶员对加速踏板或者制动踏板的操作有关，而驾驶员对加速踏板或者制动踏板的操作仅由个人经验进行判断，从而导致对回馈力矩的大小进行调节的准确性较低。In the above technical solution, when the vehicle controller 10 detects that the driver releases the accelerator pedal, that is, the vehicle is in the coasting feedback energy recovery state, the magnitude of the feedback torque is determined mainly according to the vehicle speed. When the vehicle controller 10 detects that the driver is stepping on the brake pedal, that is, the vehicle is in the braking energy recovery state, the braking feedback torque is determined according to the opening degree of the brake pedal and the vehicle speed. The driver's operation of the accelerator pedal or the brake pedal only affects part of the brake energy recovery, and the brake energy recovery may be affected by other factors. For example, the feedback torque of the brake energy recovery is also affected by the traffic state. Influence, when the vehicle is driving in a congested situation, because the distance between the vehicles is relatively close, if the driver depresses the brake pedal, the driver expects to decelerate in the shortest time. Increase the feedback torque of the motor to shorten the braking time, or when the vehicle is driving on a smooth highway, because the distance between the vehicles is large, at this time, if the driver steps on the brake pedal, the driver The staff expects to extend the sliding distance to avoid deceleration too fast and cause the accelerator pedal to be stepped on again. At this time, it is reasonable to reduce the feedback torque of the motor to increase the braking time. However, the braking energy recovery method described above is only related to the vehicle speed and the operation of the accelerator pedal or the brake pedal by the driver, and the operation of the accelerator pedal or the brake pedal by the driver is judged only by personal experience, thereby causing the feedback torque. The size of the adjustment is less accurate.
针对该技术问题，本发明实施例再提供一种制动能量回收方法，在进行制动能量回收时，能够自动根据车辆在预设时长内的速度信息获取车辆自身的运行参数，并基于获取的运行参数预测车辆所处的运行道路的交通状态，从而可以根据交通状态自适应地调整回馈力矩的大小，比如，当交通状态为市区拥堵状态时，可适当增加回馈力矩，当交通状态为高速畅通状态时，则可以适当减小回馈力矩，避免了由于仅凭驾驶员的经验对回馈力矩的大小进行调节的不准确性，相应地就提高了所确定的回馈力矩的准确性，能够更好地进行制动能量的回收。For the technical problem, the embodiment of the present invention further provides a braking energy recovery method, which can automatically acquire the operating parameters of the vehicle according to the speed information of the vehicle within a preset time period when performing braking energy recovery, and based on the acquired The operating parameter predicts the traffic state of the running road on which the vehicle is located, so that the magnitude of the feedback torque can be adaptively adjusted according to the traffic state. For example, when the traffic state is an urban congestion state, the feedback torque can be appropriately increased, and when the traffic state is high speed When the state is unblocked, the feedback torque can be appropriately reduced, and the inaccuracy of adjusting the magnitude of the feedback torque by the driver's experience alone is avoided, and the accuracy of the determined feedback torque is improved accordingly, which is better. The braking energy is recovered.
进一步地，在本发明实施例中，由于在进行制动能量回收时能够根据交通状态自适应地调整回馈力矩的大小，从而尽量避免了由于回馈力矩过大而需要再次踩下加速踏板，造成再次耗损电能为车辆提供驱动力带来的能量浪费。还可以尽量避免因为回馈力矩过小而造成的制动能量回收不充分的问题，进而可以提高车辆的能量利用率。Further, in the embodiment of the present invention, since the magnitude of the feedback torque can be adaptively adjusted according to the traffic state when braking energy recovery is performed, it is avoided as much as possible that the accelerator pedal needs to be depressed again due to excessive feedback torque. The loss of electrical energy is a waste of energy from the driving force provided by the vehicle. It is also possible to avoid the problem that the braking energy recovery is insufficient due to the feedback torque being too small, thereby improving the energy utilization rate of the vehicle.
且在本发明实施例中，直接根据车辆自身的车速获取的运动参数确定车辆所处的交通状态，无需增加额外的信息检测装置或传感器检测外界的环境参数，既使得确定的交通状态符合当前的实际情况，又节省了硬件成本。In the embodiment of the present invention, the traffic state obtained by the vehicle is directly determined according to the motion parameter acquired by the vehicle's own vehicle speed, and no additional information detecting device or sensor is needed to detect the external environment parameter, so that the determined traffic state conforms to the current state. The actual situation saves hardware costs.
接下来将结合附图介绍本发明实施例所提供的方法。请参考图2，为本发明实施例提供的一种制动能量回收方法的流程图，该方法可以以如图1所示的制动能量回收***实现，该方法可以包括如下步骤：Next, the method provided by the embodiment of the present invention will be described with reference to the accompanying drawings. Please refer to FIG. 2 , which is a flowchart of a braking energy recovery method according to an embodiment of the present invention. The method may be implemented by using a braking energy recovery system as shown in FIG. 1 , and the method may include the following steps:
S201：车辆上的制动能量回收***基于车辆在预设时长内的速度信息获取车辆在预设时长内的运行参数信息。S201: The braking energy recovery system on the vehicle acquires operating parameter information of the vehicle within a preset time period based on the speed information of the vehicle within a preset time period.
在车辆进行制动能量回收时，车辆的制动能量回收***首先获取在预设时长内车辆的速度信息，然后基于获取的速度信息，获取车辆在该预设时长内的运行参数信息。When the vehicle performs braking energy recovery, the braking energy recovery system of the vehicle first acquires speed information of the vehicle within a preset time period, and then acquires operating parameter information of the vehicle within the preset time length based on the acquired speed information.
在本发明实施例中，所述制动状态为所述车辆处于主动减速或不主动加速的状态。具体来讲，所述主动减速状态可以是车辆的驾驶员踩下车辆的制动踏板时的状态，当然，若车辆不存在制动踏板，比如，通过语音控制的EV，那么主动减速状态可以是车辆在驾驶员在通过语音指令控制车辆减速时的状态，在此就不一一举例了。所述不主动加速状态可以是车辆的加速踏板处于松开时的状态，即车辆的驾驶员没有踩下加速踏板的状态，比如， 可以是车辆处于滑行时的状态，当然，若车辆不存在加速踏板，比如，通过语音控制的EV，那么主动减速状态可以是车辆在驾驶员没有通过语音指令控制车辆减速或者加速时的状态，在此就不一一举例了。在下面的描述中，将以所述车辆为具有制动踏板以及加速踏板的EV来进行详细说明。In an embodiment of the invention, the braking state is a state in which the vehicle is actively decelerating or not actively accelerating. Specifically, the active deceleration state may be a state when the driver of the vehicle steps on the brake pedal of the vehicle. Of course, if the brake pedal is not present in the vehicle, for example, by an EV controlled by voice, the active deceleration state may be The state of the vehicle when the driver controls the deceleration of the vehicle by voice command is not exemplified here. The inactive acceleration state may be a state in which the accelerator pedal of the vehicle is in a state of being released, that is, a state in which the driver of the vehicle does not step on the accelerator pedal, for example, may be in a state when the vehicle is coasting, and of course, if the vehicle does not have acceleration The pedal, for example, an EV controlled by voice, the active deceleration state may be a state in which the vehicle does not control the deceleration or acceleration of the vehicle by a voice command, and is not exemplified herein. In the following description, the vehicle will be described in detail as an EV having a brake pedal and an accelerator pedal.
本发明实施例中，为了使获取的速度信息能够反映车辆的运行状况，并尽量减小制动能量回收***的工作负荷，在获取车速时，可以预先确定预设时长，从而只获取在该预设时长内的车速。所述预设时长大于车辆在任意一次处于制动状态的时长，比如，车辆平均处于制动状态的时长为50秒，则在本发明实施例中，预设时长可以设置为处于制动状态的时长的10倍，以t表示预设时长，则t＝500秒。或者预设时长也可以根据实际计算需要调整，比如，设置t为统计的某城市高峰时段从一个红绿灯路口到下一个红绿灯路口的平均通行时间的整数倍。当然，本领域技术人员也可以将预设时长设置为其他的值，本发明实施例不对此进行限定。In the embodiment of the present invention, in order to make the acquired speed information reflect the running condition of the vehicle and minimize the workload of the braking energy recovery system, when the vehicle speed is acquired, the preset duration may be determined in advance, so that only the pre-acquisition is obtained. Set the speed of the vehicle within the length of time. The preset duration is greater than the duration of the vehicle in any of the braking states. For example, if the vehicle is in the braking state for an average of 50 seconds, in the embodiment of the present invention, the preset duration may be set to be in the braking state. 10 times the duration, t is the preset duration, then t = 500 seconds. Or the preset duration can also be adjusted according to actual calculations. For example, set t is an integer multiple of the average transit time of a city peak hour from a traffic light intersection to the next traffic light intersection. Of course, those skilled in the art can also set the preset duration to other values, which is not limited in the embodiment of the present invention.
在获取EV在预设时长内的车速v后，则车速v来获取车辆的运行参数信息。运行参数信息包括如下的任意一种或几种：平均车速V Avg、平均运行车速V1 Avg、停车时间比例η、平均运行加速度A Avg、平均加速度A1 Avg和平均减速度A2 Avg。其中，平均运行车速V1 Avg表示在预设时长内EV的车速不为零的时间内EV的平均车速，平均运行加速度A Avg表示在预设时长内EV的车速不为零的时间内EV的平均加速度，当然，运行参数信息还可能包括其他的参数信息，本发明实施例不作限制。 After acquiring the vehicle speed v of the EV within the preset time period, the vehicle speed v is used to acquire the operating parameter information of the vehicle. The operating parameter information includes any one or more of the following: average vehicle speed V Avg , average running vehicle speed V1 Avg , parking time ratio η, average running acceleration A Avg , average acceleration A1 Avg, and average deceleration A2 Avg . Wherein, the average running speed V1 Avg represents the average speed of the EV in the time when the speed of the EV is not zero within the preset time period, and the average running acceleration A Avg represents the average EV in the time when the speed of the EV is not zero within the preset time period. Acceleration, of course, the operating parameter information may also include other parameter information, which is not limited in the embodiment of the present invention.
根据获取的在预设时长内的车速v计算EV在预设时长内的运行参数信息，一种计算方法如下：Calculate the operating parameter information of the EV within the preset time length according to the obtained vehicle speed v within the preset time length. One calculation method is as follows:
平均车速V Avg：
Figure PCTCN2018073971-appb-000001
Average speed V Avg :
Figure PCTCN2018073971-appb-000001
平均运行车速V1 Avg：
Figure PCTCN2018073971-appb-000002
其中t’为EV在预设时长内的车速不为零的时刻； Average running speed V1 Avg :
Figure PCTCN2018073971-appb-000002
Where t' is the moment when the EV speed is not zero within the preset time period;
停车时间比例η：
Figure PCTCN2018073971-appb-000003
Parking time ratio η:
Figure PCTCN2018073971-appb-000003
平均运行加速度A Avg:
Figure PCTCN2018073971-appb-000004
其中t’为EV在预设时长内的速度不为零的时刻； Average running acceleration A Avg :
Figure PCTCN2018073971-appb-000004
Where t' is the moment when the speed of the EV is not zero within the preset duration;
平均加速度A1 Avg:
Figure PCTCN2018073971-appb-000005
其中t acc为EV进入加速状态的时刻； Average acceleration A1 Avg :
Figure PCTCN2018073971-appb-000005
Where t acc is the moment when the EV enters the acceleration state;
平均减速度A2 Avg:
Figure PCTCN2018073971-appb-000006
其中t dec为EV减速的时刻； Average deceleration A2 Avg :
Figure PCTCN2018073971-appb-000006
Where t dec is the time when the EV is decelerating;
需注意的是，如上的计算方法只是举例，本发明实施例并不限制获得运行参数信息的 方式。由以上计算，得到EV在预设时长内的运行参数向量[V Avg,V1 Avg,η,A Avg，A1 Avg,A2 Avg]，该运行参数向量中的所有参数并不一定全部使用，比如，可以只选用A1 Avg,A2 Avg，至于究竟选择哪些参数作为运行参数向量的一部分，本发明实施例不作限制。在实际应用中，可以只计算需要使用的运行参数信息，对于不使用的运行参数信息无需进行计算，提高计算效率。或者也可以将上述的运行参数信息全部进行计算，然后从已经计算好的多个运行参数信息中选择其中一部分或全部来使用，这样更方便做选择。在本发明实施例中同样不作限制。 It should be noted that the above calculation method is only an example, and the embodiment of the present invention does not limit the manner in which the operation parameter information is obtained. From the above calculation, the operating parameter vector [V Avg , V1 Avg , η , A Avg , A1 Avg , A2 Avg ] of the EV within the preset duration is obtained, and all parameters in the running parameter vector are not necessarily all used, for example, It is possible to use only A1 Avg , A2 Avg , as to which parameters are selected as part of the operating parameter vector, which is not limited in the embodiment of the present invention. In practical applications, only the running parameter information that needs to be used can be calculated, and no calculation is needed for the running parameter information that is not used, thereby improving the calculation efficiency. Alternatively, all of the above-mentioned operational parameter information may be calculated, and then some or all of the plurality of operational parameter information that have been calculated may be selected for use, which is more convenient for selection. The same is not limited in the embodiment of the present invention.
S202：制动能量回收***基于运行参数信息，预测车辆所处的运行道路的当前的交通状态。S202: The braking energy recovery system predicts a current traffic state of the running road where the vehicle is located based on the operating parameter information.
在本发明实施例中，制动能量回收***中需要存储多个预设交通状态以及各个预设交通状态对应的运行参数信息。即，用一组运行参数信息表征一个预设交通状态，且制动能量回收***中可以仅存储各个预设交通状态的标识信息，比如，可以为各个预设交通状态进行编号，则该标识信息即为各个预设交通状态的编号，从而，制动能量回收***中存储的各个预设交通状态对应的运行参数信息即为一个编号对应的一组运行参数信息，从而使制动能量回收***根据编号或者一组运行参数信息便能区分各个预设交通状态。为了使存储的各个预设交通状态对应的运行参数信息能够准确地反映出EV在不同预设交通状态中的运行状态，本发明实施例中的预设交通状态对应的运行参数信息包括但不限于通过如下方式获取：In the embodiment of the present invention, the braking energy recovery system needs to store a plurality of preset traffic states and operating parameter information corresponding to each preset traffic state. That is, a set of operational parameter information is used to represent a preset traffic state, and the braking energy recovery system can store only the identification information of each preset traffic state. For example, each preset traffic state can be numbered, and the identification information is That is, the number of each preset traffic state, so that the operating parameter information corresponding to each preset traffic state stored in the braking energy recovery system is a set of operating parameter information corresponding to a number, so that the braking energy recovery system is based on The number or a set of operating parameter information can distinguish each preset traffic state. The operation parameter information corresponding to the preset traffic state in the embodiment of the present invention includes, but is not limited to, the operating parameter information corresponding to the preset preset traffic state, which can accurately reflect the running state of the EV in different preset traffic states. Obtained as follows:
1、计算方式：1, calculation method:
预先采集大量的真实运行数据。比如，采集该EV在1个月内多个时刻的实时车速，或者，以各种车型的EV作为采集对象，采集每种类型的EV在1个月内多个时刻的实时车速。当然，为了使采集的数据更加准确，也可以采集EV在2个月、3个月或者甚至更长时间内的车速。然后，基于采集到的车速，计算每一个运动学片段的特征参数信息的值，其中，每一个运动学片段即EV从一次怠速开始到下一次怠速开始的时间段。最后，利用聚类分析的方法，分析各个运动学片段的特征参数信息的值之间的差异值，将差异值小于预设阈值的多组特征参数信息分为一个类别，每一个类别即为车辆的一种预设交通状态。比如，可以将所有运动学片段的特征参数信息分为5类，分别是市区拥堵、市区畅通、近郊中低速、远郊中高速和高速状态。当然，为了能够更加准确地区分不同的预设交通状态，也可以将上述运动学片段的特征参数信息分为更多种类别，本发明实施例对此不作限定。Collect a large amount of real running data in advance. For example, the real-time vehicle speed of the EV at multiple times in one month is collected, or the EV of each vehicle type is used as an acquisition target, and the real-time vehicle speed of each type of EV at multiple times in one month is collected. Of course, in order to make the collected data more accurate, it is also possible to collect the speed of the EV in 2 months, 3 months or even longer. Then, based on the collected vehicle speed, the value of the feature parameter information of each kinematic segment is calculated, wherein each kinematic segment is a period of time from the start of one idle speed to the start of the next idle speed. Finally, the cluster analysis method is used to analyze the difference value between the values of the characteristic parameter information of each kinematic fragment, and the plurality of sets of characteristic parameter information whose difference value is smaller than the preset threshold is divided into one category, and each category is a vehicle. A preset traffic state. For example, the characteristic parameter information of all kinematic segments can be divided into five categories, namely urban congestion, urban smooth flow, suburban low-speed, suburban high-speed and high-speed state. Of course, in order to be able to more accurately distinguish different preset traffic states, the feature parameter information of the kinematic segments may be further classified into a plurality of categories, which is not limited in the embodiment of the present invention.
在计算方式中，由于EV的各个预设交通状态是根据EV的实时车速获取的，因此，各个预设交通状态对应的运行参数信息能够准确地反映该预设交通状态的特征。但由于需要采集大量的真实运行数据，在具体实施时需要花费较长的时间。鉴于此，本发明实施例再提供另一种较高效地获取各个预设交通状态对应的运行参数信息的方式，即如下的第2种方式。In the calculation mode, since the preset traffic states of the EV are acquired according to the real-time vehicle speed of the EV, the operation parameter information corresponding to each preset traffic state can accurately reflect the characteristics of the preset traffic state. However, due to the need to collect a large amount of real running data, it takes a long time to implement it. In view of this, the embodiment of the present invention further provides another manner of acquiring operating parameter information corresponding to each preset traffic state more efficiently, that is, the following second manner.
2、聚类分析方式：2, cluster analysis method:
由于地图等应用软件或者车联网***在工作过程中，会采集EV的位置信息以及移动速度信息等来预测某个路段的实时路况，因此，本发明实施例中在获取各个预设交通状态对应的运行参数信息时，可以通过地图或车联网***等第三方获取各种路况的特征参数作 为预设交通状态的特征参数信息。当然，车联网或者地图等第三方预测的实时路况可能只包括道路拥堵或者道路畅通两种情况，为了获取各种不同的预设交通状态，还可以在从第三方获取大量信息后，再进行聚类分析，从而获取多个预设交通状态的特征参数信息。In the working process, an application software such as a map or a car network system collects the position information of the EV and the moving speed information to predict the real-time road condition of a certain road segment. Therefore, in the embodiment of the present invention, each of the preset traffic states is acquired. When the parameter information is run, the feature parameters of various road conditions may be acquired by a third party such as a map or a car network system as the feature parameter information of the preset traffic state. Of course, the real-time road conditions predicted by third parties such as the Internet of Vehicles or maps may only include road congestion or smooth roads. In order to obtain various preset traffic states, it is also possible to collect a large amount of information from a third party. Class analysis to obtain feature parameter information of a plurality of preset traffic states.
需要说明的是，在实际运用中，获取预设交通状态的特征参数信息的方式不限于上述描述的方式，每种预设交通状态的特征参数信息可以与步骤S401中的运行参数信息相同，比如，步骤S401中的运行参数信息包括V Avg,V1 Avg,η,A Avg，A1 Avg,以及A2 Avg等参数，则每种预设交通状态的特征参数信息也包括V Avg,V1 Avg,η,A Avg，A1 Avg,以及A2 Avg等参数。其中，预设交通状态的特征参数信息所包括的参数可以多于步骤S401中的运行参数信息包括的参数，比如，步骤S401中的运行参数信息包括V Avg,V1 Avg这两个参数，而每种预设交通状态的特征参数信息可以包括V Avg,V1 Avg,η,A Avg，A1 Avg,以及A2 Avg等参数。总之，预设交通状态的特征参数信息中可以包括尽量多的参数，以能够与各种运行参数信息相比较。至于预设交通状态的特征参数信息和预设交通状态的运行参数信息各自包括哪些参数，在本发明实施例中不作限定。 It should be noted that, in actual application, the manner of acquiring the feature parameter information of the preset traffic state is not limited to the manner described above, and the feature parameter information of each preset traffic state may be the same as the operation parameter information in step S401, for example, The operation parameter information in step S401 includes parameters such as V Avg , V1 Avg , η, A Avg , A1 Avg , and A2 Avg , and the characteristic parameter information of each preset traffic state also includes V Avg , V1 Avg , η, A Avg , A1 Avg , and A2 Avg parameters. The parameter parameter information of the preset traffic state may include more parameters than the parameter included in the operation parameter information in step S401. For example, the operation parameter information in step S401 includes two parameters, V Avg and V1 Avg . The characteristic parameter information of the preset traffic state may include parameters such as V Avg , V1 Avg , η, A Avg , A1 Avg , and A2 Avg . In summary, the characteristic parameter information of the preset traffic state may include as many parameters as possible to be able to be compared with various operating parameter information. The parameters of the parameter information of the preset traffic state and the operating parameter information of the preset traffic state are not limited in the embodiment of the present invention.
在获取预设交通状态的特征参数信息后，制动能量回收***则存储与各个预设交通状态对应的的特征参数信息。比如，制动能量回收***中存储有市区拥堵状态和高速状态，其中，市区拥堵状态对应的特征参数信息为：停车时长大于预设时长的1/4，平均车速小于50kmph，平均加速度小于0.3m/s 2；高速状态对应的特征参数信息为：停车时长小于预设时长的1/10，平均车速大于70kmph，平均加速度大于0.5m/s 2。 After acquiring the characteristic parameter information of the preset traffic state, the braking energy recovery system stores the feature parameter information corresponding to each preset traffic state. For example, the braking energy recovery system stores an urban congestion state and a high-speed state, wherein the characteristic parameter information corresponding to the urban congestion state is: the parking time is longer than 1/4 of the preset duration, the average vehicle speed is less than 50 kmph, and the average acceleration is less than 0.3m/s 2 ; The characteristic parameter information corresponding to the high speed state is: the parking time is less than 1/10 of the preset duration, the average vehicle speed is greater than 70kmph, and the average acceleration is greater than 0.5m/s 2 .
当制动能量回收***按照S401的方式获取EV的运行参数信息后，将获取的运行参数信息与各种预设交通状态的特征参数信息进行匹配，与该运行参数信息相匹配的特征参数信息对应的预设交通状态为EV当前的交通状态。After the braking energy recovery system acquires the operating parameter information of the EV according to the method of S401, the obtained operating parameter information is matched with the characteristic parameter information of various preset traffic states, and the characteristic parameter information matching the operating parameter information corresponds to The default traffic status is the current traffic status of the EV.
其中，将获取的运行参数信息与各种预设交通状态的特征参数信息进行比较判定的方法有多种，本发明实施例以如下两种方法为例进行详细说明：There are a plurality of methods for comparing and determining the obtained operating parameter information with the characteristic parameter information of various preset traffic states. The following two methods are used as an example for detailed description in the embodiment of the present invention:
1、模糊逻辑方法：1, fuzzy logic method:
在本发明实施例中，制动能量回收***建立预存的每种特征参数信息与该特征参数信息的取值范围之间的隶属函数，该隶属函数可以为三角形隶属函数或者梯形隶属函数或者正态型隶属函数等。In the embodiment of the present invention, the braking energy recovery system establishes a membership function between each of the pre-stored feature parameter information and the value range of the feature parameter information, and the membership function may be a triangular membership function or a trapezoidal membership function or a normal state. Type membership function, etc.
比如，预设交通状态中包含的特征参数信息为步骤S401中介绍的平均运行车速、停车时间比例、及平均加速度，从而制动能量回收***中可以存储平均运行车速的三角形隶属函数，本文中将其称为第一三角形隶属函数，如图3A所示，制动能量回收***中还存储停车时间比例的三角形隶属函数，本文中称其为第二三角形隶属函数，如图3B所示，以及制动能量回收***中还存储平均加速度的三角形隶属函数，本文中将其称为第三三角形隶属函数，如图3C所示。从图3A-3C可以看出，每个特征参数信息的隶属函数均由多个不同的分段函数构成。比如，图3A中的第一三角形隶属函数由车速小于50kmph的低速对应的第一部分隶属函数、车速大于30kmph且小于等于80kmph的中速对应的第二部分隶属函数以及车速大于70kmph的高速对应的第三部分隶属函数组成；图3B中的第二三角形隶属函数由停车比例小于0.12的低停车比例对应第一部分隶属函数、停车比例大于0.11且小于0.24的中停车比例对应的第二部分隶属函数以及停车比例大于0.23的高停车比例对应的第三部分隶属函数组成；图3C中的第三三角形隶属函数由加速度的绝对值小于0.3的低加速度对应的第一部分隶属函数、加速度的绝对值大于0.25且小于0.6的中加速 度对应的第二部分隶属函数以及加速度的绝对值大于0.5的高加速度对应的第三部分隶属函数组成，其中，中加速度对应的第二部分隶属函数以及高加速度对应的第三部分隶属函数分别由与加速度为0对称的两个隶属函数组成。For example, the characteristic parameter information included in the preset traffic state is the average running speed, the parking time ratio, and the average acceleration introduced in step S401, so that the triangular energy membership function of the average running vehicle speed can be stored in the braking energy recovery system. It is called a first triangular membership function. As shown in FIG. 3A, the brake energy recovery system also stores a triangular membership function of the parking time ratio, which is referred to herein as a second triangular membership function, as shown in FIG. 3B. The triangular membership function of the average acceleration is also stored in the dynamic energy recovery system, which is referred to herein as the third triangular membership function, as shown in FIG. 3C. As can be seen from Figures 3A-3C, the membership functions of each feature parameter information are composed of a plurality of different segmentation functions. For example, the first triangular membership function in FIG. 3A is composed of a first partial membership function corresponding to a low speed with a vehicle speed of less than 50 kmph, a second partial membership function corresponding to a medium speed of a vehicle speed greater than 30 kmph and less than or equal to 80 kmph, and a high speed corresponding to a vehicle speed greater than 70 kmph. The three-part membership function is composed; the second triangular membership function in FIG. 3B is composed of a low-parking ratio with a parking ratio of less than 0.12 corresponding to the first partial membership function, a parking ratio of more than 0.11 and less than 0.24, and a second partial membership function corresponding to the parking ratio and parking The third part membership function corresponding to the high parking ratio of the ratio greater than 0.23; the third triangular membership function in FIG. 3C corresponds to the first partial membership function corresponding to the low acceleration of the absolute value of the acceleration less than 0.3, and the absolute value of the acceleration is greater than 0.25 and less than The second partial membership function corresponding to the medium acceleration of 0.6 and the third partial membership function corresponding to the high acceleration of the acceleration greater than 0.5, wherein the second partial membership function corresponding to the medium acceleration and the third part corresponding to the high acceleration are attached The function is symmetrically symmetrical with the acceleration 0 The two membership functions are composed.
这样，当制动能量回收***获取该EV的运行参数信息后，则将运行参数信息与建立的隶属函数进行比较，确定与该运行参数信息对应的分段函数。比如，确定运行参数信息中的平均车速在第一三角形隶属函数中对应的分段函数、停车时间比例在第二三角形隶属函数中对应的分段函数、以及平均加速度在第三三角形隶属函数中对应的分段函数。以图3A为例，当车辆的平均车速为60kmph时，该车速对应图3A中的第一三角形隶属函数的中速对应的第二部分隶属函数。Thus, when the braking energy recovery system acquires the operating parameter information of the EV, the operating parameter information is compared with the established membership function to determine a segmentation function corresponding to the operating parameter information. For example, determining that the average vehicle speed in the operating parameter information corresponds to a segmentation function in the first triangular membership function, a corresponding segmentation function of the parking time ratio in the second triangular membership function, and an average acceleration corresponding to the third triangular membership function Segmentation function. Taking FIG. 3A as an example, when the average vehicle speed of the vehicle is 60 kmph, the vehicle speed corresponds to the second partial membership function corresponding to the medium speed of the first triangular membership function in FIG. 3A.
在制动能量回收***确定与运行参数信息对应的分段函数后，则根据分段函数预测EV当前的交通状态。After the braking energy recovery system determines the segmentation function corresponding to the operating parameter information, the current traffic state of the EV is predicted according to the segmentation function.
比如，EV的运行参数对应的分段函数为第一三角形隶属函数里的低速对应的第一部分隶属函数、第二三角形隶属函数里的高停车比例对应的第三部分隶属函数以及第三三角形隶属函数里的低加速度对应的第一部分隶属函数；而预存的市区拥堵状态对应的特征参数信息为：停车时长大于预设时长的1/4，平均车速小于50kmph，平均加速度小于0.3m/s 2，其对应的分段函数为第二三角形隶属函数里的高停车比例对应的第三部分隶属函数、第一三角形隶属函数里的低速对应的第一部分隶属函数以及第三三角形隶属函数里的低加速度对应的第一部分隶属函数；预存的高速状态对应的特征参数信息为：停车时长小于预设时长的1/10，平均车速大于70kmph，平均加速度大于0.5m/s 2，其对应的分段函数为第二三角形隶属函数里的低停车比例对应的第一部分隶属函数、第一三角形隶属函数里的高速对应的第三部分隶属函数以及第一三角形隶属函数里的高加速度对应的第三部分隶属函数，因此，预存的市区拥堵状态与EV当前的运行参数信息相匹配，从而，通过分段函数预测出EV当前的交通状态。 For example, the segmentation function corresponding to the operating parameter of the EV is the first partial membership function corresponding to the low speed in the first triangular membership function, the third partial membership function corresponding to the high parking ratio in the second triangular membership function, and the third triangular membership function. The low acceleration corresponds to the first part of the membership function; and the pre-stored urban congestion state corresponds to the characteristic parameter information: the parking time is longer than 1/4 of the preset duration, the average vehicle speed is less than 50kmph, and the average acceleration is less than 0.3m/s 2 . The corresponding piecewise function is a third partial membership function corresponding to the high parking ratio in the second triangular membership function, a low-speed corresponding first partial membership function in the first triangular membership function, and a low acceleration corresponding in the third triangular membership function. The first part of the membership function; the characteristic parameter information corresponding to the pre-stored high-speed state is: the parking time is less than 1/10 of the preset duration, the average vehicle speed is greater than 70kmph, and the average acceleration is greater than 0.5m/s 2 , and the corresponding segmentation function is The first part of the membership function corresponding to the low parking ratio in the two triangular membership functions, the first three a high-speed corresponding third-part membership function in the shape membership function and a third-part membership function corresponding to the high acceleration in the first triangular membership function, so that the pre-stored urban congestion state matches the current operational parameter information of the EV, thereby The current traffic state of the EV is predicted by the piecewise function.
当然，为了便于更为精确地计算，制动能量回收***除了可以建立隶属函数外，还可以进一步根据预设交通状态及对应的特征参数信息，建立用于预测当前的交通状态的模糊规则库，该模糊规则库中的映射规则可以是二维映射，也可以是三维映射，在此不作限定。下面以模糊规则库中的映射规则为是二维映射为例进行说明。在上述的三角隶属函数中，分别获取了平均运行车速V1 Avg为低速、中速以及高速的分段函数，平均加速度A Avg为小加速度、中加速度以及大加速度的分段函数，停车时间比例η为低停车比例、中停车比例及高停车比例的分段函数，因此，模糊规则库中也以平均运行车速V1 Avg分为低速、中速以及高速，平均加速度A Avg分为小加速度、中加速度以及大加速度，停车时间比例η分为低停车比例、中停车比例及高停车比例建立模糊规则，如表1-表3所示。表1为平均加速度A Avg为小加速度时，根据平均运行车速V1 Avg以及停车时间比例η预测当前的交通状态的模糊推理规则，下文中称为模糊推理规则一，表2为平均加速度A Avg为中加速度时，根据平均运行车速V1 Avg以及停车时间比例η预测当前的交通状态的模糊推理规则，下文中称为模糊推理规则二，表3为平均加速度A Avg为大加速度时，根据平均运行车速V1 Avg以及停车时间比例η预测当前的交通状态的模糊推理规则，下文中称为模糊推理规则三。当然，本领域技术人员也可以根据停车时间比例η的不同，建立与表1-表3类似的平均加速度A Avg及平均运行车速V1 Avg，在本发明实施例中不作限定。 Of course, in order to facilitate more accurate calculation, the braking energy recovery system can further establish a fuzzy rule base for predicting the current traffic state according to the preset traffic state and the corresponding feature parameter information, in addition to the membership function. The mapping rule in the fuzzy rule base may be a two-dimensional mapping or a three-dimensional mapping, which is not limited herein. The following is an example in which the mapping rule in the fuzzy rule base is a two-dimensional mapping. In the above-mentioned triangular membership function, the piecewise function of the average running speed V1 Avg is obtained as low speed, medium speed and high speed respectively, and the average acceleration A Avg is a piecewise function of small acceleration, medium acceleration and large acceleration, and the parking time ratio η It is a piecewise function for low parking ratio, medium parking ratio and high parking ratio. Therefore, the fuzzy rule base is also divided into low speed, medium speed and high speed with average running speed V1 Avg . The average acceleration A Avg is divided into small acceleration and medium acceleration. And the large acceleration, the parking time ratio η is divided into a low parking ratio, a medium parking ratio and a high parking ratio to establish a fuzzy rule, as shown in Table 1 - Table 3. Table 1 is the fuzzy inference rule for predicting the current traffic state based on the average running vehicle speed V1 Avg and the parking time ratio η when the average acceleration A Avg is a small acceleration. Hereinafter, the fuzzy inference rule 1 is shown, and the average acceleration A Avg is when the acceleration, the average running speed V1 Avg parking time and the ratio η prediction fuzzy inference rules of the current traffic state, hereinafter referred to as a fuzzy inference rule two, table 3 is the average acceleration a Avg large acceleration, based on the average operating speed V1 Avg and the parking time ratio η predict the fuzzy inference rules of the current traffic state, hereinafter referred to as fuzzy inference rule 3. Of course, a person skilled in the art can also establish an average acceleration A Avg and an average running speed V1 Avg similar to those of Tables 1 to 3 according to the difference of the parking time ratio η, which is not limited in the embodiment of the present invention.
表1 交通状态的模糊推理规则一Table 1 Fuzzy Inference Rule 1 of Traffic Status
小加速度Small acceleration 低停车比例Low parking ratio 中停车比例Medium parking ratio 高停车比例High parking ratio
低速Low speed 近郊中低速状态Suburban low speed 近郊中低速状态Suburban low speed 市区拥堵状态Urban congestion
中速Medium speed 远郊中高速状态High-speed state in the outer suburbs 市区畅通状态Uninterrupted urban condition 市区畅通状态Uninterrupted urban condition
高速high speed 高速状态High speed state 远郊中高速状态High-speed state in the outer suburbs 市区畅通状态Uninterrupted urban condition
表2 交通状态的模糊推理规则二Table 2 Fuzzy Inference Rule 2 of Traffic Status
中加速度Medium acceleration 低停车比例Low parking ratio 中停车比例Medium parking ratio 高停车比例High parking ratio
低速Low speed 近郊中低速状态Suburban low speed 近郊中低速状态Suburban low speed 市区畅通状态Uninterrupted urban condition
中速Medium speed 远郊中高速状态High-speed state in the outer suburbs 近郊中低速状态Suburban low speed 市区畅通状态Uninterrupted urban condition
高速high speed 高速状态High speed state 远郊中高速状态High-speed state in the outer suburbs 远郊中高速状态High-speed state in the outer suburbs
表3 交通状态的模糊推理规则三Table 3 Fuzzy inference rules for traffic conditions
大加速度Large acceleration 低停车比例Low parking ratio 中停车比例Medium parking ratio 高停车比例High parking ratio
低速Low speed 近郊中低速状态Suburban low speed 近郊中低速状态Suburban low speed 近郊中低速状态Suburban low speed
中速Medium speed 远郊中高速状态High-speed state in the outer suburbs 远郊中高速状态High-speed state in the outer suburbs 市区畅通状态Uninterrupted urban condition
高速high speed 高速状态High speed state 远郊中高速状态High-speed state in the outer suburbs 远郊中高速状态High-speed state in the outer suburbs
这样，制动能量回收***根据存储的隶属函数确定该EV的平均加速度为小加速度或者中加速度或者大加速度后，则可以根据不同加速度对应的模糊规则预测EV当前的交通状态。表1中，当EV的平均加速度为小加速度时，若该EV的平均运行速度为低速且停车时间比例为中停车比例，则预测该EV当前的交通状态为近郊中低速状态；表2中，当EV的平均加速度为中加速度时，若该EV的平均运行速度为高速且停车时间比例为低停车比例，则预测该EV当前的交通状态为高速状态；表3中，当EV的平均加速度为大加速度时，若该EV的平均运行速度为中速且停车时间比例为高停车比例，则预测该EV当前的交通状态为市区畅通状态。In this way, after the braking energy recovery system determines that the average acceleration of the EV is a small acceleration or a medium acceleration or a large acceleration according to the stored membership function, the current traffic state of the EV can be predicted according to the fuzzy rule corresponding to the different accelerations. In Table 1, when the average acceleration of the EV is a small acceleration, if the average running speed of the EV is low speed and the parking time ratio is the medium parking ratio, the current traffic state of the EV is predicted to be a suburban low-speed state; in Table 2, When the average acceleration of the EV is medium acceleration, if the average running speed of the EV is high speed and the parking time ratio is a low parking ratio, the current traffic state of the EV is predicted to be a high speed state; in Table 3, when the average acceleration of the EV is In the case of large acceleration, if the average running speed of the EV is medium speed and the parking time ratio is a high parking ratio, it is predicted that the current traffic state of the EV is an urban unblocked state.
从而，通过隶属函数将EV的运行参数模糊化，然后通过模糊规则库及模糊推理法，预测EV当前的交通状态。Thus, the operating parameters of the EV are fuzzified by the membership function, and then the current traffic state of the EV is predicted by the fuzzy rule base and the fuzzy inference method.
2、简化的竞争型神经网络方法：2. Simplified competitive neural network method:
为了更为准确地预测当前的交通状态，本发明实施例还可以采用竞争型神经网络方法将EV的运行参数信息与预设交通状态的特征参数信息进行比较判定。如图4所示，为本发明实施例中采用的简化的竞争型神经网络的结构图。首先将制动能量回收***获取的多个运行参数信息作为该竞争型神经网络的输入向量，然后将输入向量分别与该竞争型神经网络中的每个神经元的权值向量W i相乘，当有多个输入向量时，则多个输入向量中的每个输入向量分别与每个神经元的权值向量相乘，相乘后的结果作为竞争层传输函数的输入。从而，当某一神经元的权值向量与输入向量最为接近时，则该权值向量对应的竞争层传输函数的输出为1，从而赢得竞争。 In order to more accurately predict the current traffic state, the embodiment of the present invention may also use a competitive neural network method to compare and determine the operating parameter information of the EV with the characteristic parameter information of the preset traffic state. As shown in FIG. 4, it is a structural diagram of a simplified competitive neural network used in an embodiment of the present invention. A plurality of operating parameter information of the braking energy recovery system is first acquired as the input vector of the neural network competition, and then multiplies the input vector and the respective values of the weights of each neuron in a neural network Competitive vector W i, When there are multiple input vectors, each of the plurality of input vectors is multiplied by the weight vector of each neuron, respectively, and the multiplied result is used as the input to the competition layer transfer function. Thus, when the weight vector of a certain neuron is closest to the input vector, the output of the competition layer transfer function corresponding to the weight vector is 1 to win the competition.
将上述竞争型神经网络应用到预测当前的交通状态中，具体实现方式为：The above competitive neural network is applied to predict the current traffic state, and the specific implementation manner is:
将制动能量回收***获取的车辆的运行参数信息作为该竞争型神经网络的输入向量。比如，获取的运行参数信息包括平均车速V Avg、平均运行车速V1 Avg、停车时间比例η、平均加速度A1 Avg、以及平均减速度A2 Avg，运行参数信息构成的输入向量为[V Avg,V1 Avg,η,A1 Avg,A2 Avg]。该竞争型神经网络中的神经元将输入向量与由已知的预设交通状态中的每个预设 交通状态的特征参数信息构成的特征向量[V Avg_prei,V1 Avg_prei,η _prei,A1 Avg_prei,A2 Avg_prei]进行比较,获取输入向量与每个预设交通状态的特征向量之间的差值，确定多个差值中的最小值，与最小值对应的预设交通状态即为该EV当前的交通状态。其中i＝1，2，3，4，5…N，分别代表已知的预设交通状态中每个预设交通状态对应的特征参数信息，如[V Avg_pre1,V1 Avg_pre1,η _pre1,A1 Avg_pre1,A2 Avg_pre1]为市区拥堵状态的特征参数信息组成的向量，[V Avg_pre2,V1 Avg_pre2,η _pre2,A1 Avg_pre2,A2 Avg_pre2]为高速状态的特征参数信息组成的向量。 The operating parameter information of the vehicle acquired by the braking energy recovery system is used as an input vector of the competitive neural network. For example, the acquired operating parameter information includes the average vehicle speed V Avg , the average running speed V1 Avg , the parking time ratio η, the average acceleration A1 Avg , and the average deceleration A2 Avg , and the input vector composed of the operating parameter information is [V Avg , V1 Avg , η, A1 Avg , A2 Avg ]. The neuron in the competitive neural network combines the input vector with a feature vector [V Avg_prei , V1 Avg_prei , η _prei , A1 Avg_prei , which is composed of characteristic parameter information of each preset traffic state in the known preset traffic state. A2 Avg_prei ] compares, obtains a difference between the input vector and the feature vector of each preset traffic state, determines a minimum value among the plurality of differences, and the preset traffic state corresponding to the minimum value is the current EV Traffic status. Where i=1, 2, 3, 4, 5...N, respectively represent characteristic parameter information corresponding to each preset traffic state in the known preset traffic state, such as [V Avg_pre1 , V1 Avg_pre1 , η _pre1 , A1 Avg_pre1 , A2 Avg_pre1 ] is a vector composed of characteristic parameter information of urban congestion state, [V Avg_pre2 , V1 Avg_pre2 , η _pre2 , A1 Avg_pre2 , A2 Avg_pre2 ] is a vector composed of characteristic parameter information of a high speed state.
为了简化求取多个差值中的最小值的计算量，请参考图5，为本发明实施例将上述竞争型神经网络应用到预测当前的交通状态中的一种计算方法示意图，当获取输入向量与每个预设交通状态的特征向量之间的差值后，首先对获取的每个差值进行归一化处理，比如，将每个差值分别进行取绝对值运算，然后分别求取各个绝对值的反正切函数(actan)，再分别将各个反正切函数的值乘以2/pi，从而获得归一化结果，然后将归一化的结果与权重向量W相乘，其中，权重向量W中包含各特征参数对应的权重值，其中，各特征参数信息的参数类型与各运行参数信息的参数类型一一对应，比如，W＝[W 1、W 2、W 3、W 4、W 5]，即平均车速V Avg对应的权重值为W 1、平均运行车速V1 Avg对应的权重值为W 2、停车时间比例η对应的权重值为W 3、平均加速度A1 Avg对应的权重值为W 4、平均减速度A2 Avg对应的权重值为W 5。将归一化结果与权重向量相乘后，对每个相乘后的结果取绝对值，最后获取每个取绝对值后的向量的模值，以此表示当前的交通状态与预设交通状态的差异大小，最后，确定与模值中的最小值对应的预设交通状态为该EV当前的交通状态。 In order to simplify the calculation of the minimum of the plurality of differences, please refer to FIG. 5, which is a schematic diagram of a calculation method for applying the competitive neural network to predicting the current traffic state according to an embodiment of the present invention. After the difference between the vector and the feature vector of each preset traffic state, each of the obtained differences is first normalized, for example, each difference is separately subjected to an absolute value operation, and then separately obtained. The inverse tangent function (actan) of each absolute value is multiplied by 2/pi for each inverse tangent function, respectively, to obtain a normalized result, and then the normalized result is multiplied by the weight vector W, wherein the weight The vector W includes a weight value corresponding to each feature parameter, wherein the parameter type of each feature parameter information corresponds to the parameter type of each operation parameter information, for example, W=[W 1 , W 2 , W 3 , W 4 , W 5 ], that is, the weight value corresponding to the average vehicle speed V Avg is W 1 , the weight value corresponding to the average running vehicle speed V1 Avg is W 2 , the weight value corresponding to the parking time ratio η is W 3 , and the weight value corresponding to the average acceleration A1 Avg to W 4 Weight average deceleration value corresponding to A2 Avg weight W 5. After multiplying the normalized result by the weight vector, the absolute value is obtained for each multiplied result, and finally the modulus value of each vector after taking the absolute value is obtained, thereby indicating the current traffic state and the preset traffic state. The difference in size, and finally, the preset traffic state corresponding to the minimum value in the modulus value is determined as the current traffic state of the EV.
S203：制动能量回收***基于与当前的交通状态匹配的回馈力矩映射关系信息，确定车辆在当前的交通状态下处于制动状态时的回馈力矩。S203: The braking energy recovery system determines the feedback torque when the vehicle is in the braking state in the current traffic state based on the feedback torque mapping relationship information matched with the current traffic state.
其中，回馈力矩用于指示在车辆进入制动状态时用于对车辆产生制动作用的力矩。Wherein, the feedback torque is used to indicate a moment for braking the vehicle when the vehicle enters the braking state.
在本发明实施例中，制动能量回收***中预先存储与各个预设交通状态对应的制动力矩映射关系。比如，与市区拥堵状态对应的制动力矩映射关系1，与高速状态对应的制动力矩映射关系2，不同的预设交通状态对应不同的制动力矩映射关系。当制动能量回收***确定该EV当前的交通状态后，制动能量回收***则需要根据该交通状态及EV的运行参数信息，计算该EV在当前的交通状态下，进入制动状态时的回馈力矩。In the embodiment of the present invention, the braking torque recovery relationship corresponding to each preset traffic state is stored in advance in the braking energy recovery system. For example, the braking torque mapping relationship 1 corresponding to the urban congestion state and the braking torque mapping relationship 2 corresponding to the high speed state, the different preset traffic states correspond to different braking torque mapping relationships. After the braking energy recovery system determines the current traffic state of the EV, the braking energy recovery system needs to calculate the feedback of the EV in the current traffic state when entering the braking state according to the traffic state and the operating parameter information of the EV. Torque.
在本发明实施例中，回馈力矩映射关系信息中包括至少一组车辆的速度、制动程度以及回馈力矩之间的映射关系，其中，制动程度表征车辆进入制动状态前的速度与车辆进入制动状态后的速度的变化量，在本发明实施例中，通过EV的制动踏板的开度来表征制动程度；当然，本领域技术人员应该知道，当EV有其他能够对EV产生制动作用的制动装置时，也可以通过检测该制动装置的状态来表征制动程度，在此不作限制。In the embodiment of the present invention, the feedback torque mapping relationship information includes a mapping relationship between a speed, a braking degree, and a feedback torque of at least one group of vehicles, wherein the braking degree represents a speed before the vehicle enters the braking state and the vehicle enters The amount of change in speed after the braking state, in the embodiment of the present invention, the degree of braking is characterized by the opening degree of the brake pedal of the EV; of course, those skilled in the art should know that when the EV has other factors capable of generating EV In the case of a moving brake device, the degree of braking can also be characterized by detecting the state of the brake device, which is not limited herein.
以EV的制动踏板的开度来表征制动程度为例，任意一个制动力矩映射关系中至少包括车速、制动踏板开度以及制动力矩三者之间的映射关系，该制动力矩映射关系可以通过表格或者图表形式存储，或者也可以通过其他形式存储。如表4所示，为以表格形式存储的与市区拥堵状态对应的制动力矩映射关系的示例：Taking the braking degree of the EV brake pedal as an example, any one of the braking torque mapping relationships includes at least a mapping relationship between the vehicle speed, the brake pedal opening degree, and the braking torque, the braking torque. The mapping relationship can be stored in the form of a table or a chart, or it can be stored in other forms. As shown in Table 4, an example of the braking torque mapping relationship corresponding to the urban congestion state stored in a tabular form:
表4Table 4
Figure PCTCN2018073971-appb-000007
Figure PCTCN2018073971-appb-000007
表4中，当车速为30kmph、制动踏板开度为0时，制动力矩为150Nm；当车速为40kmph、制动踏板开度为20％时，制动力矩为230Nm。当然，本领域技术人员可以根据实际使用需要设置不同的参数值，在此不作限定。In Table 4, when the vehicle speed is 30kmph and the brake pedal opening is 0, the braking torque is 150Nm; when the vehicle speed is 40kmph and the brake pedal opening is 20%, the braking torque is 230Nm. Of course, a person skilled in the art can set different parameter values according to actual needs, which is not limited herein.
当制动能量回收***确定EV当前的交通状态后，则从多种制动力矩映射关系中选择与当前的交通状态对应的制动力矩映射关系，以计算EV在当前的交通状态下，进入制动状态时的回馈力矩。After the braking energy recovery system determines the current traffic state of the EV, the braking torque mapping relationship corresponding to the current traffic state is selected from the plurality of braking torque mapping relationships to calculate the entry speed of the EV under the current traffic state. The feedback torque in the moving state.
在本发明实施例中，制动能量回收***计算在当前的交通状态下进入制动状态时的回馈力矩的方式可以有如下三种情况：In the embodiment of the present invention, the braking energy recovery system can calculate the feedback torque when entering the braking state in the current traffic state, and can have the following three situations:
1、查询方式：1. Query method:
当制动能量回收***确定与当前的交通状态对应的制动力矩映射关系后，则根据EV在进入制动状态时获取的EV的速度信息以及EV的制动踏板的开度，通过查询该制动力矩映射关系直接得到当前时刻的回馈力矩，从而简便快速的确定出用于制动能量回收的回馈力矩。比如，EV当前的交通状态为市区拥堵状态，与市区拥堵状态对应的制动力矩映射关系为表4，制动能量回收***获取EV在进入制动状态时的车速为50kmph、制动踏板开度为20％，从而通过查询表4，确定EV当前的回馈力矩为250Nm。After the braking energy recovery system determines the braking torque mapping relationship corresponding to the current traffic state, the speed information of the EV acquired when the EV enters the braking state and the opening degree of the brake pedal of the EV are checked by the system. The dynamic torque mapping relationship directly obtains the feedback torque at the current moment, thereby easily and quickly determining the feedback torque for braking energy recovery. For example, the current traffic state of the EV is an urban congestion state, and the braking torque mapping relationship corresponding to the urban congestion state is Table 4. The braking energy recovery system acquires a vehicle speed of 50 kmph when the EV enters the braking state, and the brake pedal The opening is 20%, so by querying Table 4, it is determined that the current feedback torque of the EV is 250 Nm.
为了使制动能量回收***确定的回馈力矩更加符合EV的性能指标，比如，回馈力矩应小于等于EV的蓄电池所能支持的最大制动力矩，且回馈力矩应小于等于EV的电动机所支持的最大制动力矩，因此，本发明实施例提供计算回馈力矩的第2种方式，在第2种方式中，将EV的蓄电池所能支持的最大制动力矩和EV的电动机所支持的最大制动力矩作为确定EV当前的回馈力矩的影响因素。In order to make the feedback torque determined by the braking energy recovery system more in line with the performance index of the EV, for example, the feedback torque should be less than or equal to the maximum braking torque that the EV battery can support, and the feedback torque should be less than or equal to the maximum supported by the EV motor. Braking torque, therefore, the second embodiment of the present invention provides a second way of calculating the feedback torque. In the second mode, the maximum braking torque that the EV battery can support and the maximum braking torque supported by the EV motor. As a factor in determining the current feedback torque of the EV.
2、选择最小值的方式：2. The way to choose the minimum value:
当制动能量回收***确定与当前的交通状态对应的制动力矩映射关系后，首先，根据EV在进入制动状态时获取的EV的速度信息以及EV的制动踏板的开度，通过查询制动力矩映射关系得到当前时刻的初始制动力矩，然后确定该EV在进入制动状态时，EV的电动机能够支持的最大制动力矩以及该EV的蓄电池能够支持的最大制动力矩。最后从初始制动力矩、EV的电动机能够支持的最大制动力矩以及EV的蓄电池能够支持的最大制动力矩中选择最小值作为EV的回馈力矩。After the braking energy recovery system determines the braking torque mapping relationship corresponding to the current traffic state, first, according to the speed information of the EV acquired when the EV enters the braking state and the opening degree of the brake pedal of the EV, the inquiry system is adopted. The dynamic torque mapping relationship obtains the initial braking torque at the current moment, and then determines the maximum braking torque that the EV motor can support when entering the braking state and the maximum braking torque that the EV battery can support. Finally, the minimum value is selected as the feedback torque of the EV from the initial braking torque, the maximum braking torque that the EV motor can support, and the maximum braking torque that the EV battery can support.
为了能够使EV的回馈力矩更加符合EV的驾驶员的制动意图，比如，当EV的防抱死制动***(Antilock Brake System，ABS)处于启动状态时，无论制动能量回收***确定的初始回馈力矩为多大，此时EV的驾驶员期望EV禁止制动能量回收，因此，本发明实施例提供计算回馈力矩的第3种方式，在第3种方式中，将EV的驾驶员的制动意图作为确定EV当前的回馈力矩的影响因素。In order to make the feedback torque of the EV more in line with the braking intention of the EV driver, for example, when the EV anti-lock braking system (ABS) is in the starting state, regardless of the initial determination determined by the braking energy recovery system How large is the feedback torque? At this time, the driver of the EV expects the EV to prohibit the recovery of the braking energy. Therefore, the embodiment of the present invention provides a third way of calculating the feedback torque, and in the third mode, the driver of the EV is braked. The intention is to determine the influencing factor of the current feedback torque of the EV.
3、结合EV的驾驶员的制动意图的方式：3. The way to combine the driver's intention of the EV:
当制动能量回收***确定与当前的交通状态对应的制动力矩映射关系后，首先，根据EV在进入制动状态时获取的EV的速度信息以及EV的制动踏板的开度，通过查询制动力矩映射关系得到当前时刻的初始制动力矩，然后，制动能量回收***根据该EV在进入制动状态时的档位状态、主动减速状态、主动加速状态以及防抱死制动***(Antilock Brake System，ABS)中的任意一种或多种因素来确定该EV的驾驶员的制动意图，最后根据驾驶员的制动意图、初始制动力矩、EV在进入制动状态时电动机能够支持的最大制动力矩以及该EV在进入制动状态时蓄电池能够支持的最大制动力矩，确定EV的回馈力矩。请 参考图6，为制动能量回收***通过结合EV的驾驶员的制动意图的方式计算EV在制动状态下的回馈力矩的方法流程图，图中T Bat为蓄电池允许的实时最大回馈力矩绝对值，T M为电动机允许的实时最大回馈力矩绝对值，T为EV的回馈力矩。在本发明实施例中，主动减速状态可以通过检测EV的制动踏板的开度来表征，主动加速状态可以通过检测EV的加速踏板的开度来表征，当然，也可以采用其他方式表征主动减速状态和主动加速状态，在此不作限制。 After the braking energy recovery system determines the braking torque mapping relationship corresponding to the current traffic state, first, according to the speed information of the EV acquired when the EV enters the braking state and the opening degree of the brake pedal of the EV, the inquiry system is adopted. The dynamic torque mapping relationship obtains the initial braking torque at the current moment. Then, the braking energy recovery system is based on the gear state, the active deceleration state, the active acceleration state, and the anti-lock braking system (Antilock) when the EV enters the braking state. Any one or more factors in the Brake System, ABS) determine the braking intention of the driver of the EV, and finally the motor can support the braking intention according to the driver, the initial braking torque, and the EV when entering the braking state. The maximum braking torque and the maximum braking torque that the battery can support when the EV enters the braking state determine the feedback torque of the EV. Please refer to FIG. 6 , which is a flow chart of a method for calculating the feedback torque of the EV in the braking state by the braking energy recovery system by combining the braking intention of the driver of the EV. In the figure, T Bat is the real-time maximum feedback torque allowed by the battery. Absolute value, T M is the absolute value of the real-time maximum feedback torque allowed by the motor, and T is the feedback torque of the EV. In the embodiment of the present invention, the active deceleration state can be characterized by detecting the opening degree of the brake pedal of the EV, and the active acceleration state can be characterized by detecting the opening degree of the accelerator pedal of the EV. Of course, the active deceleration can also be characterized by other means. State and active acceleration state are not limited here.
具体来讲，确定回馈力矩的过程如下：Specifically, the process of determining the feedback torque is as follows:
首先，制动能量回收***确定EV的档位是否为倒档(Reverse，R档)或者前进档(Drive，D档)，若不为R/D档，则认为驾驶员无回馈制动意图，此时，确定EV的回馈力矩为0；First, the braking energy recovery system determines whether the gear position of the EV is reverse (R, R) or forward (Drive, D). If it is not R/D, the driver is considered to have no feedback intention. At this time, it is determined that the feedback torque of the EV is 0;
若档位为R/D档，则制动能量回收***进一步确定制动踏板是否处于踩下状态，即制动能量回收***是否检测到制动信号，若制动能量回收***检测到制动信号，则确定制动踏板处于踩下状态；若制动能量回收***未检测到制动信号，则确定制动踏板处于放松状态。若制动踏板处于踩下状态，且ABS或者车身电子稳定***(Electronic Stability Program，ESP)未启动，则制动回收***从初始制动力矩、EV在进入制动状态时电动机能够支持的最大制动力矩以及该EV在进入制动状态时蓄电池能够支持的最大制动力矩中确定最小值作为EV的回馈力矩。当然，制动能量回收***中也可以存储ABS***的状态与制动力矩的映射关系或ESP***的状态与制动力矩的映射关系，从而可以根据ABS***的状态或ESP***的状态确定出另一个制动力矩，将该另一个制动力矩与初始制动力矩、EV在进入制动状态时电动机能够支持的最大制动力矩以及该EV在进入制动状态时蓄电池能够支持的最大制动力矩进行比较，确定这四个数据中的最小值作为EV的回馈力矩；If the gear position is the R/D gear, the brake energy recovery system further determines whether the brake pedal is in a depressed state, that is, whether the brake energy recovery system detects the brake signal, and if the brake energy recovery system detects the brake signal , it is determined that the brake pedal is in the depressed state; if the brake energy recovery system does not detect the brake signal, it is determined that the brake pedal is in the relaxed state. If the brake pedal is depressed and the ABS or the Electronic Stability Program (ESP) is not activated, the brake recovery system will support the maximum braking torque from the initial braking torque and the EV when entering the braking state. The dynamic torque and the maximum braking torque that the battery can support when the EV enters the braking state determine the minimum value as the feedback torque of the EV. Of course, the braking energy recovery system can also store the mapping relationship between the state of the ABS system and the braking torque or the relationship between the state of the ESP system and the braking torque, so that the state of the ABS system or the state of the ESP system can be determined. a braking torque, the other braking torque and the initial braking torque, the maximum braking torque that the motor can support when the EV enters the braking state, and the maximum braking torque that the battery can support when the EV enters the braking state Comparing, determining the minimum of the four data as the feedback torque of the EV;
若档位为R/D档，制动踏板处于放松状态，而加速踏板处于踩下状态，或者加速踏板处于放松状态但ABS/ESP处于启动状态，则制动能量回收***确定EV的回馈力矩为0，其中，制动能量回收***确定加速踏板是否处于踩下状态，即制动能量回收***是否检测到油门信号，若制动能量回收***检测到油门信号，则确定加速踏板处于踩下状态；若制动能量回收***未检测到油门信号，则确定加速踏板处于放松状态；If the gear is in the R/D position, the brake pedal is in the relaxed state, and the accelerator pedal is in the depressed state, or the accelerator pedal is in the relaxed state but the ABS/ESP is in the starting state, the braking energy recovery system determines that the EV feedback torque is 0, wherein the braking energy recovery system determines whether the accelerator pedal is in a depressed state, that is, whether the brake energy recovery system detects the throttle signal, and if the brake energy recovery system detects the throttle signal, determining that the accelerator pedal is in the depressed state; If the brake energy recovery system does not detect the throttle signal, it is determined that the accelerator pedal is in a relaxed state;
若档位为R/D档，制动踏板处于踩下状态，加速踏板处于放松状态且ABS/ESP处于为启动状态，则制动能量回收***根据本发明实施例前述的方法进行回馈力矩的计算。If the gear position is the R/D gear, the brake pedal is in the depressed state, the accelerator pedal is in the relaxed state, and the ABS/ESP is in the activated state, the braking energy recovery system performs the calculation of the feedback torque according to the method described in the embodiment of the present invention. .
从而将该EV的驾驶员的制动意图结合到确定回馈力矩的计算过程中，实现制动能量回收***对回馈力矩的智能调节。Thereby, the braking intention of the driver of the EV is combined into the calculation process of determining the feedback torque, and the intelligent adjustment of the feedback torque by the braking energy recovery system is realized.
S204：所述制动能量回收***基于回馈力矩将车辆的制动能量转化为电能，并将电能存储至车辆的蓄电池中。S204: The braking energy recovery system converts braking energy of the vehicle into electrical energy based on a feedback torque, and stores the electrical energy into a battery of the vehicle.
当制动能量回收***确定EV当前的回馈力矩后，则控制制动能量回收***中的各个部件根据该回馈力矩回收EV的制动能量，从而将EV的制动能量转化为电能存储在EV的蓄电池中。在本发明实施例中，制动能量回收***将EV的制动能量转化为电能的过程与现有技术中将制动能量转化为电能的转化方法相同，在此不再赘述。After the braking energy recovery system determines the current feedback torque of the EV, each component in the control braking energy recovery system recovers the braking energy of the EV according to the feedback torque, thereby converting the braking energy of the EV into electrical energy stored in the EV. In the battery. In the embodiment of the present invention, the process of converting the braking energy of the EV into the electric energy by the braking energy recovery system is the same as the conversion method of converting the braking energy into the electric energy in the prior art, and details are not described herein again.
在本发明实施例中，进行制动能量回收时，无需增加额外的信息检测装置或传感器，根据车辆自身的车速获取的运行参数确定车辆所处的交通状态，在确定出当前的交通状态后，根据当前的交通状态自适应地调整回馈力矩的大小，可以避免由于仅凭驾驶员的经验对回馈力矩的大小进行调节的不准确性，也尽量避免由于回馈力矩过大或过小导致的能量利用率低，可以实现以较低的硬件成本，来提高对电动机的回馈力矩的大小进行调节的准 确性、提高能量的利用率的效果。In the embodiment of the present invention, when braking energy recovery is performed, it is not necessary to add an additional information detecting device or sensor, and the traffic state obtained by the vehicle according to the operating parameter acquired by the vehicle's own vehicle speed is determined, and after determining the current traffic state, By adaptively adjusting the magnitude of the feedback torque according to the current traffic state, it is possible to avoid the inaccuracy of adjusting the magnitude of the feedback torque only by the driver's experience, and also avoid the energy utilization caused by the excessive or too small feedback torque. The low rate can achieve an effect of adjusting the accuracy of the feedback torque of the motor and improving the utilization of energy with a lower hardware cost.
下面结合说明书附图介绍本发明实施例提供的电动汽车。The electric vehicle provided by the embodiment of the present invention will be described below with reference to the accompanying drawings.
请参考图7，图7为本发明实施例提供的电动汽车的可能的结构示意图，该电动汽车具体可以是BEV或者混合动力汽车(Hybrid Electric Vehicle，HEV)或者PHEV或者燃料电池汽车(Fuel cell vehicles，FCEV)等，该电动汽车用以实现如图2所示的方法中的部分步骤或全部步骤，具体的配置可以依据实际需要确定。Please refer to FIG. 7. FIG. 7 is a schematic structural diagram of an electric vehicle according to an embodiment of the present invention. The electric vehicle may be a BEV or a Hybrid Electric Vehicle (HEV) or a PHEV or a fuel cell vehicle. , FCEV), etc., the electric vehicle is used to implement some or all of the steps in the method shown in FIG. 2, and the specific configuration may be determined according to actual needs.
具体的，当图7所示的结构为本发明实施例提供的电动汽车时，控制器701用于：用于基于该电动汽车在预设时长内的速度信息，获取该电动汽车在该预设时长内的运行参数信息；其中，该预设时长大于该电动汽车在任意一次处于制动状态的时长，该制动状态为该电动汽车处于主动减速或不主动加速的状态；基于该运行参数信息，预测该电动汽车所处的运行道路的当前的交通状态；基于与该当前的交通状态匹配的回馈力矩映射关系信息，确定该电动汽车在该当前的交通状态下处于该制动状态时的回馈力矩；其中，该回馈力矩映射关系信息中包括至少一组电动汽车的速度、制动程度以及回馈力矩之间的映射关系，该回馈力矩用于指示在该电动汽车处于该制动状态时用于对该电动汽车产生制动作用的力矩，该制动程度表征该电动汽车处于该制动状态前的速度与该电动汽车处于该制动状态后的速度的变化量；电动机702，用于在该车辆处于该制动状态时，基于该回馈力矩将该车辆的制动能量转化为所述电能；蓄电池703，用于存储该电能。Specifically, when the structure shown in FIG. 7 is an electric vehicle provided by an embodiment of the present invention, the controller 701 is configured to: acquire, according to the speed information of the electric vehicle within a preset time period, the electric vehicle at the preset The operating parameter information in the duration; wherein the preset duration is greater than the length of the electric vehicle in any braking state, the braking state is a state in which the electric vehicle is actively decelerating or not actively accelerating; based on the operating parameter information Predicting a current traffic state of the running road on which the electric vehicle is located; determining feedback of the electric vehicle when the braking state is in the current traffic state based on the feedback torque mapping relationship information matching the current traffic state a torque; wherein the feedback torque mapping relationship information includes a mapping relationship between a speed, a braking degree, and a feedback torque of the at least one group of electric vehicles, the feedback torque is used to indicate that the electric vehicle is used when the braking state is in the braking state a torque that produces a braking effect on the electric vehicle, the degree of braking characterizing the speed of the electric vehicle before the braking state And a change amount of the speed of the electric vehicle after the braking state; the motor 702 is configured to convert the braking energy of the vehicle into the electric energy based on the feedback torque when the vehicle is in the braking state; 703, for storing the electrical energy.
在实际应用中，请参考图8，为图7所示的电动汽车在执行如图2所示的方法的具体实现示意图。In practical applications, please refer to FIG. 8 , which is a schematic diagram of a specific implementation of the method shown in FIG. 2 for the electric vehicle shown in FIG. 7 .
S801：控制器701基于电动汽车在预设时长内的速度信息，获取电动汽车在预设时长内的运行参数信息；S801: The controller 701 acquires operation parameter information of the electric vehicle within a preset time period based on the speed information of the electric vehicle within a preset time period;
S802：控制器701基于运行参数信息，预测电动汽车所处的运行道路的当前的交通状态；S802: The controller 701 predicts a current traffic state of the running road where the electric vehicle is located based on the operation parameter information;
S803：控制器701基于与当前的交通状态匹配的回馈力矩映射关系信息，确定电动汽车在当前的交通状态下处于制动状态时的回馈力矩。S803: The controller 701 determines a feedback torque when the electric vehicle is in a braking state in a current traffic state, based on the feedback torque mapping relationship information matched with the current traffic state.
控制器701执行步骤S801-S803的过程可以参考图2所示的步骤S201-S203，在此不再赘述。For the process of the controller 701 to perform the steps S801-S803, reference may be made to the steps S201-S203 shown in FIG. 2, and details are not described herein again.
S804：控制器701将回馈力矩发送给电动机702，则电动机702接收该回馈力矩指令。S804: The controller 701 sends the feedback torque to the motor 702, and the motor 702 receives the feedback torque command.
当控制器701确定车辆的回馈力矩后，则生成回馈力矩指令，通过该回馈力矩指令向电动机702指示回馈力矩，并控制电动机702进入回馈发电状态。When the controller 701 determines the feedback torque of the vehicle, a feedback torque command is generated, by which the feedback torque is commanded to the motor 702, and the motor 702 is controlled to enter the feedback power generation state.
S805：电动机702基于该回馈力矩将制动能量转化为电能。S805: The motor 702 converts braking energy into electrical energy based on the feedback torque.
电动机702接收控制器701发送的回馈力矩指令后，则将电动机702的工作状态调整为发电制动状态，并根据该指令中的回馈力矩将制动能量转化为电能。After receiving the feedback torque command sent by the controller 701, the motor 702 adjusts the operating state of the motor 702 to the generating braking state, and converts the braking energy into electrical energy according to the feedback torque in the command.
S806：电动机702将电能传输给蓄电池703。S806: The motor 702 transmits power to the battery 703.
电动机702在将制动能量转化为电能时，将转化的电能传输给蓄电池703存储，从而完成制动能量回收的过程。The electric motor 702 transmits the converted electric energy to the storage battery 703 for storage when converting the braking energy into electric energy, thereby completing the process of braking energy recovery.
请参考图9，图9为本发明实施例提供的电动汽车的可能的结构框图，该电动汽车包括采集单元901、确定单元902、计算单元903以及执行单元904。Please refer to FIG. 9. FIG. 9 is a block diagram showing a possible structure of an electric vehicle according to an embodiment of the present invention. The electric vehicle includes an acquisition unit 901, a determination unit 902, a calculation unit 903, and an execution unit 904.
在实际应用中，采集单元901、确定单元902以及计算单元903对应的实体装置可以为图7中的控制器701，执行单元904对应的实体装置可以为图7中的电动机702以及蓄 电池703，在本发明实施例中不作限制。In an actual application, the physical device corresponding to the collecting unit 901, the determining unit 902, and the calculating unit 903 may be the controller 701 in FIG. 7, and the physical device corresponding to the executing unit 904 may be the motor 702 and the battery 703 in FIG. The embodiment of the invention is not limited.
本发明实施例中的电动汽车可以用于执行上述图2所示的实施例提供的方法，对于该电动汽车中的各模块所实现的功能等，可参考如前方法部分的描述，在此不多赘述。The electric vehicle in the embodiment of the present invention can be used to perform the method provided in the embodiment shown in FIG. 2 above. For the functions and functions implemented by the modules in the electric vehicle, reference may be made to the description of the previous method section, and More details.
在本发明实施例中，应该理解到，所揭露的***和方法，可以通过其它的方式实现。例如，以上所描述的***实施例仅仅是示意性的，例如，所述整车控制器或电动机控制器的划分，仅仅为一种逻辑功能划分，实际实现时可以有另外的划分方式，例如将整车控制器和电动机控制器集成到一个结构中，或一些特征可以忽略，或不执行。另一点，所显示或讨论的相互之间的耦合或直接耦合或通信连接可以是通过一些接口，各个部件的间接耦合或通信连接，可以是电性或其它的形式。In the embodiments of the present invention, it should be understood that the disclosed system and method may be implemented in other manners. For example, the system embodiment described above is merely illustrative. For example, the division of the vehicle controller or the motor controller is only a logical function division, and the actual implementation may have another division manner, for example, The vehicle controller and motor controller are integrated into one structure, or some features can be ignored or not executed. In addition, the coupling or direct coupling or communication connection between the components shown or discussed may be through some interfaces, indirect coupling or communication connections of the various components, and may be electrical or otherwise.
在上述发明实施例中，可以全部或部分地通过软件、硬件、固件或者其任意组合来实现。当使用软件实现时，可以全部或部分地以计算机程序产品的形式实现。所述计算机程序产品包括一个或多个计算机指令。在计算机上加载和执行所述计算机程序指令时，全部或部分地产生按照本发明实施例所述的流程或功能。所述计算机可以是通用计算机、专用计算机、计算机网络、或者其他可编程装置。所述计算机指令可以存储在计算机可读存储介质中，或者从一个计算机可读存储介质向另一个可读存储介质传输，例如，所述计算机指令可以从一个网站站点、计算机、服务器或数据中心通过有线(例如同轴电缆、光纤、数字用户线(DSL))或无线(例如红外、无线、微波等)方式向另一个网站站点、计算机、服务器或数据中心进行传输。所述计算机可读存储介质可以是计算机能够存取的任何可用介质或者是包含一个或多个可用介质集成的服务器、数据中心等数据存储设备。所述可用介质可以是磁性介质，(例如，软盘、硬盘、磁带)、光介质(例如，DVD)、或者半导体介质(例如，固态硬盘Solid State Disk(SSD))等。In the above described embodiments of the invention, it may be implemented in whole or in part by software, hardware, firmware or any combination thereof. When implemented in software, it may be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, the processes or functions described in accordance with embodiments of the present invention are generated in whole or in part. The computer can be a general purpose computer, a special purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer readable storage medium or transferred from one computer readable storage medium to another readable storage medium, for example, the computer instructions can be passed from a website site, computer, server or data center Wired (eg, coaxial cable, fiber optic, digital subscriber line (DSL)) or wireless (eg, infrared, wireless, microwave, etc.) to another website site, computer, server, or data center. The computer readable storage medium can be any available media that can be accessed by a computer or a data storage device such as a server, data center, or the like that includes one or more available media. The usable medium may be a magnetic medium (eg, a floppy disk, a hard disk, a magnetic tape), an optical medium (eg, a DVD), or a semiconductor medium (eg, a solid state disk (SSD)) or the like.
以上实施例仅用以对本发明实施例的技术方案进行详细介绍，但以上实施例的说明只是用于帮助理解本发明实施例的方法及其核心思想，不应该理解为对本申请的限制。本领域技术人员在本发明实施例揭露的技术范围内，可轻易想到的变化或替换，都应涵盖在本发明实施例的保护范围之内。The above embodiments are only used to describe the technical solutions of the embodiments of the present invention, but the description of the above embodiments is only used to help understand the method and the core ideas of the embodiments of the present invention, and should not be construed as limiting the present application. Those skilled in the art, within the scope of the technical scope of the present invention, are susceptible to variations and substitutions within the scope of the present invention.

Claims (20)

  1. 一种制动能量回收方法，其特征在于，包括：A braking energy recovery method, comprising:
    车辆的制动能量回收***基于所述车辆在预设时长内的速度信息，获取所述车辆在所述预设时长内的运行参数信息；其中，所述预设时长大于所述车辆在任意一次处于制动状态的时长，所述制动状态为所述车辆处于主动减速或不主动加速的状态；The braking energy recovery system of the vehicle acquires operating parameter information of the vehicle within the preset time length based on the speed information of the vehicle within a preset time period; wherein the preset time length is greater than the vehicle at any time a duration of the braking state, wherein the braking state is a state in which the vehicle is actively decelerating or not actively accelerating;
    基于所述运行参数信息，预测所述车辆所处的运行道路的当前的交通状态；Predicting a current traffic state of the running road on which the vehicle is located based on the operating parameter information;
    基于与所述当前的交通状态匹配的回馈力矩映射关系信息，确定所述车辆在所述当前的交通状态下处于所述制动状态时的回馈力矩；其中，所述回馈力矩映射关系信息中包括至少一组车辆的速度、制动程度以及回馈力矩之间的映射关系，所述回馈力矩用于指示在所述车辆处于所述制动状态时用于对所述车辆产生制动作用的力矩，所述制动程度表征所述车辆处于所述制动状态前的速度与所述车辆处于所述制动状态后的速度的变化量；And determining, according to the feedback torque mapping relationship information that matches the current traffic state, a feedback torque when the vehicle is in the braking state in the current traffic state; wherein the feedback torque mapping relationship information includes a mapping relationship between a speed, a degree of braking, and a feedback torque of at least one group of vehicles, the feedback torque being used to indicate a moment for generating a braking effect on the vehicle when the vehicle is in the braking state, The degree of braking characterizes a change in speed of the vehicle before the braking state and a speed of the vehicle after the braking state is in the braking state;
    在所述车辆处于所述制动状态时，基于所述回馈力矩将所述车辆的制动能量转化为电能，并将所述电能存储至所述车辆的蓄电池中。When the vehicle is in the braking state, braking energy of the vehicle is converted into electrical energy based on the feedback torque, and the electrical energy is stored into a battery of the vehicle.
  2. 如权利要求1所述的方法，其特征在于，基于所述运行参数信息，预测所述车辆所处的运行道路的当前的交通状态，包括：The method of claim 1, wherein predicting a current traffic state of the running road on which the vehicle is located based on the operating parameter information comprises:
    获取至少一个预设交通状态的至少一个特征参数信息；Obtaining at least one characteristic parameter information of at least one preset traffic state;
    确定与所述运行参数信息相匹配的特征参数信息对应的预设交通状态为所述当前的交通状态。Determining, by the feature parameter information that matches the operation parameter information, a preset traffic state is the current traffic state.
  3. 如权利要求2所述的方法，其特征在于，确定与所述运行参数信息相匹配的特征参数信息对应的预设交通状态为所述当前的交通状态，包括：The method of claim 2, wherein determining the preset traffic state corresponding to the feature parameter information that matches the operating parameter information is the current traffic state, including:
    获取所述至少一个特征参数信息中的每个特征参数信息与所述每个特征参数信息的取值范围之间的隶属函数；Obtaining a membership function between each of the at least one feature parameter information and a value range of each of the feature parameter information;
    根据所述隶属函数确定与所述运行参数信息相匹配的特征参数信息。Feature parameter information that matches the operational parameter information is determined according to the membership function.
  4. 如权利要求3所述的方法，其特征在于，所述特征参数信息包括所述车辆在一次减速开始到下一次减速开始的间隔时长内的平均车速、所述车辆在所述间隔时长内处于速度为零的总时长与所述间隔时长的比例、以及所述车辆在所述间隔时长内处于速度不为零的状态内的平均加速度；The method according to claim 3, wherein said characteristic parameter information comprises an average vehicle speed of said vehicle during an interval from the start of one deceleration to the start of the next deceleration, and said vehicle is at a speed within said interval duration a ratio of the total duration of zero to the interval duration, and an average acceleration of the vehicle in a state where the speed is not zero during the interval duration;
    获取所述至少一个特征参数信息中的每个特征参数信息与所述每个特征参数信息的取值范围之间的隶属函数，包括：Obtaining a membership function between each feature parameter information of the at least one feature parameter information and a value range of each feature parameter information, including:
    获取所述平均车速与所述平均车速的取值范围之间的第一隶属函数、所述比例与所述比例的取值范围之间的第二隶属函数、以及所述平均加速度与所述平均加速度的取值范围之间的第三隶属函数；其中，所述第一隶属函数、所述第二隶属函数及所述第三隶属函数均为分段函数。Obtaining a first membership function between the average vehicle speed and a range of values of the average vehicle speed, a second membership function between the ratio and a range of values of the ratio, and the average acceleration and the average a third membership function between the range of values of the acceleration; wherein the first membership function, the second membership function, and the third membership function are both piecewise functions.
  5. 如权利要求4所述的方法，其特征在于，根据所述隶属函数确定与所述运行参数信息相匹配的特征参数信息，包括：The method of claim 4, wherein determining the feature parameter information that matches the operating parameter information according to the membership function comprises:
    确定所述运行参数信息中的平均车速在所述第一隶属函数中对应的第一分段函数、所述运行参数信息中的比例在所述第二隶属函数中对应的第二分段函数、以及所述运行参数信息中的平均加速度在所述第三隶属函数中对应的第三分段函数；Determining, in the first parameter function, a corresponding first segment function, a ratio of the operation parameter information in the first membership function, a second segmentation function corresponding to the second membership function, And a third piecewise function corresponding to the average acceleration in the operating parameter information in the third membership function;
    确定与所述第一分段函数、所述第二分段函数以及所述第三分段函数相匹配的特征参数信息。Feature parameter information that matches the first segmentation function, the second segmentation function, and the third segmentation function is determined.
  6. 如权利要求2所述的方法，其特征在于，确定与所述运行参数信息相匹配的特征参数信息对应的预设交通状态为所述当前的交通状态，包括：The method of claim 2, wherein determining the preset traffic state corresponding to the feature parameter information that matches the operating parameter information is the current traffic state, including:
    计算所述运行参数信息与所述至少一个特征参数信息的至少一个差异值；Calculating at least one difference value between the operation parameter information and the at least one feature parameter information;
    确定所述至少一个差异值中的最小值；Determining a minimum of the at least one difference value;
    确定与所述最小值对应的预设交通状态为所述当前的交通状态。Determining a preset traffic state corresponding to the minimum value as the current traffic state.
  7. 如权利要求6所述的方法，其特征在于，计算所述运行参数信息与所述至少一个特征参数信息中的第一特征参数信息的差异值，包括：The method according to claim 6, wherein calculating a difference value between the operation parameter information and the first feature parameter information of the at least one feature parameter information comprises:
    计算所述运行参数信息与所述第一特征参数信息的差值；所述第一特征参数信息为所述至少一个特征参数信息中的任意一个特征参数信息；Calculating a difference between the operation parameter information and the first feature parameter information; the first feature parameter information is any one of the at least one feature parameter information;
    将所述差值进行归一化处理，得到归一化的差值；Normalizing the difference to obtain a normalized difference;
    将所述归一化的差值与为所述第一特征参数信息设置的权重向量相乘，得到差值向量；Multiplying the normalized difference value by a weight vector set for the first feature parameter information to obtain a difference vector;
    确定所述差值向量的模值为所述运行参数信息与所述第一特征参数信息的差异值。Determining a modulus value of the difference vector as a difference value between the operation parameter information and the first feature parameter information.
  8. 如权利要求2-7任一所述的方法，其特征在于，基于与所述当前的交通状态匹配的回馈力矩映射关系信息，确定所述车辆在所述当前的交通状态下处于所述制动状态时的回馈力矩，包括：The method according to any one of claims 2-7, wherein the vehicle is determined to be in the braking in the current traffic state based on feedback torque mapping relationship information that matches the current traffic state. The feedback torque in the state, including:
    从至少一个回馈力矩映射关系信息中获取与所述当前的交通状态匹配的回馈力矩映射关系信息；其中，所述至少一个预设交通状态与所述至少一个回馈力矩映射关系信息一一对应；Obtaining feedback torque mapping relationship information that matches the current traffic state from the at least one feedback torque mapping relationship information; wherein the at least one preset traffic state and the at least one feedback torque mapping relationship information are in one-to-one correspondence;
    基于所述车辆的速度及所述车辆的制动程度，从所述回馈力矩映射关系信息中确定所述车辆的所述回馈力矩。The feedback torque of the vehicle is determined from the feedback torque mapping relationship information based on a speed of the vehicle and a degree of braking of the vehicle.
  9. 如权利要求2-7任一所述的方法，其特征在于，基于与所述当前的交通状态匹配的回馈力矩映射关系信息，确定所述车辆在所述当前的交通状态下处于所述制动状态时的回馈力矩，包括：The method according to any one of claims 2-7, wherein the vehicle is determined to be in the braking in the current traffic state based on feedback torque mapping relationship information that matches the current traffic state. The feedback torque in the state, including:
    从至少一个回馈力矩映射关系信息中获取与所述当前的交通状态匹配的回馈力矩映射关系信息；其中，所述至少一个预设交通状态与所述至少一个回馈力矩映射关系信息一一对应；Obtaining feedback torque mapping relationship information that matches the current traffic state from the at least one feedback torque mapping relationship information; wherein the at least one preset traffic state and the at least one feedback torque mapping relationship information are in one-to-one correspondence;
    从所述回馈力矩映射关系信息中确定与所述车辆的速度及所述车辆的制动程度相应的回馈力矩；Determining, from the feedback torque mapping relationship information, a feedback torque corresponding to a speed of the vehicle and a braking degree of the vehicle;
    确定所述车辆在所述制动状态时，所述电动机支持的第一最大回馈力矩；Determining a first maximum feedback torque supported by the motor when the vehicle is in the braking state;
    确定所述蓄电池在当前时刻支持的第二最大回馈力矩；Determining a second maximum feedback torque supported by the battery at a current time;
    确定与所述车辆的速度及所述车辆的制动程度相应的回馈力矩、所述第一最大回馈力矩以及所述第二最大回馈力矩中的最小值为所述车辆的所述回馈力矩。Determining a minimum of a feedback torque corresponding to a speed of the vehicle and a degree of braking of the vehicle, the first maximum feedback torque, and the second maximum feedback torque is the feedback torque of the vehicle.
  10. 如权利要求2-7任一所述的方法，其特征在于，基于与所述当前的交通状态匹配的回馈力矩映射关系信息，确定所述车辆在所述当前的交通状态下处于所述制动状态时的回馈力矩，包括：The method according to any one of claims 2-7, wherein the vehicle is determined to be in the braking in the current traffic state based on feedback torque mapping relationship information that matches the current traffic state. The feedback torque in the state, including:
    从至少一个回馈力矩映射关系信息中获取与所述当前的交通状态匹配的回馈力矩映射关系信息；其中，所述至少一个预设交通状态与所述至少一个回馈力矩映射关系 信息一一对应；Obtaining feedback torque mapping relationship information that matches the current traffic state from the at least one feedback torque mapping relationship information; wherein the at least one preset traffic state and the at least one feedback torque mapping relationship information are in one-to-one correspondence;
    从所述回馈力矩映射关系信息中确定与所述车辆的速度及所述车辆的制动程度相应的回馈力矩；Determining, from the feedback torque mapping relationship information, a feedback torque corresponding to a speed of the vehicle and a braking degree of the vehicle;
    确定所述车辆在所述制动状态时，所述电动机支持的第一最大回馈力矩；Determining a first maximum feedback torque supported by the motor when the vehicle is in the braking state;
    确定所述蓄电池在当前时刻支持的第二最大回馈力矩；Determining a second maximum feedback torque supported by the battery at a current time;
    基于所述车辆的档位状态、主动减速状态、主动加速状态以及防抱死制动状态中的至少一种因素，确定所述车辆的驾驶员的制动意图；Determining a braking intention of a driver of the vehicle based on at least one of a gear state of the vehicle, an active deceleration state, an active acceleration state, and an anti-lock braking state;
    基于所述驾驶员的制动意图、与所述车辆的速度及所述车辆的制动程度相应的回馈力矩、所述第一最大回馈力矩以及所述第二最大回馈力矩，确定所述车辆的所述回馈力矩。Determining the vehicle based on a braking intention of the driver, a feedback torque corresponding to a speed of the vehicle and a braking degree of the vehicle, the first maximum feedback torque, and the second maximum feedback torque The feedback torque.
  11. 一种电动汽车，其特征在于，包括控制器、电动机和蓄电池，其中：An electric vehicle characterized by comprising a controller, an electric motor and a battery, wherein:
    控制器，用于基于所述电动汽车在预设时长内的速度信息，获取所述电动汽车在所述预设时长内的运行参数信息；其中，所述预设时长大于所述电动汽车在任意一次处于制动状态的时长，所述制动状态为所述电动汽车处于主动减速或不主动加速的状态；基于所述运行参数信息，预测所述电动汽车所处的运行道路的当前的交通状态；基于与所述当前的交通状态匹配的回馈力矩映射关系信息，确定所述电动汽车在所述当前的交通状态下处于所述制动状态时的回馈力矩；其中，所述回馈力矩映射关系信息中包括至少一组电动汽车的速度、制动程度以及回馈力矩之间的映射关系，所述回馈力矩用于指示在所述电动汽车处于所述制动状态时用于对所述电动汽车产生制动作用的力矩，所述制动程度表征所述电动汽车处于所述制动状态前的速度与所述电动汽车处于所述制动状态后的速度的变化量；a controller, configured to acquire, according to the speed information of the electric vehicle within a preset duration, operation parameter information of the electric vehicle within the preset duration; wherein the preset duration is greater than the electric vehicle is at any a duration in a braking state in which the electric vehicle is in an active deceleration or an inactive acceleration state; and based on the operating parameter information, predicting a current traffic state of a running road on which the electric vehicle is located And determining, according to the feedback torque mapping relationship information that matches the current traffic state, a feedback torque when the electric vehicle is in the braking state in the current traffic state; wherein the feedback torque mapping relationship information A mapping relationship between a speed, a braking degree, and a feedback torque of at least one group of electric vehicles, the feedback torque being used to indicate that the electric vehicle is used to generate the electric vehicle when the electric vehicle is in the braking state a moment of action that characterizes the speed of the electric vehicle before the braking state and the electric vehicle The amount of change speed state after the brake;
    电动机，用于在所述电动汽车处于所述制动状态时，基于所述回馈力矩将所述电动汽车的制动能量转化为电能；An electric motor, configured to convert braking energy of the electric vehicle into electric energy based on the feedback torque when the electric vehicle is in the braking state;
    蓄电池，用于存储所述电能。a battery for storing the electrical energy.
  12. 如权利要求11所述的电动汽车，其特征在于，所述控制器基于所述运行参数信息，预测所述车辆所处的运行道路的当前的交通状态时，具体用于：The electric vehicle according to claim 11, wherein the controller is configured to: when predicting a current traffic state of the running road on which the vehicle is located based on the operating parameter information, specifically:
    获取至少一个预设交通状态的至少一个特征参数信息；Obtaining at least one characteristic parameter information of at least one preset traffic state;
    确定与所述运行参数信息相匹配的特征参数信息对应的预设交通状态为所述当前的交通状态。Determining, by the feature parameter information that matches the operation parameter information, a preset traffic state is the current traffic state.
  13. 如权利要求12所述的电动汽车，其特征在于，所述控制器确定与所述运行参数信息相匹配的特征参数信息对应预设交通状态为所述当前的交通状态时，具体用于：The electric vehicle according to claim 12, wherein the controller determines that the feature parameter information that matches the operation parameter information corresponds to the preset traffic state as the current traffic state, and is specifically used to:
    获取所述至少一个特征参数信息中的每个特征参数信息与所述每个特征参数信息的取值范围之间的隶属函数；Obtaining a membership function between each of the at least one feature parameter information and a value range of each of the feature parameter information;
    根据所述隶属函数确定与所述运行参数信息相匹配的特征参数信息。Feature parameter information that matches the operational parameter information is determined according to the membership function.
  14. 如权利要求13所述的电动汽车，其特征在于，所述特征参数信息包括所述车辆在一次减速开始到下一次减速开始的间隔时长内的平均车速、所述车辆在所述间隔时长内处于速度为零的总时长与所述间隔时长的比例、以及所述车辆在所述间隔时长内处于速度不为零的状态内的平均加速度；The electric vehicle according to claim 13, wherein said characteristic parameter information includes an average vehicle speed of said vehicle in an interval length from the start of one deceleration to the start of the next deceleration, and said vehicle is in said interval duration a ratio of a total duration of zero speed to the interval duration, and an average acceleration of the vehicle in a state where the speed is not zero during the interval duration;
    所述控制器获取所述至少一个特征参数信息中的每个特征参数信息与所述每个特征参数信息的取值范围之间的隶属函数时，具体用于：When the controller acquires a membership function between each of the at least one feature parameter information and the value range of each of the feature parameter information, the controller is specifically configured to:
    获取所述平均车速与所述平均车速的取值范围之间的第一隶属函数、所述比例与所述比例的取值范围之间的第二隶属函数、以及所述平均加速度与所述平均加速度的取值范围之间的第三隶属函数；其中，所述第一隶属函数、所述第二隶属函数及所述第三隶属函数均为分段函数。Obtaining a first membership function between the average vehicle speed and a range of values of the average vehicle speed, a second membership function between the ratio and a range of values of the ratio, and the average acceleration and the average a third membership function between the range of values of the acceleration; wherein the first membership function, the second membership function, and the third membership function are both piecewise functions.
  15. 如权利要求14所述的电动汽车，其特征在于，所述控制器根据所述隶属函数确定与所述运行参数信息相匹配的特征参数信息时，具体用于：The electric vehicle according to claim 14, wherein the controller determines, according to the membership function, characteristic parameter information that matches the operating parameter information, specifically for:
    确定所述运行参数信息中的平均车速在所述第一隶属函数中对应的第一分段函数、所述运行参数信息中的比例在所述第二隶属函数中对应的第二分段函数、以及所述运行参数信息中的平均加速度在所述第三隶属函数中对应的第三分段函数；Determining, in the first parameter function, a corresponding first segment function, a ratio of the operation parameter information in the first membership function, a second segmentation function corresponding to the second membership function, And a third piecewise function corresponding to the average acceleration in the operating parameter information in the third membership function;
    确定与所述第一分段函数、所述第二分段函数以及所述第三分段函数相匹配的特征参数信息。Feature parameter information that matches the first segmentation function, the second segmentation function, and the third segmentation function is determined.
  16. 如权利要求12所述的电动汽车，其特征在于，所述控制器确定与所述运行参数信息相匹配的特征参数信息对应的预设交通状态为所述当前的交通状态时，具体用于：The electric vehicle according to claim 12, wherein the controller determines that the preset traffic state corresponding to the feature parameter information that matches the operating parameter information is the current traffic state, specifically for:
    计算所述运行参数信息与所述至少一个特征参数信息的至少一个差异值；Calculating at least one difference value between the operation parameter information and the at least one feature parameter information;
    确定所述至少一个差异值中的最小值；Determining a minimum of the at least one difference value;
    确定与所述最小值对应的预设交通状态为所述当前的交通状态。Determining a preset traffic state corresponding to the minimum value as the current traffic state.
  17. 如权利要求16所述的电动汽车，其特征在于，所述控制器计算所述运行参数信息与所述至少一个特征参数信息中的第一特征参数信息的差异值时，具体用于：The electric vehicle according to claim 16, wherein the controller is configured to: when calculating the difference value between the operating parameter information and the first feature parameter information of the at least one feature parameter information, specifically:
    计算所述运行参数信息与所述第一特征参数信息的差值；所述第一特征参数信息为所述至少一个特征参数信息中的任意一个特征参数信息；Calculating a difference between the operation parameter information and the first feature parameter information; the first feature parameter information is any one of the at least one feature parameter information;
    将所述差值进行归一化处理，得到归一化的差值；Normalizing the difference to obtain a normalized difference;
    将所述归一化的差值与为所述第一特征参数信息设置的权重向量相乘，得到差值向量；Multiplying the normalized difference value by a weight vector set for the first feature parameter information to obtain a difference vector;
    确定所述差值向量的模值为所述运行参数信息与所述第一特征参数信息的差异值。Determining a modulus value of the difference vector as a difference value between the operation parameter information and the first feature parameter information.
  18. 如权利要求12-17任一所述的电动汽车，其特征在于，所述控制器基于与所述当前的交通状态匹配的回馈力矩映射关系信息，确定所述车辆在所述当前的交通状态下处于所述制动状态时的回馈力矩时，具体用于：The electric vehicle according to any one of claims 12-17, wherein said controller determines that said vehicle is in said current traffic state based on feedback torque mapping relationship information that matches said current traffic state When the feedback torque is in the braking state, it is specifically used to:
    从至少一个回馈力矩映射关系信息中获取与所述当前的交通状态匹配的回馈力矩映射关系信息；其中，所述至少一个预设交通状态与所述至少一个回馈力矩映射关系信息一一对应；Obtaining feedback torque mapping relationship information that matches the current traffic state from the at least one feedback torque mapping relationship information; wherein the at least one preset traffic state and the at least one feedback torque mapping relationship information are in one-to-one correspondence;
    基于所述车辆的速度及所述车辆的制动程度，从所述回馈力矩映射关系信息中确定所述车辆的所述回馈力矩。The feedback torque of the vehicle is determined from the feedback torque mapping relationship information based on a speed of the vehicle and a degree of braking of the vehicle.
  19. 如权利要求12-17任一所述的电动汽车，其特征在于，所述控制器基于与所述当前的交通状态匹配的回馈力矩映射关系信息，确定所述车辆在所述当前的交通状态下处于所述制动状态时的回馈力矩时，具体用于：The electric vehicle according to any one of claims 12-17, wherein said controller determines that said vehicle is in said current traffic state based on feedback torque mapping relationship information that matches said current traffic state When the feedback torque is in the braking state, it is specifically used to:
    从至少一个回馈力矩映射关系信息中获取与所述当前的交通状态匹配的回馈力矩映射关系信息；其中，所述至少一个预设交通状态与所述至少一个回馈力矩映射关系信息一一对应；Obtaining feedback torque mapping relationship information that matches the current traffic state from the at least one feedback torque mapping relationship information; wherein the at least one preset traffic state and the at least one feedback torque mapping relationship information are in one-to-one correspondence;
    从所述回馈力矩映射关系信息中确定与所述车辆的速度及所述车辆的制动程度相 应的回馈力矩；Determining, from the feedback torque mapping relationship information, a feedback torque corresponding to the speed of the vehicle and the braking degree of the vehicle;
    确定所述车辆在所述制动状态时，所述电动机支持的第一最大回馈力矩；Determining a first maximum feedback torque supported by the motor when the vehicle is in the braking state;
    确定所述车辆的蓄电池在当前时刻支持的第二最大回馈力矩；Determining a second maximum feedback torque supported by the battery of the vehicle at a current time;
    确定与所述车辆的速度及所述车辆的制动程度相应的回馈力矩、所述第一最大回馈力矩以及所述第二最大回馈力矩中的最小值为所述车辆的所述回馈力矩。Determining a minimum of a feedback torque corresponding to a speed of the vehicle and a degree of braking of the vehicle, the first maximum feedback torque, and the second maximum feedback torque is the feedback torque of the vehicle.
  20. 如权利要求12-17任一所述的电动汽车，其特征在于，所述控制器基于与所述当前的交通状态匹配的回馈力矩映射关系信息，确定所述车辆在所述当前的交通状态下处于所述制动状态时的回馈力矩时，具体用于：The electric vehicle according to any one of claims 12-17, wherein said controller determines that said vehicle is in said current traffic state based on feedback torque mapping relationship information that matches said current traffic state When the feedback torque is in the braking state, it is specifically used to:
    从至少一个回馈力矩映射关系信息中获取与所述当前的交通状态匹配的回馈力矩映射关系信息；其中，所述至少一个预设交通状态与所述至少一个回馈力矩映射关系信息一一对应；Obtaining feedback torque mapping relationship information that matches the current traffic state from the at least one feedback torque mapping relationship information; wherein the at least one preset traffic state and the at least one feedback torque mapping relationship information are in one-to-one correspondence;
    从所述回馈力矩映射关系信息中确定与所述车辆的速度及所述车辆的制动程度相应的回馈力矩；Determining, from the feedback torque mapping relationship information, a feedback torque corresponding to a speed of the vehicle and a braking degree of the vehicle;
    确定所述车辆在所述制动状态时，所述电动机支持的第一最大回馈力矩；Determining a first maximum feedback torque supported by the motor when the vehicle is in the braking state;
    确定所述车辆的蓄电池在当前时刻支持的第二最大回馈力矩；Determining a second maximum feedback torque supported by the battery of the vehicle at a current time;
    基于所述车辆的档位状态、主动减速状态、主动加速状态以及防抱死制动***的状态中的至少一种因素，确定所述车辆的驾驶员的制动意图；Determining a braking intention of a driver of the vehicle based on at least one of a gear state of the vehicle, an active deceleration state, an active acceleration state, and a state of an anti-lock braking system;
    基于所述驾驶员的制动意图、与所述车辆的速度及所述车辆的制动程度相应的回馈力矩、所述第一最大回馈力矩以及所述第二最大回馈力矩，确定所述车辆的所述回馈力矩。Determining the vehicle based on a braking intention of the driver, a feedback torque corresponding to a speed of the vehicle and a braking degree of the vehicle, the first maximum feedback torque, and the second maximum feedback torque The feedback torque.
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