CN115503506A - Energy management optimization method and device for electric automobile - Google Patents

Energy management optimization method and device for electric automobile Download PDF

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Publication number
CN115503506A
CN115503506A CN202211337407.4A CN202211337407A CN115503506A CN 115503506 A CN115503506 A CN 115503506A CN 202211337407 A CN202211337407 A CN 202211337407A CN 115503506 A CN115503506 A CN 115503506A
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driving
working condition
brake
characteristic value
value
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郭展岩
张鹏
侯亚飞
文涛
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Chongqing Changan Automobile Co Ltd
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Chongqing Changan Automobile Co Ltd
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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
    • B60L15/00—Methods, circuits, or devices for controlling the traction-motor speed of electrically-propelled vehicles
    • B60L15/20—Methods, circuits, or devices for controlling the traction-motor speed of electrically-propelled vehicles for control of the vehicle or its driving motor to achieve a desired performance, e.g. speed, torque, programmed variation of speed
    • B60L15/2045—Methods, circuits, or devices for controlling the traction-motor speed of electrically-propelled vehicles for control of the vehicle or its driving motor to achieve a desired performance, e.g. speed, torque, programmed variation of speed for optimising the use of energy
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
    • B60W40/00—Estimation or calculation of non-directly measurable driving parameters for road vehicle drive control systems not related to the control of a particular sub unit, e.g. by using mathematical models
    • B60W40/08—Estimation or calculation of non-directly measurable driving parameters for road vehicle drive control systems not related to the control of a particular sub unit, e.g. by using mathematical models related to drivers or passengers
    • B60W40/09—Driving style or behaviour

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  • Transportation (AREA)
  • Mechanical Engineering (AREA)
  • Physics & Mathematics (AREA)
  • Automation & Control Theory (AREA)
  • Mathematical Physics (AREA)
  • Power Engineering (AREA)
  • Electric Propulsion And Braking For Vehicles (AREA)

Abstract

The application discloses an energy management optimization method and device for an electric automobile, wherein the method comprises the following steps: extracting a driving style characteristic value of a current driver according to opening data of an accelerator pedal and a brake pedal of a vehicle within a preset time length, and acquiring motor power and brake power corresponding to the opening data; identifying the current working condition of the electric automobile according to the driving style characteristic value, and calculating the average power consumption value of the motor and the brake according to the motor power and the brake power; and when the current working condition meets the preset optimization condition, optimizing the energy distribution threshold value corresponding to the current working condition into the average power consumption value of the motor and the brake. According to the embodiment of the application, the driving style characteristic value of the current driver can be extracted, the power consumption requirement corresponding to the driving style is added into the energy management strategy, the energy management strategies under different driving styles are optimized, the vehicle using experience of a user is improved while the applicability and the reliability of a vehicle are improved, and the driving and riding diversified requirements of the user are met.

Description

Energy management optimization method and device for electric automobile
Technical Field
The application relates to the technical field of automobile control, in particular to an energy management optimization method and device for an electric automobile.
Background
In the related technology, a vehicle speed signal and an accelerator pedal opening signal of a vehicle can be collected, when the sampling condition is met, the accelerator opening signal of the vehicle is sampled, one period of sampling is completed after the accumulated sampling time reaches a set value, the accelerator opening and the accelerator opening change rate of each sampling point of the vehicle in the accumulated sampling time are divided through a calibrated style curve graph, and the final driving style is determined.
However, in the related art, only the driving style of the user can be analyzed, which has certain limitations, reduces the applicability of the vehicle, reduces the vehicle using experience of the user, cannot meet the diversified requirements of the user for driving, and needs to be solved urgently.
Disclosure of Invention
The application provides an energy management optimization method and device for an electric automobile, and aims to solve the technical problems that in the related art, only the driving style of a user can be analyzed, certain limitation is achieved, the applicability of a vehicle is reduced, the vehicle using experience of the user is reduced, and the driving and riding diversification requirements of the user cannot be met.
An embodiment of a first aspect of the present application provides an energy management optimization method for an electric vehicle, including the following steps: extracting a driving style characteristic value of a current driver according to opening data of an accelerator pedal and a brake pedal of a vehicle within a preset time length, and acquiring motor power and brake power corresponding to the opening data; identifying the current working condition of the electric automobile according to the driving style characteristic value, and calculating the average power consumption value of the motor and the brake according to the motor power and the brake power; and when the current working condition meets a preset optimization condition, optimizing the energy distribution threshold value corresponding to the current working condition into the average power consumption value of the motor and the brake.
According to the technical means, the driving style characteristic value of the current driver can be extracted, the power consumption requirements corresponding to the driving style are added into the energy management strategy, and the energy management strategies under different driving styles are optimized, so that the applicability and the reliability of the vehicle are improved, the vehicle using experience of a user is improved, and the driving and riding diversified requirements of the user are met.
Optionally, in an embodiment of the present application, the preset optimization condition is that the current operating condition is a violent driving operating condition or a soft driving operating condition.
According to the technical means, the energy distribution under the working conditions of different driving severity degrees can be continuously adjusted according to the driving habits of the user, the utilization rate of electric energy is improved, meanwhile, the energy consumption of the vehicle is reduced, and the driving requirements of the user are effectively met.
Optionally, in an embodiment of the application, the identifying, according to the driving style characteristic value, a current working condition of the electric vehicle includes: comparing the driving style characteristic value with a preset violent driving characteristic value and a preset soft driving characteristic value respectively; and determining the current working condition as the violent driving working condition or the soft driving working condition according to the comparison result, wherein the preset soft driving characteristic value is smaller than the preset violent driving characteristic value.
According to the technical means, the driving style can identify the current specific working condition, the feasibility of energy management optimization is effectively improved, and the personalized experience of driving of the user is improved.
Optionally, in an embodiment of the present application, the calculation formula of the driving style characteristic value is:
Figure BDA0003915016410000021
wherein, P i Is the ith accelerator pedal opening value, B, collected within the time t i For the ith brake pedal opening value collected over time t,
Figure BDA0003915016410000022
the number of data sampled at the frequency n in the time interval t is shown, k1 is a proportionality coefficient of an accelerator pedal opening value, and k2 is a proportionality coefficient of a brake pedal opening value.
According to the technical means, the driving style characteristic value of the current driver can be extracted according to the opening data of the accelerator pedal and the brake pedal of the vehicle within a certain time, and the performability of energy management optimization is effectively improved.
Optionally, in an embodiment of the present application, the calculation formula of the average power consumption value of the motor and the brake is:
Figure BDA0003915016410000023
wherein p is i For the ith motor power, acquired over time t, b i For the ith brake power collected over time t,
Figure BDA0003915016410000024
the number of data sampled at the frequency n in the time interval t is shown, k1 is a proportionality coefficient of an accelerator pedal opening value, and k2 is a proportionality coefficient of a brake pedal opening value.
According to the technical means, the energy distribution of the motor and the brake under different driving styles can be adjusted in real time according to the driving characteristics and the power consumption level within a certain time, and the utilization rate of electric energy is effectively improved.
An embodiment of a second aspect of the present application provides an energy management optimization device for an electric vehicle, including: the acquisition module is used for extracting a driving style characteristic value of a current driver according to opening data of an accelerator pedal and a brake pedal of a vehicle within a preset time length, and acquiring motor power and brake power corresponding to the opening data; the calculation module is used for identifying the current working condition of the electric automobile according to the driving style characteristic value and calculating the average power consumption value of the motor and the brake according to the motor power and the brake power; and the optimization module is used for optimizing the average power consumption values of the motor and the brake by using the energy distribution threshold corresponding to the current working condition when the current working condition meets the preset optimization condition.
Optionally, in an embodiment of the present application, the preset optimization condition is that the current operating condition is a violent driving operating condition or a soft driving operating condition.
Optionally, in an embodiment of the present application, the calculation module includes: the comparison unit is used for comparing the driving style characteristic value with a preset violent driving characteristic value and a preset soft driving characteristic value respectively; and the determining unit is used for determining that the current working condition is the violent driving working condition or the soft driving working condition according to the comparison result, wherein the preset soft driving characteristic value is smaller than the preset violent driving characteristic value.
Optionally, in an embodiment of the present application, the calculation formula of the driving style characteristic value is:
Figure BDA0003915016410000031
wherein, P i Is the ith accelerator pedal opening value, B, collected within the time t i The ith brake pedal opening value collected during the time t,
Figure BDA0003915016410000032
the number of data sampled at the frequency n in the time interval t is shown, k1 is a proportionality coefficient of an accelerator pedal opening value, and k2 is a proportionality coefficient of a brake pedal opening value.
Optionally, in an embodiment of the present application, the calculation formula of the average power consumption value of the motor and the brake is:
Figure BDA0003915016410000033
wherein p is i For the ith motor power, b, collected over time t i For the i-th brake power collected during time t,
Figure BDA0003915016410000034
the number of data sampled at the frequency n in the time interval t is shown, k1 is a proportionality coefficient of an accelerator pedal opening value, and k2 is a proportionality coefficient of a brake pedal opening value.
An embodiment of a third aspect of the present application provides a vehicle, comprising: the energy management optimization method of the electric automobile comprises a memory, a processor and a computer program stored on the memory and capable of running on the processor, wherein the processor executes the program to realize the energy management optimization method of the electric automobile according to the embodiment.
A fourth aspect of the present application provides a computer-readable storage medium storing a computer program, which when executed by a processor implements the energy management optimization method for an electric vehicle as above.
The beneficial effect of this application:
(1) The driving style of the embodiment of the application can identify the current specific working condition, the feasibility of energy management optimization is effectively improved, and the personalized experience of driving of a user is improved.
(2) According to the embodiment of the application, the energy distribution under the working conditions of different driving severity degrees can be continuously adjusted according to the driving habits of the user, the utilization rate of electric energy is improved, the energy consumption of a vehicle is reduced, and the driving requirements of the user are effectively met.
(3) According to the embodiment of the application, the driving style characteristic value of the current driver can be extracted, the power consumption requirement corresponding to the driving style is added into the energy management strategy, and the energy management strategies under different driving styles are optimized, so that the vehicle using experience of a user is improved while the applicability and the reliability of the vehicle are improved, and the driving and riding diversification requirements of the user are met.
Additional aspects and advantages of the present application will be set forth in part in the description which follows and, in part, will be obvious from the description, or may be learned by practice of the present application.
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The foregoing and/or additional aspects and advantages of the present application will become apparent and readily appreciated from the following description of the embodiments, taken in conjunction with the accompanying drawings of which:
fig. 1 is a flowchart of an energy management optimization method for an electric vehicle according to an embodiment of the present disclosure;
FIG. 2 is a flow chart of calculating driving style characteristic values according to an embodiment of the present application;
FIG. 3 is a flow chart of calculating an average power consumption value of the motor and brake according to an embodiment of the present application;
fig. 4 is a schematic structural diagram of an energy management optimization device of an electric vehicle according to an embodiment of the present application;
fig. 5 is a schematic structural diagram of a vehicle according to an embodiment of the present application.
10-an energy management optimization device of the electric automobile; 100-acquisition module, 200-calculation module and 300-optimization module; 501-memory, 502-processor and 503-communication interface.
Detailed Description
Reference will now be made in detail to embodiments of the present application, examples of which are illustrated in the accompanying drawings, wherein like or similar reference numerals refer to the same or similar elements or elements having the same or similar function throughout. The embodiments described below with reference to the drawings are exemplary and intended to be used for explaining the present application and should not be construed as limiting the present application.
The following describes an energy management optimization method and device for an electric vehicle according to an embodiment of the present application with reference to the drawings. In order to solve the problems that only the driving style of a user can be analyzed in the related technologies mentioned in the background technology center, the method has certain limitations, the applicability of the vehicle is reduced, the vehicle using experience of the user is reduced, and the diversified requirements of the driving of the user cannot be met, the method provides the energy management optimization method for the electric vehicle. Therefore, the technical problems that in the related art, only the driving style of a user can be analyzed, certain limitation is realized, the applicability of a vehicle is reduced, the vehicle using experience of the user is reduced, and the driving and riding diversification requirements of the user cannot be met are solved.
Specifically, fig. 1 is a schematic flowchart of an energy management optimization method for an electric vehicle according to an embodiment of the present application.
As shown in fig. 1, the energy management optimization method for an electric vehicle includes the following steps:
in step S101, a driving style characteristic value of a current driver is extracted according to opening data of an accelerator pedal and a brake pedal of a vehicle within a preset time period, and motor power and brake power corresponding to the opening data are collected.
It can be understood that, in the embodiment of the present application, the driving style characteristic value of the current driver in the following steps may be extracted according to the opening data of the accelerator pedal and the brake pedal of the Vehicle within a certain time period, for example, the driving style characteristic value may be a violent driving characteristic value, a soft driving characteristic value, and the like, and the motor power and the brake power corresponding to the opening data are collected, for example, the motor power and the brake power under the opening of the accelerator pedal and the brake pedal may be collected and stored respectively by a VCU (Vehicle Control Unit, a whole Vehicle controller of an electric Vehicle), so as to ensure that the average power consumption values of the motor and the brake in the following steps may be calculated, so as to ensure real-time performance and accuracy of the collected data, and improve performability of energy management optimization.
For example, when the vehicle is running, the current time may be used as a reference point, the opening data of the accelerator pedal and the opening data of the brake pedal are collected for a period of time t, then the opening data of the accelerator pedal and the opening data of the brake pedal are collected as sample data at a sampling frequency n within a time interval t, and the driving style characteristic value X is calculated based on the formula in the following steps.
In an embodiment of the present application, the calculation formula of the driving style characteristic value X is as follows:
Figure BDA0003915016410000051
wherein, P i Is the ith accelerator pedal opening value, B, collected within the time t i The ith brake pedal opening value collected during the time t,
Figure BDA0003915016410000052
the number of data sampled at the frequency n in the time interval t is shown, k1 is a proportionality coefficient of an accelerator pedal opening value, and k2 is a proportionality coefficient of a brake pedal opening value.
In addition, in the above formula
Figure BDA0003915016410000053
Is an integer greater than 0, when present
Figure BDA0003915016410000054
When there is a decimal point, an integer value before the decimal point is taken as a sample number value, wherein t and n can be adjusted according to different requirements, generally, the more sample data, the more accurate the analyzed characteristic value X is, k1 and k2 are calibration values determined according to drivability, so that a finished automobile manufacturer can analyze and calibrate in a test before the automobile comes into the market, and k1+ k2=1.
It should be noted that the preset time period is set by a person skilled in the art according to actual situations, and is not specifically limited herein.
In step S102, the current working condition of the electric vehicle is identified according to the driving style characteristic value, and the average power consumption value of the motor and the brake is calculated according to the motor power and the brake power.
It can be understood that, the embodiment of the application can identify the current working condition of the electric automobile according to the driving style characteristic value, such as a violent driving working condition, a soft driving working condition and the like, and calculate the average power consumption value of the motor and the brake according to the motor power and the brake power, so that the driving characteristic and the power consumption level in the period of time can be analyzed, the energy distribution under the working conditions of different driving violence degrees can be continuously adjusted according to the driving habits of users, and the adaptability of energy consumption management and control is improved.
In one embodiment of the application, identifying the current working condition of the electric vehicle according to the driving style characteristic value includes: comparing the driving style characteristic value with a preset violent driving characteristic value and a preset soft driving characteristic value respectively; and determining the current working condition to be a violent driving working condition or a soft driving working condition according to the comparison result, wherein the preset soft driving characteristic value is smaller than the preset violent driving characteristic value.
For example, the driving style characteristic value X can be compared with a violent driving characteristic value A0 and a soft driving characteristic value A1 of the vehicle, when the X is larger than or equal to the A0, the vehicle is considered to be in a violent driving working condition at the moment, when the X is smaller than or equal to the A1, the vehicle is considered to be in a soft driving working condition at the moment, wherein the A1 is smaller than the A0, therefore, the feasibility of energy management optimization is effectively improved, and the personalized experience of driving of a user is improved.
In addition, in the embodiment of the application, while the accelerator pedal opening data and the brake pedal opening data are collected within the time t, the VCU collects and stores the motor power and the brake power at corresponding openings respectively, and calculates the average power consumption value W of the motor and the brake within the time interval t based on the formula in the following steps, that is:
Figure BDA0003915016410000061
wherein p is i For the ith motor power, acquired over time t, b i For the i-th brake power collected during time t,
Figure BDA0003915016410000062
the number of data sampled at the frequency n in the time interval t is shown, k1 is a proportionality coefficient of an accelerator pedal opening value, and k2 is a proportionality coefficient of a brake pedal opening value.
It should be noted that the preset soft driving characteristic value and the preset hard driving characteristic value are set by those skilled in the art according to actual situations, and are not limited in particular.
In step S103, when the current working condition meets the preset optimization condition, the energy distribution threshold corresponding to the current working condition is optimized as the average power consumption value of the motor and the brake.
It can be understood that, in the embodiment of the present application, when the current working condition meets the optimization condition in the following steps, the energy distribution threshold corresponding to the current working condition is optimized as the average power consumption value of the motor and the brake, for example, the threshold when the vehicle is driven intensely may be W0, and the threshold when the vehicle is driven softly may be W1.
In one embodiment of the application, the preset optimization condition is that the current working condition is a violent driving working condition or a soft driving working condition.
In some embodiments, the optimization condition is that the current working condition is a violent driving working condition or a soft driving working condition, the embodiment of the application can adjust the energy distribution to the motor and the brake in real time under different driving styles, in other words, the embodiment of the application can continuously adjust the energy distribution under the working conditions of different driving violence degrees according to the driving habits of users, the adaptability of energy consumption management and control is strong, the energy distribution is optimal, the utilization rate of electric energy is improved, the energy consumption of vehicles is reduced, and the driving requirements of the users are effectively met.
The working principle of the embodiment of the present application is explained in detail below with a specific embodiment, as shown in fig. 2.
Step S201: and acquiring the opening values of the accelerator pedal and the brake pedal of n sampling points within the time range t to ensure the real-time performance and the accuracy of the acquired data.
Step S202: and calculating a driving style characteristic value X based on the opening degree value of the accelerator pedal and the opening degree value of the brake pedal.
Step S203: and comparing the driving style characteristic value X with the violent driving characteristic value A0 and the soft driving characteristic value A1 to obtain the driving style, and improving the performability of energy management optimization.
The working principle of the embodiment of the present application is explained in detail with a specific embodiment as shown in fig. 3.
Step S301: and acquiring the motor power corresponding to the accelerator pedal opening value and the brake power corresponding to the brake pedal opening value of n sampling points in the time range t.
Step S302: and calculating the corresponding average power W under the driving style X based on the motor power and the brake power.
Step S303: and applying W as an energy distribution threshold corresponding to a violent driving style and a soft driving style so as to realize real-time adjustment of energy distribution on the motor and the brake under different driving styles according to the driving characteristics and the power consumption level in the period of time, thereby effectively improving the utilization rate of electric energy.
According to the energy management optimization method for the electric automobile, the driving style characteristic value of the current driver can be extracted according to the opening data of the accelerator pedal and the brake pedal of the automobile within a certain time period, the motor power and the brake power corresponding to the opening data are collected, the current working condition of the electric automobile is identified according to the driving style characteristic value, the average power consumption value of the motor and the brake is calculated according to the motor power and the brake power, and when the current working condition meets the optimization condition, the energy distribution threshold corresponding to the current working condition is optimized to be the average power consumption value of the motor and the brake, so that the applicability and the reliability of the automobile are improved, the automobile using experience of a user is improved, and the diversified driving requirements of the user are met. Therefore, the technical problems that in the related art, only the driving style of a user can be analyzed, certain limitation is realized, the applicability of a vehicle is reduced, the vehicle using experience of the user is reduced, and the driving and riding diversification requirements of the user cannot be met are solved.
Next, an energy management optimization apparatus for an electric vehicle according to an embodiment of the present application will be described with reference to the drawings.
Fig. 4 is a block diagram schematically illustrating an energy management optimization apparatus for an electric vehicle according to an embodiment of the present application.
As shown in fig. 4, the energy management optimization apparatus 10 for an electric vehicle includes: an acquisition module 100, a calculation module 200 and an optimization module 300.
Specifically, the collecting module 100 is configured to extract a driving style characteristic value of a current driver according to opening data of an accelerator pedal and a brake pedal of a vehicle within a preset time period, and collect motor power and brake power corresponding to the opening data.
And the calculating module 200 is used for identifying the current working condition of the electric automobile according to the driving style characteristic value and calculating the average power consumption value of the motor and the brake according to the motor power and the brake power.
And the optimizing module 300 is configured to optimize the average power consumption values of the motor and the brake by using the energy distribution threshold corresponding to the current working condition when the current working condition meets the preset optimizing condition.
Optionally, in an embodiment of the present application, the preset optimization condition is that the current operating condition is a violent driving condition or a soft driving condition.
Optionally, in an embodiment of the present application, the computing module 200 includes: a comparison unit and a determination unit.
The comparison unit is used for comparing the driving style characteristic value with a preset violent driving characteristic value and a preset soft driving characteristic value respectively.
And the determining unit is used for determining the current working condition to be a violent driving working condition or a soft driving working condition according to the comparison result, wherein the preset soft driving characteristic value is smaller than the preset violent driving characteristic value.
Optionally, in an embodiment of the present application, the calculation formula of the driving style characteristic value is:
Figure BDA0003915016410000081
wherein, P i Is the ith accelerator pedal opening value, B, collected within the time t i The ith brake pedal opening value collected during the time t,
Figure BDA0003915016410000082
the number of data sampled at the frequency n in the time interval t is shown, k1 is a proportionality coefficient of an accelerator pedal opening value, and k2 is a proportionality coefficient of a brake pedal opening value.
Optionally, in an embodiment of the present application, the calculation formula of the average power consumption value of the motor and the brake is as follows:
Figure BDA0003915016410000083
wherein p is i For the ith motor power, b, collected over time t i For the i-th brake power collected during time t,
Figure BDA0003915016410000084
the number of data sampled at the frequency n in the time interval t is shown, k1 is a proportionality coefficient of an accelerator pedal opening value, and k2 is a proportionality coefficient of a brake pedal opening value.
It should be noted that the foregoing explanation of the embodiment of the energy management optimization method for an electric vehicle is also applicable to the energy management optimization device for an electric vehicle in this embodiment, and details are not repeated here.
According to the energy management optimization device for the electric automobile, the driving style characteristic value of the current driver can be extracted according to the opening data of the accelerator pedal and the brake pedal of the automobile within a certain time, the motor power and the brake power corresponding to the opening data are collected, the current working condition of the electric automobile is identified according to the driving style characteristic value, the average power consumption value of the motor and the brake is calculated according to the motor power and the brake power, and when the current working condition meets the optimization condition, the energy distribution threshold corresponding to the current working condition is optimized to be the average power consumption value of the motor and the brake, so that the applicability and the reliability of the automobile are improved, the automobile using experience of a user is improved, and the diversified driving requirements of the user are met. Therefore, the technical problems that in the related art, only the driving style of a user can be analyzed, certain limitation is realized, the applicability of a vehicle is reduced, the vehicle using experience of the user is reduced, and the driving and riding diversification requirements of the user cannot be met are solved.
Fig. 5 is a schematic structural diagram of a vehicle according to an embodiment of the present application. The vehicle may include:
a memory 501, a processor 502, and a computer program stored on the memory 501 and executable on the processor 502.
The processor 502 executes the program to implement the energy management optimization method for the electric vehicle provided in the above-described embodiments.
Further, the vehicle further includes:
a communication interface 503 for communication between the memory 501 and the processor 502.
A memory 501 for storing computer programs that can be run on the processor 502.
The memory 501 may comprise high-speed RAM memory, and may also include non-volatile memory (non-volatile memory), such as at least one disk memory.
If the memory 501, the processor 502 and the communication interface 503 are implemented independently, the communication interface 503, the memory 501 and the processor 502 may be connected to each other through a bus and perform communication with each other. The bus may be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, an Extended ISA (EISA) bus, or the like. The bus may be divided into an address bus, a data bus, a control bus, etc. For ease of illustration, only one thick line is shown in FIG. 5, but this is not intended to represent only one bus or type of bus.
Alternatively, in practical implementation, if the memory 501, the processor 502 and the communication interface 503 are integrated on a chip, the memory 501, the processor 502 and the communication interface 503 may complete communication with each other through an internal interface.
The processor 502 may be a Central Processing Unit (CPU), an Application Specific Integrated Circuit (ASIC), or one or more Integrated circuits configured to implement embodiments of the present Application.
Embodiments of the present application further provide a computer-readable storage medium, on which a computer program is stored, where the program is executed by a processor to implement the above energy management optimization method for an electric vehicle.
In the description of the present specification, reference to the description of "one embodiment," "some embodiments," "an example," "a specific example," or "some examples" or the like means that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the present application. In this specification, the schematic representations of the terms used above are not necessarily intended to refer to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or N embodiments or examples. Furthermore, various embodiments or examples and features of different embodiments or examples described in this specification can be combined and combined by one skilled in the art without contradiction.
Furthermore, the terms "first", "second" and "first" are used for descriptive purposes only and are not to be construed as indicating or implying relative importance or to implicitly indicate the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one such feature. In the description of the present application, "N" means at least two, e.g., two, three, etc., unless specifically limited otherwise.
Any process or method descriptions in flow charts or otherwise described herein may be understood as representing modules, segments, or portions of code which include one or N executable instructions for implementing steps of a custom logic function or process, and alternate implementations are included within the scope of the preferred embodiment of the present application in which functions may be executed out of order from that shown or discussed, including substantially concurrently or in reverse order, depending on the functionality involved, as would be understood by those reasonably skilled in the art of the embodiments of the present application.
The logic and/or steps represented in the flowcharts or otherwise described herein, e.g., an ordered listing of executable instructions that can be considered to implement logical functions, can be embodied in any computer-readable medium for use by or in connection with an instruction execution system, apparatus, or device, such as a computer-based system, processor-containing system, or other system that can fetch the instructions from the instruction execution system, apparatus, or device and execute the instructions. For the purposes of this description, a "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transport the program for use by or in connection with the instruction execution system, apparatus, or device. More specific examples (a non-exhaustive list) of the computer-readable medium would include the following: an electrical connection (electronic device) having one or N wires, a portable computer diskette (magnetic device), a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disc read-only memory (CDROM). Additionally, the computer-readable medium could even be paper or another suitable medium upon which the program is printed, as the program can be electronically captured, via optical scanning of the paper or other medium, then compiled, interpreted or otherwise processed in a suitable manner if necessary, and then stored in a computer memory.
It should be understood that portions of the present application may be implemented in hardware, software, firmware, or a combination thereof. In the above embodiments, the N steps or methods may be implemented in software or firmware stored in a memory and executed by a suitable instruction execution system. If implemented in hardware as in another embodiment, any one or combination of the following techniques, which are known in the art, may be used: a discrete logic circuit having a logic gate circuit for implementing a logic function on a data signal, an application specific integrated circuit having an appropriate combinational logic gate circuit, a Programmable Gate Array (PGA), a Field Programmable Gate Array (FPGA), or the like.
It will be understood by those skilled in the art that all or part of the steps carried by the method for implementing the above embodiments may be implemented by hardware related to instructions of a program, which may be stored in a computer readable storage medium, and when the program is executed, the program includes one or a combination of the steps of the method embodiments.
In addition, functional units in the embodiments of the present application may be integrated into one processing module, or each unit may exist alone physically, or two or more units are integrated into one module. The integrated module can be realized in a hardware mode, and can also be realized in a software functional module mode. The integrated module, if implemented in the form of a software functional module and sold or used as a stand-alone product, may also be stored in a computer readable storage medium.
The storage medium mentioned above may be a read-only memory, a magnetic or optical disk, etc. Although embodiments of the present application have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting the present application, and that variations, modifications, substitutions and alterations may be made to the above embodiments by those of ordinary skill in the art within the scope of the present application.

Claims (10)

1. An energy management optimization method of an electric automobile is characterized by comprising the following steps:
extracting a driving style characteristic value of a current driver according to opening data of an accelerator pedal and a brake pedal of a vehicle within a preset time length, and acquiring motor power and brake power corresponding to the opening data;
identifying the current working condition of the electric automobile according to the driving style characteristic value, and calculating the average power consumption value of the motor and the brake according to the motor power and the brake power; and
and when the current working condition meets a preset optimization condition, optimizing the energy distribution threshold value corresponding to the current working condition into the average power consumption value of the motor and the brake.
2. The method according to claim 1, wherein the preset optimization condition is that the current working condition is a violent driving working condition or a soft driving working condition.
3. The method according to claim 2, wherein the identifying the current working condition of the electric vehicle according to the driving style characteristic value comprises:
comparing the driving style characteristic value with a preset violent driving characteristic value and a preset soft driving characteristic value respectively;
and determining the current working condition as the violent driving working condition or the soft driving working condition according to the comparison result, wherein the preset soft driving characteristic value is smaller than the preset violent driving characteristic value.
4. The method according to claim 1, wherein the driving style characteristic value is calculated by the formula:
Figure FDA0003915016400000011
wherein, P i Is the ith accelerator pedal opening value, B, collected within the time t i The ith brake pedal opening value collected during the time t,
Figure FDA0003915016400000012
the number of data sampled at the frequency n in the time interval t is shown, k1 is a proportionality coefficient of an accelerator pedal opening value, and k2 is a proportionality coefficient of a brake pedal opening value.
5. The method of claim 4, wherein the motor to brake average power consumption value is calculated by the formula:
Figure FDA0003915016400000013
wherein p is i For the ith motor power, b, collected over time t i For the i-th brake power collected during time t,
Figure FDA0003915016400000014
the number of data sampled at the frequency n in the time interval t is shown, k1 is a proportionality coefficient of an accelerator pedal opening value, and k2 is a proportionality coefficient of a brake pedal opening value.
6. An energy management optimization device for an electric vehicle, comprising:
the acquisition module is used for extracting a driving style characteristic value of a current driver according to opening data of an accelerator pedal and a brake pedal of a vehicle within a preset time length, and acquiring motor power and brake power corresponding to the opening data;
the calculation module is used for identifying the current working condition of the electric automobile according to the driving style characteristic value and calculating the average power consumption value of the motor and the brake according to the motor power and the brake power; and
and the optimization module is used for optimizing the average power consumption values of the motor and the brake by using the energy distribution threshold corresponding to the current working condition when the current working condition meets the preset optimization condition.
7. The device according to claim 6, wherein the preset optimization condition is that the current working condition is a violent driving working condition or a soft driving working condition.
8. The apparatus of claim 7, wherein the computing module comprises:
the comparison unit is used for comparing the driving style characteristic value with a preset violent driving characteristic value and a preset soft driving characteristic value respectively;
and the determining unit is used for determining that the current working condition is the violent driving working condition or the soft driving working condition according to the comparison result, wherein the preset soft driving characteristic value is smaller than the preset violent driving characteristic value.
9. A vehicle, characterized by comprising: memory, a processor and a computer program stored on the memory and executable on the processor, the processor executing the program to implement the energy management optimization method of an electric vehicle according to any one of claims 1 to 5.
10. A computer-readable storage medium, on which a computer program is stored, the program being executed by a processor for implementing the method for optimizing energy management of an electric vehicle according to any one of claims 1 to 5.
CN202211337407.4A 2022-10-28 2022-10-28 Energy management optimization method and device for electric automobile Pending CN115503506A (en)

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Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
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