WO2018086218A1 - 车辆制动能量的回收方法和装置 - Google Patents

车辆制动能量的回收方法和装置 Download PDF

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Publication number
WO2018086218A1
WO2018086218A1 PCT/CN2016/112756 CN2016112756W WO2018086218A1 WO 2018086218 A1 WO2018086218 A1 WO 2018086218A1 CN 2016112756 W CN2016112756 W CN 2016112756W WO 2018086218 A1 WO2018086218 A1 WO 2018086218A1
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WIPO (PCT)
Prior art keywords
vehicle
speed
safe
safety
distance
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PCT/CN2016/112756
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English (en)
French (fr)
Inventor
刘祖齐
郑建锋
马金博
黄佳俊
张伟
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华为技术有限公司
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Application filed by 华为技术有限公司 filed Critical 华为技术有限公司
Priority to EP23152343.2A priority Critical patent/EP4219218A3/en
Priority to EP16921257.8A priority patent/EP3536538B1/en
Priority to KR1020197016304A priority patent/KR102225006B1/ko
Priority to JP2019524084A priority patent/JP2019537414A/ja
Publication of WO2018086218A1 publication Critical patent/WO2018086218A1/zh
Priority to US16/407,977 priority patent/US11260756B2/en
Priority to US17/673,132 priority patent/US11919422B2/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
    • 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
    • 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
    • B60L15/2009—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 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
    • B60L7/18—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
    • B60L7/20—Braking by supplying regenerated power to the prime mover of vehicles comprising engine-driven generators
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60T—VEHICLE BRAKE CONTROL SYSTEMS OR PARTS THEREOF; BRAKE CONTROL SYSTEMS OR PARTS THEREOF, IN GENERAL; ARRANGEMENT OF BRAKING ELEMENTS ON VEHICLES IN GENERAL; PORTABLE DEVICES FOR PREVENTING UNWANTED MOVEMENT OF VEHICLES; VEHICLE MODIFICATIONS TO FACILITATE COOLING OF BRAKES
    • B60T1/00—Arrangements of braking elements, i.e. of those parts where braking effect occurs specially for vehicles
    • B60T1/02—Arrangements of braking elements, i.e. of those parts where braking effect occurs specially for vehicles acting by retarding wheels
    • B60T1/10—Arrangements of braking elements, i.e. of those parts where braking effect occurs specially for vehicles acting by retarding wheels by utilising wheel movement for accumulating energy, e.g. driving air compressors
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/04—Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
    • 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
    • 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
    • B60L2240/14—Acceleration
    • B60L2240/16—Acceleration longitudinal
    • 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
    • B60L2240/40—Drive Train control parameters
    • B60L2240/42—Drive Train control parameters related to electric machines
    • 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
    • B60L2240/40—Drive Train control parameters
    • B60L2240/42—Drive Train control parameters related to electric machines
    • B60L2240/421—Speed
    • 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
    • B60L2240/40—Drive Train control parameters
    • B60L2240/42—Drive Train control parameters related to electric machines
    • B60L2240/423—Torque
    • 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
    • B60L2240/60—Navigation input
    • B60L2240/64—Road conditions
    • B60L2240/642—Slope of road
    • 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
    • B60L2240/60—Navigation input
    • B60L2240/68—Traffic data
    • 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
    • B60L2240/70—Interactions with external data bases, e.g. traffic centres
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60Y—INDEXING SCHEME RELATING TO ASPECTS CROSS-CUTTING VEHICLE TECHNOLOGY
    • B60Y2200/00—Type of vehicle
    • B60Y2200/90—Vehicles comprising electric prime movers
    • B60Y2200/91—Electric vehicles
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
    • Y02T10/00—Road transport of goods or passengers
    • Y02T10/60—Other road transportation technologies with climate change mitigation effect
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    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
    • Y02T10/00—Road transport of goods or passengers
    • Y02T10/60—Other road transportation technologies with climate change mitigation effect
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    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
    • Y02T90/00—Enabling technologies or technologies with a potential or indirect contribution to GHG emissions mitigation
    • Y02T90/10—Technologies relating to charging of electric vehicles
    • Y02T90/16—Information or communication technologies improving the operation of electric vehicles

Definitions

  • the present application relates to energy recovery technologies, and in particular, to a method and apparatus for recovering braking energy of a vehicle.
  • the problem of the driving range is usually solved by increasing the utilization rate of the EV energy.
  • the braking energy recovery can be used to improve the energy utilization rate of the EV.
  • 1 is a system frame diagram of vehicle braking energy recovery in the prior art.
  • the brake energy recovery and control decisions of the existing EV pass through the accelerator pedal, the brake pedal, and the clutch pedal opening degree. Operation identification, obtaining the brake signal, the throttle signal, the clutch signal, and then calculating the braking torque command according to the maximum allowable charging current, the state of charge of the battery (State of Charge; SOC), and the vehicle controller synthesizing the motor speed. Control the motor for energy feedback.
  • SOC state of charge of Charge
  • the existing braking energy recovery method is based on the driver's operation on the pedal and the passive recovery of the braking energy of the battery and the motor state, so that the recovery rate of the braking energy is low.
  • the embodiment of the present application provides a method and a device for recovering braking energy of a vehicle, which are used to improve the recovery rate of braking energy.
  • an embodiment of the present application provides a method for recovering braking energy of a vehicle, where the method includes:
  • the motor of the vehicle is controlled to perform braking energy recovery based on the target torque.
  • the method for recovering the braking energy of the vehicle provided by the above first aspect is that, by acquiring the current position information of the vehicle, the current road scene is determined according to the current position information of the vehicle, and the current road scene is determined according to the mapping relationship between the road scene and the weight. Corresponding weights are determined, and the safety distance and safety speed of the vehicle are determined according to the weight, and the target torque is determined according to the safety distance and the safe speed of the vehicle. Finally, the braking energy recovery of the motor of the vehicle is controlled according to the target torque.
  • the target torque is determined to recover the braking energy, that is, the information perceived by the vehicle, the road scene is judged, and different road scenes are given differently.
  • the weights are calculated to calculate the safety distance and the safe speed, and then the target deceleration is determined and the torque is distributed, thereby increasing the recovery rate of the braking energy.
  • the weight includes the weight of the safety distance and the weight of the safety speed
  • the safety distance and the safe speed of the vehicle are determined according to the weight, including:
  • the safe speed of the vehicle is calculated based on the weight of the safe speed of the vehicle.
  • the weights of the safety distance and the safety speed of the vehicle may be the same or different for the same scene.
  • the method before calculating the safety distance of the vehicle according to the weight of the safety distance of the vehicle, the method further includes:
  • Calculating the safety distance of the vehicle according to the weight of the safety distance of the vehicle including:
  • L safe is a safe distance
  • the safety distance weight includes two parameters, namely ⁇ 1i and ⁇ 2i .
  • the obstacle includes a moving or stationary object, such as an object including other vehicles or railings.
  • the method before calculating the safe speed of the vehicle according to the weight of the safe speed of the vehicle, the method further includes:
  • V 1 is the relative speed of the obstacle and the vehicle
  • Calculating the safety distance of the vehicle according to the weight of the safety distance of the vehicle including:
  • V safe ⁇ 1i *V 1 + ⁇ 2i *V 2 ;
  • V safe is a safe speed
  • the safety speed includes two parameters, ⁇ 1i and ⁇ 2i .
  • Calculating the safety distance of the vehicle according to the weight of the safety distance of the vehicle including:
  • L safe is a safe distance
  • the safety distance weight includes three parameters, namely ⁇ 1i , ⁇ 2i and ⁇ 3i .
  • the method before calculating the safe speed of the vehicle according to the weight of the safe speed of the vehicle, the method further includes:
  • V 1 is the relative speed of the obstacle and the vehicle
  • Calculating the safe speed of the vehicle based on the weight of the safe speed of the vehicle including:
  • V safe ⁇ 1i *V 1 + ⁇ 2i *V 2 + ⁇ 3i *V 3 ;
  • V safe is a safe speed
  • the safety speed includes three parameters, namely ⁇ 1i , ⁇ 2i and ⁇ 3i .
  • the recovery method of the vehicle braking energy provided by each of the above possible designs can calculate the safety distance and the safe speed of the vehicle in different ways in different situations, so that the calculation method of the safety distance and the safe speed is more flexible.
  • the target torque is determined based on the safe distance and the safe speed of the vehicle, including:
  • the target torque is determined based on the target deceleration.
  • the target deceleration is calculated according to the traveling speed of the vehicle, the safe distance and the safe speed, including:
  • a trg is the target deceleration
  • V safe is the safe speed
  • L safe is the safety distance
  • v is the driving speed of the vehicle.
  • the vehicle braking energy recovery method provided by each of the above possible designs calculates the target deceleration according to the traveling speed, the safety distance and the safe speed of the vehicle, and determines the target torque according to the target deceleration and the vehicle dynamics model, so that the target is passed. Deceleration and the introduction of the vehicle dynamics model to calculate the target torque, thus achieving the goal of global energy consideration for braking energy recovery.
  • the information transmitted by the vehicle network includes at least one of a road allowed maximum speed, a road gradient, a signal state, a signal distance, an average passing speed of the current road, and a first congestion factor.
  • the road history information stored by the cloud data center includes an average time speed of the current time period, an average vehicle distance of the current time period, and a second congestion coefficient, wherein the current time period average vehicle speed and the current time period average vehicle The distance and the second congestion factor are obtained by the cloud data center based on machine learning algorithms and historical data.
  • an embodiment of the present application provides a device for recovering braking energy of a vehicle, where the device includes:
  • An acquisition module configured to acquire current location information of the vehicle
  • a determining module configured to determine a current road scene according to current location information of the vehicle
  • the determining module is further configured to determine, according to a mapping relationship between the road scenario and the weight, a weight corresponding to the current road scenario;
  • the determining module is further configured to determine a safety distance and a safe speed of the vehicle according to the weight;
  • the determining module is further configured to determine the target torque according to the safety distance and the safe speed of the vehicle;
  • a control module configured to control the motor of the vehicle to perform braking energy recovery according to the target torque.
  • the weight includes the weight of the safety distance and the weight of the safety speed
  • the determining module includes:
  • a first calculating unit configured to calculate a safety distance of the vehicle according to a weight of the safety distance of the vehicle
  • a second calculating unit configured to calculate a safe speed of the vehicle according to a weight of the safe speed of the vehicle.
  • the acquisition module is further configured to acquire information that the vehicle itself perceives
  • the determining module is further configured to determine, according to information perceived by the vehicle itself, a first safety distance L 1 , where L 1 is a relative distance between the obstacle and the vehicle;
  • the obtaining module is further configured to obtain information transmitted by the car network
  • the determining module is further configured to determine, according to information transmitted by the Internet of Vehicles, a second safety distance L 2 , where L 2 is a relative distance between the obstacle and the vehicle;
  • the first computing unit is specifically configured to:
  • L safe is a safe distance
  • the safety distance weight includes two parameters, namely ⁇ 1i and ⁇ 2i .
  • the acquisition module is further configured to acquire information that the vehicle itself perceives
  • the determining module is further configured to determine, according to the information perceived by the vehicle itself, the first safety speed V 1 , where V 1 is a relative speed of the obstacle and the vehicle;
  • the obtaining module is further configured to obtain information transmitted by the car network
  • the determining module is further configured to determine, according to information transmitted by the vehicle network, that the second safety speed V 2 , V 2 is a relative speed of the obstacle and the vehicle;
  • the second calculating unit is specifically configured to:
  • V safe ⁇ 1i *V 1 + ⁇ 2i *V 2 ;
  • V safe is a safe speed
  • the safety speed includes two parameters, ⁇ 1i and ⁇ 2i .
  • the acquisition module is further configured to acquire information that the vehicle itself perceives
  • the determining module is further configured to determine, according to information perceived by the vehicle itself, a first safety distance L 1 , where L 1 is a relative distance between the obstacle and the vehicle;
  • the obtaining module is further configured to obtain information transmitted by the car network
  • the determining module is further configured to determine, according to information transmitted by the vehicle network, a second safety distance L 2 , where L 2 is a relative distance between the obstacle and the vehicle;
  • the obtaining module is further configured to acquire road history information stored in the cloud data center;
  • the determining module is further configured to determine, according to the road history information stored in the cloud data center, a third safety distance L 3 , where L 3 is a relative distance between the obstacle and the vehicle;
  • the first computing unit is specifically configured to:
  • L safe is a safe distance
  • the safety distance weight includes three parameters, namely ⁇ 1i , ⁇ 2i and ⁇ 3i .
  • the acquisition module is further configured to acquire information that the vehicle itself perceives
  • the determining module is further configured to determine, according to the information perceived by the vehicle itself, the first safety speed V 1 , where V 1 is a relative speed of the obstacle and the vehicle;
  • the obtaining module is further configured to obtain information transmitted by the car network
  • the determining module is further configured to determine, according to information transmitted by the vehicle network, a second safety speed V 2 , V 2 being a relative speed of the obstacle and the vehicle;
  • the obtaining module is further configured to acquire road history information stored in the cloud data center;
  • the determining module is further configured to determine, according to the road history information stored in the cloud data center, a third safety speed V 3 , V 3 is a relative speed of the obstacle and the vehicle;
  • the second calculating unit is specifically configured to:
  • V safe ⁇ 1i *V 1 + ⁇ 2i *V 2 + ⁇ 3i *V 3 ;
  • V safe is a safe speed
  • the safety speed includes three parameters, namely ⁇ 1i , ⁇ 2i and ⁇ 3i .
  • the determining module is specifically used to:
  • the target torque is determined based on the target deceleration.
  • the determining module is further configured to calculate the target deceleration according to the following formula:
  • a trg is the target deceleration
  • V safe is the safe speed
  • L safe is the safety distance
  • v is the driving speed of the vehicle.
  • the information transmitted by the vehicle network includes at least one of a road allowed maximum speed, a road gradient, a signal state, a signal distance, an average passing speed of the current road, and a first congestion factor.
  • the road history information stored by the cloud data center includes an average time speed of the current time period, an average vehicle distance of the current time period, and a second congestion coefficient, wherein the current time period average vehicle speed and the current time period average vehicle The distance and the second congestion factor are obtained by the cloud data center based on machine learning algorithms and historical data.
  • an embodiment of the present application provides a vehicle, including:
  • a processor configured to acquire current location information of the vehicle
  • the processor is further configured to determine a current road scene according to current location information of the vehicle;
  • the processor is further configured to determine, according to a mapping relationship between the road scenario and the weight, a weight corresponding to the current road scenario;
  • the processor is further configured to determine a safety distance and a safety speed of the vehicle according to the weight;
  • the processor is further configured to determine the target torque according to a safe distance and a safe speed of the vehicle;
  • the processor is further configured to control braking energy recovery of the motor of the vehicle according to the target torque.
  • the weight includes the weight of the safety distance and the weight of the safety speed
  • the processor is further configured to calculate a safety distance of the vehicle according to a weight of the safety distance of the vehicle;
  • the processor is further configured to calculate a safe speed of the vehicle based on a weight of the safe speed of the vehicle.
  • the processor is further configured to acquire information perceived by the vehicle itself, and determine a first safety distance L 1 according to information perceived by the vehicle itself, where L 1 is a relative distance between the obstacle and the vehicle;
  • the processor is further configured to obtain information transmitted by the car network, and determine, according to the information transmitted by the car network, a second safety distance L 2 , where L 2 is a relative distance between the obstacle and the vehicle;
  • L safe is a safe distance
  • the safety distance weight includes two parameters, namely ⁇ 1i and ⁇ 2i .
  • the processor is further configured to acquire information that the vehicle itself perceives, and determine, according to the information perceived by the vehicle itself, the first safety speed V 1 , V 1 is a relative speed of the obstacle and the vehicle;
  • the processor is further configured to obtain information transmitted by the car network, and determine, according to the information transmitted by the car network, the second safety speed V 2 , V 2 as a relative speed of the obstacle and the vehicle;
  • V safe is a safe speed
  • the safety speed includes two parameters, ⁇ 1i and ⁇ 2i .
  • the processor is further configured to acquire information perceived by the vehicle itself, and determine a first safety distance L 1 according to information perceived by the vehicle itself, where L 1 is a relative distance between the obstacle and the vehicle;
  • the processor is further configured to obtain information transmitted by the car network, and determine, according to the information transmitted by the car network, a second safety distance L 2 , where L 2 is a relative distance between the obstacle and the vehicle;
  • the processor is further configured to acquire road history information stored in the cloud data center, and determine a third safety distance L 3 according to the road history information stored in the cloud data center, where L 3 is a relative distance between the obstacle and the vehicle;
  • L safe is a safe distance
  • the safety distance weight includes three parameters, namely ⁇ 1i , ⁇ 2i and ⁇ 3i .
  • the processor is further configured to acquire information that the vehicle itself perceives, and determine, according to the information perceived by the vehicle itself, the first safety speed V 1 , V 1 is a relative speed of the obstacle and the vehicle;
  • the processor is further configured to obtain information transmitted by the car network, and determine, according to information transmitted by the car network, a second safety speed V 2 , V 2 being a relative speed of the obstacle and the vehicle;
  • the processor is further configured to acquire road history information stored in the cloud data center, and determine, according to the road history information stored in the cloud data center, a third safety speed V 3 , V 3 as a relative speed of the obstacle and the vehicle;
  • V safe is a safe speed
  • the safety speed includes three parameters, namely ⁇ 1i , ⁇ 2i and ⁇ 3i .
  • the processor is further configured to calculate a target deceleration according to the traveling speed of the vehicle, the safety distance, and the safety speed;
  • the processor is further configured to determine the target torque based on the target deceleration.
  • the processor is further configured to calculate the target deceleration according to the following formula:
  • a trg is the target deceleration
  • V safe is the safe speed
  • L safe is the safety distance
  • v is the driving speed of the vehicle.
  • the information transmitted by the vehicle network includes at least one of a road allowed maximum speed, a road gradient, a signal state, a signal distance, an average passing speed of the current road, and a first congestion factor.
  • the road history information stored by the cloud data center includes an average time speed of the current time period, an average vehicle distance of the current time period, and a second congestion coefficient, wherein the current time period average vehicle speed and the current time period average vehicle The distance and the second congestion factor are obtained by the cloud data center based on machine learning algorithms and historical data.
  • FIG. 2 is a schematic diagram of a system architecture of a method for recovering braking energy of a vehicle according to the present application
  • Embodiment 3 is a schematic flow chart of Embodiment 1 of a method for recovering braking energy of a vehicle according to the present application;
  • FIG. 4 is a flow chart 1 for calculating a safety distance of a vehicle
  • FIG. 5 is a schematic flow chart 2 of calculating a safety distance of a vehicle
  • Figure 1 is a flow chart 1 for calculating the safe speed of the vehicle
  • Figure 2 is a flow chart 2 of calculating the safe speed of the vehicle
  • FIG. 8 is a schematic structural diagram of Embodiment 1 of a device for recovering braking energy of a vehicle according to an embodiment of the present application;
  • FIG. 9 is a schematic structural diagram of Embodiment 2 of a device for recovering braking energy of a vehicle according to an embodiment of the present disclosure
  • FIG. 10 is a schematic structural diagram of an embodiment of a vehicle according to an embodiment of the present application.
  • FIG. 2 is a schematic structural diagram of a system for braking energy recovery of a vehicle according to the present application.
  • the embodiment of the present application is applicable to a vehicle networking system, and the system includes: a sensing subsystem, a computing and fusion subsystem, execution, and decision making. Subsystem.
  • the sensing subsystem includes a self-aware unit, a car networking unit, a vehicle position locating unit, a cloud data center unit, and a driving intention recognition unit.
  • the self-aware unit is mainly a laser radar, a millimeter wave radar, a monocular camera, a multi-view camera, an acceleration sensor, etc. for measuring vehicle speed/distance/direction of a smart car device.
  • Vehicle networking unit mainly through vehicle-to-vehicle information exchange (Vehicle to Vehicle; referred to as: V2V), vehicle-to-infrastructure information exchange (Vehicle to Infrastructure (V2I) for short, vehicle-to-Pedestrian information exchange (Vehicle to People;
  • V2V vehicle-to-vehicle information exchange
  • V2I Vehicle to Infrastructure
  • V2P vehicle-to-Pedestrian information exchange
  • the vehicle position locating unit mainly includes various vehicle positioning information, such as a Global Positioning System (GPS), a Differential Global Positioning System (DGPS), a Beidou navigation system, a Galileo navigation system, Russian Genus navigation system to And the Inertial Measurement Unit (IMU) system provides the positioning accuracy of the vehicle resolution including the meter level, the centimeter level, and the lane line positioning level.
  • GPS Global Positioning System
  • DGPS Differential Global Positioning System
  • Beidou navigation system a Beidou navigation system
  • Galileo navigation system a Galileo navigation system
  • Russian Genus navigation system to And the Inertial Measurement Unit (IMU) system provides the positioning accuracy of the vehicle resolution including the meter level, the centimeter level, and the lane line positioning level.
  • the cloud data center unit mainly acquires safe driving data, such as risk level and congestion index, under different weather conditions, such as big data, machine learning, deep learning, and different weather conditions.
  • the driving intention recognition unit is configured to recognize
  • the calculation and fusion subsystem mainly comprises: a vehicle environment sensing information fusion unit, a vehicle target speed calculation unit, a vehicle target torque calculation unit and a vehicle torque distribution control unit.
  • the vehicle environment sensing information fusion unit is mainly used for comprehensively acquiring various parameters acquired by the sensing subsystem, and selecting weights of various parameters according to the type of the scene driven by the vehicle as an input of the vehicle target deceleration calculation module.
  • the execution and decision making subsystem comprises: an electric drive unit, a power battery unit, and an active mechanical brake unit.
  • the method for recovering the braking energy of the vehicle in the embodiment of the present application comprehensively considers two important factors: the inside of the vehicle (electric drive system, battery system, pedal state, control state, etc.) and the outside of the vehicle (environment, road scene, external vehicle, etc.).
  • Information input, final regenerative braking control and drive control not only optimize the regenerative braking when the driver has the corresponding braking operation; intelligently regenerative braking according to the environmental conditions without the intention of braking, achieving the overall economy of the vehicle
  • FIG. 3 is a schematic flow chart of Embodiment 1 of a method for recovering braking energy of a vehicle according to the present application.
  • the embodiment of the present application provides a method for recovering braking energy of a vehicle, which may be performed by any device that performs a method for recovering braking energy of a vehicle, and the device may be implemented by software and/or hardware. In this embodiment, the device can be integrated in a vehicle.
  • the method in this embodiment may include:
  • Step 301 Acquire current location information of the vehicle.
  • the vehicle can acquire the vehicle position locating unit in the sensing subsystem.
  • the current location information in the specific implementation process, can obtain current location information through GPS, DGPS, Beidou navigation system, Galileo navigation system, Russian Genus navigation system or IMU system.
  • Step 302 Determine a current road scene according to current location information of the vehicle.
  • the vehicle may acquire information such as the road gradient i 1 and the road speed limit V max1 in the first range by using the self-sensing unit in the sensing subsystem, wherein the first range may be, for example, a radar, a camera, or a sensor.
  • the first range may be, for example, a radar, a camera, or a sensor.
  • the maximum distance measured, such as 200m; the road allowed maximum speed V max2 , the road gradient i 2 , the signal state S light , the distance between the vehicle and the signal light L light can be obtained through the vehicle networking unit in the sensing subsystem; the current road The average traffic speed V avg1 and the first congestion coefficient f 1 can be obtained by the cloud data center unit based on a large amount of data acquired by big data, machine learning, or the like, the current road speed, the current vehicle speed average speed V avg2 , the second congestion coefficient f 2 , and the like.
  • the vehicle After acquiring the above information and acquiring the current location information of the vehicle, the vehicle determines the current road scene in combination with the identification information of the road type indicated by the map.
  • the identification information of the road type indicated by the map includes: G (national road), S (provincial road), X (county road), Y (township road), etc., or ordinary road (class A road), highway (class B) Road), intelligent network driving lane (C road), intelligent network driving road (D road).
  • the vehicle can determine the road scene where the vehicle is currently located according to the above information, the current location information of the vehicle, and the identification information of the road type indicated by the map, wherein the road scene includes a highway (scene 1), Provincial highway (Scene 2), urban road (Scene 3) or country road (Scene 4).
  • the road scene includes a highway (scene 1), Provincial highway (Scene 2), urban road (Scene 3) or country road (Scene 4).
  • the vehicle obtains a road gradient of 30 degrees
  • the maximum speed allowed by the road is 30km/h
  • the first congestion coefficient is 0.1, indicating that the environmental road slope where the vehicle is located is large, and the maximum speed allowed by the road is higher.
  • the degree of congestion of the vehicle is not high, and the vehicle determines that the location of the location in the map is Y (township) according to its current location information.
  • the current road scene is a country road.
  • the vehicle may determine the current road scene based only on the current location information, or may determine the parameter measured by at least one unit of the self-aware unit, the vehicle networking unit, or the cloud data center unit, and the current location information.
  • Current road scene For the specific determination manner of the current road scene, the embodiment is not limited herein.
  • Step 303 Determine a weight corresponding to the current road scenario according to a mapping relationship between the road scenario and the weight.
  • a mapping relationship between a road scene and a weight is stored in advance in the vehicle. After the vehicle determines the current road scene, the weight corresponding to the current road scene will be determined according to the pre-stored mapping relationship. Specifically, the weight may be allocated according to the manner of determining the current road scene. For example, if the vehicle determines the current road scene according to the parameters measured by the own sensing unit, the vehicle networking unit, or the cloud data center unit, and the current location information, Determine the weight according to the allocation method in Table 1:
  • ⁇ 1, ⁇ 2, and ⁇ 3 can be customized.
  • it is generally in the vehicle sales area, which can be customized according to the traffic regulations of the sales area and the map road conditions.
  • it can be configured before the vehicle leaves the factory, or for the aftermarket. , can be configured at the time of installation.
  • ⁇ 1, ⁇ 2, and ⁇ 3 are percentages of data measured by the self-aware unit, the vehicle networking unit, or the cloud data center unit, respectively.
  • the self-aware unit the vehicle network
  • the data measured by the unit or cloud data center unit accounts for 60%, 20%, and 20% of the weight.
  • the weights are determined according to the allocation method in Table 2:
  • the distance parameter acquired by the self-perception system has the highest proportion.
  • ⁇ 2 is a road condition of 200 meters to several kilometers, which has a relatively large impact on the driving speed of the vehicle. There is no traffic light at high speed. For urban roads, ⁇ 2 is the influence of the traffic light state, which is relatively large; Road, all rely on the safety distance data obtained by their own sensing unit.
  • Step 304 Determine a safety distance and a safety speed of the vehicle according to the weight.
  • the weight includes a weight of the safety distance and a weight of the safety speed. Therefore, in practical applications, determining the safety distance and the safety speed of the vehicle according to the weight includes: calculating the safety distance of the vehicle according to the weight of the safety distance of the vehicle, and calculating the safety speed of the vehicle according to the weight of the safety speed of the vehicle.
  • the weight of the safe distance of the vehicle and the weight of the safe speed may be the same or different.
  • the achievable manner of calculating the safety distance of the vehicle according to the weight of the safety distance of the vehicle may include the following:
  • a schematic flowchart 1 of calculating the safety distance of the vehicle, the step 304 may specifically include:
  • Step 401 Acquire information perceived by the vehicle itself, and determine a first safety distance L 1 according to information perceived by the vehicle itself, and L 1 is a relative distance between the obstacle and the vehicle.
  • the vehicle may acquire information perceived by the vehicle itself through a radar, a camera, or other types of sensors in the sensing unit, and thereby determine a relative distance L 1 between the obstacle and the vehicle, that is, a first safety distance L 1 .
  • Obstacles include moving or stationary objects, such as objects such as other vehicles or railings.
  • Step 402 Acquire information transmitted by the car network, and determine a second safety distance L 2 according to information transmitted by the car network, where L 2 is a relative distance between the obstacle and the vehicle.
  • the vehicle can obtain information transmitted by the vehicle network through V2V, V2I, V2P, etc. in the vehicle networking unit, thereby determining a relative distance L 2 between the obstacle and the vehicle, that is, a second safety distance L 2 , wherein the obstacle It may also include moving or stationary objects, such as objects including other vehicles or railings.
  • the information transmitted by the car network includes at least one of a road allowed maximum speed, a road gradient, a signal state, a signal distance, an average passing speed of the current road, and a first congestion coefficient.
  • the first congestion coefficient can be represented by any value in 0-1, and the larger the value, the representative car The more congested the car.
  • L safe is a safe distance; wherein the safety distance weight includes two parameters, respectively ⁇ 1i , ⁇ 2i .
  • the second method is as follows: Referring to FIG. 5, a flow chart 2 of calculating the safety distance of the vehicle is shown in FIG.
  • Step 501 Acquire information perceived by the vehicle itself, and determine a first safety distance L 1 according to information perceived by the vehicle itself, where L 1 is a relative distance between the obstacle and the vehicle.
  • Step 502 Obtain information transmitted by the vehicle network, and determine a second safety distance L 2 according to information transmitted by the vehicle network, where L 2 is a relative distance between the obstacle and the vehicle.
  • Step 501 - Step 502 is similar to Step 401 - Step 402, and details are not described herein again.
  • Step 503 Acquire road history information stored in the cloud data center, and determine a third safety distance L 3 according to the road history information stored in the cloud data center, where L 3 is a relative distance between the obstacle and the vehicle.
  • the vehicle can acquire safe driving data under different time periods and different meteorological conditions by using a cloud data center unit based on big data, machine learning or deep learning, that is, obtaining a relative distance L 3 between the obstacle and the vehicle, that is, the third. Safety distance L 3 .
  • the road history information stored in the cloud data center includes an average time speed of the current time period, an average vehicle distance of the current time period, and a second congestion coefficient, wherein the current time period average vehicle speed, the current time period average vehicle distance, and the second congestion coefficient It is calculated by the cloud data center based on machine learning algorithms and historical data.
  • the second congestion coefficient can be represented by any value in 0-1, and the larger the value, the more congested the vehicle.
  • L safe is a safe distance; the safety distance weight includes three parameters, namely ⁇ 1i , ⁇ 2i and ⁇ 3i .
  • the information acquired by the vehicle includes the information that the vehicle itself perceives, the information transmitted by the vehicle network, and the road history information stored in the cloud data center, the current road scene may be determined according to the mapping relationship in Table 1 of step 303.
  • the weight of the safety distance for example, the vehicle is driving under scene 2 (high speed).
  • the achievable manner of calculating the safe speed of the vehicle according to the weight of the safe speed of the vehicle may include the following:
  • the first type Referring to FIG. 6 is a schematic flowchart 1 of the process of calculating the safety speed of the vehicle, the above step 304 may specifically include:
  • Step 601 Acquire information perceived by the vehicle itself, and determine a first safety speed V 1 according to information perceived by the vehicle itself, and V 1 is a relative speed of the obstacle and the vehicle.
  • the vehicle may acquire information perceived by the vehicle itself through a radar, a camera, or other types of sensors in the sensing unit, and thereby determine a relative speed V 1 of the obstacle and the vehicle, that is, a first safety speed V 1 .
  • Obstacles include moving or stationary objects, such as objects such as other vehicles or railings.
  • Step 602 Obtain information transmitted by the car network, and determine, according to the information transmitted by the car network, the second safety speed V 2 , V 2 is the relative speed of the obstacle and the vehicle.
  • the information transmitted by the car network includes at least one of a road allowed maximum speed, a road gradient, a signal state, a signal distance, an average passing speed of the current road, and a first congestion coefficient.
  • the first congestion coefficient can be represented by any value in 0-1, and the larger the value, the more congested the vehicle.
  • V safe is a safe speed; wherein the safe speed includes two parameters, ⁇ 1i , ⁇ 2i .
  • the second type Referring to FIG. 7 is a schematic flowchart 2 of the process of calculating the safety speed of the vehicle, the foregoing step 304 may specifically include:
  • Step 701 Acquire information perceived by the vehicle itself, and determine the first safety speed V 1 according to the information perceived by the vehicle itself, and V 1 is the relative speed of the obstacle and the vehicle.
  • Step 702 Acquire information transmitted by the vehicle network, and determine, according to the information transmitted by the vehicle network, the second safety speed V 2 , V 2 is a relative speed of the obstacle and the vehicle.
  • Step 701 - Step 702 is similar to Step 601 - Step 602, and details are not described herein again.
  • Step 703 Acquire road history information stored in the cloud data center, and determine, according to the road history information stored in the cloud data center, the third safety speed V 3 , V 3 as the relative speed of the obstacle and the vehicle.
  • the road history information stored in the cloud data center includes an average time speed of the current time period, an average vehicle distance of the current time period, and a second congestion coefficient, wherein the current time period average vehicle speed, the current time period average vehicle distance, and the second congestion coefficient It is calculated by the cloud data center based on machine learning algorithms and historical data.
  • the second congestion coefficient can be represented by any value in 0-1, and the larger the value, the more congested the vehicle.
  • V safe is a safe speed; the safe speed includes three parameters, namely ⁇ 1i , ⁇ 2i and ⁇ 3i .
  • the current road scene may be determined according to the mapping relationship in Table 1 of step 303.
  • the weight of the safe speed For example, the vehicle travels under scene 2 (high speed).
  • V safe ⁇ 1i * V 1 + ⁇ 2i * V 2 + ⁇ 3i * V 3 Calculate the safe speed of the vehicle.
  • Step 305 Determine a target torque according to a safe distance and a safe speed of the vehicle.
  • the target torque is determined according to the calculated safety distance and the safety speed, so that the vehicle determines the vehicle demand according to the target torque and the driving intention of the driver.
  • Torque which controls the vehicle's motor for braking energy recovery.
  • determining the target torque according to the safety distance and the safe speed of the vehicle includes: calculating a target deceleration according to the traveling speed, the safety distance, and the safety speed of the vehicle; and determining the target torque according to the target deceleration.
  • the vehicle target deceleration calculation unit in the calculation and fusion subsystem will be able to according to the formula
  • the target deceleration is calculated, where a trg is the target deceleration, V safe is the safe speed, L safe is the safe distance, and v is the running speed of the vehicle.
  • the vehicle target torque calculation unit calculates the target torque based on the target deceleration, the road gradient i acquired by the sensing subsystem, and the vehicle dynamics model. Specifically, according to the formula Calculate the target torque.
  • G is the weight of the vehicle
  • f is the rolling resistance coefficient
  • C D is the drag coefficient
  • A is the windward area of the vehicle
  • v is the real-time vehicle speed of the vehicle
  • i is the slope of the road on which the vehicle is traveling
  • ⁇ is the conversion factor of the rotating mass
  • m is the mass of the vehicle
  • a trg is the target deceleration
  • r is the wheel radius of the vehicle
  • i g is the transmission ratio of the vehicle
  • i 0 is the main reduction ratio
  • ⁇ T is the mechanical transmission efficiency
  • T trg is the target torque.
  • T trg When T trg is greater than 0, it is the target driving torque, and when T trg is less than 0, it is the target braking torque.
  • the target torque is calculated by introducing a vehicle dynamics model, so that the purpose of braking energy recovery from the perspective of global economy can be achieved.
  • Step 306 Control the motor of the vehicle according to the target torque to perform braking energy recovery.
  • the vehicle determines the vehicle demand torque according to the driving intention recognized by the target torque and the driver intention identifying unit, and allocates the determined vehicle demand torque, thereby controlling The motor of the vehicle performs recovery of braking energy.
  • the vehicle torque distribution control unit performs torque distribution according to a motor maximum output torque in the vehicle, a motor output torque minimum value in the vehicle, a battery real-time allowable charging power in the vehicle, and a battery real-time allowable discharge power in the vehicle, Thereby controlling the motor and the active mechanical brake system jobs.
  • the method for recovering the braking energy of the vehicle determines the current road scene according to the current position information of the vehicle by acquiring the current position information of the vehicle, and determines the current road scene corresponding according to the mapping relationship between the road scene and the weight.
  • the weight is determined, and the safety distance and safety speed of the vehicle are determined according to the weight.
  • the target torque is determined according to the safety distance and the safe speed of the vehicle.
  • the braking energy recovery of the vehicle is controlled according to the target torque. Since the safe speed and the safety distance of the vehicle are determined according to the road scene in which the vehicle is currently located, the target torque is determined to recover the braking energy, that is, the information perceived by the vehicle, the road scene is judged, and different road scenes are given differently.
  • the weights are calculated to calculate the safety distance and the safe speed, and then the target deceleration is determined and the torque is distributed, thereby increasing the recovery rate of the braking energy.
  • FIG. 8 is a schematic structural diagram of Embodiment 1 of a device for recovering braking energy of a vehicle according to an embodiment of the present application.
  • the recycling device may be a stand-alone vehicle or a device integrated in the vehicle, and the device may be implemented by software, hardware or a combination of software and hardware. As shown in Figure 8, the recycling device comprises:
  • the obtaining module 11 is configured to acquire current location information of the vehicle
  • a determining module 12 configured to determine a current road scene according to current location information of the vehicle
  • the determining module 12 is further configured to determine, according to a mapping relationship between the road scene and the weight, a weight corresponding to the current road scene;
  • the determining module 12 is further configured to determine a safety distance and a safety speed of the vehicle according to the weight;
  • the determining module 12 is further configured to determine the target torque according to the safety distance and the safe speed of the vehicle;
  • the control module 13 is configured to control braking energy recovery of the motor of the vehicle according to the target torque.
  • the foregoing obtaining module 11, the determining module 12, and the control module 13 may be corresponding to a processor in a vehicle.
  • the apparatus for recovering the braking energy of the vehicle provided by the embodiment of the present application may perform the foregoing method embodiments, and the implementation principle and technical effects thereof are similar, and details are not described herein again.
  • FIG. 9 is a schematic structural diagram of Embodiment 2 of a device for recovering braking energy of a vehicle according to an embodiment of the present application.
  • the weight includes a weight of the safety distance and The weight of the security speed; the foregoing determining module 12 specifically includes:
  • a first calculating unit 121 configured to calculate a safety distance of the vehicle according to a weight of the safety distance of the vehicle
  • the second calculating unit 122 is configured to calculate a safe speed of the vehicle according to a weight of the safe speed of the vehicle.
  • the obtaining module 11 is further configured to acquire information that is perceived by the vehicle itself;
  • the determining module 12 is further configured to determine, according to the information perceived by the vehicle itself, a first safety distance L 1 , where L 1 is a relative distance between the obstacle and the vehicle;
  • the obtaining module 11 is further configured to acquire information transmitted by the car network;
  • the determining module 12 is further configured to determine, according to the information transmitted by the Internet of Vehicles, a second safety distance L 2 , where L 2 is a relative distance between the obstacle and the vehicle;
  • the first calculating unit 121 is specifically configured to:
  • L safe is a safe distance
  • the safety distance weight includes two parameters, namely ⁇ 1i and ⁇ 2i .
  • the obtaining module 11 is further configured to acquire information that is perceived by the vehicle itself;
  • the determining module 12 is further configured to determine, according to information perceived by the vehicle itself, a first safety speed V 1 , where V 1 is a relative speed of the obstacle and the vehicle;
  • the obtaining module 11 is further configured to acquire information transmitted by the car network;
  • the determining module 12 is further configured to determine, according to the information transmitted by the car network, that the second safety speed V 2 , V 2 is a relative speed of the obstacle and the vehicle;
  • the second calculating unit 122 is specifically configured to:
  • V safe ⁇ 1i * V 1 + ⁇ 2i * V 2 ;
  • V safe is a safe speed
  • the safety speed includes two parameters, namely ⁇ 1i and ⁇ 2i .
  • the obtaining module 11 is further configured to acquire information that is perceived by the vehicle itself;
  • the determining module 12 is further configured to determine, according to the information perceived by the vehicle itself, a first safety distance L 1 , where L 1 is a relative distance between the obstacle and the vehicle;
  • the obtaining module 11 is further configured to acquire information transmitted by the car network;
  • the determining module 12 is further configured to determine, according to the information transmitted by the vehicle network, a second safety distance L 2 , where L 2 is a relative distance between the obstacle and the vehicle;
  • the obtaining module 11 is further configured to acquire road history information stored in the cloud data center;
  • the determining module 12 is further configured to determine, according to the road history information stored by the cloud data center, a third safety distance L 3 , where L 3 is a relative distance between the obstacle and the vehicle;
  • the first calculating unit 121 is specifically configured to:
  • L safe is a safe distance
  • the safety distance weight includes three parameters, namely ⁇ 1i , ⁇ 2i and ⁇ 3i .
  • the obtaining module 11 is further configured to acquire information that is perceived by the vehicle itself;
  • the determining module 12 is further configured to determine, according to information perceived by the vehicle itself, a first safety speed V 1 , where V 1 is a relative speed of the obstacle and the vehicle;
  • the obtaining module 11 is further configured to acquire information transmitted by the car network;
  • the determining module 12 is further configured to determine, according to the information transmitted by the Internet of Vehicles, a second safety speed V 2 , V 2 being a relative speed of the obstacle and the vehicle;
  • the obtaining module 11 is further configured to acquire road history information stored in the cloud data center;
  • the determining module 12 is further configured to determine, according to the road history information stored by the cloud data center, a third safety speed V 3 , V 3 as a relative speed of the obstacle and the vehicle;
  • the second calculating unit 122 is specifically configured to:
  • V safe ⁇ 1i *V 1 + ⁇ 2i *V 2 + ⁇ 3i *V 3 ;
  • V safe is a safe speed
  • the safety speed includes three parameters, namely ⁇ 1i , ⁇ 2i and ⁇ 3i .
  • the determining module 12 is specifically configured to:
  • the target torque is determined based on the target deceleration.
  • the determining module 12 is further configured to calculate the target deceleration according to the following formula:
  • V safe is the safe speed
  • L safe is the safe distance
  • v is the travel speed of the vehicle.
  • the information transmitted by the vehicle network includes at least one of a road allowed maximum speed, a road gradient, a signal state, a signal distance, an average passing speed of the current road, and a first congestion coefficient.
  • the road history information stored by the cloud data center includes a current time period average vehicle speed, a current time period average vehicle distance, and a second congestion coefficient, wherein the current time period average vehicle speed, the current time period average distance, and The second congestion factor is obtained by the cloud data center based on machine learning algorithms and historical data calculations.
  • the apparatus for recovering the braking energy of the vehicle provided by the embodiment of the present application may perform the foregoing method embodiments, and the implementation principle and technical effects thereof are similar, and details are not described herein again.
  • FIG. 10 is a schematic structural diagram of an embodiment of a vehicle according to an embodiment of the present application.
  • the vehicle may include a transmitter 20, a processor 21, a memory 22, and at least one communication bus 23.
  • the communication bus 23 is used to implement a communication connection between components.
  • Memory 22 may include high speed RAM memory, and may also include non-volatile memory NVM, such as at least one disk memory, in which various programs may be stored for performing various processing functions and implementing the method steps of the present embodiments.
  • the vehicle may further include a receiver 24.
  • the receiver 24 in this embodiment may be a corresponding input interface having a communication function and a function of receiving information.
  • the transmitter 20 in this embodiment may have a corresponding communication function and The output interface for sending information functions.
  • the transmitter 20 and the receiver 24 may be integrated in one communication interface, or may be two independent communication interfaces.
  • the processor 21 is configured to acquire current location information of the vehicle.
  • the processor 21 is further configured to determine a current road scene according to the current location information of the vehicle;
  • the processor 21 is further configured to determine, according to a mapping relationship between the road scene and the weight, a weight corresponding to the current road scene;
  • the processor 21 is further configured to determine a safety distance and a safety speed of the vehicle according to the weight;
  • the processor 21 is further configured to determine the target torque according to the safety distance and the safe speed of the vehicle;
  • the processor 21 is further configured to control braking energy recovery of the motor of the vehicle according to the target torque.
  • the weight includes a weight of a safety distance and a weight of a safety speed
  • the processor 21 is further configured to calculate a safety distance of the vehicle according to a weight of the safety distance of the vehicle;
  • the processor 21 is further configured to calculate a safe speed of the vehicle according to a weight of the safe speed of the vehicle.
  • the processor 21 is further configured to acquire information that is perceived by the vehicle itself, and determine, according to the information that the vehicle itself perceives, the first safety distance L 1 , where L 1 is a relative distance between the obstacle and the vehicle;
  • the processor 21 is further configured to acquire information transmitted by the car network, and determine, according to the information transmitted by the car network, a second safety distance L 2 , where L 2 is a relative distance between the obstacle and the vehicle;
  • L safe is a safe distance
  • the safety distance weight includes two parameters, namely ⁇ 1i and ⁇ 2i .
  • the processor 21 is further configured to acquire information that is perceived by the vehicle itself, and determine, according to information that the vehicle itself perceives, that the first safety speed V 1 , V 1 is a relative speed of the obstacle and the vehicle;
  • the processor 21 is further configured to acquire information transmitted by the car network, and determine, according to the information transmitted by the car network, that the second safety speed V 2 , V 2 is a relative speed of the obstacle and the vehicle;
  • V safe is a safe speed
  • the safety speed includes two parameters, namely ⁇ 1i and ⁇ 2i .
  • the processor 21 is further configured to acquire information that is perceived by the vehicle itself, and determine, according to the information that the vehicle itself perceives, the first safety distance L 1 , where L 1 is a relative distance between the obstacle and the vehicle;
  • the processor 21 is further configured to acquire information transmitted by the car network, and determine, according to the information transmitted by the car network, a second safety distance L 2 , where L 2 is a relative distance between the obstacle and the vehicle;
  • the processor 21 is further configured to acquire road history information stored in the cloud data center, and determine a third safety distance L 3 according to the road history information stored in the cloud data center, where L 3 is a relative distance between the obstacle and the vehicle. ;
  • L safe is a safe distance
  • the safety distance weight includes three parameters, namely ⁇ 1i , ⁇ 2i and ⁇ 3i .
  • the processor 21 is further configured to acquire information that is perceived by the vehicle itself, and determine, according to information that the vehicle itself perceives, that the first safety speed V 1 , V 1 is a relative speed of the obstacle and the vehicle;
  • the processor 21 is further configured to acquire information transmitted by the car network, and determine, according to the information transmitted by the car network, that the second safety speed V 2 , V 2 is a relative speed of the obstacle and the vehicle;
  • the processor 21 is further configured to acquire road history information stored in the cloud data center, and determine, according to the road history information stored in the cloud data center, a third safety speed V 3 , V 3 as a relative speed of the obstacle and the vehicle. ;
  • V safe is a safe speed
  • the safety speed includes three parameters, namely ⁇ 1i , ⁇ 2i and ⁇ 3i .
  • the processor 21 is further configured to calculate a target deceleration according to the traveling speed of the vehicle, the safety distance, and the safety speed;
  • the processor 21 is further configured to determine the target torque according to the target deceleration.
  • the processor 21 is further configured to calculate the target deceleration according to the following formula:
  • V safe is the safe speed
  • L safe is the safe distance
  • v is the travel speed of the vehicle.
  • the information transmitted by the vehicle network includes at least one of a road allowed maximum speed, a road gradient, a signal state, a signal distance, an average passing speed of the current road, and a first congestion coefficient.
  • the road history information stored by the cloud data center includes a current time period average vehicle speed, a current time period average vehicle distance, and a second congestion coefficient, wherein the current time period average vehicle speed, the current time period average distance, and The second congestion factor is obtained by the cloud data center based on machine learning algorithms and historical data calculations.
  • the vehicle provided in the embodiment of the present application may perform the foregoing method embodiments, and the implementation principles and technical effects thereof are similar, and details are not described herein again.
  • the disclosed system, apparatus, and method may be implemented in other manners.
  • the device embodiments described above are merely illustrative.
  • the division of the modules or units is only a logical function division.
  • there may be another division manner for example, multiple units or components may be used. Combinations can be integrated into another system, or some features can be ignored or not executed.
  • the mutual coupling or direct coupling or communication connection shown or discussed may be an indirect coupling or communication connection through some interface, device or unit, and may be in an electrical, mechanical or other form.
  • the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, may be located in one place, or may be distributed to multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of the embodiment.
  • each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.
  • the above integrated unit can be implemented in the form of hardware or in the form of a software functional unit.
  • the integrated unit if implemented in the form of a software functional unit and sold or used as a standalone product, may be stored in a computer readable storage medium.
  • a computer readable storage medium A number of instructions are included to cause a computer device (which may be a personal computer, server, or network device, etc.) or a processor to perform all or part of the steps of the methods described in various embodiments of the present application.
  • the foregoing storage medium includes: a U disk, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk, and the like. .

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Abstract

一种车辆制动能量的回收方法和装置，该方法包括：获取车辆当前的位置信息；根据所述车辆当前的位置信息确定当前的道路场景；根据道路场景与权重之间的映射关系确定所述当前的道路场景；根据所述权重确定所述车辆的安全距离和安全速度；根据所述车辆的安全距离和安全速度确定所述目标扭矩；根据所述目标扭矩控制所述车辆的电机进行制动能量回收。所述车辆制动能量的回收方法和装置可以提高制动能量的回收率。

Description

车辆制动能量的回收方法和装置 技术领域
本申请涉及能量回收技术，尤其涉及一种车辆制动能量的回收方法和装置。
背景技术
当前大气污染严重、雾霾频发，环境保护问题受到广泛关注，因此，电动汽车(Electric Vehicle，简称：EV)的发展受到了各国的重视。然而，续驶里程是阻碍EV推广的最大问题。
在现有技术中，通常采用提高EV能量利用率的方式解决续驶里程的问题，具体地，可以采用制动能量回收来提高EV的能量利用率。图1为现有技术中车辆制动能量回收的***框架图，如图1所示，现有的EV的制动能量回收及控制决策均通过对加速踏板、制动踏板、离合踏板开度的操作识别，获取制动信号、油门信号、离合信号，进而根据最大允许充电电流、电池荷电状态信号(State of Charge；简称：SOC)，整车控制器再综合电机转速算出制动扭矩命令，控制电机进行能量回馈。
然而，现有的制动能量回收方式是根据驾驶员对踏板的操作和电池、电机状态被动地进行制动能量的回收，使得制动能量的回收率较低。
发明内容
本申请实施例提供一种车辆制动能量的回收方法和装置，用以提高制动能量的回收率。
第一方面，本申请实施例提供一种车辆制动能量的回收方法，该方法包括：
获取车辆当前的位置信息；
根据该车辆当前的位置信息确定当前的道路场景；
根据道路场景与权重之间的映射关系确定该当前的道路场景对应的权重；
根据该权重确定该车辆的安全距离和安全速度；
根据该车辆的安全距离和安全速度确定该目标扭矩；
根据该目标扭矩控制该车辆的电机进行制动能量回收。
上述第一方面提供的车辆制动能量的回收方法，由于通过获取车辆当前的位置信息，根据车辆当前的位置信息确定当前的道路场景，根据道路场景与权重之间的映射关系确定当前的道路场景对应的权重，并根据权重确定车辆的安全距离和安全速度，再根据车辆的安全距离和安全速度确定目标扭矩，最后根据目标扭矩控制车辆的电机进行制动能量回收。由于根据车辆当前所处的道路场景，确定车辆的安全速度和安全距离，从而确定出目标扭矩以进行制动能量的回收，即结合车辆感知的信息、判断道路场景、对不同的道路场景赋予不同的权重，从而计算安全距离和安全速度，再确定目标减速度并分配扭矩，从而提高了制动能量的回收率。
在一种可能的设计中，该权重包括安全距离的权重和安全速度的权重；
该根据该权重确定该车辆的安全距离和安全速度，包括：
根据该车辆的安全距离的权重计算该车辆的安全距离；
根据该车辆的安全速度的权重计算该车辆的安全速度。
在上述设计中，对同一个场景，车辆的安全距离的权重和安全速度的权重可以相同，也可以不相同。
在一种可能的设计中，该根据该车辆的安全距离的权重计算该车辆的安全距离之前，该方法还包括：
获取车辆自身感知的信息，并根据该车辆自身感知的信息确定第一安全距离L1，L1为障碍物与该车辆的相对距离；
获取车联网传输的信息，并根据该车联网传输的信息确定第二安全距离L2，L2为该障碍物与该车辆的相对距离；
该根据该车辆的安全距离的权重计算该车辆的安全距离，包括：
根据公式Lsafe＝δ1i*L1+δ2i*L2计算该车辆的安全距离；
其中，Lsafe为安全距离；
其中，该安全距离权重包括两个参数，分别为δ1i，δ2i。
在上述设计中，障碍物包括移动或静止的物体，例如包括其他车辆或栏杆等物体。
在一种可能的设计中，该根据该车辆的安全速度的权重计算该车辆的安全速度之前，该方法还包括：
获取车辆自身感知的信息，并根据该车辆自身感知的信息确定第一安全速度V1，V1为障碍物与该车辆的相对速度；
获取车联网传输的信息，并根据该车联网传输的信息确定第二安全速度V2，V2为障碍物与该车辆的相对速度；
根据该车辆的安全距离的权重计算该车辆的安全距离，包括：
根据公式Vsafe＝δ1i*V1+δ2i*V2计算该车辆的安全距离；
其中，Vsafe为安全速度；
其中，该安全速度包括两个参数，分别为δ1i，δ2i。
在一种可能的设计中，该根据该车辆的安全距离的权重计算该车辆的安全距离之前，该方法还包括：
获取车辆自身感知的信息，并根据该车辆自身感知的信息确定第一安全距离L1，L1为障碍物与该车辆的相对距离；
获取车联网传输的信息，并根据该车联网传输的信息确定第二安全距离L2，L2为障碍物与该车辆的相对距离；
获取云数据中心存储的道路历史信息，并根据该云数据中心存储的道路历史信息确定第三安全距离L3，L3为障碍物与该车辆的相对距离；
该根据该车辆的安全距离的权重计算该车辆的安全距离，包括：
根据公式Lsafe＝δ1i*L1+δ2i*L2+δ3i*L3计算该车辆的安全距离；
其中，Lsafe为安全距离；
其中，该安全距离权重包括三个参数，分别为δ1i，δ2i和δ3i。
在一种可能的设计中，该根据该车辆的安全速度的权重计算该车辆的安全速度之前，该方法还包括：
获取车辆自身感知的信息，并根据该车辆自身感知的信息确定第一安全速度V1，V1为障碍物与该车辆的相对速度；
获取车联网传输的信息，并根据该车联网传输的信息确定第二安全速度V2，V2为该障碍物与该车辆的相对速度；
获取云数据中心存储的道路历史信息，并根据该云数据中心存储的道路 历史信息确定第三安全速度V3，V3为障碍物与该车辆的相对速度；
根据该车辆的安全速度的权重计算该车辆的安全速度，包括：
根据公式Vsafe＝δ1i*V1+δ2i*V2+δ3i*V3计算该车辆的安全速度；
其中，Vsafe为安全速度；
其中，该安全速度包括三个参数，分别为δ1i，δ2i以及δ3i。
上述各可能的设计所提供的车辆制动能量的回收方法，在不同的情况下可以采用不同的方式计算车辆的安全距离和安全速度，使得安全距离和安全速度的计算方式更加灵活。
在一种可能的设计中，该根据该车辆的安全距离和安全速度确定该目标扭矩，包括：
根据该车辆的行驶速度、该安全距离和该安全速度计算目标减速度；
根据该目标减速度确定该目标扭矩。
在上述可能的设计中，该根据该车辆的行驶速度、该安全距离和该安全速度计算目标减速度，包括：
根据下列公式计算该目标减速度：
Figure PCTCN2016112756-appb-000001
其中，atrg为目标减速度，Vsafe为安全速度，Lsafe为安全距离，v为该车辆的行驶速度。
上述各可能的设计所提供的车辆制动能量的回收方法，根据车辆的行驶速度、安全距离和安全速度计算目标减速度，并根据目标减速度和车辆动力学模型确定目标扭矩，这样，通过目标减速度以及引入车辆动力学模型计算目标扭矩，因而可以达到从全局经济性考虑以进行制动能量回收的目的。
在一种可能的设计中，该车联网传输的信息包括道路允许最高速度、道路坡度、信号灯状态、信号灯距离、当前道路的平均通行车速以及第一拥堵系数中的至少一个。
在一种可能的设计中，该云数据中心存储的道路历史信息包括当前时间段平均车速、当前时间段平均车距以及第二拥堵系数，其中，该当前时间段平均车速、当前时间段平均车距以及第二拥堵系数是该云数据中心基于机器学习算法和历史数据计算获得的。
第二方面，本申请实施例提供一种车辆制动能量的回收装置，该装置包括：
获取模块，用于获取车辆当前的位置信息；
确定模块，用于根据该车辆当前的位置信息确定当前的道路场景；
该确定模块，还用于根据道路场景与权重之间的映射关系确定该当前的道路场景对应的权重；
该确定模块，还用于根据该权重确定该车辆的安全距离和安全速度；
该确定模块，还用于根据该车辆的安全距离和安全速度确定该目标扭矩；
控制模块，用于根据该目标扭矩控制该车辆的电机进行制动能量回收。
在一种可能的设计中，该权重包括安全距离的权重和安全速度的权重；
该确定模块，包括：
第一计算单元，用于根据该车辆的安全距离的权重计算该车辆的安全距离；
第二计算单元，用于根据该车辆的安全速度的权重计算该车辆的安全速度。
在一种可能的设计中，该获取模块，还用于获取车辆自身感知的信息；
该确定模块，还用于根据该车辆自身感知的信息确定第一安全距离L1，L1为障碍物与该车辆的相对距离；
该获取模块，还用于获取车联网传输的信息；
该确定模块，还用于根据该车联网传输的信息确定第二安全距离L2，L2为该障碍物与该车辆的相对距离；
该第一计算单元，具体用于：
根据公式Lsafe＝δ1i*L1+δ2i*L2计算该车辆的安全距离；
其中，Lsafe为安全距离；
其中，该安全距离权重包括两个参数，分别为δ1i，δ2i。
在一种可能的设计中，该获取模块，还用于获取车辆自身感知的信息；
该确定模块，还用于根据该车辆自身感知的信息确定第一安全速度V1，V1为障碍物与该车辆的相对速度；
该获取模块，还用于获取车联网传输的信息；
该确定模块，还用于根据该车联网传输的信息确定第二安全速度V2，V2为 障碍物与该车辆的相对速度；
该第二计算单元，具体用于：
根据公式Vsafe＝δ1i*V1+δ2i*V2计算该车辆的安全距离；
其中，Vsafe为安全速度；
其中，该安全速度包括两个参数，分别为δ1i，δ2i。
在一种可能的设计中，该获取模块，还用于获取车辆自身感知的信息；
该确定模块，还用于根据该车辆自身感知的信息确定第一安全距离L1，L1为障碍物与该车辆的相对距离；
该获取模块，还用于获取车联网传输的信息；
该确定模块，还用于根据该车联网传输的信息确定第二安全距离L2，L2为障碍物与该车辆的相对距离；
该获取模块，还用于获取云数据中心存储的道路历史信息；
该确定模块，还用于根据该云数据中心存储的道路历史信息确定第三安全距离L3，L3为障碍物与该车辆的相对距离；
该第一计算单元，具体用于：
根据公式Lsafe＝δ1i*L1+δ2i*L2+δ3i*L3计算该车辆的安全距离；
其中，Lsafe为安全距离；
其中，该安全距离权重包括三个参数，分别为δ1i，δ2i和δ3i。
在一种可能的设计中，该获取模块，还用于获取车辆自身感知的信息；
该确定模块，还用于根据该车辆自身感知的信息确定第一安全速度V1，V1为障碍物与该车辆的相对速度；
该获取模块，还用于获取车联网传输的信息；
该确定模块，还用于根据该车联网传输的信息确定第二安全速度V2，V2为该障碍物与该车辆的相对速度；
该获取模块，还用于获取云数据中心存储的道路历史信息；
该确定模块，还用于根据该云数据中心存储的道路历史信息确定第三安全速度V3，V3为障碍物与该车辆的相对速度；
该第二计算单元，具体用于：
根据公式Vsafe＝δ1i*V1+δ2i*V2+δ3i*V3计算该车辆的安全速度；
其中，Vsafe为安全速度；
其中，该安全速度包括三个参数，分别为δ1i，δ2i以及δ3i。
在一种可能的设计中，该确定模块，具体用于：
根据该车辆的行驶速度、该安全距离和该安全速度计算目标减速度；
根据该目标减速度确定该目标扭矩。
在一种可能的设计中，该确定模块，还用于根据下列公式计算该目标减速度：
Figure PCTCN2016112756-appb-000002
其中，atrg为目标减速度，Vsafe为安全速度，Lsafe为安全距离，v为该车辆的行驶速度。
在一种可能的设计中，该车联网传输的信息包括道路允许最高速度、道路坡度、信号灯状态、信号灯距离、当前道路的平均通行车速以及第一拥堵系数中的至少一个。
在一种可能的设计中，该云数据中心存储的道路历史信息包括当前时间段平均车速、当前时间段平均车距以及第二拥堵系数，其中，该当前时间段平均车速、当前时间段平均车距以及第二拥堵系数是该云数据中心基于机器学习算法和历史数据计算获得的。
上述第二方面以及第二方面的各可能的设计所提供的车辆制动能量的回收装置，其有益效果可以参照上述第一方面以及第一方面的各可能的设计所带来的有益效果，在此不再赘述。
第三方面，本申请实施例提供一种车辆，包括：
处理器，用于获取车辆当前的位置信息；
该处理器，还用于根据该车辆当前的位置信息确定当前的道路场景；
该处理器，还用于根据道路场景与权重之间的映射关系确定该当前的道路场景对应的权重；
该处理器，还用于根据该权重确定该车辆的安全距离和安全速度；
该处理器，还用于根据该车辆的安全距离和安全速度确定该目标扭矩；
该处理器，还用于根据该目标扭矩控制该车辆的电机进行制动能量回收。
在一种可能的设计中，该权重包括安全距离的权重和安全速度的权重；
该处理器，还用于根据该车辆的安全距离的权重计算该车辆的安全距离；
该处理器，还用于根据该车辆的安全速度的权重计算该车辆的安全速度。
在一种可能的设计中，该处理器，还用于获取车辆自身感知的信息，并根据该车辆自身感知的信息确定第一安全距离L1，L1为障碍物与该车辆的相对距离；
该处理器，还用于获取车联网传输的信息，并根据该车联网传输的信息确定第二安全距离L2，L2为该障碍物与该车辆的相对距离；
该处理器，还用于根据公式Lsafe＝δ1i*L1+δ2i*L2计算该车辆的安全距离；
其中，Lsafe为安全距离；
其中，该安全距离权重包括两个参数，分别为δ1i，δ2i。
在一种可能的设计中，该处理器，还用于获取车辆自身感知的信息，并根据该车辆自身感知的信息确定第一安全速度V1，V1为障碍物与该车辆的相对速度；
该处理器，还用于获取车联网传输的信息，并根据该车联网传输的信息确定第二安全速度V2，V2为障碍物与该车辆的相对速度；
该处理器，还用于根据公式Vsafe＝δ1i*V1+δ2i*V2计算该车辆的安全距离；
其中，Vsafe为安全速度；
其中，该安全速度包括两个参数，分别为δ1i，δ2i。
在一种可能的设计中，该处理器，还用于获取车辆自身感知的信息，并根据该车辆自身感知的信息确定第一安全距离L1，L1为障碍物与该车辆的相对距离；
该处理器，还用于获取车联网传输的信息，并根据该车联网传输的信息确定第二安全距离L2，L2为障碍物与该车辆的相对距离；
该处理器，还用于获取云数据中心存储的道路历史信息，并根据该云数据中心存储的道路历史信息确定第三安全距离L3，L3为障碍物与该车辆的相对距离；
该处理器，还用于根据公式Lsafe＝δ1i*L1+δ2i*L2+δ3i*L3计算该车辆的安全距离；
其中，Lsafe为安全距离；
其中，该安全距离权重包括三个参数，分别为δ1i，δ2i和δ3i。
在一种可能的设计中，该处理器，还用于获取车辆自身感知的信息，并根据该车辆自身感知的信息确定第一安全速度V1，V1为障碍物与该车辆的相对速度；
该处理器，还用于获取车联网传输的信息，并根据该车联网传输的信息确定第二安全速度V2，V2为该障碍物与该车辆的相对速度；
该处理器，还用于获取云数据中心存储的道路历史信息，并根据该云数据中心存储的道路历史信息确定第三安全速度V3，V3为障碍物与该车辆的相对速度；
该处理器，还用于根据公式Vsafe＝δ1i*V1+δ2i*V2+δ3i*V3计算该车辆的安全速度；
其中，Vsafe为安全速度；
其中，该安全速度包括三个参数，分别为δ1i，δ2i以及δ3i。
在一种可能的设计中，该处理器，还用于根据该车辆的行驶速度、该安全距离和该安全速度计算目标减速度；
该处理器，还用于根据该目标减速度确定该目标扭矩。
在一种可能的设计中，该处理器，还用于根据下列公式计算该目标减速度：
Figure PCTCN2016112756-appb-000003
其中，atrg为目标减速度，Vsafe为安全速度，Lsafe为安全距离，v为该车辆的行驶速度。
在一种可能的设计中，该车联网传输的信息包括道路允许最高速度、道路坡度、信号灯状态、信号灯距离、当前道路的平均通行车速以及第一拥堵系数中的至少一个。
在一种可能的设计中，该云数据中心存储的道路历史信息包括当前时间段平均车速、当前时间段平均车距以及第二拥堵系数，其中，该当前时间段平均车速、当前时间段平均车距以及第二拥堵系数是该云数据中心基于机器学习算法和历史数据计算获得的。
上述第三方面以及第三方面的各可能的设计所提供的车辆，其有益效果可以参照上述第一方面以及第一方面的各可能的设计所带来的有益效果，在 此不再赘述。
附图说明
图1为现有技术中车辆制动能量回收的***框架图；
图2为本申请车辆制动能量回收方法的***架构示意图；
图3为本申请车辆制动能量的回收方法实施例一的流程示意图；
图4所示的计算车辆的安全距离的流程示意图一；
图5所示的计算车辆的安全距离的流程示意图二；
图6所示的计算车辆的安全速度的流程示意图一；
图7所示的计算车辆的安全速度的流程示意图二；
图8为本申请实施例提供的车辆制动能量的回收装置实施例一的结构示意图；
图9为本申请实施例提供的车辆制动能量的回收装置实施例二的结构示意图；
图10为本申请实施例提供的车辆实施例的结构示意图。
具体实施方式
图2为本申请车辆制动能量回收方法的***架构示意图，如图2所示，本申请实施例适用于车联网***中，该***包括：感知子***、计算及融合子***、执行及决策子***。
其中，感知子***包含自身感知单元、车联网单元、车辆位置定位单元、云数据中心单元和驾驶意图识别单元。自身感知单元，主要是智能汽车装置的测量车辆速度/距离/方向的激光雷达、毫米波雷达、单目摄像头、多目摄像头，加速度传感器等。车联网单元，主要是通过车对车的信息交换(Vehicle to Vehicle；简称：V2V)、车对基础设施的信息交换(Vehicle to Infrastructure；简称V2I)、车对行人的信息交换(Vehicle to People；简称V2P)获取的周边环境的参数的模块，提供车、路、网协同的关键环境参数。车辆位置定位单元，主要是各种车辆定位信息，比如全球定位***(Global Positioning System；简称：GPS)、差分全球定位***(Differential Global Positioning System；简称：DGPS)、北斗导航***、伽利略导航***、俄罗斯格纳斯导航***，以 及惯性测量单元(Inertial Measurement Unit；简称：IMU)***等，提供车辆分辨率包含米级、厘米级、车道线定位级别的定位精度。云数据中心单元，主要是获取各路况基于大数据、机器学习、深度学习获取的不同时段、不同气象条件下的安全驾驶数据，比如风险等级、拥堵指数。驾驶意图识别单元用于根据加速踏板的状态、制动踏板的状态和车辆操控的状态识别驾驶员驾驶意图。
计算及融合子***主要包含：车辆环境感知信息融合单元、车辆目标速度计算单元、车辆目标扭矩计算单元和车辆扭矩分配控制单元。车辆环境感知信息融合单元，主要用于综合感知子***获取的各种参数，根据车辆行驶的场景类型，对各种参数的权重进行选择，作为车辆目标减速度计算模块的输入。
执行及决策子***包括：电驱动单元、动力电池单元、主动机械制动单元组成。
本申请实施例中的车辆制动能量的回收方法，综合考虑了车内(电驱***、电池***、踏板状态、操控状态等)和车外(环境、道路场景、外界车辆等)两种重要信息输入，进行最终的再生制动控制与驱动控制，不仅在驾驶员有相应制动操作时优化再生制动；无制动意图时根据环境情况，智能地进行再生制动，达到车辆全局经济性，同时避免急加急减，尽量不使用机械制动，使续驶里程达到最优控制。
下面以具体地实施例对本申请的技术方案进行详细说明。下面这几个具体的实施例可以相互结合，对于相同或相似的概念或过程可能在某些实施例不再赘述。
图3为本申请车辆制动能量的回收方法实施例一的流程示意图。本申请实施例提供了一种车辆制动能量的回收方法，该方法可以由任意执行车辆制动能量的回收方法的装置来执行，该装置可以通过软件和/或硬件实现。本实施例中，该装置可以集成在车辆中。
在上述图2所示***架构的基础上，如图3所示，本实施例的方法可以包括：
步骤301、获取车辆当前的位置信息。
在本实施例中，车辆可以通过感知子***中的车辆位置定位单元获取其 当前的位置信息，在具体的实现过程中，可以通过GPS、DGPS、北斗导航***、伽利略导航***、俄罗斯格纳斯导航***或IMU***等获取当前的位置信息。
步骤302、根据车辆当前的位置信息确定当前的道路场景。
在本实施例中，车辆可以通过感知子***中的自身感知单元获取第一范围内的道路坡度i1、道路限速Vmax1等信息，其中，第一范围例如可以为雷达、摄像头或传感器能够测量的最大距离，如200m等；可以通过感知子***中的车联网单元获取道路允许最高速度Vmax2、道路坡度i2、信号灯状态Slight、车辆与信号灯之间的距离Llight；当前道路的平均通行车速Vavg1、第一拥堵系数f1；可以通过云数据中心单元基于大数据、机器学***均车速Vavg2和第二拥堵系数f2等。
车辆在获取到上述各信息，并获取到车辆当前的位置信息之后，将结合地图标示的道路类型的标识信息，确定当前的道路场景。其中，地图标示的道路类型的标识信息包括：G(国道)、S(省道)、X(县道)、Y(乡道)等，或者普通公路(A类路)、高速公路(B类路)、智能网联驾驶专用车道(C类路)、智能网联驾驶专用道路(D类路)等。在实际应用中，车辆根据上述各信息、车辆当前的位置信息以及地图标示的道路类型的标识信息，即可确定出车辆当前所处的道路场景，其中，道路场景包括高速公路(场景1)、省级公路(场景2)、城市道路(场景3)或乡村公路(场景4)等。举例来说，若车辆获取到道路坡度为30度，道路允许的最高速度为30km/h，且第一拥堵系数为0.1，则说明车辆所在的环境道路坡度较大，且道路允许的最高速度较低，车辆的拥堵程度不高，车辆根据其当前的位置信息，确定出其所在的位置在地图中的标识为Y(乡道)，此时，可以确定当前的道路场景为乡村公路。
需要进行说明的是，车辆可以仅根据当前的位置信息确定当前的道路场景，也可以根据自身感知单元、车联网单元或云数据中心单元中的至少一个单元测量的参数，结合当前的位置信息确定当前的道路场景。对于当前的道路场景的具体确定方式，本实施例在此不作限制。
步骤303、根据道路场景与权重之间的映射关系确定当前的道路场景对应的权重。
在本实施例中，在车辆中预先存储有道路场景与权重之间的映射关系， 当车辆确定出当前的道路场景之后，将根据预先存储的映射关系，确定当前的道路场景对应的权重。具体地，可以根据确定当前的道路场景的方式，分配权重，例如：若车辆根据自身感知单元、车联网单元或云数据中心单元测量的参数，结合当前的位置信息确定当前的道路场景，则将根据表1中的分配方式确定权重：
表1
Figure PCTCN2016112756-appb-000004
其中，δ1、δ2、δ3可以自定义配置。例如：一般在车辆销售区域，可以结合所销售区域的交通法规和地图道路工况等进行自定义配置等，另外，对于前装市场，可以在车辆出厂前就可以配置好，或者对于后装市场，可以在安装时进行配置。
需要进行说明的是，δ1、δ2、δ3分别为自身感知单元、车联网单元或云数据中心单元测量的数据占权重的百分比，举例来说，对于场景1(高速)，自身感知单元、车联网单元或云数据中心单元测量的数据占权重的百分比60％、20％、20％。
若车辆根据自身感知单元和车联网单元测量的参数，结合当前的位置信息确定当前的道路场景，则将根据表2中的分配方式确定权重：
表2
场景序号 δ1(％) δ2(％)
场景1(高速) 80 20
场景2(城市) 70 30
场景3(县道) 90 10
场景4(乡道) 100 0
其中，在场景1、场景2、场景3和场景4中，自身感知***获取的距离参数占比最高。
另外，对于高速路况，δ2就是200米到几公里的路况，对车辆的行车速度有比较大的影响，高速无红绿灯；对于城市道路，δ2就是红绿灯状态的影响，占比比较大；而对于乡道，全部依靠自身感知单元获取的安全距离数据。
步骤304、根据权重确定车辆的安全距离和安全速度。
在本实施例中，可选地，权重包括安全距离的权重和安全速度的权重。因此，在实际应用中，根据权重确定车辆的安全距离和安全速度，包括：根据车辆的安全距离的权重计算车辆的安全距离，根据车辆的安全速度的权重计算车辆的安全速度。
另外，对同一个场景，车辆的安全距离的权重和安全速度的权重可以相同，也可以不相同。
可选地，根据车辆的安全距离的权重计算车辆的安全距离的可实现方式可以包括如下几种：
第一种：参见图4所示的计算车辆的安全距离的流程示意图一，上述步骤304具体可以包括：
步骤401、获取车辆自身感知的信息，并根据车辆自身感知的信息确定第一安全距离L1，L1为障碍物与车辆的相对距离。
具体地，车辆可以通过自身感知单元中的雷达、摄像头或其他类型的传感器等，获取车辆自身感知的信息，进而确定出障碍物与车辆的相对距离L1，即第一安全距离L1，其中，障碍物包括移动或静止的物体，例如包括其他车辆或栏杆等物体。
步骤402、获取车联网传输的信息，并根据车联网传输的信息确定第二安全距离L2，L2为障碍物与车辆的相对距离。
具体地，车辆可以通过车联网单元中的V2V、V2I、V2P等，获取车联网传输的信息，进而确定出障碍物与车辆的相对距离L2，即第二安全距离L2，其中，障碍物也可以包括移动或静止的物体，例如包括其他车辆或栏杆等物体。
可选地，车联网传输的信息包括道路允许最高速度、道路坡度、信号灯状态、信号灯距离、当前道路的平均通行车速以及第一拥堵系数中的至少一个。其中，第一拥堵系数可以用0-1中的任意数值表示，数值越大，代表车 辆越拥堵。
步骤403、根据公式Lsafe＝δ1i*L1+δ2i*L2计算车辆的安全距离。
其中，Lsafe为安全距离；其中，安全距离权重包括两个参数，分别为δ1i，δ2i。具体地，由于车辆获取的信息包括车辆自身感知的信息和车联网传输的信息，因此，可以根据步骤303的表2中的映射关系，确定当前的道路场景的安全距离的权重。举例来说，车辆行驶在场景1(高速)下，此种场景下，安全距离的权重参数δ1i＝80％、δ2i＝20％，表示为(0.8 0.2)。另外，在获取到第一安全距离L1和第二安全距离L2之后，将根据公式Lsafe＝δ1i*L1+δ2i*L2计算车辆的安全距离。
第二种：参见图5所示的计算车辆的安全距离的流程示意图二，上述步骤304具体可以包括：
步骤501、获取车辆自身感知的信息，并根据车辆自身感知的信息确定第一安全距离L1，L1为障碍物与车辆的相对距离。
步骤502、获取车联网传输的信息，并根据车联网传输的信息确定第二安全距离L2，L2为障碍物与车辆的相对距离。
步骤501-步骤502与步骤401-步骤402类似，此处不再赘述。
步骤503、获取云数据中心存储的道路历史信息，并根据云数据中心存储的道路历史信息确定第三安全距离L3，L3为障碍物与车辆的相对距离。
具体地，车辆可以通过云数据中心单元基于大数据、机器学习或深度学习等方式，获取不同时段、不同气象条件下的安全驾驶数据，即获取障碍物与车辆的相对距离L3，即第三安全距离L3。
可选地，云数据中心存储的道路历史信息包括当前时间段平均车速、当前时间段平均车距以及第二拥堵系数，其中，当前时间段平均车速、当前时间段平均车距以及第二拥堵系数是云数据中心基于机器学习算法和历史数据计算获得的。其中，第二拥堵系数可以用0-1中的任意数值表示，数值越大，代表车辆越拥堵。
步骤504、根据公式Lsafe＝δ1i*L1+δ2i*L2+δ3i*L3计算车辆的安全距离。
其中，Lsafe为安全距离；安全距离权重包括三个参数，分别为δ1i，δ2i和δ3i。具体地，由于车辆获取的信息包括车辆自身感知的信息、车联网传输的信息以及云数据中心存储的道路历史信息，因此，可以根据步骤303的表1中的 映射关系，确定当前的道路场景的安全距离的权重，举例来说，车辆行驶在场景2(高速)下，此种场景下，安全距离的权重参数δ1i＝70％、δ2i＝30％、δ3i＝0，表示为(0.7 0.3 0)。另外，在获取到第一安全距离L1、第二安全距离L2和第三安全距离L3之后，将根据公式Lsafe＝δ1i*L1+δ2i*L2+δ3i*L3计算车辆的安全距离。
可选地，根据车辆的安全速度的权重计算车辆的安全速度的可实现方式可以包括如下几种：
第一种：参见图6所示的计算车辆的安全速度的流程示意图一，上述步骤304具体可以包括：
步骤601、获取车辆自身感知的信息，并根据车辆自身感知的信息确定第一安全速度V1，V1为障碍物与车辆的相对速度。
具体地，车辆可以通过自身感知单元中的雷达、摄像头或其他类型的传感器等，获取车辆自身感知的信息，进而确定出障碍物与车辆的相对速度V1，即第一安全速度V1，其中，障碍物包括移动或静止的物体，例如包括其他车辆或栏杆等物体。
步骤602、获取车联网传输的信息，并根据车联网传输的信息确定第二安全速度V2，V2为障碍物与车辆的相对速度。
具体地，车辆可以通过车联网单元中的V2V、V2I、V2P等，获取车联网传输的信息，进而确定出障碍物与车辆的相对速度V2，即第二安全速度V2，其中，障碍物也可以包括移动或静止的物体，例如包括其他车辆或栏杆等物体。
可选地，车联网传输的信息包括道路允许最高速度、道路坡度、信号灯状态、信号灯距离、当前道路的平均通行车速以及第一拥堵系数中的至少一个。其中，第一拥堵系数可以用0-1中的任意数值表示，数值越大，代表车辆越拥堵。
步骤603、根据公式Vsafe＝δ1i*V1+δ2i*V2计算车辆的安全速度。
其中，Vsafe为安全速度；其中，安全速度包括两个参数，分别为δ1i，δ2i。具体地，由于车辆获取的信息包括车辆自身感知的信息和车联网传输的信息，因此，可以根据步骤303的表2中的映射关系，确定当前的道路场景的安全速度的权重。举例来说，车辆行驶在场景1(高速)下，此种场景下，安全 速度距离的权重参数δ1i＝80％、δ2i＝20％，表示为(0.8 0.2)。另外，在获取到第一安全速度V1和第二安全速度V2之后，将根据公式Vsafe＝δ1i*V1+δ2i*V2计算车辆的安全速度。
第二种：参见图7所示的计算车辆的安全速度的流程示意图二，上述步骤304具体可以包括：
步骤701、获取车辆自身感知的信息，并根据车辆自身感知的信息确定第一安全速度V1，V1为障碍物与车辆的相对速度。
步骤702、获取车联网传输的信息，并根据车联网传输的信息确定第二安全速度V2，V2为障碍物与车辆的相对速度。
步骤701-步骤702与步骤601-步骤602类似，此处不再赘述。
步骤703、获取云数据中心存储的道路历史信息，并根据云数据中心存储的道路历史信息确定第三安全速度V3，V3为障碍物与车辆的相对速度。
具体地，车辆可以通过云数据中心单元基于大数据、机器学习或深度学习等方式，获取不同时段、不同气象条件下的安全驾驶数据，即获取障碍物与车辆的相对速度V3，即第三安全速度V3。
可选地，云数据中心存储的道路历史信息包括当前时间段平均车速、当前时间段平均车距以及第二拥堵系数，其中，当前时间段平均车速、当前时间段平均车距以及第二拥堵系数是云数据中心基于机器学习算法和历史数据计算获得的。其中，第二拥堵系数可以用0-1中的任意数值表示，数值越大，代表车辆越拥堵。
步骤704、根据公式Vsafe＝δ1i*V1+δ2i*V2+δ3i*V3计算车辆的安全速度。
其中，Vsafe为安全速度；安全速度包括三个参数，分别为δ1i，δ2i以及δ3i。具体地，由于车辆获取的信息包括车辆自身感知的信息、车联网传输的信息以及云数据中心存储的道路历史信息，因此，可以根据步骤303的表1中的映射关系，确定当前的道路场景的安全速度的权重。举例来说，车辆行驶在场景2(高速)下，此种场景下，安全速度的权重参数δ1i＝70％、δ2i＝30％、δ3i＝0，表示为(0.7 0.3 0)。另外，在获取到第一安全速度V1、第二安全速度V2和第三安全速度V3之后，将根据公式Vsafe＝δ1i*V1+δ2i*V2+δ3i*V3计算车辆的安全速度。
步骤305、根据车辆的安全距离和安全速度确定目标扭矩。
在本实施例中，在计算出车辆的安全距离和安全速度之后，将根据计算出的安全距离和安全速度，确定目标扭矩，以使车辆根据该目标扭矩和驾驶员的驾驶意图确定整车需求扭矩，从而控制车辆的电机进行制动能量的回收。
可选地，根据车辆的安全距离和安全速度确定目标扭矩，包括：根据车辆的行驶速度、安全距离和安全速度计算目标减速度；根据目标减速度确定目标扭矩。
在具体地实现过程中，计算及融合子***中的车辆目标减速度计算单元将可以根据公式
Figure PCTCN2016112756-appb-000005
计算目标减速度，其中，atrg为目标减速度，Vsafe为安全速度，Lsafe为安全距离，v为车辆的行驶速度。
在确定出目标减速度之后，车辆目标扭矩计算单元根据目标减速度、感知子***获取的道路坡度i以及车辆动力学模型计算目标转矩。具体地，可以根据公式
Figure PCTCN2016112756-appb-000006
计算目标扭矩。其中，G为车辆的重量；f为滚动阻力系数；CD为风阻系数；A为车辆的迎风面积；v为车辆行驶的实时车速；i为车辆行驶道路的坡度；δ为旋转质量换算系数；m为车辆的质量；atrg为目标减速度；r为车辆的车轮半径；ig为车辆的变速器传动比；i0为主减速比；ηT为机械传动效率；Ttrg为目标扭矩。
当Ttrg大于0时为目标驱动扭矩，当Ttrg小于0时为目标制动扭矩。
本实施例中，通过引入车辆动力学模型计算目标扭矩，因而可以达到从全局经济性考虑以进行制动能量回收的目的。
步骤306、根据目标扭矩控制车辆的电机进行制动能量回收。
在本实施例中，车辆在确定出目标扭矩之后，将根据该目标扭矩和驾驶员意图识别单元识别出的驾驶意图确定整车需求扭矩，并对确定出的整车需求扭矩进行分配，从而控制车辆的电机进行制动能量的回收。
具体地，车辆扭矩分配控制单元根据车辆中电机可输出扭矩最大值、车辆中的电机可输出扭矩最小值、车辆中的电池实时允许充电功率和车辆中的电池实时允许放电功率，进行扭矩分配，从而控制电机与主动机械制动*** 工作。
本申请实施例提供的车辆制动能量的回收方法，通过获取车辆当前的位置信息，根据车辆当前的位置信息确定当前的道路场景，根据道路场景与权重之间的映射关系确定当前的道路场景对应的权重，并根据权重确定车辆的安全距离和安全速度，再根据车辆的安全距离和安全速度确定目标扭矩，最后根据目标扭矩控制车辆的电机进行制动能量回收。由于根据车辆当前所处的道路场景，确定车辆的安全速度和安全距离，从而确定出目标扭矩以进行制动能量的回收，即结合车辆感知的信息、判断道路场景、对不同的道路场景赋予不同的权重，从而计算安全距离和安全速度，再确定目标减速度并分配扭矩，从而提高了制动能量的回收率。
图8为本申请实施例提供的车辆制动能量的回收装置实施例一的结构示意图。该回收装置可以为独立的车辆，还可以为集成在车辆中的装置，该装置可以通过软件、硬件或者软硬件结合的方式实现。如图8所示，该回收装置包括：
获取模块11，用于获取车辆当前的位置信息；
确定模块12，用于根据所述车辆当前的位置信息确定当前的道路场景；
所述确定模块12，还用于根据道路场景与权重之间的映射关系确定所述当前的道路场景对应的权重；
所述确定模块12，还用于根据所述权重确定所述车辆的安全距离和安全速度；
所述确定模块12，还用于根据所述车辆的安全距离和安全速度确定所述目标扭矩；
控制模块13，用于根据所述目标扭矩控制所述车辆的电机进行制动能量回收。
可选的，上述获取模块11、确定模块12和控制模块13对应可以为车辆中的处理器。
本申请实施例提供的车辆制动能量的回收装置，可以执行上述方法实施例，其实现原理和技术效果类似，在此不再赘述。
图9为本申请实施例提供的车辆制动能量的回收装置实施例二的结构示意图。在上述实施例的基础上，进一步地，所述权重包括安全距离的权重和 安全速度的权重；上述确定模块12，具体包括：
第一计算单元121，用于根据所述车辆的安全距离的权重计算所述车辆的安全距离；
第二计算单元122，用于根据所述车辆的安全速度的权重计算所述车辆的安全速度。
可选地，所述获取模块11，还用于获取车辆自身感知的信息；
所述确定模块12，还用于根据所述车辆自身感知的信息确定第一安全距离L1，L1为障碍物与所述车辆的相对距离；
所述获取模块11，还用于获取车联网传输的信息；
所述确定模块12，还用于根据所述车联网传输的信息确定第二安全距离L2，L2为所述障碍物与所述车辆的相对距离；
所述第一计算单元121，具体用于：
根据公式Lsafe＝δ1i*L1+δ2i*L2计算所述车辆的安全距离；
其中，Lsafe为安全距离；
其中，所述安全距离权重包括两个参数，分别为δ1i，δ2i。
可选地，所述获取模块11，还用于获取车辆自身感知的信息；
所述确定模块12，还用于根据所述车辆自身感知的信息确定第一安全速度V1，V1为障碍物与所述车辆的相对速度；
所述获取模块11，还用于获取车联网传输的信息；
所述确定模块12，还用于根据所述车联网传输的信息确定第二安全速度V2，V2为障碍物与所述车辆的相对速度；
所述第二计算单元122，具体用于：
根据公式Vsafe＝δ1i*V1+δ2i*V2计算所述车辆的安全距离；
其中，Vsafe为安全速度；
其中，所述安全速度包括两个参数，分别为δ1i，δ2i。
可选地，所述获取模块11，还用于获取车辆自身感知的信息；
所述确定模块12，还用于根据所述车辆自身感知的信息确定第一安全距离L1，L1为障碍物与所述车辆的相对距离；
所述获取模块11，还用于获取车联网传输的信息；
所述确定模块12，还用于根据所述车联网传输的信息确定第二安全距离L2，L2为障碍物与所述车辆的相对距离；
所述获取模块11，还用于获取云数据中心存储的道路历史信息；
所述确定模块12，还用于根据所述云数据中心存储的道路历史信息确定第三安全距离L3，L3为障碍物与所述车辆的相对距离；
所述第一计算单元121，具体用于：
根据公式Lsafe＝δ1i*L1+δ2i*L2+δ3i*L3计算所述车辆的安全距离；
其中，Lsafe为安全距离；
其中，所述安全距离权重包括三个参数，分别为δ1i，δ2i和δ3i。
可选地，所述获取模块11，还用于获取车辆自身感知的信息；
所述确定模块12，还用于根据所述车辆自身感知的信息确定第一安全速度V1，V1为障碍物与所述车辆的相对速度；
所述获取模块11，还用于获取车联网传输的信息；
所述确定模块12，还用于根据所述车联网传输的信息确定第二安全速度V2，V2为所述障碍物与所述车辆的相对速度；
所述获取模块11，还用于获取云数据中心存储的道路历史信息；
所述确定模块12，还用于根据所述云数据中心存储的道路历史信息确定第三安全速度V3，V3为障碍物与所述车辆的相对速度；
所述第二计算单元122，具体用于：
根据公式Vsafe＝δ1i*V1+δ2i*V2+δ3i*V3计算所述车辆的安全速度；
其中，Vsafe为安全速度；
其中，所述安全速度包括三个参数，分别为δ1i，δ2i以及δ3i。
可选地，所述确定模块12，具体用于：
根据所述车辆的行驶速度、所述安全距离和所述安全速度计算目标减速度；
根据所述目标减速度确定所述目标扭矩。
可选地，所述确定模块12，还用于根据下列公式计算所述目标减速度：
Figure PCTCN2016112756-appb-000007
其中，atrg为目标减速度，Vsafe为安全速度，Lsafe为安全距离，v为所述车辆的行驶速度。
可选地，所述车联网传输的信息包括道路允许最高速度、道路坡度、信号灯状态、信号灯距离、当前道路的平均通行车速以及第一拥堵系数中的至少一个。
可选地，所述云数据中心存储的道路历史信息包括当前时间段平均车速、当前时间段平均车距以及第二拥堵系数，其中，所述当前时间段平均车速、当前时间段平均车距以及第二拥堵系数是所述云数据中心基于机器学习算法和历史数据计算获得的。
本申请实施例提供的车辆制动能量的回收装置，可以执行上述方法实施例，其实现原理和技术效果类似，在此不再赘述。
图10为本申请实施例提供的车辆实施例的结构示意图。如图10所示，该车辆可以包括发送器20、处理器21、存储器22和至少一个通信总线23。通信总线23用于实现元件之间的通信连接。存储器22可能包含高速RAM存储器，也可能还包括非易失性存储NVM，例如至少一个磁盘存储器，存储器22中可以存储各种程序，用于完成各种处理功能以及实现本实施例的方法步骤。另外，该车辆还可以包括接收器24，本实施例中的接收器24可以为相应的具有通信功能和接收信息功能的输入接口，本实施例中的发送器20可以为相应的具有通信功能和发送信息功能的输出接口。可选的，该发送器20和接收器24可以集成在一个通信接口中，也可以分别为独立的两个通信接口。
本实施例中，处理器21，用于获取车辆当前的位置信息；
该处理器21，还用于根据所述车辆当前的位置信息确定当前的道路场景；
该处理器21，还用于根据道路场景与权重之间的映射关系确定所述当前的道路场景对应的权重；
该处理器21，还用于根据所述权重确定所述车辆的安全距离和安全速度；
该处理器21，还用于根据所述车辆的安全距离和安全速度确定所述目标扭矩；
该处理器21，还用于根据所述目标扭矩控制所述车辆的电机进行制动能量回收。
可选地，所述权重包括安全距离的权重和安全速度的权重；
该处理器21，还用于根据所述车辆的安全距离的权重计算所述车辆的安全距离；
该处理器21，还用于根据所述车辆的安全速度的权重计算所述车辆的安全速度。
可选地，该处理器21，还用于获取车辆自身感知的信息，并根据所述车辆自身感知的信息确定第一安全距离L1，L1为障碍物与所述车辆的相对距离；
该处理器21，还用于获取车联网传输的信息，并根据所述车联网传输的信息确定第二安全距离L2，L2为所述障碍物与所述车辆的相对距离；
该处理器21，还用于根据公式Lsafe＝δ1i*L1+δ2i*L2计算所述车辆的安全距离；
其中，Lsafe为安全距离；
其中，所述安全距离权重包括两个参数，分别为δ1i，δ2i。
可选地，该处理器21，还用于获取车辆自身感知的信息，并根据所述车辆自身感知的信息确定第一安全速度V1，V1为障碍物与所述车辆的相对速度；
该处理器21，还用于获取车联网传输的信息，并根据所述车联网传输的信息确定第二安全速度V2，V2为障碍物与所述车辆的相对速度；
该处理器21，还用于根据公式Vsafe＝δ1i*V1+δ2i*V2计算所述车辆的安全距离；
其中，Vsafe为安全速度；
其中，所述安全速度包括两个参数，分别为δ1i，δ2i。
可选地，该处理器21，还用于获取车辆自身感知的信息，并根据所述车辆自身感知的信息确定第一安全距离L1，L1为障碍物与所述车辆的相对距离；
该处理器21，还用于获取车联网传输的信息，并根据所述车联网传输的信息确定第二安全距离L2，L2为障碍物与所述车辆的相对距离；
该处理器21，还用于获取云数据中心存储的道路历史信息，并根据所述云数据中心存储的道路历史信息确定第三安全距离L3，L3为障碍物与所述车辆的相对距离；
该处理器21，还用于根据公式Lsafe＝δ1i*L1+δ2i*L2+δ3i*L3计算所述车辆的安全距离；
其中，Lsafe为安全距离；
其中，所述安全距离权重包括三个参数，分别为δ1i，δ2i和δ3i。
可选地，该处理器21，还用于获取车辆自身感知的信息，并根据所述车辆自身感知的信息确定第一安全速度V1，V1为障碍物与所述车辆的相对速度；
该处理器21，还用于获取车联网传输的信息，并根据所述车联网传输的信息确定第二安全速度V2，V2为所述障碍物与所述车辆的相对速度；
该处理器21，还用于获取云数据中心存储的道路历史信息，并根据所述云数据中心存储的道路历史信息确定第三安全速度V3，V3为障碍物与所述车辆的相对速度；
该处理器21，还用于根据公式Vsafe＝δ1i*V1+δ2i*V2+δ3i*V3计算所述车辆的安全速度；
其中，Vsafe为安全速度；
其中，所述安全速度包括三个参数，分别为δ1i，δ2i以及δ3i。
可选地，该处理器21，还用于根据所述车辆的行驶速度、所述安全距离和所述安全速度计算目标减速度；
该处理器21，还用于根据所述目标减速度确定所述目标扭矩。
可选地，该处理器21，还用于根据下列公式计算所述目标减速度：
Figure PCTCN2016112756-appb-000008
其中，atrg为目标减速度，Vsafe为安全速度，Lsafe为安全距离，v为所述车辆的行驶速度。
可选地，所述车联网传输的信息包括道路允许最高速度、道路坡度、信号灯状态、信号灯距离、当前道路的平均通行车速以及第一拥堵系数中的至少一个。
可选地，所述云数据中心存储的道路历史信息包括当前时间段平均车速、当前时间段平均车距以及第二拥堵系数，其中，所述当前时间段平均车速、当前时间段平均车距以及第二拥堵系数是所述云数据中心基于机器学习算法和历史数据计算获得的。
本申请实施例提供的车辆，可以执行上述方法实施例，其实现原理和技术效果类似，在此不再赘述。
所属领域的技术人员可以清楚地了解到，为描述的方便和简洁，仅以上 述各功能模块的划分进行举例说明，实际应用中，可以根据需要而将上述功能分配由不同的功能模块完成，即将装置的内部结构划分成不同的功能模块，以完成以上描述的全部或者部分功能。上述描述的***，装置和单元的具体工作过程，可以参考前述方法实施例中的对应过程，在此不再赘述。
在本申请所提供的几个实施例中，应该理解到，所揭露的***，装置和方法，可以通过其它的方式实现。例如，以上所描述的装置实施例仅仅是示意性的，例如，所述模块或单元的划分，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，例如多个单元或组件可以结合或者可以集成到另一个***，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，装置或单元的间接耦合或通信连接，可以是电性，机械或其它的形式。
所述作为分离部件说明的单元可以是或者也可以不是物理上分开的，作为单元显示的部件可以是或者也可以不是物理单元，即可以位于一个地方，或者也可以分布到多个网络单元上。可以根据实际的需要选择其中的部分或者全部单元来实现本实施例方案的目的。
另外，在本申请各个实施例中的各功能单元可以集成在一个处理单元中，也可以是各个单元单独物理存在，也可以两个或两个以上单元集成在一个单元中。上述集成的单元既可以采用硬件的形式实现，也可以采用软件功能单元的形式实现。
所述集成的单元如果以软件功能单元的形式实现并作为独立的产品销售或使用时，可以存储在一个计算机可读取存储介质中。基于这样的理解，本申请的技术方案本质上或者说对现有技术做出贡献的部分或者该技术方案的全部或部分可以以软件产品的形式体现出来，该计算机软件产品存储在一个存储介质中，包括若干指令用以使得一台计算机设备(可以是个人计算机，服务器，或者网络设备等)或处理器(processor)执行本申请各个实施例所述方法的全部或部分步骤。而前述的存储介质包括：U盘、移动硬盘、只读存储器(ROM，Read-Only Memory)、随机存取存储器(RAM，Random Access Memory)、磁碟或者光盘等各种可以存储程序代码的介质。

Claims (20)

  1. 一种车辆制动能量的回收方法，其特征在于，所述方法包括：
    获取车辆当前的位置信息；
    根据所述车辆当前的位置信息确定当前的道路场景；
    根据道路场景与权重之间的映射关系确定所述当前的道路场景对应的权重；
    根据所述权重确定所述车辆的安全距离和安全速度；
    根据所述车辆的安全距离和安全速度确定目标扭矩；
    根据所述目标扭矩控制所述车辆的电机进行制动能量回收。
  2. 根据权利要求1所述的方法，其特征在于，所述权重包括安全距离的权重和安全速度的权重；
    所述根据所述权重确定所述车辆的安全距离和安全速度，包括：
    根据所述车辆的安全距离的权重计算所述车辆的安全距离；
    根据所述车辆的安全速度的权重计算所述车辆的安全速度。
  3. 根据权利要求2所述的方法，其特征在于，所述根据所述车辆的安全距离的权重计算所述车辆的安全距离之前，所述方法还包括：
    获取车辆自身感知的信息，并根据所述车辆自身感知的信息确定第一安全距离L1，L1为障碍物与所述车辆的相对距离；
    获取车联网传输的信息，并根据所述车联网传输的信息确定第二安全距离L2，L2为所述障碍物与所述车辆的相对距离；
    所述根据所述车辆的安全距离的权重计算所述车辆的安全距离，包括：
    根据公式Lsafe＝δ1i*L1+δ2i*L2计算所述车辆的安全距离；
    其中，Lsafe为安全距离；
    其中，所述安全距离权重包括两个参数，分别为δ1i，δ2i。
  4. 根据权利要求2或3所述的方法，其特征在于，所述根据所述车辆的安全速度的权重计算所述车辆的安全速度之前，所述方法还包括：
    获取车辆自身感知的信息，并根据所述车辆自身感知的信息确定第一安全速度V1，V1为障碍物与所述车辆的相对速度；
    获取车联网传输的信息，并根据所述车联网传输的信息确定第二安全速 度V2，V2为障碍物与所述车辆的相对速度；
    根据所述车辆的安全速度的权重计算所述车辆的安全速度，包括：
    根据公式Vsafe＝δ1i*V1+δ2i*V2计算所述车辆的安全速度；
    其中，Vsafe为安全速度；
    其中，所述安全速度包括两个参数，分别为δ1i，δ2i。
  5. 根据权利要求2所述的方法，其特征在于，所述根据所述车辆的安全距离的权重计算所述车辆的安全距离之前，所述方法还包括：
    获取车辆自身感知的信息，并根据所述车辆自身感知的信息确定第一安全距离L1，L1为障碍物与所述车辆的相对距离；
    获取车联网传输的信息，并根据所述车联网传输的信息确定第二安全距离L2，L2为障碍物与所述车辆的相对距离；
    获取云数据中心存储的道路历史信息，并根据所述云数据中心存储的道路历史信息确定第三安全距离L3，L3为障碍物与所述车辆的相对距离；
    所述根据所述车辆的安全距离的权重计算所述车辆的安全距离，包括：
    根据公式Lsafe＝δ1i*L1+δ2i*L2+δ3i*L3计算所述车辆的安全距离；
    其中，Lsafe为安全距离；
    其中，所述安全距离权重包括三个参数，分别为δ1i，δ2i和δ3i。
  6. 根据权利要求2或5所述的方法，其特征在于，所述根据所述车辆的安全速度的权重计算所述车辆的安全速度之前，所述方法还包括：
    获取车辆自身感知的信息，并根据所述车辆自身感知的信息确定第一安全速度V1，V1为障碍物与所述车辆的相对速度；
    获取车联网传输的信息，并根据所述车联网传输的信息确定第二安全速度V2，V2为所述障碍物与所述车辆的相对速度；
    获取云数据中心存储的道路历史信息，并根据所述云数据中心存储的道路历史信息确定第三安全速度V3，V3为障碍物与所述车辆的相对速度；
    根据所述车辆的安全速度的权重计算所述车辆的安全速度，包括：
    根据公式Vsafe＝δ1i*V1+δ2i*V2+δ3i*V3计算所述车辆的安全速度；
    其中，Vsafe为安全速度；
    其中，所述安全速度包括三个参数，分别为δ1i，δ2i以及δ3i。
  7. 根据权利要求2至6任一所述的方法，其特征在于，所述根据所述车辆的安全距离和安全速度确定目标扭矩，包括：
    根据所述车辆的行驶速度、所述安全距离和所述安全速度计算目标减速度；
    根据所述目标减速度确定所述目标扭矩。
  8. 根据权利要求7所述的方法，其特征在于，所述根据所述车辆的行驶速度、所述安全距离和所述安全速度计算目标减速度，包括：
    根据下列公式计算所述目标减速度：
    Figure PCTCN2016112756-appb-100001
    其中，atrg为目标减速度，Vsafe为安全速度，Lsafe为安全距离，v为所述车辆的行驶速度。
  9. 根据权利要求2至8任一所述的方法，其特征在于，所述车联网传输的信息包括道路允许最高速度、道路坡度、信号灯状态、信号灯距离、当前道路的平均通行车速以及第一拥堵系数中的至少一个。
  10. 根据权利要求5至9任一所述的方法，其特征在于，所述云数据中心存储的道路历史信息包括当前时间段平均车速、当前时间段平均车距以及第二拥堵系数，其中，所述当前时间段平均车速、当前时间段平均车距以及第二拥堵系数是所述云数据中心基于机器学习算法和历史数据计算获得的。
  11. 一种车辆制动能量的回收装置，其特征在于，所述装置包括：
    获取模块，用于获取车辆当前的位置信息；
    确定模块，用于根据所述车辆当前的位置信息确定当前的道路场景；
    所述确定模块，还用于根据道路场景与权重之间的映射关系确定所述当前的道路场景对应的权重；
    所述确定模块，还用于根据所述权重确定所述车辆的安全距离和安全速度；
    所述确定模块，还用于根据所述车辆的安全距离和安全速度确定目标扭矩；
    控制模块，用于根据所述目标扭矩控制所述车辆的电机进行制动能量回收。
  12. 根据权利要求11所述的装置，其特征在于，所述权重包括安全距离的权重和安全速度的权重；
    所述确定模块，包括：
    第一计算单元，用于根据所述车辆的安全距离的权重计算所述车辆的安全距离；
    第二计算单元，用于根据所述车辆的安全速度的权重计算所述车辆的安全速度。
  13. 根据权利要求12所述的装置，其特征在于，
    所述获取模块，还用于获取车辆自身感知的信息；
    所述确定模块，还用于根据所述车辆自身感知的信息确定第一安全距离L1，L1为障碍物与所述车辆的相对距离；
    所述获取模块，还用于获取车联网传输的信息；
    所述确定模块，还用于根据所述车联网传输的信息确定第二安全距离L2，L2为所述障碍物与所述车辆的相对距离；
    所述第一计算单元，具体用于：
    根据公式Lsafe＝δ1i*L1+δ2i*L2计算所述车辆的安全距离；
    其中，Lsafe为安全距离；
    其中，所述安全距离权重包括两个参数，分别为δ1i，δ2i。
  14. 根据权利要求12或13所述的装置，其特征在于，
    所述获取模块，还用于获取车辆自身感知的信息；
    所述确定模块，还用于根据所述车辆自身感知的信息确定第一安全速度V1，V1为障碍物与所述车辆的相对速度；
    所述获取模块，还用于获取车联网传输的信息；
    所述确定模块，还用于根据所述车联网传输的信息确定第二安全速度V2，V2为障碍物与所述车辆的相对速度；
    所述第二计算单元，具体用于：
    根据公式Vsafe＝δ1i*V1+δ2i*V2计算所述车辆的安全距离；
    其中，Vsafe为安全速度；
    其中，所述安全速度包括两个参数，分别为δ1i，δ2i。
  15. 根据权利要求12所述的装置，其特征在于，
    所述获取模块，还用于获取车辆自身感知的信息；
    所述确定模块，还用于根据所述车辆自身感知的信息确定第一安全距离L1，L1为障碍物与所述车辆的相对距离；
    所述获取模块，还用于获取车联网传输的信息；
    所述确定模块，还用于根据所述车联网传输的信息确定第二安全距离L2，L2为障碍物与所述车辆的相对距离；
    所述获取模块，还用于获取云数据中心存储的道路历史信息；
    所述确定模块，还用于根据所述云数据中心存储的道路历史信息确定第三安全距离L3，L3为障碍物与所述车辆的相对距离；
    所述第一计算单元，具体用于：
    根据公式Lsafe＝δ1i*L1+δ2i*L2+δ3i*L3计算所述车辆的安全距离；
    其中，Lsafe为安全距离；
    其中，所述安全距离权重包括三个参数，分别为δ1i，δ2i和δ3i。
  16. 根据权利要求12或15所述的装置，其特征在于，
    所述获取模块，还用于获取车辆自身感知的信息；
    所述确定模块，还用于根据所述车辆自身感知的信息确定第一安全速度V1，V1为障碍物与所述车辆的相对速度；
    所述获取模块，还用于获取车联网传输的信息；
    所述确定模块，还用于根据所述车联网传输的信息确定第二安全速度V2，V2为所述障碍物与所述车辆的相对速度；
    所述获取模块，还用于获取云数据中心存储的道路历史信息；
    所述确定模块，还用于根据所述云数据中心存储的道路历史信息确定第三安全速度V3，V3为障碍物与所述车辆的相对速度；
    所述第二计算单元，具体用于：
    根据公式Vsafe＝δ1i*V1+δ2i*V2+δ3i*V3计算所述车辆的安全速度；
    其中，Vsafe为安全速度；
    其中，所述安全速度包括三个参数，分别为δ1i，δ2i以及δ3i。
  17. 根据权利要求12至16任一所述的装置，其特征在于，所述确定模 块，具体用于：
    根据所述车辆的行驶速度、所述安全距离和所述安全速度计算目标减速度；
    根据所述目标减速度确定所述目标扭矩。
  18. 根据权利要求17所述的装置，其特征在于，所述确定模块，还用于根据下列公式计算所述目标减速度：
    Figure PCTCN2016112756-appb-100002
    其中，atrg为目标减速度，Vsafe为安全速度，Lsafe为安全距离，v为所述车辆的行驶速度。
  19. 根据权利要求12至18任一所述的装置，其特征在于，所述车联网传输的信息包括道路允许最高速度、道路坡度、信号灯状态、信号灯距离、当前道路的平均通行车速以及第一拥堵系数中的至少一个。
  20. 根据权利要求15至19任一所述的装置，其特征在于，所述云数据中心存储的道路历史信息包括当前时间段平均车速、当前时间段平均车距以及第二拥堵系数，其中，所述当前时间段平均车速、当前时间段平均车距以及第二拥堵系数是所述云数据中心基于机器学习算法和历史数据计算获得的。
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