WO2024022305A1 - 车辆及其能量管理方法和能量管理装置、可读存储介质 - Google Patents

车辆及其能量管理方法和能量管理装置、可读存储介质 Download PDF

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Publication number
WO2024022305A1
WO2024022305A1 PCT/CN2023/108980 CN2023108980W WO2024022305A1 WO 2024022305 A1 WO2024022305 A1 WO 2024022305A1 CN 2023108980 W CN2023108980 W CN 2023108980W WO 2024022305 A1 WO2024022305 A1 WO 2024022305A1
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Prior art keywords
vehicle
battery soc
working condition
predicted
road
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PCT/CN2023/108980
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English (en)
French (fr)
Inventor
杨冬生
朱福堂
王春生
沈涛
武金龙
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比亚迪股份有限公司
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Publication of WO2024022305A1 publication Critical patent/WO2024022305A1/zh

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Classifications

    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
    • B60W20/00—Control systems specially adapted for hybrid vehicles
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
    • B60W20/00—Control systems specially adapted for hybrid vehicles
    • B60W20/10—Controlling the power contribution of each of the prime movers to meet required power demand
    • B60W20/13—Controlling the power contribution of each of the prime movers to meet required power demand in order to stay within battery power input or output limits; in order to prevent overcharging or battery depletion
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
    • B60W20/00—Control systems specially adapted for hybrid vehicles
    • B60W20/10—Controlling the power contribution of each of the prime movers to meet required power demand
    • B60W20/15—Control strategies specially adapted for achieving a particular effect
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
    • B60W40/00—Estimation or calculation of non-directly measurable driving parameters for road vehicle drive control systems not related to the control of a particular sub unit, e.g. by using mathematical models
    • B60W40/02—Estimation or calculation of non-directly measurable driving parameters for road vehicle drive control systems not related to the control of a particular sub unit, e.g. by using mathematical models related to ambient conditions
    • B60W40/06—Road conditions
    • B60W40/076—Slope angle of the road
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
    • B60W40/00—Estimation or calculation of non-directly measurable driving parameters for road vehicle drive control systems not related to the control of a particular sub unit, e.g. by using mathematical models
    • B60W40/10—Estimation or calculation of non-directly measurable driving parameters for road vehicle drive control systems not related to the control of a particular sub unit, e.g. by using mathematical models related to vehicle motion
    • B60W40/105—Speed
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
    • B60W40/00—Estimation or calculation of non-directly measurable driving parameters for road vehicle drive control systems not related to the control of a particular sub unit, e.g. by using mathematical models
    • B60W40/10—Estimation or calculation of non-directly measurable driving parameters for road vehicle drive control systems not related to the control of a particular sub unit, e.g. by using mathematical models related to vehicle motion
    • B60W40/107—Longitudinal acceleration
    • 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
    • B60L58/00—Methods or circuit arrangements for monitoring or controlling batteries or fuel cells, specially adapted for electric vehicles
    • B60L58/40—Methods or circuit arrangements for monitoring or controlling batteries or fuel cells, specially adapted for electric vehicles for controlling a combination of batteries and fuel cells
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
    • B60W2520/00—Input parameters relating to overall vehicle dynamics
    • B60W2520/10—Longitudinal speed
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
    • B60W2520/00—Input parameters relating to overall vehicle dynamics
    • B60W2520/10—Longitudinal speed
    • B60W2520/105—Longitudinal acceleration
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
    • B60W2552/00—Input parameters relating to infrastructure
    • B60W2552/15—Road slope, i.e. the inclination of a road segment in the longitudinal direction

Definitions

  • the present disclosure relates to the field of vehicle technology, and in particular, to a vehicle energy management method, a vehicle energy management device, a computer-readable storage medium and a vehicle.
  • the energy management strategy of the vehicle control is mainly to meet the power demand, maintain the battery SOC (State of Charge, state of charge), and the system's working efficiency as the control criteria.
  • SOC State of Charge, state of charge
  • the energy management control strategy rationally allocates various powers.
  • the power of the power source is combined with the efficiency characteristics of the power source to improve the driving efficiency of the system.
  • energy management strategies only control energy based on the vehicle's own operating conditions, which cannot achieve optimal control and achieve optimal system driving efficiency.
  • the present disclosure aims to solve one of the technical problems in the related art, at least to a certain extent.
  • the first purpose of the present disclosure is to propose a vehicle energy management method, which divides the predicted working conditions according to the working condition data of the vehicle on the pre-driving road, and manages the vehicle based on the SOC change path with the smallest energy consumption under the predicted working conditions. Energy management can achieve optimal system operating efficiency under different predicted working conditions, and ultimately achieve global optimization of user driving conditions.
  • a second object of the present disclosure is to provide an energy management device for a vehicle.
  • a third object of the present disclosure is to provide a computer-readable storage medium.
  • a fourth object of the present disclosure is to provide a vehicle.
  • the first embodiment of the present disclosure proposes an energy management method for a vehicle, which includes: obtaining a pre-travel road of the vehicle, and the pre-travel road is divided into at least one predicted working condition according to the working condition data of the pre-travel road. ; According to the working condition data corresponding to each predicted working condition, the battery SOC variation range of the vehicle under each predicted working condition is determined; according to the battery SOC variation range of the vehicle under each predicted working condition, the number of times the vehicle is on the pre-driving road is determined. A battery SOC change path; the vehicle's energy management is performed according to the battery SOC change path among multiple battery SOC change paths that can minimize the energy consumption of the vehicle on the pre-driving road.
  • the vehicle is divided into predicted working conditions according to the working condition data of the pre-driving road, and the energy management of the vehicle is performed based on the battery SOC change path with the smallest energy consumption under the predicted working conditions, which can be achieved
  • the system operation efficiency is optimal under different time windows or predicted working conditions, and ultimately the global optimization of user driving conditions is achieved.
  • vehicle energy management method may also have the following additional technical features:
  • the working condition data includes: slope data and speed limit data
  • the method of determining at least one predicted working condition includes: dividing the pre-driving road into at least one road section according to the working condition data; obtaining at least one road section historical driving parameters within the vehicle, and road parameter data is determined based on the historical driving parameters, where the road parameter data includes: at least one of: average vehicle speed, average acceleration, average uphill gradient, average downhill gradient, standard deviation of vehicle speed, and standard deviation of acceleration. kind; match the road parameter data with the road parameter data of multiple predicted working conditions; when the matching is successful, divide the vehicle's pre-driving road into one or more combinations of predicted working conditions.
  • the battery SOC variation range of the first predicted working condition of the pre-driving road is determined based on the actual battery SOC of the vehicle at the starting point of the pre-driving road and the operating condition data of the first predicted working condition; the pre-driving road
  • the battery SOC variation range of the non-first predicted working condition is determined based on the working condition data of the non-first predicted working condition and the battery SOC variation range of the previous predicted working condition other than the first predicted working condition.
  • the upper limit value of the battery SOC variation range under the predicted operating conditions is the battery SOC at the end of the vehicle's operation in the predicted operating conditions in the power generation mode; the lower limit value of the battery SOC variation range under the predicted operating conditions is the vehicle Battery SOC at the end of predicted operating conditions in pure electric mode.
  • determining a battery SOC change path among multiple battery SOC change paths of the vehicle on the pre-driving road includes: selecting a target SOC value in the battery SOC change range of each predicted working condition. ;According to each target SOC value, one battery SOC change path among multiple battery SOC change paths is obtained.
  • obtaining the working condition data of the vehicle on the pre-driving road includes: determining the starting position and the end position of the vehicle pre-driving; and obtaining the working condition data from the starting position to the end position.
  • the second embodiment of the present disclosure proposes an energy management device for a vehicle, including: obtaining The module is used to obtain the pre-travel road of the vehicle.
  • the pre-travel road is divided into at least one predicted working condition according to the working condition data of the pre-driving road; the second determination module is used to determine based on the working condition data corresponding to each predicted working condition.
  • the energy management module is used to manage the energy of the vehicle according to the battery SOC change path among multiple battery SOC change paths that can minimize the energy consumption of the vehicle on the pre-driving road.
  • the predicted working conditions are divided according to the working condition data of the vehicle on the pre-driving road, and the energy management of the vehicle is based on the battery SOC change path with the smallest energy consumption under the predicted working conditions, which can realize different
  • the system operation efficiency is optimal under the time window or predicted working conditions, and ultimately the global optimization of the user's driving conditions is achieved.
  • a third embodiment of the present disclosure also provides a computer-readable storage medium on which a vehicle energy management program is stored.
  • the vehicle energy management program is executed by a processor, the above-mentioned vehicle energy management program is implemented. management methods.
  • the system operating efficiency optimization under different time windows or predicted working conditions can be achieved, and ultimately the global optimization of the user's driving conditions can be achieved.
  • a fourth embodiment of the present disclosure also provides a vehicle, including a memory, a processor, and a vehicle energy management program stored in the memory and executable on the processor.
  • the processor executes the energy management of the vehicle. program, the above-mentioned vehicle energy management method is implemented.
  • the system operating efficiency can be optimized under different time windows or predicted working conditions, and ultimately the global optimization of the user's driving conditions can be achieved.
  • Figure 1 is a block diagram of a vehicle energy management method according to an embodiment of the present disclosure
  • Figure 2 is a schematic diagram of predicted operating conditions according to an embodiment of the present disclosure
  • Figure 3 is a schematic diagram of a battery SOC change path according to an embodiment of the present disclosure
  • Figure 4 is a flow chart of a vehicle energy management method according to a specific embodiment of the present disclosure.
  • Figure 5 is a comparative schematic diagram of a vehicle energy management method according to a specific embodiment of the present disclosure
  • FIG. 6 is a block diagram of a vehicle energy management device according to an embodiment of the present disclosure.
  • Figure 7 is a block diagram of a vehicle according to one embodiment of the present disclosure.
  • the purpose of implementing the energy management control strategy is mainly to meet power requirements, maintain SOC, and the efficiency of components and systems, and adjust the working status of the power system through control, such as engine operating speed and working torque, etc. This improves system efficiency and reduces vehicle fuel consumption.
  • the above energy management strategy improves system efficiency by optimizing and adjusting the system working status, it does not take into account the user's driving conditions and cannot plan the system working status according to the user's driving conditions. It only optimizes the working efficiency of the system's current transient state. , resulting in suboptimal fuel economy and inability to achieve global optimization under the entire driving condition.
  • the present disclosure proposes a vehicle energy management method.
  • FIG. 1 is a block diagram of a vehicle energy management method according to an embodiment of the present disclosure.
  • the energy management method of the vehicle may include:
  • the map of the user's pre-driving road is obtained, thereby obtaining the working condition data of the pre-driving road, such as road condition speed. , slope and other signal data, combined with big data analysis, the pre-driving road is divided into different predicted working conditions, the battery SOC change range is calculated under each predicted working condition, and multiple battery SOC change paths are carried out within the battery SOC change range. Planning, calculate the energy consumption of each battery SOC change path, use the battery SOC change path with the smallest energy consumption as the optimal battery SOC change path under the predicted working conditions, and use this battery SOC change path to perform energy management on the vehicle.
  • the optimal battery SOC change path under each predicted working condition on the pre-driving road can be obtained, and the battery SOC is balanced and planned based on the current battery state of charge, thereby planning and controlling the battery power to achieve the optimal battery SOC under the entire driving condition. the global optimum.
  • the above-mentioned vehicle pre-travel road can be confirmed based on the starting point and end point selected by the user on the map, or the vehicle pre-travel road can be predicted and determined based on the current location and driving direction of the vehicle.
  • the specific situation can be determined according to the actual situation. Make settings.
  • obtaining the working condition data of the vehicle on the pre-driving road includes: determining the starting position and the end position of the vehicle pre-driving; and obtaining the working condition data from the starting position to the end position.
  • the user selects the starting point A and the ending point E in the map interface of the terminal display.
  • the map determines the starting point A and the ending point E based on the starting point A and the ending point E.
  • Point E generates multiple pairs of driving roads and displays them to the user.
  • the user selects one of the driving roads as a pre-driving road, and then obtains the working condition data of the vehicle's pre-driving road from point A to point E, as shown in Figure 2 shows that through statistical analysis of road condition data, it is identified that the driving conditions in section AB are predicted working conditions 1, the driving conditions in section BC are predicted working conditions 2, the driving conditions in section CD are predicted working conditions 3, and the driving conditions in section DE are predicted working conditions 3.
  • the working condition is predicted working condition 4. Therefore, this method is used to convert the vehicle driving road condition AE segment into AB segment (predicted working condition 1) + BC segment (predicted working condition 2) + CD segment (predicted working condition 3) + DE segment (predicted working condition 4 ) predicted operating condition combination.
  • the working condition data includes: slope data and speed limit data; determining at least one predicted working condition of the vehicle on the pre-driving road based on the working condition data includes: dividing the pre-driving road into at least A road section; obtain the historical driving parameters in at least one road section, and determine the road parameter data based on the historical driving parameters.
  • the road parameter data includes: average vehicle speed, average acceleration, average uphill gradient, average downhill gradient, and vehicle speed standards. At least one of difference and acceleration standard deviation; match road parameter data with road parameter data of multiple predicted working conditions; when the matching is successful, divide the vehicle's pre-travel road into one or more combinations of predicted working conditions .
  • this disclosure first divides vehicle driving conditions into 16 representative predicted conditions, such as severe urban congestion conditions, moderate urban congestion conditions, mild urban congestion conditions, and urban expressway conditions. conditions, highway conditions, suburban conditions, mountain road conditions, etc., and save the road parameter data corresponding to each predicted working condition.
  • 16 representative predicted conditions such as severe urban congestion conditions, moderate urban congestion conditions, mild urban congestion conditions, and urban expressway conditions. conditions, highway conditions, suburban conditions, mountain road conditions, etc.
  • the working condition data corresponding to the pre-driving road can be automatically retrieved based on the pre-driving road, such as speed limit, slope and other signal data, and then statistical analysis of the working condition data is performed , and divide the pre-driving road into several road sections based on the working condition data, and then obtain the speed, acceleration and other data of historical vehicles when passing through the road corresponding to the working condition data based on big data analysis, and then the vehicle speed, acceleration and other data can be retrieved through big data
  • the acceleration is calculated according to the basic calculation formula to obtain the corresponding parameter data such as average vehicle speed, average acceleration, standard deviation of vehicle speed, and standard deviation of acceleration.
  • the road parameter data of the predicted working conditions corresponding to the working condition data of the vehicle on the pre-driving road is determined, that is, it is identified as
  • the predicted working conditions corresponding to this working condition data are used to divide the working condition data of the vehicle's pre-driving road into Several representative predicted operating conditions can be one or a combination of multiple predicted operating conditions.
  • the above working condition data determined based on the pre-driving road can be obtained based on road planning. For example, assuming that the obtained working condition data of the pre-driving road includes speed limit data of 60Km/h and speed limit data of 80Km/h, At this time, the pre-driving road can be divided into road section a corresponding to the speed limit data of 60Km/h and road section b with the speed limit data of 80Km/h.
  • road parameter data including: average vehicle speed, average acceleration, vehicle speed standard deviation, and acceleration standard deviation as an example
  • the vehicle speed, acceleration and other driving parameter data of historical vehicles traveling through a road section are obtained, and based on the average Calculation formula and standard deviation calculation formula calculate the average speed, average acceleration, speed standard deviation and acceleration standard deviation of the vehicle passing through the road section a, and compare it with the road parameter data corresponding to the pre-stored predicted road conditions.
  • the predicted working conditions corresponding to the preset road parameter data are determined to be the predicted working conditions corresponding to the road section a.
  • the predicted working conditions corresponding to road section b can be obtained.
  • the predicted working conditions corresponding to the above-mentioned road section a and road section b may be the same or different. That is to say, the pre-driving road can be pre-segmented according to its corresponding working condition data, and then the predicted working conditions corresponding to the pre-segmented road sections are obtained based on the determined historical driving parameters, thereby obtaining all the predicted working conditions corresponding to the pre-driving road. condition.
  • the above historical driving parameters may also include the driving parameters of the vehicle currently driving on the road section, which is not limited here.
  • the above-mentioned road parameter data is used to identify and divide the predicted working conditions.
  • the road parameter data under different predicted working conditions have obvious differences, and the combination can be set according to the actual situation.
  • determining the battery SOC variation range of the vehicle under each predicted operating condition is determined based on the operating condition data corresponding to each predicted operating condition, including: obtaining the current actual battery SOC of the vehicle; Battery SOC and the working condition data corresponding to the first predicted working condition determined according to the vehicle's driving direction, determine the battery SOC change range of the vehicle under the first predicted working condition; for at least one predicted working condition except the first predicted working condition For each predicted working condition other than the predicted working condition, the battery SOC changing range of the vehicle under the predicted working conditions is determined based on the working condition data corresponding to the predicted working condition and the battery SOC changing range corresponding to the previous predicted working condition of the predicted working condition.
  • the battery consumption of the vehicle under the predicted working condition is calculated, and based on the vehicle's current actual battery SOC, the vehicle's battery consumption at the end of the first predicted working condition is predicted.
  • Battery SOC thereby determining the variation range of battery SOC under the first predicted operating condition.
  • determine the initial battery SOC under the second predicted working condition based on the variation range of the battery SOC under the first predicted working condition, and combine it with the predicted battery consumption under the second predicted working condition to calculate the end of the second predicted working condition.
  • the battery SOC of the vehicle is used to determine the battery variation range under the second predicted working condition, and by analogy, the initial battery SOC of the third predicted working condition is determined based on the battery variation range under the second predicted working condition, thereby determining the battery SOC for the third predicted working condition.
  • the battery SOC variation range under each predicted operating condition on the pre-driving road is determined.
  • the battery SOC variation range of the vehicle under the first predicted operating condition is determined based on the current actual battery SOC of the vehicle and the operating condition data corresponding to the first predicted operating condition determined according to the driving direction of the vehicle. , including: determining the first SOC based on the actual battery SOC and the working condition data corresponding to the first predicted working condition.
  • the first SOC is the battery SOC at the end of the vehicle's first predicted working condition in power generation mode; based on the actual battery SOC and The working condition data corresponding to the first predicted working condition determines the second SOC.
  • the second SOC is the battery SOC when the vehicle runs in pure electric mode at the end of the first predicted working condition; the first SOC is used as the battery SOC upper limit value.
  • SOC is used as the lower limit of battery SOC to obtain the battery SOC variation range of the vehicle under the first predicted operating condition;
  • the battery SOC changing range of the vehicle under the predicted working conditions including: According to the working conditions corresponding to the predicted working conditions
  • the data and the battery SOC change range corresponding to the previous predicted working condition are used to determine the third SOC.
  • the third SOC is the battery SOC at the end of the vehicle's operation in the power generation mode in the predicted working condition; according to the working condition data corresponding to the predicted working condition and the previous The battery SOC variation range corresponding to the predicted working conditions is determined, and the fourth SOC is the battery SOC at the end of the vehicle's pure electric mode operation under the predicted working conditions; the third SOC is used as the battery SOC upper limit, and the fourth SOC is used as the battery The SOC lower limit value is used to obtain the battery SOC variation range of the vehicle under predicted operating conditions.
  • the first predicted working condition is predicted working condition 1 corresponding to segment AB.
  • the initial battery SOC of the vehicle at point A is F.
  • the vehicle adopts pure power generation mode that is, under the predicted working condition 1 from point A to point B
  • the first SOC is G, that is, the upper limit of the battery SOC in the predicted working condition 1 is G.
  • the vehicle adopts pure electric mode that is, under the predicted working condition 1 from point A to point B, the vehicle is fully charged and the battery is in a discharged state.
  • the second SOC of point B is determined to be I, that is, the lower limit of the battery SOC in the predicted operating condition 1 is I. From this, the battery variation range under the predicted operating condition 1 can be determined to be [I, G]. Assume that F of the actual battery SOC under predicted working condition 1 is 70%, the upper limit value G of battery SOC at point B is 75%, and the lower limit value I of battery SOC is 65%, then the range of change of battery SOC under predicted working condition 1 is: [65%,75%].
  • the battery SOC change range of the vehicle under the predicted working condition 2 is determined.
  • the upper limit of battery SOC in predicted working condition 1 is G as the initial battery SOC in predicted working condition 2.
  • the vehicle adopts pure power generation mode, that is, under predicted working condition 2, the vehicle uses all fuel and the battery is in a charging state to determine point C.
  • the third SOC is J, that is, the upper limit of the battery SOC in the predicted working condition 2 is J.
  • the lower limit value of the battery SOC of the predicted working condition 1 is I as the initial battery SOC of the predicted working condition 2.
  • the vehicle adopts pure electric mode, that is, the vehicle is fully charged under the predicted working condition 2 from point B to point C, and the battery is in The discharge state is used to determine the fourth SOC at point C to be L, that is, the lower limit of the battery SOC in the predicted working condition 2 is L. Therefore, the battery SOC change range under the predicted working condition 2 is determined to be [L, J].
  • determining a battery SOC change path among multiple battery SOC change paths of the vehicle on the pre-driving road includes: selecting a target SOC from the SOC change range corresponding to each predicted operating condition. value; obtain one battery SOC change path among multiple battery SOC change paths based on each target SOC value.
  • the target SOC value of the AB segment prediction working condition 1 also includes the battery SOC upper limit value G and the battery SOC lower limit value I.
  • the AB segment Predicted working condition 1 can form three battery SOC change paths: F-G, F-H, and F-I.
  • the initial battery SOC of the predicted working condition 2 of the BC segment can be the target SOC value of the AB segment, which includes G, H, and I.
  • the preset reference battery SOC of the BC segment includes J, K, and L.
  • the BC segment can form G-J , G-K, G-L, H-J, H-K, H-L, I-J, I-K and I-L nine SOC change paths.
  • the predicted operating condition 3 in the CD segment can form fifteen battery SOC change paths
  • obtaining the SOC change path with the smallest energy consumption among multiple battery SOC change paths includes: obtaining the fuel consumption and power consumption of each battery SOC change path; accumulating the fuel consumption and power consumption to find the battery SOC change with the lowest The path is used as the target battery SOC change path.
  • the fuel consumption and power consumption of different battery SOC change paths are calculated, the battery SOC change path with the smallest fuel consumption and power consumption is compared and selected as the optimal battery SOC change path, and the optimal battery SOC change path is used as the prediction tool.
  • the target battery SOC change path of the condition is used to control the vehicle for energy management.
  • the fuel consumption and power consumption of the battery SOC change path F-G, F-H and F-I are respectively obtained, and the fuel consumption and power consumption of the battery SOC change path F-I are calculated to be the smallest, so
  • the battery SOC change path F-I is used as the target battery SOC change path in segment AB, and I is the optimal battery SOC at the end of predicted working condition 1.
  • the target SOC change path of the BC segment is I-J
  • the target SOC change path of the CD segment is J-P
  • the target SOC change path of the DE segment is P-U.
  • the target battery SOC change path under the pre-driving road AE is F-I-J-P-U.
  • the energy management method of the vehicle further includes: the difference between the actual battery SOC under the current predicted operating conditions and the corresponding battery SOC in the SOC change path with minimum energy consumption is greater than the set threshold, Or when the vehicle's pre-driving route changes, multiple battery SOC change paths are reacquired.
  • the above setting threshold can be set according to the actual situation.
  • the above-mentioned battery SOC obtained from the optimal path is used as the optimal battery SOC for each predicted working condition, that is, the fuel consumption and energy consumption of the user working condition are minimized and the global efficiency is optimized.
  • the SOC change path with minimum energy consumption in segment AB is F-I
  • the optimal battery SOC in segment AB is I.
  • the set threshold is 2%.
  • the actual battery SOC is used as the initial battery SOC of the BC segment vehicle, and multiple battery SOC change paths are reacquired and determined through energy consumption comparison.
  • the optimal battery SOC change path and use this to manage the energy of the vehicle. If the difference between the vehicle's actual battery SOC and I is ⁇ SOC ⁇ 2%, the vehicle operation is controlled according to the originally obtained battery SOC change path with the smallest energy consumption, that is, IJ is used as the battery SOC change path of the BC segment to perform vehicle energy management.
  • the working condition data of the pre-driving road is obtained based on the positioning signal of the global navigation system, high-precision map data, intelligent network connection 4G/5G data, etc., and the driving route changes, the working condition data will be re-processed. Obtain and re-execute the planning of the battery SOC change path.
  • this disclosure takes into account the large amount of calculations in the actual vehicle software program, and can complete the combined calculation of known predicted working conditions in the simulation platform, and input the battery SOC change path into the vehicle controller as the energy management planning strategy logic. After identifying the current driving road conditions of the vehicle, it automatically plans and selects the optimal battery SOC change path, reducing the calculation amount and calculation time of the vehicle controller, which is simple and easy to implement.
  • the energy management method of the vehicle may include the following steps:
  • S103 Divide the pre-driving road into at least one road section according to the working condition data.
  • S104 Obtain historical driving parameters in at least one road section, and determine road parameter data based on the historical driving parameters.
  • S108 Determine the first SOC based on the actual battery SOC and the working condition data corresponding to the predicted working condition 1.
  • S109 Determine the second SOC based on the actual battery SOC and the working condition data corresponding to the predicted working condition 1.
  • S111 Determine the third SOC based on the operating condition data corresponding to the predicted operating condition 2 and the battery SOC change range corresponding to the predicted operating condition 1.
  • S112 Determine the fourth SOC based on the operating condition data corresponding to the predicted operating condition 2 and the battery SOC variation range corresponding to the predicted operating condition 1.
  • S114 Determine multiple battery SOC change paths of the vehicle on the pre-driving road based on the battery SOC change range of the vehicle under various predicted operating conditions.
  • the battery SOC of the vehicle is planned, and a comparison diagram as shown in Figure 5 can be formed, in which the SOC planning energy management strategy adopts the disclosed energy management method.
  • the SOC control achieved and the SOC-free planning energy management strategy are SOC control achieved by using energy management methods in related technologies.
  • This disclosure is based on the predicted battery SOC intelligent planning energy management control strategy, which divides the driving conditions of the vehicle into different predicted working conditions, balances the battery SOC based on the predicted working conditions and the current battery state of charge, and based on the current energy management strategy for The driver's driving needs and SOC planning control the battery power to achieve the optimal system operating efficiency under different time windows or predicted working conditions, and ultimately achieve the global optimization of the user's driving conditions.
  • the working condition data of the vehicle on the pre-driving road is obtained, at least one predicted working condition of the vehicle on the pre-driving road is determined based on the working condition data, and based on each predicted working condition,
  • the working condition data corresponding to the conditions are used to determine the battery SOC change range of the vehicle under each predicted working condition.
  • the battery SOC change range of the vehicle under each predicted working condition multiple battery SOC change paths of the vehicle on the pre-driving road are determined. , and then perform energy management on the vehicle according to the battery SOC change path with the smallest energy consumption among multiple battery SOC change paths.
  • this method divides the vehicle into predicted working conditions based on the working condition data of the vehicle on the pre-driving road, and performs energy management on the vehicle based on the battery SOC change path with the smallest energy consumption under the predicted working conditions, which can achieve different time windows or predicted working conditions.
  • the system operation efficiency under the conditions is optimal, and ultimately the global optimization of user driving conditions is achieved.
  • the present disclosure also provides an energy management device for a vehicle.
  • the energy management device of the vehicle may include: an acquisition module 10 , a first determination module 20 , a second determination module 30 , a third determination module 40 and an energy management module 50 .
  • the acquisition module 10 is used to acquire the working condition data of the vehicle on the pre-driving road.
  • the first determination module 20 is configured to determine at least one predicted working condition of the vehicle on the pre-driving road based on the working condition data.
  • the second determination module 30 is used to determine the battery SOC variation range of the vehicle operating under each predicted operating condition based on the operating condition data corresponding to each predicted operating condition.
  • the third determination module 40 is used to determine whether the vehicle is on the pre-driving road based on the battery SOC variation range of the vehicle under various predicted operating conditions. Multiple battery SOC change paths.
  • the energy management module 50 is used to perform energy management on the vehicle according to the battery SOC change path with the smallest energy consumption among the multiple battery SOC change paths.
  • the working condition data includes: slope data and speed limit data.
  • the first determination module 20 determines at least one predicted working condition of the vehicle on the pre-driving road according to the working condition data, specifically for: according to the working condition data Divide the pre-driving road into at least one road section; obtain historical driving parameters in at least one road section, and determine road parameter data based on the historical driving parameters, where the road parameter data includes: average vehicle speed, average acceleration, average uphill slope, At least one of the average downhill gradient, vehicle speed standard deviation, and acceleration standard deviation; match the road parameters with road parameter data of multiple predicted working conditions; when the matching is successful, divide the vehicle's pre-driving road into one or more A combination of predicted operating conditions.
  • the second determination module 30 determines the operating conditions of the vehicle under the first predicted operating condition based on the current actual battery SOC of the vehicle and the operating condition data corresponding to the first predicted operating condition determined according to the driving direction of the vehicle.
  • the battery SOC variation range is specifically used to determine the first SOC based on the actual battery SOC and the working condition data corresponding to the first predicted working condition.
  • the first SOC is the battery SOC at the end of the first predicted working condition of the vehicle in power generation mode. ; Based on the actual battery SOC and the working condition data corresponding to the first predicted working condition, determine the second SOC.
  • the second SOC is the battery SOC at the end of the first predicted working condition in pure electric mode; use the first SOC as the battery SOC The upper limit value, the second SOC is used as the lower limit value of the battery SOC, and the battery SOC variation range of the vehicle under the first predicted operating condition is obtained;
  • the second determination module 30 determines the battery SOC variation range of the vehicle under the predicted working conditions based on the working condition data corresponding to the predicted working conditions and the battery SOC changing range corresponding to the previous predicted working condition of the predicted working conditions, and is specifically used to:
  • the working condition data corresponding to the predicted working condition and the battery SOC change range corresponding to the previous predicted working condition are used to determine the third SOC.
  • the third SOC is the battery SOC at the end of the vehicle's power generation mode in the predicted working condition; corresponding to the predicted working condition
  • the working condition data and the battery SOC change range corresponding to the previous predicted working condition are used to determine the fourth SOC.
  • the fourth SOC is the battery SOC at the end of the vehicle's pure electric mode operation under the predicted working conditions; the third SOC is used as the upper limit of the battery SOC. value, the fourth SOC is used as the battery SOC lower limit value, and the battery SOC variation range of the vehicle under the predicted operating conditions is obtained.
  • the third determination module 40 determines a battery SOC change path among multiple battery SOC change paths of the vehicle on the pre-driving road, specifically for: SOC change corresponding to each predicted operating condition. Select a target SOC value from the range; obtain a battery SOC change path among multiple battery SOC change paths based on each target SOC value.
  • the third determination module 40 is further configured to: the difference between the actual battery SOC under the current predicted operating conditions and the corresponding battery SOC change path with minimum energy consumption is greater than the set threshold. , or when the vehicle's pre-driving road changes, multiple battery SOC change paths are reacquired.
  • the energy management method of the vehicle further includes: the difference between the actual battery SOC under the current predicted operating conditions and the corresponding battery SOC in the SOC change path with minimum energy consumption is greater than the set threshold, Or when the vehicle's pre-driving route changes, multiple battery SOC change paths are reacquired.
  • the acquisition module 10 acquires the working condition data of the vehicle on the pre-driving road, including: determining the starting position and the end position of the vehicle's pre-driving; and acquiring the working condition data from the starting position to the end position.
  • the working condition data of the vehicle on the pre-driving road is obtained through the acquisition module, and the first determination module determines at least one predicted working condition of the vehicle on the pre-driving road based on the working condition data, The second determination module determines the battery SOC variation range of the vehicle under each predicted working condition based on the working condition data corresponding to each predicted working condition. The third determination module determines the battery SOC variation range based on the battery SOC variation range of the vehicle under each predicted working condition. The energy management module performs energy management on the vehicle based on the battery SOC change path with the smallest energy consumption among the multiple battery SOC change paths.
  • the device divides the predicted working conditions according to the working condition data of the vehicle on the pre-driving road, and performs energy management on the vehicle based on the battery SOC change path with the smallest energy consumption under the predicted working conditions, which can realize different time windows or predicted working conditions.
  • the system operation efficiency is optimal, and ultimately the global optimization of user driving conditions is achieved.
  • the present disclosure also provides a computer-readable storage medium.
  • the computer-readable storage medium has a vehicle energy management program stored thereon.
  • the vehicle energy management program is executed by the processor, the above-mentioned vehicle energy management method is implemented.
  • the system operating efficiency optimization under different time windows or predicted working conditions can be achieved, and ultimately the global optimization of the user's driving conditions can be achieved.
  • the present disclosure also provides a vehicle.
  • the vehicle 100 in the embodiment of the present disclosure includes a memory 110 , a processor 120 and a vehicle energy management program stored on the memory 110 and executable on the processor 120 .
  • the processor 120 executes the vehicle energy management program. When, the above-mentioned vehicle energy management method is implemented.
  • the processor 120 may be configured to execute the above method embodiments according to instructions in the computer program.
  • the processor 120 may include, but is not limited to:
  • DSP Digital Signal Processor
  • ASIC Application Specific Integrated Circuit
  • FPGA Field Programmable Gate Array
  • the memory 110 includes, but is not limited to:
  • Non-volatile memory can be read-only memory (Read-Only Memory, ROM), programmable read-only memory (Programmable ROM, PROM), erasable programmable read-only memory (Erasable PROM, EPROM), electrically removable memory. Erase programmable read-only memory (Electrically EPROM, EEPROM) or flash memory. Volatile memory may be Random Access Memory (RAM), which is used as an external cache.
  • RAM Random Access Memory
  • RAM static random access memory
  • DRAM dynamic random access memory
  • DRAM synchronous dynamic random access memory
  • SDRAM double data rate synchronous dynamic random access memory
  • Double Data Rate SDRAM DDR SDRAM
  • ESDRAM enhanced synchronous dynamic random access memory
  • SLDRAM synchronous link dynamic random access memory
  • Direct Rambus RAM Direct Rambus RAM
  • the computer program may be divided into one or more modules, and the one or more modules are stored in the memory 110 and executed by the processor 120 to complete the tasks provided by the present disclosure.
  • the one or more modules may be a series of computer program instruction segments capable of completing specific functions, and the instruction segments are used to describe the execution process of the computer program in the vehicle 100 .
  • the vehicle 100 may also include:
  • Transceiver 130 the transceiver 130 may be connected to the processor 120 or the memory 110 .
  • the processor 120 can control the transceiver 130 to communicate with other devices. For example, it can send information or data to other devices, or receive information or data sent by other devices.
  • Transceiver 130 may include a transmitter and a receiver.
  • the transceiver 130 may further include an antenna, and the number of antennas may be one or more.
  • bus system where in addition to the data bus, the bus system also includes a power bus, a control bus and a status signal bus.
  • the system operating efficiency can be optimized under different time windows or predicted working conditions, and ultimately the global optimization of the user's driving conditions can be achieved.
  • a "computer-readable medium” may be any A device that stores, communicates, disseminates or transmits programs for use by or in conjunction with instruction execution systems, devices or devices.
  • Non-exhaustive list of computer readable media include the following: electrical connections with one or more wires (electronic device), portable computer disk cartridges (magnetic device), random access memory (RAM), Read-only memory (ROM), erasable and programmable read-only memory (EPROM or flash memory), fiber optic devices, and portable compact disc read-only memory (CDROM).
  • the computer-readable medium may even be paper or other suitable medium on which the program may be printed, as the paper or other medium may be optically scanned, for example, and subsequently edited, interpreted, or otherwise suitable as necessary. process to obtain the program electronically and then store it in computer memory.
  • various parts of the present disclosure may be implemented in hardware, software, firmware, or combinations thereof.
  • various steps or methods may be implemented in software or firmware stored in a memory and executed by a suitable instruction execution system.
  • a logic gate circuit with a logic gate circuit for implementing a logic function on a data signal.
  • Discrete logic circuits application specific integrated circuits with suitable combinational logic gates, programmable gate arrays (PGA), field programmable gate arrays (FPGA), etc.
  • first and second are used for descriptive purposes only and cannot be understood as indicating or implying relative importance or implicitly indicating the quantity of indicated technical features. Therefore, features defined as “first” and “second” may explicitly or implicitly include at least one of these features.
  • plural means at least two, such as two, three, etc., unless otherwise expressly and specifically limited.
  • installation”, “connection”, “connection”, “fixing” and other terms should be understood in a broad sense. For example, it can be a fixed connection or a detachable connection.

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Abstract

一种车辆及其能量管理方法和能量管理装置、可读存储介质，其中，方法包括：获取车辆的预行驶道路，预行驶道路根据工况数据被划分为至少一个预测工况；根据各个预测工况对应的工况数据，确定车辆在各个预测工况运行下的电池SOC变化范围；根据车辆在各个预测工况运行下的电池SOC变化范围，确定车辆在预行驶道路上的多个电池SOC变化路径；根据多个电池SOC变化路径中运行能耗最小的电池SOC变化路径对车辆进行能量管理。

Description

车辆及其能量管理方法和能量管理装置、可读存储介质
相关申请的交叉引用
本公开要求于2022年07月25日提交的申请号为202210877503.1，名称为“车辆及其能量管理方法和能量管理装置、可读存储介质”的中国专利申请的优先权，其全部内容通过引用结合在本公开中。
技术领域
本公开涉及车辆技术领域，尤其涉及一种车辆的能量管理方法、一种车辆的能量管理装置、一种计算机可读存储介质和一种车辆。
背景技术
当前混合动力汽车为了合理地对多动力源能量耦合系统进行管理，通过设置能量管理控制策略对多动力源的功率或转矩进行分配、对机械制动和电能量回收进行协调，在保证车辆动力性、安全性及舒适性的基础上，提升系统效率，改善车辆的节能减排性能。
整车控制的能量管理策略主要是以满足功率需求、维持电池SOC(State Of Charge，荷电状态)、以及系统的工作效率为控制准则，当车辆运行时，能量管理控制策略通过合理分配各动力源的功率并结合动力源的效率特点，来提高系统的驱动效率。在相关技术中，能量管理策略仅基于车辆自身的运行状况来进行能量控制，无法实现最优控制，难以达到系统驱动效率的最优。
公开内容
本公开旨在至少在一定程度上解决相关技术中的技术问题之一。为此，本公开的第一个目的在于提出一种车辆的能量管理方法，根据车辆在预行驶道路的工况数据划分预测工况，并基于预测工况下能耗最小的SOC变化路径对车辆进行能量管理，可实现不同预测工况下的系统运行效率最优，最终实现用户行驶工况的全局最优。
本公开的第二个目的在于提出一种车辆的能量管理装置。
本公开的第三个目的在于提出一种计算机可读存储介质。
本公开的第四个目的在于提出一种车辆。
为达到上述目的，本公开第一方面实施例提出了一种车辆的能量管理方法，包括：获取车辆的预行驶道路，预行驶道路根据预行驶道路的工况数据被划分为至少一个预测工况； 根据各个预测工况对应的工况数据，确定车辆在各个预测工况运行下的电池SOC变化范围；根据车辆在各个预测工况运行下的电池SOC变化范围，确定车辆在预行驶道路上的多个电池SOC变化路径；根据多个电池SOC变化路径中能够使车辆在预行驶道路运行能耗最小的电池SOC变化路径对车辆进行能量管理。
根据本公开实施例的车辆的能量管理方法，根据车辆在预行驶道路的工况数据划分为预测工况，并基于预测工况下能耗最小的电池SOC变化路径对车辆进行能量管理，可实现不同时间窗或预测工况下的系统运行效率最优，最终实现用户行驶工况的全局最优。
另外，根据本公开上述实施例的车辆的能量管理方法，还可以具有如下的附加技术特征：
根据本公开的一个实施例，工况数据包括：坡度数据和限速数据，确定至少一个预测工况的方式包括：根据工况数据将预行驶道路划分为至少一个道路区间；获取至少一个道路区间内的历史行驶参数，并根据历史行驶参数确定道路参数数据，其中，道路参数数据包括：平均车速、平均加速度、平均上坡坡度、平均下坡坡度、车速标准差、加速度标准差中的至少一种；将道路参数数据与多个预测工况的道路参数数据进行匹配；在匹配成功时，将车辆的预行驶道路划分为一个或者多个预测工况的组合。
根据本公开的一个实施例，预行驶道路的首个预测工况的电池SOC变化范围是根据车辆在预行驶道路起点的实际电池SOC和首个预测工况的工况数据确定的；预行驶道路的非首个预测工况的电池SOC变化范围是根据非首个预测工况的工况数据和非首个预测工况的前一个预测工况的电池SOC变化范围确定的。
根据本公开的一个实施例，预测工况的电池SOC变化范围的上限值是车辆以发电模式在预测工况运行结束时的电池SOC；预测工况的电池SOC变化范围的下限值是车辆以纯电模式在预测工况运行结束时的电池SOC。
根据本公开的一个实施例，确定车辆在预行驶道路上的多个电池SOC变化路径中的一个电池SOC变化路径，包括：分别在每个预测工况的电池SOC变化范围中选取一个目标SOC值；根据各个目标SOC值得到多个电池SOC变化路径中的一个电池SOC变化路径。
根据本公开的一个实施例，在当前预测工况下的实际电池SOC与对应的能耗最小的SOC变化路径中电池SOC之间的差值大于设定阈值，或者车辆的预行驶道路发生变化时，重新获取多个电池SOC变化路径。
根据本公开的一个实施例，获取车辆在预行驶道路的工况数据，包括：确定车辆预行驶的起始位置和终点位置；获取起始位置到终点位置的工况数据。
为达到上述目的，本公开第二方面实施例提出了一种车辆的能量管理装置，包括：获取 模块，用于获取车辆的预行驶道路，预行驶道路根据预行驶道路的工况数据被划分为至少一个预测工况；第二确定模块，用于根据各个预测工况对应的工况数据，确定车辆在各个预测工况运行下的电池SOC变化范围；第三确定模块，用于根据车辆在各个预测工况运行下的电池SOC变化范围，确定车辆在预行驶道路上的多个电池SOC变化路径；能量管理模块，用于根据多个电池SOC变化路径中能够使车辆在预行驶道路运行能耗最小的电池SOC变化路径对车辆进行能量管理。
根据本公开实施例的车辆的能量管理装置，根据车辆在预行驶道路的工况数据划分预测工况，并基于预测工况下能耗最小的电池SOC变化路径对车辆进行能量管理，可实现不同时间窗或预测工况下的系统运行效率最优，最终实现用户行驶工况的全局最优。
为达到上述目的，本公开第三方面实施例还提出了一种计算机可读存储介质，其上存储有车辆的能量管理程序，该车辆的能量管理程序被处理器执行时实现上述的车辆的能量管理方法。
根据本公开实施例的计算机可读存储介质，基于上述的车辆的能量管理方法，可实现不同时间窗或预测工况下的系统运行效率最优，最终实现用户行驶工况的全局最优。
为达到上述目的，本公开第四方面实施例还提出了一种车辆，包括存储器、处理器及存储在存储器上并可在处理器上运行的车辆的能量管理程序，处理器执行车辆的能量管理程序时，实现上述的车辆的能量管理方法。
根据本公开实施例的车辆，基于上述的车辆的能量管理方法，可实现不同时间窗或预测工况下的系统运行效率最优，最终实现用户行驶工况的全局最优。
本公开附加的方面和优点将在下面的描述中部分给出，部分将从下面的描述中变得明显，或通过本公开的实践了解到。
附图说明
图1为根据本公开一个实施例的车辆的能量管理方法的方框示意图；
图2为根据本公开一个实施例的预测工况划分示意图；
图3为根据本公开一个实施例的电池SOC变化路径示意图；
图4为根据本公开一个具体实施例的车辆的能量管理方法的流程图；
图5为根据本公开一个具体实施例的车辆的能量管理方法的对比示意图；
图6为根据本公开一个实施例的车辆的能量管理装置的方框示意图；
图7为根据本公开一个实施例的车辆的方框示意图。
具体实施方式
下面详细描述本公开的实施例，实施例的示例在附图中示出，其中自始至终相同或类似的标号表示相同或类似的元件或具有相同或类似功能的元件。下面通过参考附图描述的实施例是示例性的，旨在用于解释本公开，而不能理解为对本公开的限制。
下面参考附图描述本公开实施例提出的车辆的能量管理方法、车辆的能量管理装置、计算机可读存储介质和车辆。
相关技术中，当车辆运行时，能量管理控制策略的实施目的主要是满足功率需求、维持SOC、以及零部件与系统的效率，通过控制调整动力系统工作状态，如发动机运行转速和工作扭矩等，进而提升系统工作效率、降低车辆燃油消耗。上述能量管理策略虽然通过优化调整系统工作状态来提升系统工作效率，但未考虑到用户行驶工况需求，无法根据用户的行驶工况规划系统工作状态，仅优化了系统当前瞬态状态的工作效率，导致次优的燃料经济性，无法实现整个行驶工况下的全局优化。为解决上述问题，本公开提出了一种车辆的能量管理方法。
图1为根据本公开一个实施例的车辆的能量管理方法的方框示意图。
如图1所示，本公开实施例的车辆的能量管理方法可包括：
S1，获取车辆在预行驶道路的工况数据；
S2，根据工况数据确定车辆在预行驶道路的至少一个预测工况；
S3，根据各个预测工况对应的工况数据，确定车辆在各个预测工况运行下的电池SOC变化范围；
S4，根据车辆在各个预测工况运行下的电池SOC变化范围，确定车辆在预行驶道路上的多个电池SOC变化路径；
S5，根据多个电池SOC变化路径中能耗最小的电池SOC变化路径对车辆进行能量管理。
示例性地，根据全球导航系统的定位信号、高精度地图数据、智能网联的4G/5G数据等，获取用户预行驶道路的地图，从而获取预行驶道路的工况数据，例如道路工况车速、坡度等信号数据，并结合大数据分析将预行驶道路划分为不同的预测工况，计算每个预测工况下电池SOC的变化范围，并在电池SOC变化范围内进行多条电池SOC变化路径的规划，计算每条电池SOC变化路径的能耗，以能耗最小的电池SOC变化路径作为该预测工况下的最优电池SOC变化路径，以该电池SOC变化路径对车辆进行能量管理。以此类推，可获得预行驶道路中的每个预测工况下的最优电池SOC变化路径，并基于当前电池的荷电状态均衡规划电池SOC，从而规划控制电池功率，达到整个行驶工况下的全局最优。
需要说明的是，上述车辆预行驶道路可根据用户在地图中选择的起点和终点进行确认，也可根据车辆当前所处位置以及行驶方向，对车辆预行驶道路进行预测确定，具体可根据实际情况进行设定。
在本公开的一个实施例中，获取车辆在预行驶道路的工况数据，包括：确定车辆预行驶的起始位置和终点位置；获取起始位置到终点位置的工况数据。
示例性地，假设用户需要从A点行驶到E点，首先用户在终端显示器的地图界面中对起始位置A点和终止位置E点的位置进行选取，地图根据起始位置A点和终点位置E点生成多条对行驶道路，并向用户进行展示，用户选择其中一条行驶道路作为预行驶道路，然后对车辆由A点到E点车辆预行驶道路的工况数据进行获取，如图2所示，通过对道路工况数据进行统计分析，识别出AB段行驶工况为预测工况1，BC段行驶工况为预测工况2，CD段行驶工况为预测工况3，DE段行驶工况为预测工况4。由此，通过此方法将车辆行驶道路工况AE段转化为AB段(预测工况1)+BC段(预测工况2)+CD段(预测工况3)+DE段(预测工况4)的预测工况组合。
根据本公开的一个实施例，工况数据包括：坡度数据和限速数据，根据工况数据确定车辆在预行驶道路的至少一个预测工况，包括：根据工况数据将预行驶道路划分为至少一个道路区间；获取至少一个道路区间内的历史行驶参数，并根据历史行驶参数确定道路参数数据，其中，道路参数数据包括：平均车速、平均加速度、平均上坡坡度、平均下坡坡度、车速标准差、加速度标准差中的至少一种；将道路参数数据与多个预测工况的道路参数数据进行匹配；在匹配成功时，将车辆的预行驶道路划分为一个或者多个预测工况的组合。
具体而言，本公开首先将车辆行驶道路工况划分为16中具有代表性的预测工况，如城市严重拥堵工况、城市中度拥堵工况、城市轻度拥堵工况、城市快速路工况、高速工况、市郊工况、山路工况等，并将每个预测工况对应的道路参数数据进行保存。
当用户基于终端显示屏的地图确定预行驶道路后，根据预行驶道路可自动调取预行驶道路所对应的工况数据，例如，限速、坡度等信号数据，然后对工况数据进行统计分析，并根据工况数据将预行驶道路划分为若干道路区间，进而基于大数据分析获取历史车辆通过该工况数据对应道路时的速度、加速度等数据，然后可通过大数据调取的车辆速度、加速度根据基本计算公式计算得到所对应的平均车速、平均加速度、车速标准差、加速度标准差等参数数据。基于已计算的车辆对应的道路参数数据与预先存储的预测工况对应的道路参数数据进行对比识别，确定车辆在预行驶道路的工况数据所对应的预测工况的道路参数数据，即识别为该工况数据对应的预测工况，以此将车辆的预行驶道路的工况数据划分为 若干个代表性的预测工况，可以是一个或者多个预测工况的组合。
需要说明的是，上述根据预行驶道路确定的工况数据可基于道路规划获取，举例来说，假设获取的预行驶道路的工况数据包括限速数据60Km/h和限速数据80Km/h，此时可将预行驶道路划分为限速数据60Km/h对应的a道路区间和限速数据为80Km/h的b段道路区间。以道路参数数据包括：平均车速、平均加速度、车速标准差、加速度标准差为例，首先基于大数据分析获得历史车辆在行驶通过a道路区间中的车速、加速度等行驶参数数据，并根据平均数计算公式和标准差计算公式计算得到车辆通过a道路区间的平均车速、平均加速度、车速标准差和加速度标准差，并与预先存储的预测公况对应的道路参数数据相比较，当计算获得的道路参数数据处于预设道路参数数据范围内时，确定该预设道路参数数据对应的预测工况为a道路区间对应的预测工况。以此类推，可获得b道路区间对应的预测工况。可以理解的是，上述a道路区间和b道路区间所对应的预测工况可以相同也可以不同。也就是说，根据预行驶道路可根据其对应的工况数据进行预分段，然后基于确定的历史行驶参数获取预分割道路区间对应的预测工况，从而得到预行驶道路所对应的全部预测工况。
需要说明的是，上述仅作为本公开的一个可实现方式，具体可根据实际情况进行设定。例如，上述历史行驶参数还可包括当前行驶在道路区间上车辆的行驶参数，此处不作限制。
另外需要说明的是，上述道路参数数据用于预测工况的识别划分，通常不同预测工况下道路参数数据具有明显的区别，具体可根据实际情况进行组合设定。
在本公开的一个实施例中，根据各个预测工况对应的工况数据，确定车辆在各个预测工况运行下的电池SOC变化范围，包括：获取车辆当前的实际电池SOC；根据车辆当前的实际电池SOC和按照车辆的行驶方向确定的首个预测工况对应的工况数据，确定车辆在首个预测工况运行下的电池SOC变化范围；针对至少一个预测工况中除首个预测工况之外的每个预测工况，根据预测工况对应的工况数据和预测工况的前一个预测工况对应的电池SOC变化范围，确定车辆在预测工况运行下的电池SOC变化范围。
也就是说，根据车辆在首个预测工况的工况数据，计算车辆在该预测工况运行下电池的消耗量，并基于车辆当前的实际电池SOC，预测首个预测工况结束时车辆的电池SOC，从而确定首个预测工况下电池SOC的变化范围。并根据首个预测工况下电池SOC的变化范围确定第二个预测工况下的初始电池SOC，并结合第二个预测工况下预测的电池消耗量，计算第二个预测工况结束时车辆的电池SOC，由此确定第二个预测工况下的电池变化范围，以此类推，根据第二个预测工况下的电池变化范围确定第三个预测工况的初始电池SOC，从而对预行驶道路中的各个预测工况运行下的电池SOC变化范围进行确定。
在本公开的一个实施例中，根据车辆当前的实际电池SOC和按照车辆的行驶方向确定的首个预测工况对应的工况数据，确定车辆在首个预测工况运行下的电池SOC变化范围，包括：根据实际电池SOC和首个预测工况对应的工况数据，确定第一SOC，第一SOC为车辆以发电模式在首个预测工况运行结束时的电池SOC；根据实际电池SOC和首个预测工况对应的工况数据，确定第二SOC，第二SOC为车辆以纯电模式在首个预测工况运行结束时的电池SOC；将第一SOC作为电池SOC上限值，第二SOC作为电池SOC下限值，得到车辆在首个预测工况运行下的电池SOC变化范围；
根据预测工况对应的工况数据和预测工况的前一个预测工况对应的电池SOC变化范围，确定车辆在预测工况运行下的电池SOC变化范围，包括：根据预测工况对应的工况数据和前一个预测工况对应的电池SOC变化范围，确定第三SOC，第三SOC为车辆以发电模式在预测工况运行结束时的电池SOC；根据预测工况对应的工况数据和前一个预测工况对应的电池SOC变化范围，确定第四SOC，第四SOC为车辆以纯电模式在预测工况运行结束时的电池SOC；将第三SOC作为电池SOC上限值，第四SOC作为电池SOC下限值，得到车辆在预测工况运行下的电池SOC变化范围。
示例性地，继续以如图2所示的预测工况划分为例，其首个预测工况为AB段对应的预测工况1。如图3所示，车辆处于A点的初始电池SOC为F，以车辆采用纯发电模式即在A点至B点的预测工况1下车辆全部使用燃料，电池处于充电状态，来确定B点的第一SOC为G，即预测工况1的电池SOC上限值为G，以车辆采用纯电模式即在A点至B点的预测工况1下车辆全部电，电池处于放电状态，来确定B点的第二SOC为I，即预测工况1的电池SOC下限值为I，由此，可以确定预测工况1下的电池变化范围为[I,G]。假设预测工况1下的实际电池SOC的F为70％，B点的电池SOC上限值G为75％，电池SOC下限值I为65％，则预测工况1下电池SOC变化范围为[65％,75％]。
然后，根据预测工况2对应的工况数据和预测工况2的前一个预测工况即预测工况1对应的电池SOC变化范围，来确定车辆在预测工况2运行下的电池SOC变化范围。首先，以预测工况1的电池SOC上限值为G作为预测工况2的初始电池SOC，车辆采用纯发电模式即预测工况2下车辆全部使用燃料，电池处于充电状态，来确定C点的第三SOC为J，即预测工况2的电池SOC上限值为J。然后，以预测工况1的电池SOC下限值为I作为预测工况2的初始电池SOC，以车辆采用纯电模式即在B点至C点的预测工况2下车辆全部电，电池处于放电状态，来确定C点的第四SOC为L，即预测工况2的电池SOC下限值为L，由此，确定预测工况2下电池SOC变化范围为[L，J]。
在本公开的一个实施例中，确定车辆在预行驶道路上的多个电池SOC变化路径中的一个电池SOC变化路径，包括：分别在每个预测工况对应的SOC变化范围中选取一个目标SOC值；根据各个目标SOC值得到多个电池SOC变化路径中的一个电池SOC变化路径。
具体而言，继续参照如图2、图3所示，A点的初始电池SOC为F，假设在电池SOC变化范围[I,G]内选取点H作为目标SOC值，则F-H为一条预测工况1的电池SOC变化路径。
如图3所示，AB段预测工况1的目标SOC值除上述处于电池SOC变化范围内的H点外，还包括电池SOC上限值G和电池SOC下限值I，此时，AB段预测工况1可形成F-G、F-H、F-I三个电池SOC变化路径。BC段预测工况2的初始电池SOC可为AB段的目标SOC值，即包括G、H、I，BC段的预设参考电池SOC包括J、K、L，此时，BC段可形成G-J、G-K、G-L、H-J、H-K、H-L、I-J、I-K和I-L九个SOC变化路径。以此类推，CD段预测工况3可形成十五个电池SOC变化路径，DE段预测工况4可形成三十个SOC变化路径。基于上述内容可知，车辆当前行驶道路AE工况下可形成3×9×15×30＝12150组SOC变化路径。
需要说明的是，每个预测工况的目标SOC值的数量越多，所产生的电池SOC变化路径的数量也就越多，则预测工况下的能耗最小的电池SOC变化路径的确定精度也就越高，同时所达到的车辆的能量管理效果也就越好。
根据本公开的一个实施例，获取多个电池SOC变化路径中能耗最小的SOC变化路径，包括：获取每个电池SOC变化路径的油耗和电耗；将油耗和电耗累加最低的电池SOC变化路径作为目标电池SOC变化路径。
也就是说，通过计算得到不同电池SOC变化路径的油耗和电耗，对比选取油耗和电耗最小的电池SOC变化路径作为最优电池SOC变化路径，并将最优电池SOC变化路径作为该预测工况的目标电池SOC变化路径，以控制车辆进行能量管理。如图3所示，以AB段的预测工况1为例，分别获取电池SOC变化路径F-G、F-H和F-I的油耗和电耗，并计算得到电池SOC变化路径F-I的油耗和电耗最小，因此以电池SOC变化路径F-I作为AB段的目标电池SOC变化路径，I为预测工况1结束时的最优电池SOC。以此类推，确定BC段的目标SOC变化路径为I-J，CD段的目标SOC变化路径为J-P，DE段的目标SOC变化路径为P-U，则预行驶道路AE下的目标电池SOC变化路径为F-I-J-P-U。
根据本公开的一个实施例，该车辆的能量管理方法还包括：在当前预测工况下的实际电池SOC与对应的能耗最小的SOC变化路径中电池SOC之间的差值大于设定阈值，或者车辆的预行驶道路发生变化时，重新获取多个电池SOC变化路径。其中，上述设定阈值可根据实际情况进行设定。
具体而言，将上述获取最优路径的电池SOC作为各预测工况的最优电池SOC，即实现用户工况油耗、能耗最小和全局效率最优。以图3为例，AB段的能耗最小的SOC变化路径为F-I，AB段的最优电池SOC为I，假设设定阈值为2％，获取车辆到达B点的车辆实际电池SOC，将实际电池SOC与I相比较，当车辆实际电池SOC与I差值ΔSOC>2％时，以实际电池SOC作为BC段车辆的初始电池SOC，重新获取多个电池SOC变化路径，并通过能耗比较确定最优电池SOC变化路径，并以此对车辆进行能量管理。若车辆实际电池SOC与I差值ΔSOC≤2％，则按照原获取的能耗最小的电池SOC变化路径控制车辆运行，即以IJ作为BC段的电池SOC变化路径，来进行车辆的能量管理。
另外，若根据全球导航系统的定位信号、高精度地图数据、智能网联的4G/5G数据等，获取用户的预行驶道路的工况数据、行驶路线发生变化时，则重新对工况数据进行获取，重新执行电池SOC变化路径的规划。
进一步地，本公开考虑到实车软件程序计算量较大，可在仿真平台中完成已知预测工况的组合计算，将电池SOC变化路径作为能量管理规划策略逻辑输入至整车控制器中，在识别车辆当前行驶道路工况后自动规划选取最优电池SOC变化路径，减少整车控制器计算量和计算时间，简单易行。
作为本公开的一个具体实施例，以预行驶道路划分为预测工况1和预测工况2为例，如图4所示，该车辆的能量管理方法可包括以下步骤：
S101，确定车辆预行驶道路的起始位置和终点位置。
S102，获取起始位置到终点位置的工况数据。
S103，根据工况数据将预行驶道路划分为至少一个道路区间。
S104，获取至少一个道路区间内的历史行驶参数，并根据历史行驶参数确定道路参数数据。
S105，将道路参数数据与多个预测工况的道路参数数据进行匹配。
S106，在匹配成功时，将车辆的预行驶道路划分为预测工况1和预测工况2的组合。
S107，获取当前预测工况下的实际电池SOC。
S108，根据实际电池SOC和预测工况1对应的工况数据，确定第一SOC。
S109，根据实际电池SOC和预测工况1对应的工况数据，确定第二SOC。
S110，确定预测工况1运行下的电池SOC变化范围。
S111，根据预测工况2对应的工况数据和预测工况1对应的电池SOC变化范围，确定第三SOC。
S112，根据预测工况2对应的工况数据和预测工况1对应的电池SOC变化范围，确定第四SOC。
S113，确定车辆在预测工况2运行下的电池SOC变化范围。
S114，根据车辆在各个预测工况运行下的电池SOC变化范围，确定车辆在预行驶道路上的多个电池SOC变化路径。
S115，获取每个电池SOC变化路径的油耗和电耗。
S116，将油耗和电耗累加最低的电池SOC变化路径作为目标电池SOC变化路径。
S117，根据目标电池SOC变化路径对车辆进行能量管理。
进一步地，根据上述车辆地能量管理方法作为SOC规划能量管理策略，对车辆的电池SOC进行规划，可形成如图5所示的对比示意图，其中，SOC规划能量管理策略为采用本公开能量管理方法所实现的SOC控制，无SOC规划能量管理策略为采用相关技术中能量管理方法所实现的SOC控制。本公开基于预测的电池SOC智能规划能量管理控制策略，将车辆的行驶工况划分为不同的预测工况，基于预测工况和当前电池的荷电状态均衡规划电池SOC，基于当前能量管理策略对于驾驶员行驶需求和SOC规划控制电池功率，实现不同时间窗或预测工况下的系统运行效率最优，最终实现用户行驶工况的全局最优。
综上，根据本公开实施例的车辆的能量管理方法，首先，获取车辆在预行驶道路的工况数据，根据工况数据确定车辆在预行驶道路的至少一个预测工况，并根据各个预测工况对应的工况数据，确定车辆在各个预测工况运行下的电池SOC变化范围，根据车辆在各个预测工况运行下的电池SOC变化范围确定车辆在预行驶道路上的多个电池SOC变化路径，然后根据多个电池SOC变化路径中能耗最小的电池SOC变化路径对车辆进行能量管理。由此，该方法根据车辆在预行驶道路的工况数据划分为预测工况，并基于预测工况下能耗最小的电池SOC变化路径对车辆进行能量管理，可实现不同时间窗或预测工况下的系统运行效率最优，最终实现用户行驶工况的全局最优。
对应上述实施例，本公开还提出了一种车辆的能量管理装置。
如图6所示，本公开实施例的车辆的能量管理装置可包括：获取模块10、第一确定模块20、第二确定模块30、第三确定模块40和能量管理模块50。
其中，获取模块10用于获取车辆在预行驶道路的工况数据。第一确定模块20用于根据工况数据确定车辆在预行驶道路的至少一个预测工况。第二确定模块30用于根据各个预测工况对应的工况数据，确定车辆在各个预测工况运行下的电池SOC变化范围。第三确定模块40用于根据车辆在各个预测工况运行下的电池SOC变化范围，确定车辆在预行驶道路上 的多个电池SOC变化路径。能量管理模块50用于根据多个电池SOC变化路径中能耗最小的电池SOC变化路径对车辆进行能量管理。
根据本公开的一个实施例，工况数据包括：坡度数据和限速数据，第一确定模块20根据工况数据确定车辆在预行驶道路的至少一个预测工况，具体用于：根据工况数据将预行驶道路划分为至少一个道路区间；获取至少一个道路区间内的历史行驶参数，并根据历史行驶参数确定道路参数数据，其中，道路参数数据包括：平均车速、平均加速度、平均上坡坡度、平均下坡坡度、车速标准差、加速度标准差中的至少一种；将道路参数与多个预测工况的道路参数数据进行匹配；在匹配成功时，将车辆的预行驶道路划分为一个或者多预测工况的组合。
根据本公开的一个实施例，第二确定模块30根据车辆当前的实际电池SOC和按照车辆的行驶方向确定的首个预测工况对应的工况数据，确定车辆在首个预测工况运行下的电池SOC变化范围，具体用于：根据实际电池SOC和首个预测工况对应的工况数据，确定第一SOC，第一SOC为车辆以发电模式在首个预测工况运行结束时的电池SOC；根据实际电池SOC和首个预测工况对应的工况数据，确定第二SOC，第二SOC为车辆以纯电模式在首个预测工况运行结束时的电池SOC；将第一SOC作为电池SOC上限值，第二SOC作为电池SOC下限值，得到车辆在首个预测工况运行下的电池SOC变化范围；
第二确定模块30根据预测工况对应的工况数据和预测工况的前一个预测工况对应的电池SOC变化范围，确定车辆在预测工况运行下的电池SOC变化范围，具体用于：根据预测工况对应的工况数据和前一个预测工况对应的电池SOC变化范围，确定第三SOC，第三SOC为车辆以发电模式在预测工况运行结束时的电池SOC；根据预测工况对应的工况数据和前一个预测工况对应的电池SOC变化范围，确定第四SOC，第四SOC为车辆以纯电模式在预测工况运行结束时的电池SOC；将第三SOC作为电池SOC上限值，第四SOC作为电池SOC下限值，得到车辆在预测工况运行下的电池SOC变化范围。
根据本公开的一个实施例，第三确定模块40确定车辆在预行驶道路上的多个电池SOC变化路径中的一个电池SOC变化路径，具体用于：分别在每个预测工况对应的SOC变化范围中选取一个目标SOC值；根据各个目标SOC值得到多个电池SOC变化路径中的一个电池SOC变化路径。
根据本公开的一个实施例，第三确定模块40还用于：在当前预测工况下的实际电池SOC与对应的能耗最小的电池SOC变化路径中电池SOC之间的差值大于设定阈值，或者车辆的预行驶道路发生变化时，重新获取多个电池SOC变化路径。
根据本公开的一个实施例，该车辆的能量管理方法还包括：在当前预测工况下的实际电池SOC与对应的能耗最小的SOC变化路径中电池SOC之间的差值大于设定阈值，或者车辆的预行驶道路发生变化时，重新获取多个电池SOC变化路径。
根据本公开的一个实施例，获取模块10获取车辆在预行驶道路的工况数据，包括：确定车辆预行驶的起始位置和终点位置；获取起始位置到终点位置的工况数据。
需要说明的是，本公开实施例的车辆的能量管理装置中未披露的细节，请参照本公开上述实施例的车辆的能量管理方法中所披露的细节，具体这里不再赘述。
综上，根据本公开实施例的车辆的能量管理装置，通过获取模块获取车辆在预行驶道路的工况数据，第一确定模块根据工况数据确定车辆在预行驶道路的至少一个预测工况，第二确定模块根据各个预测工况对应的工况数据，确定车辆在各个预测工况运行下的电池SOC变化范围，第三确定模块根据车辆在各个预测工况运行下的电池SOC变化范围，确定车辆在预行驶道路上的多个电池SOC变化路径，能量管理模块根据多个电池SOC变化路径中能耗最小的电池SOC变化路径对车辆进行能量管理。由此，该装置根据车辆在预行驶道路的工况数据划分预测工况，并基于预测工况下能耗最小的电池SOC变化路径对车辆进行能量管理，可实现不同时间窗或预测工况下的系统运行效率最优，最终实现用户行驶工况的全局最优。
对应上述实施例，本公开还提出了一种计算机可读存储介质。
本公开实施例的计算机可读存储介质，其上存储有车辆的能量管理程序，该车辆的能量管理程序被处理器执行时实现上述的车辆的能量管理方法。
根据本公开实施例的计算机可读存储介质，基于上述的车辆的能量管理方法，可实现不同时间窗或预测工况下的系统运行效率最优，最终实现用户行驶工况的全局最优。
对应上述实施例，本公开还提出了一种车辆。
如图7所示，本公开实施例的车辆100包括存储器110、处理器120及存储在存储器110上并可在处理器120上运行的车辆的能量管理程序，处理器120执行车辆的能量管理程序时，实现上述的车辆的能量管理方法。
例如，该处理器120可用于根据该计算机程序中的指令执行上述方法实施例。
在本公开的一些实施例中，该处理器120可以包括但不限于：
通用处理器、数字信号处理器(Digital Signal Processor，DSP)、专用集成电路(Application Specific Integrated Circuit，ASIC)、现场可编程门阵列(Field Programmable Gate Array，FPGA)或者其他可编程逻辑器件、分立门或者晶体管逻辑器件、 分立硬件组件等等。
在本公开的一些实施例中，该存储器110包括但不限于：
易失性存储器和/或非易失性存储器。其中，非易失性存储器可以是只读存储器(Read-Only Memory，ROM)、可编程只读存储器(Programmable ROM，PROM)、可擦除可编程只读存储器(Erasable PROM，EPROM)、电可擦除可编程只读存储器(Electrically EPROM，EEPROM)或闪存。易失性存储器可以是随机存取存储器(Random Access Memory，RAM)，其用作外部高速缓存。通过示例性但不是限制性说明，许多形式的RAM可用，例如静态随机存取存储器(Static RAM，SRAM)、动态随机存取存储器(Dynamic RAM，DRAM)、同步动态随机存取存储器(Synchronous DRAM，SDRAM)、双倍数据速率同步动态随机存取存储器(Double Data Rate SDRAM，DDR SDRAM)、增强型同步动态随机存取存储器(Enhanced SDRAM，ESDRAM)、同步连接动态随机存取存储器(synch link DRAM，SLDRAM)和直接内存总线随机存取存储器(Direct Rambus RAM，DR RAM)。
在本公开的一些实施例中，该计算机程序可以被分割成一个或多个模块，该一个或者多个模块被存储在该存储器110中，并由该处理器120执行，以完成本公开提供的方法。该一个或多个模块可以是能够完成特定功能的一系列计算机程序指令段，该指令段用于描述该计算机程序在该车辆100中的执行过程。
如图7所示，该车辆100还可包括：
收发器130，该收发器130可连接至该处理器120或存储器110。
其中，处理器120可以控制该收发器130与其他设备进行通信，示例性地，可以向其他设备发送信息或数据，或接收其他设备发送的信息或数据。收发器130可以包括发射机和接收机。收发器130还可以进一步包括天线，天线的数量可以为一个或多个。
应当理解，该车辆100的各个组件通过总线系统相连，其中，总线系统除包括数据总线之外，还包括电源总线、控制总线和状态信号总线。
根据本公开实施例的车辆，基于上述的车辆的能量管理方法，可实现不同时间窗或预测工况下的系统运行效率最优，最终实现用户行驶工况的全局最优。
需要说明的是，在流程图中表示或在此以其他方式描述的逻辑和/或步骤，例如，可以被认为是用于实现逻辑功能的可执行指令的定序列表，可以具体实现在任何计算机可读介质中，以供指令执行系统、装置或设备(如基于计算机的系统、包括处理器的系统或其他可以从指令执行系统、装置或设备取指令并执行指令的系统)使用，或结合这些指令执行系统、装置或设备而使用。就本说明书而言，"计算机可读介质"可以是任何可以包含、存 储、通信、传播或传输程序以供指令执行系统、装置或设备或结合这些指令执行系统、装置或设备而使用的装置。计算机可读介质的更具体的示例(非穷尽性列表)包括以下：具有一个或多个布线的电连接部(电子装置)，便携式计算机盘盒(磁装置)，随机存取存储器(RAM)，只读存储器(ROM)，可擦除可编辑只读存储器(EPROM或闪速存储器)，光纤装置，以及便携式光盘只读存储器(CDROM)。另外，计算机可读介质甚至可以是可在其上打印所述程序的纸或其他合适的介质，因为可以例如通过对纸或其他介质进行光学扫描，接着进行编辑、解译或必要时以其他合适方式进行处理来以电子方式获得所述程序，然后将其存储在计算机存储器中。
应当理解，本公开的各部分可以用硬件、软件、固件或它们的组合来实现。在上述实施方式中，多个步骤或方法可以用存储在存储器中且由合适的指令执行系统执行的软件或固件来实现。例如，如果用硬件来实现，和在另一实施方式中一样，可用本领域公知的下列技术中的任一项或他们的组合来实现：具有用于对数据信号实现逻辑功能的逻辑门电路的离散逻辑电路，具有合适的组合逻辑门电路的专用集成电路，可编程门阵列(PGA)，现场可编程门阵列(FPGA)等。
在本说明书的描述中，参考术语“一个实施例”、“一些实施例”、“示例”、“具体示例”、或“一些示例”等的描述意指结合该实施例或示例描述的具体特征、结构、材料或者特点包含于本公开的至少一个实施例或示例中。在本说明书中，对上述术语的示意性表述不一定指的是相同的实施例或示例。而且，描述的具体特征、结构、材料或者特点可以在任何的一个或多个实施例或示例中以合适的方式结合。
此外，术语“第一”、“第二”仅用于描述目的，而不能理解为指示或暗示相对重要性或者隐含指明所指示的技术特征的数量。由此，限定有“第一”、“第二”的特征可以明示或者隐含地包括至少一个该特征。在本公开的描述中，“多个”的含义是至少两个，例如两个，三个等，除非另有明确具体的限定。在本公开中，除非另有明确的规定和限定，术语“安装”、“相连”、“连接”、“固定”等术语应做广义理解，例如，可以是固定连接，也可以是可拆卸连接，或成一体；可以是机械连接，也可以是电连接；可以是直接相连，也可以通过中间媒介间接相连，可以是两个元件内部的连通或两个元件的相互作用关系，除非另有明确的限定。对于本领域的普通技术人员而言，可以根据具体情况理解上述术语在本公开中的具体含义。尽管上面已经示出和描述了本公开的实施例，可以理解的是，上述实施例是示例性的，不能理解为对本公开的限制，本领域的普通技术人员在本公开的范围内可以对上述实施例进行变化、修改、替换和变型。

Claims (10)

  1. 一种车辆的能量管理方法，包括：
    获取所述车辆的预行驶道路，所述预行驶道路根据所述预行驶道路的工况数据被划分为至少一个预测工况；
    根据各个预测工况对应的工况数据，确定所述车辆在所述各个预测工况运行下的电池SOC变化范围；
    根据所述车辆在所述各个预测工况运行下的电池SOC变化范围，确定所述车辆在所述预行驶道路上的多个电池SOC变化路径；
    根据所述多个电池SOC变化路径中能够使所述车辆在所述预行驶道路运行能耗最小的电池SOC变化路径对所述车辆进行能量管理。
  2. 根据权利要求1所述的方法，其中，所述工况数据包括：坡度数据和限速数据，确定所述至少一个预测工况的方式包括：
    根据所述工况数据将所述预行驶道路划分为至少一个道路区间；
    获取所述至少一个道路区间内的历史行驶参数，并根据所述历史行驶参数确定道路参数数据，其中，所述道路参数数据包括：平均车速、平均加速度、平均上坡坡度、平均下坡坡度、车速标准差、加速度标准差中的至少一种；
    将所述道路参数数据与多个所述预测工况的道路参数数据进行匹配；
    在匹配成功时，将所述车辆的预行驶道路划分为一个或者多个预测工况的组合。
  3. 根据权利要求1所述的方法，其中，所述预行驶道路的首个预测工况的电池SOC变化范围是根据所述车辆在所述预行驶道路起点的实际电池SOC和所述首个预测工况的工况数据确定的；所述预行驶道路的非首个预测工况的电池SOC变化范围是根据所述非首个预测工况的工况数据和所述非首个预测工况的前一个预测工况的电池SOC变化范围确定的。
  4. 根据权利要求3所述的方法，其中，所述预测工况的电池SOC变化范围的上限值是所述车辆以发电模式在所述预测工况运行结束时的电池SOC；所述预测工况的电池SOC变化范围的下限值是所述车辆以纯电模式在所述预测工况运行结束时的电池SOC。
  5. 根据权利要求3所述的方法，其中，确定所述车辆在所述预行驶道路上的多个电池SOC变化路径中的一个电池SOC变化路径，包括：
    分别在每个预测工况的电池SOC变化范围中选取一个目标SOC值；
    根据各个目标SOC值得到多个电池SOC变化路径中的一个电池SOC变化路径。
  6. 根据权利要求1所述的方法，其中，还包括：
    在当前所述预测工况下的实际电池SOC与对应的所述能耗最小的SOC变化路径中电池SOC之间的差值大于设定阈值，或者所述车辆的预行驶道路发生变化时，重新获取多个电池SOC变化路径。
  7. 根据权利要求1所述的方法，其中，获取所述车辆在预行驶道路的工况数据，包括：
    确定所述车辆预行驶的起始位置和终点位置；
    获取所述起始位置到所述终点位置的工况数据。
  8. 一种车辆的能量管理装置，包括：
    获取模块，用于获取所述车辆的预行驶道路，所述预行驶道路根据所述预行驶道路的工况数据被划分为至少一个预测工况；
    第二确定模块，用于根据各个预测工况对应的工况数据，确定所述车辆在所述各个预测工况运行下的电池SOC变化范围；
    第三确定模块，用于根据所述车辆在所述各个预测工况运行下的电池SOC变化范围，确定所述车辆在所述预行驶道路上的多个电池SOC变化路径；
    能量管理模块，用于根据所述多个SOC变化路径中能够使所述车辆在所述预行驶道路运行能耗最小的SOC变化路径对所述车辆进行能量管理。
  9. 一种计算机可读存储介质，其上存储有车辆的能量管理程序，该车辆的能量管理程序被处理器执行时实现根据权利要求1-7中任一项所述的车辆的能量管理方法。
  10. 一种车辆，包括存储器、处理器及存储在存储器上并可在处理器上运行的车辆的能量管理程序，所述处理器执行所述车辆的能量管理程序时，实现根据权利要求1-7中任一项所述的车辆的能量管理方法。
PCT/CN2023/108980 2022-07-25 2023-07-24 车辆及其能量管理方法和能量管理装置、可读存储介质 WO2024022305A1 (zh)

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JP2001298805A (ja) * 2000-02-07 2001-10-26 Nissan Motor Co Ltd ハイブリッド車両の制御装置
JP2009298271A (ja) * 2008-06-12 2009-12-24 Aisin Aw Co Ltd ハイブリッド車両の運転支援装置、運転支援方法及びプログラム
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