Vehicle, energy management method and device thereof, and readable storage medium

Abstract

The application discloses a vehicle and an energy management method and device thereof and a readable storage medium, wherein the method comprises the following steps: acquiring a pre-travel road of the vehicle, the pre-travel road being divided into at least one predicted working condition according to working condition data; determining a battery SOC change range of the vehicle under each predicted working condition according to working condition data corresponding to each predicted working condition; determining a plurality of battery SOC change paths of the vehicle on the pre-travel road according to the battery SOC change range of the vehicle under each predicted working condition; and performing energy management on the vehicle according to a battery SOC change path with the minimum running energy consumption in the plurality of battery SOC change paths. Thus, the method divides the predicted working conditions according to the working condition data of the vehicle on the pre-travel road, and performs energy management on the vehicle based on the battery SOC change path with the minimum energy consumption in the predicted working condition, so that the system running efficiency in different predicted working conditions is optimized, and finally the global optimization of the user travel working condition is realized.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of vehicles, and particularly relates to an energy management method of a vehicle, an energy management device of a vehicle, a computer readable storage medium and a vehicle. BACKGROUND

[0002] In order to reasonably manage a multi-power-source energy coupling system, a current hybrid vehicle sets an energy management control strategy to distribute power or torque of the multi-power-source, and to coordinate mechanical braking and electric energy recovery, so as to improve system efficiency and improve energy saving and emission reduction performance of the vehicle on the basis of ensuring vehicle power performance, safety and comfort.

[0003] The energy management strategy of the whole vehicle control is mainly to meet the power demand, maintain the SOC (State Of Charge) of the battery and the working efficiency of the system as the control criterion. When the vehicle is running, the energy management control strategy improves the driving efficiency of the system by reasonably distributing the power of each power source and combining the efficiency characteristics of the power source. In the related technology, the energy management strategy only controls the energy based on the running condition of the vehicle, and cannot realize optimal control, and it is difficult to achieve the optimal system driving efficiency. SUMMARY

[0004] The present application aims to at least solve one of the technical problems in the related art. To this end, a first object of the present application is to provide an energy management method of a vehicle, which divides a predicted working condition according to working condition data of a vehicle on a predicted driving road, and manages energy of the vehicle based on a SOC change path with minimum energy consumption in the predicted working condition, so as to realize optimal system running efficiency in different predicted working conditions, and finally realize global optimization of user driving working conditions.

[0005] A second object of the present application is to provide an energy management device of a vehicle.

[0006] A third object of the present application is to provide a computer readable storage medium.

[0007] A fourth object of the present application is to provide a vehicle.

[0008] To achieve the above object, the first aspect of the present application provides a vehicle energy management method, comprising: obtaining a pre-travel road of a vehicle, the pre-travel road being divided into at least one prediction working condition according to working condition data of the pre-travel road; determining a battery SOC change range of the vehicle under each prediction working condition according to the working condition data corresponding to each prediction working condition; determining a plurality of battery SOC change paths of the vehicle on the pre-travel road according to the battery SOC change range of the vehicle under each prediction working condition; and performing energy management on the vehicle according to the battery SOC change path in the plurality of battery SOC change paths which can minimize the energy consumption of the vehicle running on the pre-travel road.

[0009] The vehicle energy management method according to the embodiments of the present application divides the pre-travel road into prediction working conditions according to the working condition data of the pre-travel road, and performs energy management on the vehicle based on the battery SOC change path with the minimum energy consumption under the prediction working condition, so that the system running efficiency under different time windows or prediction working conditions is optimized, and the global optimization of the user's travel working condition is finally achieved.

[0010] In addition, the vehicle energy management method according to the above embodiments of the present application can have the following additional technical features:

[0011] According to one embodiment of the present application, the working condition data includes slope data and speed limit data, and the manner of determining the at least one prediction working condition includes: dividing the pre-travel road into at least one road section according to the working condition data; obtaining historical travel parameters in the at least one road section, and determining road parameter data according to the historical travel parameters, wherein the road parameter data includes at least one of average speed, average acceleration, average uphill slope, average downhill slope, speed standard deviation, and acceleration standard deviation; matching the road parameter data with road parameter data of a plurality of prediction working conditions; and when the matching is successful, dividing the pre-travel road of the vehicle into a combination of one or more prediction working conditions.

[0012] According to one embodiment of the present application, the battery SOC change range of the first prediction working condition of the pre-travel road is determined according to the actual battery SOC of the vehicle at the starting point of the pre-travel road and the working condition data of the first prediction working condition; and the battery SOC change range of the non-first prediction working condition of the pre-travel road is determined according to the working condition data of the non-first prediction working condition and the battery SOC change range of the previous prediction working condition of the non-first prediction working condition.

[0013] According to one embodiment of the present application, the upper limit value of the battery SOC change range of the prediction working condition is the battery SOC of the vehicle at the end of the prediction working condition running in the power generation mode; and the lower limit value of the battery SOC change range of the prediction working condition is the battery SOC of the vehicle at the end of the prediction working condition running in the pure electric mode.

[0014] According to one embodiment of the present application, the determining of the one battery SOC change path in the plurality of battery SOC change paths of the vehicle on the pre-travel road comprises: selecting a target SOC value in each battery SOC change range of each predicted working condition respectively; and obtaining the one battery SOC change path in the plurality of battery SOC change paths according to the target SOC values.

[0015] According to one embodiment of the present application, when the difference between the actual battery SOC in the current predicted working condition and the battery SOC in the SOC change path with the minimum energy consumption is greater than a set threshold, or the pre-travel road of the vehicle changes, the plurality of battery SOC change paths are re-acquired.

[0016] According to one embodiment of the present application, the obtaining of the working condition data of the vehicle on the pre-travel road comprises: determining a starting position and an end position of the pre-travel of the vehicle; and obtaining the working condition data from the starting position to the end position.

[0017] To achieve the above object, the second aspect of the present application provides a vehicle energy management device, comprising: an obtaining module, configured to obtain a pre-travel road of a vehicle, the pre-travel road being divided into at least one predicted working condition according to working condition data of the pre-travel road; a second determining module, configured to determine a battery SOC change range of the vehicle in each predicted working condition according to working condition data corresponding to each predicted working condition; a third determining module, configured to determine a plurality of battery SOC change paths of the vehicle on the pre-travel road according to the battery SOC change range of the vehicle in each predicted working condition; and an energy management module, configured to perform energy management on the vehicle according to a battery SOC change path in the plurality of battery SOC change paths which can make the energy consumption of the vehicle on the pre-travel road minimum.

[0018] The vehicle energy management device according to the embodiments of the present application divides the predicted working conditions according to the working condition data of the vehicle on the pre-travel road, and performs energy management on the vehicle based on the battery SOC change path with the minimum energy consumption in the predicted working condition, so that the system running efficiency in different time windows or predicted working conditions is optimal, and the global optimization of the user travel working condition is finally realized.

[0019] To achieve the above object, the third aspect of the present application further provides a computer readable storage medium having a vehicle energy management program stored thereon, and the vehicle energy management program is executed by a processor to implement the vehicle energy management method.

[0020] The computer readable storage medium according to the embodiments of the present application is based on the vehicle energy management method, so that the system running efficiency in different time windows or predicted working conditions is optimal, and the global optimization of the user travel working condition is finally realized.

[0021] To achieve the above objectives, a fourth aspect of this application also proposes a vehicle, including a memory, a processor, and a vehicle energy management program stored in the memory and executable on the processor. When the processor executes the vehicle energy management program, it implements the above-described vehicle energy management method.

[0022] According to the vehicle embodiment of this application, based on the above-described vehicle energy management method, the optimal system operating efficiency can be achieved under different time windows or predicted operating conditions, ultimately achieving the global optimal user driving conditions.

[0023] Additional aspects and advantages of this application will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of this application. Attached Figure Description

[0024] Figure 1 This is a block diagram illustrating a vehicle energy management method according to an embodiment of this application;

[0025] Figure 2 This is a schematic diagram illustrating the predicted operating conditions according to an embodiment of this application;

[0026] Figure 3 This is a schematic diagram of the battery SOC change path according to an embodiment of this application;

[0027] Figure 4 This is a flowchart of a vehicle energy management method according to a specific embodiment of this application;

[0028] Figure 5 This is a comparative schematic diagram of a vehicle energy management method according to a specific embodiment of this application;

[0029] Figure 6 This is a block diagram of a vehicle energy management device according to an embodiment of this application;

[0030] Figure 7 This is a block diagram of a vehicle according to one embodiment of the present application. Detailed Implementation

[0031] The embodiments of this application are described in detail below. Examples of the embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and intended to explain this application, and should not be construed as limiting this application.

[0032] The energy management method, energy management device, computer-readable storage medium, and vehicle of this application are described below with reference to the accompanying drawings.

[0033] In related technologies, when a vehicle is running, the purpose of energy management control strategies is mainly to meet power demands, maintain State of Charge (SOC), and ensure the efficiency of components and systems. This is achieved by controlling and adjusting the operating state of the powertrain, such as engine speed and torque, thereby improving system efficiency and reducing fuel consumption. While these energy management strategies improve system efficiency by optimizing and adjusting the system's operating state, they do not consider the user's driving conditions and cannot plan the system's operating state according to those conditions. They only optimize the system's efficiency in the current transient state, resulting in suboptimal fuel economy and failing to achieve global optimization across all driving conditions. To address these issues, this application proposes a vehicle energy management method.

[0034] Figure 1 This is a block diagram illustrating a vehicle energy management method according to an embodiment of this application.

[0035] like Figure 1 As shown, the energy management method for a vehicle according to an embodiment of this application may include:

[0036] S1, acquire the vehicle's operating condition data on the pre-driving road;

[0037] S2, determine at least one predicted operating condition for the vehicle on the pre-driving road based on the operating condition data;

[0038] S3, based on the operating data corresponding to each predicted operating condition, determines the range of battery SOC variation of the vehicle under each predicted operating condition.

[0039] S4. Based on the range of battery SOC change under various predicted operating conditions, determine multiple battery SOC change paths of the vehicle on the pre-driving road.

[0040] S5 manages vehicle energy based on the battery SOC change path with the lowest energy consumption among multiple battery SOC change paths.

[0041] Specifically, based on positioning signals from the global navigation system, high-precision map data, and 4G / 5G data from intelligent connected vehicles, a map of the user's pre-driving route is obtained. This allows for the acquisition of operational data for the pre-driving route, such as vehicle speed and gradient. Combined with big data analysis, the pre-driving route is divided into different predicted operating conditions. The range of battery SOC variation under each predicted operating condition is calculated, and multiple battery SOC variation paths are planned within this range. The energy consumption of each battery SOC variation path is calculated, and the battery SOC variation path with the lowest energy consumption is selected as the optimal battery SOC variation path for that predicted operating condition. Energy management of the vehicle is then based on this path. This process is repeated to obtain the optimal battery SOC variation path for each predicted operating condition on the pre-driving route. Furthermore, based on the current battery state of charge, battery SOC is planned and balanced to control battery power, achieving global optimization across all driving conditions.

[0042] It should be noted that the above-mentioned vehicle pre-driving route can be confirmed based on the starting point and destination selected by the user on the map, or it can be predicted and determined based on the vehicle's current location and driving direction. The specific settings can be made according to the actual situation.

[0043] In one embodiment of this application, obtaining the vehicle's operating condition data on the pre-driving road includes: determining the starting position and ending position of the vehicle's pre-driving; and obtaining operating condition data from the starting position to the ending position.

[0044] Specifically, assuming a user needs to travel from point A to point E, the user first selects the starting point A and the ending point E on the map interface of the terminal display. The map generates multiple driving routes based on the starting point A and the ending point E and displays them to the user. The user selects one of these driving routes as the pre-driving route. Then, the operating data of the vehicle's pre-driving route from point A to point E is obtained, such as... Figure 2 As shown, through statistical analysis of road condition data, the driving conditions of segment AB are identified as predicted condition 1, segment BC as predicted condition 2, segment CD as predicted condition 3, and segment DE as predicted condition 4. Therefore, this method transforms the vehicle driving road condition segment AE into a predicted condition combination of segment AB (predicted condition 1) + segment BC (predicted condition 2) + segment CD (predicted condition 3) + segment DE (predicted condition 4).

[0045] According to one embodiment of this application, the driving condition data includes: gradient data and speed limit data. Determining at least one predicted driving condition for the vehicle on the pre-driving road based on the driving condition data includes: dividing the pre-driving road into at least one road interval based on the driving condition data; obtaining historical driving parameters within the at least one road interval, and determining road parameter data based on the historical driving parameters, wherein the road parameter data includes at least one of: average vehicle speed, average acceleration, average uphill gradient, average downhill gradient, vehicle speed standard deviation, and acceleration standard deviation; matching the road parameter data with road parameter data of multiple predicted driving conditions; and when a match is successful, dividing the vehicle's pre-driving road into a combination of one or more predicted driving conditions.

[0046] Specifically, this application first divides the road conditions for vehicle driving into 16 representative predicted conditions, such as severe urban congestion, moderate urban congestion, light urban congestion, urban expressway, highway, suburban, and mountain road conditions, and saves the road parameter data corresponding to each predicted condition.

[0047] Once the user determines the planned route based on the map displayed on the terminal screen, the system automatically retrieves the corresponding driving condition data, such as speed limits and gradient signals. This data is then statistically analyzed, and the planned route is divided into several road sections. Furthermore, based on big data analysis, historical vehicle speed and acceleration data for each road section are obtained. The vehicle speed and acceleration data retrieved from the big data database are then used to calculate the corresponding average speed, average acceleration, speed standard deviation, and acceleration standard deviation using basic formulas. The calculated road parameters are compared with pre-stored predicted driving condition parameters to identify the predicted driving condition corresponding to the vehicle's current driving condition data. This process divides the vehicle's current driving route driving condition data into several representative predicted driving conditions, which can be one or a combination of multiple predicted driving conditions.

[0048] It should be noted that the operating condition data determined based on the pre-driving road can be obtained from road planning. For example, assuming the obtained operating condition data for the pre-driving road includes speed limits of 60 km / h and 80 km / h, the pre-driving road can be divided into road segment a corresponding to the speed limit of 60 km / h and road segment b corresponding to the speed limit of 80 km / h. Taking road parameter data including average vehicle speed, average acceleration, speed standard deviation, and acceleration standard deviation as an example, firstly, based on big data analysis, historical vehicle speed, acceleration, and other driving parameter data in road segment a are obtained. Then, the average vehicle speed, average acceleration, speed standard deviation, and acceleration standard deviation of the vehicle in road segment a are calculated according to the mean and standard deviation calculation formulas. These are compared with the pre-stored road parameter data corresponding to the predicted operating conditions. When the calculated road parameter data is within the preset road parameter data range, the predicted operating condition corresponding to the preset road parameter data is determined as the predicted operating condition for road segment a. Similarly, the predicted operating conditions for road segment b can be obtained. It is understandable that the predicted driving conditions for road section a and road section b mentioned above can be the same or different. That is to say, the road to be driven can be pre-segmented based on its corresponding driving condition data, and then the predicted driving conditions corresponding to the pre-segmented road sections can be obtained based on the determined historical driving parameters, thereby obtaining all the predicted driving conditions corresponding to the road to be driven.

[0049] It should be noted that the above is only one possible implementation method for this application, and the specific settings can be made according to the actual situation. For example, the above historical driving parameters may also include the driving parameters of vehicles currently traveling on the road section, which is not limited here.

[0050] It should also be noted that the road parameter data mentioned above is used for the identification and classification of predicted working conditions. Usually, the road parameter data under different predicted working conditions are significantly different, and specific combinations can be set according to the actual situation.

[0051] In one embodiment of this application, determining the battery SOC variation range of a vehicle under each predicted operating condition based on the operating condition data corresponding to each predicted operating condition includes: obtaining the vehicle's current actual battery SOC; determining the battery SOC variation range of the vehicle under the first predicted operating condition based on the vehicle's current actual battery SOC and the operating condition data corresponding to the first predicted operating condition determined according to the vehicle's driving direction; and for each predicted operating condition other than the first predicted operating condition, determining the battery SOC variation range of the vehicle under the predicted operating condition based on the operating condition data corresponding to the predicted operating condition and the battery SOC variation range corresponding to the previous predicted operating condition.

[0052] In other words, based on the vehicle's operating data under the first predicted operating condition, the battery consumption under that predicted condition is calculated. Based on the vehicle's current actual battery SOC, the battery SOC at the end of the first predicted operating condition is predicted, thus determining the range of battery SOC variation under the first predicted operating condition. Then, based on the range of battery SOC variation under the first predicted operating condition, the initial battery SOC for the second predicted operating condition is determined. Combining this with the predicted battery consumption under the second predicted operating condition, the battery SOC at the end of the second predicted operating condition is calculated, thus determining the range of battery SOC variation under the second predicted operating condition. This process is repeated, and based on the range of battery SOC variation under the second predicted operating condition, the initial battery SOC for the third predicted operating condition is determined. This process thus determines the range of battery SOC variation under each predicted operating condition during the pre-driving route.

[0053] In one embodiment of this application, the range of battery SOC variation of the vehicle under the first predicted operating condition is determined based on the vehicle's current actual battery SOC and the operating condition data corresponding to the first predicted operating condition determined according to the vehicle's driving direction. This includes: determining a first SOC based on the actual battery SOC and the operating condition data corresponding to the first predicted operating condition, wherein the first SOC is the battery SOC of the vehicle in power generation mode at the end of the first predicted operating condition; determining a second SOC based on the actual battery SOC and the operating condition data corresponding to the first predicted operating condition, wherein the second SOC is the battery SOC of the vehicle in pure electric mode at the end of the first predicted operating condition; and using the first SOC as the upper limit of battery SOC and the second SOC as the lower limit of battery SOC to obtain the range of battery SOC variation of the vehicle under the first predicted operating condition.

[0054] Based on the operating condition data corresponding to the predicted operating condition and the battery SOC variation range corresponding to the previous predicted operating condition, the battery SOC variation range of the vehicle under the predicted operating condition is determined, including: determining a third SOC based on the operating condition data corresponding to the predicted operating condition and the battery SOC variation range corresponding to the previous predicted operating condition, where the third SOC is the battery SOC at the end of the predicted operating condition operation in power generation mode; determining a fourth SOC based on the operating condition data corresponding to the predicted operating condition and the battery SOC variation range corresponding to the previous predicted operating condition, where the fourth SOC is the battery SOC at the end of the predicted operating condition operation in pure electric mode; using the third SOC as the upper limit of battery SOC and the fourth SOC as the lower limit of battery SOC, the battery SOC variation range of the vehicle under the predicted operating condition is obtained.

[0055] Specifically, continue with, as Figure 2 Taking the predicted working condition division shown as an example, the first predicted working condition is predicted working condition 1 corresponding to segment AB. Figure 3As shown, the initial battery SOC of the vehicle at point A is F. Assuming the vehicle operates in pure power generation mode (i.e., under predicted condition 1 from point A to point B, the vehicle uses only fuel and the battery is charging), the first SOC at point B is determined to be G, which is the upper limit of the battery SOC under predicted condition 1. Assuming the vehicle operates in pure electric mode (i.e., under predicted condition 1 from point A to point B, the vehicle uses only electricity and the battery is discharging), the second SOC at point B is determined to be I, which is the lower limit of the battery SOC under predicted condition 1. Therefore, the battery SOC variation range under predicted condition 1 can be determined to be [I, G]. Assuming the actual battery SOC F under predicted condition 1 is 70%, the upper limit of battery SOC G at point B is 75%, and the lower limit of battery SOC I is 65%, then the battery SOC variation range under predicted condition 1 is [65%, 75%].

[0056] Then, based on the operating data corresponding to Predicted Operating Condition 2 and the battery SOC variation range corresponding to the previous Predicted Operating Condition 1, the battery SOC variation range of the vehicle under Predicted Operating Condition 2 is determined. First, the upper limit of the battery SOC in Predicted Operating Condition 1, G, is used as the initial battery SOC for Predicted Operating Condition 2. Assuming the vehicle operates in pure power generation mode (i.e., the vehicle uses fuel entirely and the battery is charging) under Predicted Operating Condition 2, the third SOC at point C is determined to be J, meaning the upper limit of the battery SOC for Predicted Operating Condition 2 is J. Then, the lower limit of the battery SOC in Predicted Operating Condition 1, I, is used as the initial battery SOC for Predicted Operating Condition 2. Assuming the vehicle operates in pure electric mode (i.e., the vehicle is fully charged and the battery is discharging) under Predicted Operating Condition 2 from point B to point C, the fourth SOC at point C is determined to be L, meaning the lower limit of the battery SOC for Predicted Operating Condition 2 is L. Therefore, the battery SOC variation range under Predicted Operating Condition 2 is determined to be [L, J].

[0057] In one embodiment of this application, determining one of the multiple battery SOC change paths of a vehicle on a pre-driving road includes: selecting a target SOC value within the SOC change range corresponding to each predicted operating condition; and obtaining one of the multiple battery SOC change paths based on each target SOC value.

[0058] Specifically, continue to refer to, for example Figure 2 , Figure 3 As shown, the initial battery SOC at point A is F. Assuming that point H is selected as the target SOC value within the battery SOC variation range [I,G], then FH is a predicted battery SOC variation path for operating condition 1.

[0059] like Figure 3As shown, the target SOC value for predicted condition 1 in segment AB includes not only point H, which is within the range of battery SOC variation, but also the upper limit of battery SOC G and the lower limit of battery SOC I. Therefore, predicted condition 1 in segment AB can form three battery SOC variation paths: FG, FH, and FI. The initial battery SOC for predicted condition 2 in segment BC can be the target SOC value of segment AB, including G, H, and I. The preset reference battery SOC for segment BC includes J, K, and L. Therefore, segment BC can form nine SOC variation paths: GJ, GK, GL, HJ, HK, HL, IJ, IK, and IL. Similarly, predicted condition 3 in segment CD can form fifteen battery SOC variation paths, and predicted condition 4 in segment DE can form thirty SOC variation paths. Based on the above, it can be seen that under the current driving road AE condition, 3 × 9 × 15 × 30 = 12150 sets of SOC variation paths can be formed.

[0060] It should be noted that the more target SOC values ​​there are for each predicted operating condition, the more battery SOC change paths are generated. This results in a higher accuracy in determining the battery SOC change path with the lowest energy consumption under the predicted operating condition, and ultimately, a better energy management effect for the vehicle.

[0061] According to one embodiment of this application, obtaining the SOC change path with the lowest energy consumption among multiple battery SOC change paths includes: obtaining the fuel consumption and electricity consumption of each battery SOC change path; and taking the battery SOC change path with the lowest cumulative fuel consumption and electricity consumption as the target battery SOC change path.

[0062] In other words, by calculating the fuel consumption and energy consumption of different battery SOC change paths, the optimal battery SOC change path is selected based on the comparison of fuel consumption and energy consumption. This optimal battery SOC change path is then used as the target battery SOC change path for the predicted operating condition to control the vehicle's energy management. Figure 3 As shown, taking the predicted driving condition 1 of segment AB as an example, the fuel consumption and energy consumption of battery SOC change paths FG, FH, and FI are obtained respectively. The calculation shows that battery SOC change path FI has the lowest fuel consumption and energy consumption. Therefore, battery SOC change path FI is taken as the target battery SOC change path for segment AB, and I is the optimal battery SOC at the end of predicted driving condition 1. Similarly, the target SOC change path for segment BC is determined as IJ, the target SOC change path for segment CD is JP, and the target SOC change path for segment DE is PU. Therefore, the target battery SOC change path under the pre-driving road AE is FIJPU.

[0063] According to one embodiment of this application, the energy management method for the vehicle further includes: when the difference between the actual battery SOC under the current predicted operating condition and the battery SOC in the corresponding path with the lowest energy consumption exceeds a set threshold, or when the vehicle's pre-driving route changes, re-acquiring multiple battery SOC change paths. The aforementioned set threshold can be set according to actual conditions.

[0064] Specifically, the battery SOC obtained from the optimal path is taken as the optimal battery SOC for each predicted operating condition, thus achieving minimum fuel consumption and energy consumption for the user's operating condition and optimal global efficiency. Figure 3 For example, the minimum SOC change path for segment AB is FI, and the optimal battery SOC for segment AB is I. Assuming a threshold of 2%, the actual battery SOC of the vehicle upon reaching point B is obtained. This actual battery SOC is compared with I. If the difference between the actual battery SOC and I (ΔSOC > 2%), the actual battery SOC is used as the initial battery SOC for segment BC. Multiple battery SOC change paths are then obtained again, and the optimal battery SOC change path is determined through energy consumption comparison. This optimal path is then used for vehicle energy management. If the difference between the actual battery SOC and I (ΔSOC ≤ 2%), the vehicle operation is controlled according to the previously obtained minimum energy SOC change path, i.e., IJ is used as the battery SOC change path for segment BC for vehicle energy management.

[0065] In addition, if the operating condition data of the user's pre-driving road is obtained based on the positioning signal of the global navigation system, high-precision map data, and 4G / 5G data of intelligent network, and the driving route changes, the operating condition data will be obtained again and the battery SOC change path planning will be re-executed.

[0066] Furthermore, considering the large computational load of the actual vehicle software program, this application can complete the combined calculation of known and predicted operating conditions in the simulation platform, and input the battery SOC change path as the energy management planning strategy logic into the vehicle controller. After identifying the current driving road conditions of the vehicle, the optimal battery SOC change path is automatically planned and selected, reducing the computational load and calculation time of the vehicle controller, which is simple and easy to implement.

[0067] As a specific embodiment of this application, taking the pre-driving road divided into predicted driving condition 1 and predicted driving condition 2 as an example, as follows: Figure 4 As shown, the energy management method for this vehicle may include the following steps:

[0068] S101, determine the starting and ending points of the road the vehicle is to travel.

[0069] S102, acquire the working condition data from the starting position to the ending position.

[0070] S103, based on the operating condition data, divide the pre-driving road into at least one road section.

[0071] S104, obtain historical driving parameters within at least one road section, and determine road parameter data based on the historical driving parameters.

[0072] S105 matches road parameter data with road parameter data for multiple predicted working conditions.

[0073] S106, when a match is successful, the vehicle's pre-driving road is divided into a combination of predicted condition 1 and predicted condition 2.

[0074] S107, obtain the actual battery SOC under the current predicted operating conditions.

[0075] S108, determine the first SOC based on the actual battery SOC and the operating condition data corresponding to the predicted operating condition 1.

[0076] S109, determine the second SOC based on the actual battery SOC and the operating condition data corresponding to the predicted operating condition 1.

[0077] S110, determine the range of battery SOC variation under predicted operating condition 1.

[0078] S111, determine the third SOC based on the operating condition data corresponding to predicted operating condition 2 and the battery SOC variation range corresponding to predicted operating condition 1.

[0079] S112, determine the fourth SOC based on the operating condition data corresponding to predicted operating condition 2 and the battery SOC variation range corresponding to predicted operating condition 1.

[0080] S113, determine the range of battery SOC variation for the vehicle under predicted operating condition 2.

[0081] S114, based on the range of battery SOC change under various predicted operating conditions, determine multiple battery SOC change paths of the vehicle on the pre-driving road.

[0082] S115, obtain the fuel consumption and energy consumption for each battery SOC change path.

[0083] S116, the battery SOC change path with the lowest cumulative fuel consumption and electricity consumption is taken as the target battery SOC change path.

[0084] S117 performs energy management on the vehicle based on the target battery SOC change path.

[0085] Furthermore, based on the aforementioned vehicle energy management method as a SOC planning energy management strategy, the vehicle's battery SOC can be planned to form, as follows: Figure 5The comparison diagrams shown illustrate that the SOC planning energy management strategy is the SOC control implemented using the energy management method of this application, while the energy management strategy without SOC planning is the SOC control implemented using energy management methods in related technologies. This application's intelligent planning energy management control strategy based on predicted battery SOC divides the vehicle's driving conditions into different predicted conditions. It plans the battery SOC based on the predicted conditions and the current battery state of charge, and controls the battery power based on the current energy management strategy's consideration of the driver's driving needs and SOC planning. This achieves optimal system operating efficiency under different time windows or predicted conditions, ultimately achieving global optimization of the user's driving conditions.

[0086] In summary, the vehicle energy management method according to the embodiments of this application first acquires the vehicle's operating condition data on the pre-driving road, determines at least one predicted operating condition of the vehicle on the pre-driving road based on the operating condition data, and determines the battery SOC variation range of the vehicle under each predicted operating condition based on the operating condition data corresponding to each predicted operating condition. Then, it determines multiple battery SOC variation paths of the vehicle on the pre-driving road based on the battery SOC variation range of the vehicle under each predicted operating condition, and finally performs energy management on the vehicle based on the battery SOC variation path with the lowest energy consumption among the multiple battery SOC variation paths. Therefore, this method divides the vehicle's operating condition data on the pre-driving road into predicted operating conditions and performs energy management on the vehicle based on the battery SOC variation path with the lowest energy consumption under the predicted operating conditions, achieving optimal system operating efficiency under different time windows or predicted operating conditions, and ultimately achieving global optimization of the user's driving conditions.

[0087] Corresponding to the above embodiments, this application also proposes a vehicle energy management device.

[0088] like Figure 6 As shown, the energy management device for a vehicle in this application embodiment 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.

[0089] The acquisition module 10 acquires operating condition data of the vehicle on the pre-driving road. The first determination module 20 determines at least one predicted operating condition of the vehicle on the pre-driving road based on the operating condition data. The second determination module 30 determines the battery SOC change range of the vehicle under each predicted operating condition based on the operating condition data corresponding to each predicted operating condition. The third determination module 40 determines multiple battery SOC change paths of the vehicle on the pre-driving road based on the battery SOC change range of the vehicle under each predicted operating condition. The energy management module 50 performs energy management of the vehicle based on the battery SOC change path with the lowest energy consumption among the multiple battery SOC change paths.

[0090] According to one embodiment of this application, the driving condition data includes: gradient data and speed limit data. The first determining module 20 determines at least one predicted driving condition for the vehicle on the pre-driving road based on the driving condition data. Specifically, it is used to: divide the pre-driving road into at least one road interval based on the driving condition data; obtain historical driving parameters within the at least one road interval, and determine road parameter data based on the historical driving parameters, wherein the road parameter data includes at least one of: average vehicle speed, average acceleration, average uphill gradient, average downhill gradient, vehicle speed standard deviation, and acceleration standard deviation; match the road parameters with the road parameter data of multiple predicted driving conditions; and when the matching is successful, divide the vehicle's pre-driving road into a combination of one or more predicted driving conditions.

[0091] According to one embodiment of this application, the second determining module 30 determines the range of battery SOC variation of the vehicle under the first predicted operating condition based on the vehicle's current actual battery SOC and the operating condition data corresponding to the first predicted operating condition determined according to the vehicle's driving direction. Specifically, it is used to: determine a first SOC based on the actual battery SOC and the operating condition data corresponding to the first predicted operating condition, wherein the first SOC is the battery SOC of the vehicle in power generation mode at the end of the first predicted operating condition; determine a second SOC based on the actual battery SOC and the operating condition data corresponding to the first predicted operating condition, wherein the second SOC is the battery SOC of the vehicle in pure electric mode at the end of the first predicted operating condition; and use the first SOC as the upper limit of battery SOC and the second SOC as the lower limit of battery SOC to obtain the range of battery SOC variation of the vehicle under the first predicted operating condition.

[0092] The second determining module 30 determines the battery SOC variation range of the vehicle under the predicted operating condition based on the operating condition data corresponding to the predicted operating condition and the battery SOC variation range corresponding to the previous predicted operating condition. Specifically, it is used to: determine a third SOC based on the operating condition data corresponding to the predicted operating condition and the battery SOC variation range corresponding to the previous predicted operating condition, wherein the third SOC is the battery SOC of the vehicle in power generation mode at the end of the predicted operating condition; determine a fourth SOC based on the operating condition data corresponding to the predicted operating condition and the battery SOC variation range corresponding to the previous predicted operating condition, wherein the fourth SOC is the battery SOC of the vehicle in pure electric mode at the end of the predicted operating condition; and use the third SOC as the upper limit of the battery SOC and the fourth SOC as the lower limit of the battery SOC to obtain the battery SOC variation range of the vehicle under the predicted operating condition.

[0093] According to one embodiment of this application, the third determining module 40 determines one of the multiple battery SOC change paths of the vehicle on the pre-driving road, specifically used for: selecting a target SOC value in the SOC change range corresponding to each predicted operating condition; and obtaining one of the multiple battery SOC change paths based on each target SOC value.

[0094] According to one embodiment of this application, the third determining module 40 is further configured to: re-acquire multiple battery SOC change paths when the difference between the actual battery SOC under the current predicted operating condition and the battery SOC change path with the lowest energy consumption is greater than a set threshold, or when the vehicle's pre-driving road changes.

[0095] According to one embodiment of this application, the energy management method for the vehicle further includes: when the difference between the actual battery SOC under the current predicted operating condition and the corresponding SOC change path with the lowest energy consumption is greater than a set threshold, or when the vehicle's pre-driving road changes, re-acquiring multiple battery SOC change paths.

[0096] According to one embodiment of this application, the acquisition module 10 acquires the vehicle's operating condition data on the pre-driving road, including: determining the starting position and ending position of the vehicle's pre-driving; and acquiring operating condition data from the starting position to the ending position.

[0097] It should be noted that for details not disclosed in the vehicle energy management device of the embodiments of this application, please refer to the details disclosed in the vehicle energy management method of the above embodiments of this application, which will not be repeated here.

[0098] In summary, the vehicle energy management device according to the embodiments of this application acquires the vehicle's operating condition data on the pre-driving road through an acquisition module. A first determining module determines at least one predicted operating condition of the vehicle on the pre-driving road based on the operating condition data. A second determining module determines the battery SOC change range of the vehicle under each predicted operating condition based on the operating condition data corresponding to each predicted operating condition. A third determining module determines multiple battery SOC change paths of the vehicle on the pre-driving road based on the battery SOC change range of the vehicle under each predicted operating condition. The energy management module performs energy management on the vehicle based on the battery SOC change path with the lowest energy consumption among the multiple battery SOC change paths. Therefore, this device divides the predicted operating conditions based on the vehicle's operating condition data on the pre-driving road and performs energy management on the vehicle based on the battery SOC change path with the lowest energy consumption under the predicted operating conditions. This can achieve optimal system operating efficiency under different time windows or predicted operating conditions, ultimately achieving global optimization of the user's driving conditions.

[0099] Corresponding to the above embodiments, this application also proposes a computer-readable storage medium.

[0100] The computer-readable storage medium of this application embodiment stores a vehicle energy management program thereon, which, when executed by a processor, implements the above-described vehicle energy management method.

[0101] According to the computer-readable storage medium of the embodiments of this application, based on the above-described vehicle energy management method, the optimal system operating efficiency can be achieved under different time windows or predicted operating conditions, and ultimately the global optimal user driving conditions can be achieved.

[0102] Corresponding to the above embodiments, this application also proposes a vehicle.

[0103] like Figure 7 As shown, the vehicle 100 in this embodiment includes a memory 110, a processor 120, and a vehicle energy management program stored in the memory 110 and executable on the processor 120. When the processor 120 executes the vehicle energy management program, it implements the above-described vehicle energy management method.

[0104] For example, the processor 120 can be used to execute the above-described method embodiments according to instructions in the computer program.

[0105] In some embodiments of this application, the processor 120 may include, but is not limited to:

[0106] General-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc.

[0107] In some embodiments of this application, the memory 110 includes, but is not limited to:

[0108] Volatile memory and / or non-volatile memory. Non-volatile memory may be read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), or flash memory. Volatile memory may be random access memory (RAM), which serves as an external cache. By way of example, but not limitation, many forms of RAM are available, such as static random access memory (SRAM), dynamic random access memory (DRAM), synchronous dynamic random access memory (SDRAM), double data rate synchronous dynamic random access memory (DDR SDRAM), enhanced synchronous dynamic random access memory (ESDRAM), synchronous linked dynamic random access memory (SLDRAM), and direct memory bus RAM (DRRAM).

[0109] In some embodiments of this application, the computer program may be divided into one or more modules, which are stored in the memory 110 and executed by the processor 120 to perform the method provided in this application. The one or more modules may be a series of computer program instruction segments capable of performing specific functions, which describe the execution process of the computer program in the vehicle 100.

[0110] like Figure 7 As shown, the vehicle 100 may also include:

[0111] Transceiver 130, which can be connected to processor 120 or memory 110.

[0112] The processor 120 can control the transceiver 130 to communicate with other devices; specifically, it can send information or data to other devices or receive information or data sent by other devices. The transceiver 130 may include a transmitter and a receiver. The transceiver 130 may further include antennas, and the number of antennas may be one or more.

[0113] It should be understood that the various components of the vehicle 100 are connected through a bus system, which includes a data bus, a power bus, a control bus, and a status signal bus.

[0114] According to the vehicle embodiment of this application, based on the above-described vehicle energy management method, the optimal system operating efficiency can be achieved under different time windows or predicted operating conditions, ultimately achieving the global optimal user driving conditions.

[0115] It should be noted that the logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (such as a computer-based system, a processor-included system, or other system that can fetch and execute instructions from, an instruction execution system, apparatus, or device). For the purposes of this specification, "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transmit programs for use by, or in conjunction with, an instruction execution system, apparatus, or device. More specific examples (a non-exhaustive list) of computer-readable media include: an electrical connection having one or more wires (electronic device), a portable computer disk drive (magnetic device), random access memory (RAM), read-only memory (ROM), erasable and editable read-only memory (EPROM or flash memory), fiber optic devices, and portable optical disc read-only memory (CDROM). Alternatively, the computer-readable medium may be paper or other suitable media on which the program can be printed, since the program can be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, interpreting, or otherwise processing as necessary, and then stored in a computer memory.

[0116] It should be understood that various parts of this application can be implemented using hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented using software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, it can be implemented using any one or a combination of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0117] In the description of this specification, the references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of this application. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

[0118] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one of that feature. In the description of this application, "multiple" means at least two, such as two, three, etc., unless otherwise explicitly specified.

[0119] In this application, unless otherwise expressly specified and limited, the terms "installation," "connection," "joining," and "fixing," etc., should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral part; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; they can refer to the internal communication of two components or the interaction between two components, unless otherwise expressly limited. Those skilled in the art can understand the specific meaning of the above terms in this application according to the specific circumstances.

[0120] Although embodiments of this application have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting this application. Those skilled in the art can make changes, modifications, substitutions and variations to the above embodiments within the scope of this application.

Claims

1. A method for energy management of a vehicle, characterized in that, include: The vehicle's pre-driving route is obtained, and the pre-driving route is divided into at least one predicted driving condition based on the driving condition data of the pre-driving route. Based on the operating condition data corresponding to each predicted operating condition, the range of battery SOC variation of the vehicle under each predicted operating condition is determined. Based on the range of battery SOC change of the vehicle under each predicted operating condition, determine multiple battery SOC change paths of the vehicle on the pre-driving road; Energy management is performed on the vehicle based on the battery SOC change path that minimizes the energy consumption of the vehicle while operating on the pre-driving road from among the multiple battery SOC change paths. The battery SOC variation range for the first predicted operating condition of the pre-driving road is determined based on the vehicle's actual battery SOC at the starting point of the pre-driving road and the operating condition data for the first predicted operating condition. The battery SOC variation range for non-first predicted operating conditions of the pre-driving road is determined based on the operating condition data for non-first predicted operating conditions and the battery SOC variation range for the preceding predicted operating condition. The upper limit of the battery SOC variation range for the predicted operating conditions is the battery SOC of the vehicle at the end of the predicted operating condition in power generation mode. The lower limit of the battery SOC variation range for the predicted operating conditions is the battery SOC of the vehicle at the end of the predicted operating condition in pure electric mode.

2. The method according to claim 1, characterized in that, The operating condition data includes: slope data and speed limit data, and the method for determining the at least one predicted operating condition includes: The pre-driving road is divided into at least one road section based on the operating condition data; Historical driving parameters within the at least one road section are obtained, and road parameter data is determined based on the historical driving parameters. The road parameter data includes at least one of the following: average vehicle speed, average acceleration, average uphill gradient, average downhill gradient, standard deviation of vehicle speed, and standard deviation of acceleration. The road parameter data is matched with road parameter data for multiple predicted working conditions; When a match is successful, the vehicle's pre-driving route is divided into a combination of one or more predicted driving conditions.

3. The method according to claim 1, characterized in that, Determining one of multiple battery SOC change paths for the vehicle on the pre-driving road includes: Select a target SOC value within the battery SOC variation range for each predicted operating condition; Based on each target SOC value, one of the multiple battery SOC change paths can be obtained as the battery SOC change path.

4. The method according to claim 1, characterized in that, Also includes: If the difference between the actual battery SOC under the current predicted operating condition and the battery SOC in the corresponding path with the lowest energy consumption is greater than a set threshold, or if the vehicle's pre-driving route changes, multiple battery SOC change paths are reacquired.

5. The method according to claim 1, characterized in that, Acquire the vehicle's operating condition data on the pre-trip road, including: Determine the starting and ending positions of the vehicle's pre-departure journey; Obtain the operating condition data from the starting position to the ending position.

6. An energy management device for a vehicle, characterized in that, include: The acquisition module is used to acquire the vehicle's pre-driving road, which is divided into at least one predicted driving condition based on the driving condition data of the pre-driving road. The second determining module is used to determine the battery SOC variation range of the vehicle under each predicted operating condition based on the operating condition data corresponding to each predicted operating condition. Specifically, the battery SOC variation range for the first predicted operating condition of the pre-driving road is determined based on the vehicle's actual battery SOC at the starting point of the pre-driving road and the operating condition data for the first predicted operating condition. The battery SOC variation range for non-first predicted operating conditions of the pre-driving road is determined based on the operating condition data for the non-first predicted operating conditions and the battery SOC variation range for the preceding non-first predicted operating condition. The upper limit of the battery SOC variation range for each predicted operating condition is the battery SOC of the vehicle at the end of the predicted operating condition operation in power generation mode. The lower limit of the battery SOC variation range for each predicted operating condition is the battery SOC of the vehicle at the end of the predicted operating condition operation in pure electric mode. The third determining module is used to 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 each predicted operating condition. An energy management module is used to manage the energy of the vehicle based on the SOC change path that minimizes the energy consumption of the vehicle while driving on the pre-driving road among the multiple SOC change paths.

7. A computer-readable storage medium, characterized in that, It stores a vehicle energy management program, which, when executed by a processor, implements the vehicle energy management method according to any one of claims 1-5.

8. A vehicle, characterized in that, The system includes a memory, a processor, and a vehicle energy management program stored in the memory and executable on the processor. When the processor executes the vehicle energy management program, it implements the vehicle energy management method according to any one of claims 1-5.

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