Battery SOC lower limit determination method, apparatus and device, and readable storage medium

Abstract

The invention discloses a battery SOC lower limit determination method, device and equipment and a readable storage medium. The method comprises the steps of obtaining the number of discharging times of a battery in a vehicle and the number of times that the SOC of the discharging tail end of the battery is smaller than the maximum SOC use lower limit; determining a user driving behavior characterization coefficient according to the discharge times of the battery and the times that the SOC at the discharge tail end of the battery is smaller than the maximum SOC use lower limit; according to the user driving behavior characterization coefficient, a fuzzy control algorithm is adopted to determine the discharge SOC lower limit of the battery; wherein the discharge SOC lower limit of the battery is between the battery emptying SOC and the maximum SOC use lower limit. According to the technical scheme, on the basis of the driving behavior of the vehicle user, the fuzzy control algorithm is adopted to dynamically adjust the discharging SOC lower limit of the battery between the battery emptying SOC and the maximum SOC use lower limit, so that the cruising ability of the whole vehicle is improved, and the frequency of tail end undervoltage faults caused by SOC errors is reduced.

Description

Technical Field

[0001] This application relates to the technical field of electric vehicles, and specifically relates to a method, device, equipment and readable storage medium for determining the lower limit of battery SOC. Background Art

[0002] The available cruising range estimation of electric vehicles is strongly correlated with the battery SOC (State of Charge) usage window.

[0003] Currently, considering the existence of SOC errors, in order to avoid undervoltage faults caused by SOC errors at low battery levels, each vehicle manufacturer does not open the true lower limit of SOC for use. Generally, the maximum error of the system SOC under all working conditions is taken as the lower limit of SOC during battery discharge. However, this method will cause the true capacity of the battery not to be released, thus reducing the cruising range of the whole vehicle.

[0004] In summary, how to improve the cruising range of the whole vehicle and reduce the frequency of undervoltage faults is a technical problem that needs to be solved urgently by those skilled in the art at present. Summary of the Invention

[0005] In view of the above problems, this application provides a method, device, equipment and readable storage medium for determining the lower limit of battery SOC, which is used to improve the cruising range of the whole vehicle and reduce the frequency of undervoltage faults.

[0006] In a first aspect, this application provides a method for determining the lower limit of battery discharge SOC, including: obtaining the number of discharges of the battery in the vehicle and the number of times that the SOC at the end of battery discharge is less than the maximum SOC usage lower limit; determining a user driving behavior characterization coefficient according to the number of discharges of the battery and the number of times that the SOC at the end of battery discharge is less than the maximum SOC usage lower limit; determining the lower limit of the battery discharge SOC by using a fuzzy control algorithm according to the user driving behavior characterization coefficient; wherein, the lower limit of the battery discharge SOC is between the battery empty SOC and the maximum SOC usage lower limit.

[0007] The technical solution disclosed in the embodiments of this application determines a user driving behavior characterization coefficient according to the number of discharges of the battery in the vehicle and the number of times that the SOC at the end of battery discharge is less than the maximum SOC usage lower limit, and uses a fuzzy control algorithm to adjust the lower limit of the battery discharge SOC between the battery empty SOC and the maximum SOC usage lower limit according to the user driving behavior characterization coefficient, so as to dynamically adjust the lower limit of the battery discharge SOC based on the driving behavior of the vehicle user, thereby improving the cruising range of the whole vehicle, reducing the frequency of end undervoltage faults caused by SOC errors, improving the safety of the battery and the vehicle, and extending the service life of the battery.

[0008] In some embodiments, it further includes: obtaining the current temperature of the battery and the optimal temperature of the battery; determining a temperature comfort characterization coefficient of the battery according to the current temperature of the battery and the optimal temperature of the battery;

[0009] Determining a lower limit of the discharge SOC of the battery by using a fuzzy control algorithm according to the user driving behavior characterization coefficient includes: determining the lower limit of the discharge SOC of the battery by using the fuzzy control algorithm according to the user driving behavior characterization coefficient and the temperature comfort characterization coefficient of the battery.

[0010] By using a fuzzy control algorithm to dynamically adjust the lower limit of the discharge SOC of the battery between the empty SOC of the battery and the lower limit of the maximum SOC usage according to the user driving behavior characterization coefficient and the temperature comfort characterization coefficient of the battery, it is possible to more accurately adjust the lower limit of the discharge SOC of the battery, thereby better improving the endurance of the whole vehicle and better reducing the frequency of occurrence of end under-voltage faults caused by SOC errors, and improving the safety of the battery and the vehicle.

[0011] In some embodiments, determining the lower limit of the discharge SOC of the battery by using the fuzzy control algorithm according to the user driving behavior characterization coefficient and the temperature comfort characterization coefficient of the battery includes: taking the user driving behavior characterization coefficient K(t) and the temperature comfort characterization coefficient P(t) of the battery as input variables of the fuzzy control algorithm, and taking the dynamic lower limit SOC characterization coefficient L(t) as the output variable of the fuzzy control algorithm; setting the fuzzy subsets corresponding to the input variable and the output variable to be {LE, ME, GE}, and setting the membership functions corresponding to the input variable and the output variable; where LE is smaller, ME is medium, and GE is larger; converting a first preset fuzzy rule into a first fuzzy control table according to the fuzzy subsets corresponding to the input variable and the output variable, and inputting the first fuzzy control table, the membership function corresponding to the input variable and the membership function corresponding to the output variable into a first fuzzy controller; inputting the user driving behavior characterization coefficient and the temperature comfort characterization coefficient of the battery into the first fuzzy controller to obtain the defuzzified dynamic lower limit SOC characterization coefficient; and obtaining the lower limit of the discharge SOC of the battery by using the defuzzified dynamic lower limit SOC characterization coefficient, the lower limit of the maximum SOC usage and the empty SOC of the battery.

[0012] By taking the user driving behavior characterization coefficient and the temperature comfort characterization coefficient of the battery as input variables of the fuzzy control and taking the dynamic lower limit SOC characterization coefficient as the output variable of the fuzzy control, it is not only possible to improve the accuracy of determining the lower limit of the discharge SOC of the battery, but also possible to improve the convenience of determining the lower limit of the discharge SOC of the battery.

[0013] In some embodiments, the first fuzzy control table includes: K(t)=LE, P(t)=LE, L(t)=LE; K(t)=LE, P(t)=ME, L(t)=LE; K(t)=LE, P(t)=GE, L(t)=ME; K(t)=ME, P(t)=LE, L(t)=LE; K(t)=ME, P(t)=ME, L(t)=ME; K(t)=ME, P(t)=GE, L(t)=ME; K(t)=GE, P(t)=LE, L(t)=LE; K(t)=GE, P(t)=ME, L(t)=ME; K(t)=GE, P(t)=GE, L(t)=GE.

[0014] Through the above first fuzzy control table, the accurate determination of the lower limit of the battery discharge SOC can be achieved, so as to better improve the vehicle endurance and reduce the frequency of under-voltage faults at the end of discharge caused by SOC errors.

[0015] In some embodiments, it further includes: pre-establishing a temperature comfort characterization coefficient table of the battery in the vehicle; the temperature comfort characterization coefficient table includes the absolute value of the temperature difference between the temperature of the battery and the optimal temperature, and the temperature comfort characterization coefficient corresponding to the absolute value of the temperature difference.

[0016] Determining the temperature comfort characterization coefficient of the battery according to the current temperature of the battery and the optimal temperature of the battery includes: determining the absolute value of the temperature difference between the current temperature of the battery and the optimal temperature of the battery according to the current temperature of the battery and the optimal temperature of the battery; determining the temperature comfort characterization coefficient of the battery according to the absolute value of the temperature difference between the current temperature of the battery and the optimal temperature of the battery and the temperature comfort characterization coefficient table.

[0017] By pre-establishing a temperature comfort characterization coefficient table of the battery in the vehicle, the convenience and efficiency of determining the temperature comfort characterization coefficient of the battery can be improved, so as to improve the speed and efficiency of determining the lower limit of the battery discharge SOC.

[0018] In some embodiments, according to the user driving behavior characterization coefficient, a fuzzy control algorithm is used to determine the lower limit of the discharge SOC of the battery, including: taking the user driving behavior characterization coefficient K(t) as the input of the fuzzy control algorithm, and taking the dynamic lower limit SOC characterization coefficient L(t) as the output of the fuzzy control algorithm; setting the fuzzy subsets corresponding to the input and the output to be {LE, ME, GE}, and setting the membership functions corresponding to the input and the output; where LE is smaller, ME is medium, and GE is larger; according to the fuzzy subset corresponding to the input and the fuzzy subset corresponding to the output, converting the second preset fuzzy rule into a second fuzzy control table, and inputting the second fuzzy control table, the membership function corresponding to the input, and the membership function corresponding to the output into a second fuzzy controller; inputting the user driving behavior characterization coefficient into the second fuzzy controller to obtain the defuzzified dynamic lower limit SOC characterization coefficient; using the defuzzified dynamic lower limit SOC characterization coefficient, the maximum SOC usage lower limit, and the battery empty SOC to obtain the lower limit of the discharge SOC of the battery.

[0019] By taking the user driving behavior characterization coefficient as the input of the fuzzy control algorithm and the dynamic lower limit SOC characterization coefficient as the output of the fuzzy control algorithm, not only can the structure of the fuzzy controller be simplified and the output determination efficiency be improved, but also the dynamic adjustment of the lower limit of the discharge SOC of the battery can be realized, so as to improve the vehicle endurance and reduce the occurrence frequency of under-voltage faults.

[0020] In some embodiments, the second fuzzy control table includes: K(t) = LE, L(t) = LE; K(t) = ME, L(t) = ME; K(t) = GE, L(t) = GE.

[0021] Through the above second fuzzy control table, not only can the output determination efficiency be improved, but also the dynamic adjustment of the lower limit of the discharge SOC of the battery can be realized, so as to improve the vehicle endurance and reduce the occurrence frequency of under-voltage faults.

[0022] In some embodiments, using the defuzzified dynamic lower limit SOC characterization coefficient, the maximum SOC usage lower limit, and the battery empty SOC to obtain the lower limit of the discharge SOC of the battery, including: using the formula: the lower limit of the discharge SOC of the battery = defuzzified dynamic lower limit SOC characterization coefficient * (maximum SOC usage lower limit - battery empty SOC) + battery empty SOC, to calculate the lower limit of the discharge SOC of the battery.

[0023] Through the above, the accuracy of determining the lower limit of the discharge SOC of the battery can be improved, so as to better improve the vehicle endurance and reduce the occurrence frequency of under-voltage faults.

[0024] In some embodiments, setting the membership function corresponding to the input quantity and the membership function corresponding to the output quantity includes: setting the membership function corresponding to the input quantity as a Gaussian function and setting the membership function corresponding to the output quantity as a Gaussian function.

[0025] By selecting a Gaussian function as the membership function of the input quantity and the output quantity in the fuzzy controller, the lower limit of the discharge SOC of the battery can be smoothly adjusted and output, so as to better improve the cruising range of the whole vehicle, and better reduce the occurrence frequency of the under-voltage fault of the battery at the end of discharge due to the SOC error, and enhance the user's trust in the cruising range of the whole vehicle.

[0026] In some embodiments, obtaining the number of discharges of the battery in the vehicle and the number of times that the SOC at the end of the battery discharge is less than the lower limit of the maximum SOC usage includes: obtaining the driving state of the vehicle, the charging state of the vehicle, and the SOC of the battery in the vehicle in real time; according to the driving state of the vehicle, the charging state of the vehicle, and the SOC of the battery in the vehicle obtained in real time, obtaining the number of discharges of the battery in the vehicle and the SOC at the end of the battery discharge, and determining the number of times that the SOC at the end of the battery discharge is less than the lower limit of the maximum SOC usage according to the SOC at the end of the battery discharge and the lower limit of the maximum SOC usage.

[0027] Through the above method, the accuracy of obtaining the number of discharges of the battery in the vehicle and the number of times that the SOC at the end of the battery discharge is less than the lower limit of the maximum SOC usage can be improved, so as to improve the accuracy of determining the lower limit of the discharge SOC of the battery.

[0028] In a second aspect, the present application provides a device for determining the lower limit of the battery SOC, including: a first acquisition module, configured to acquire the number of discharges of the battery in the vehicle and the number of times that the SOC at the end of the battery discharge is less than the lower limit of the maximum SOC usage; a first determination module, configured to determine a user driving behavior characterization coefficient according to the number of discharges of the battery and the number of times that the SOC at the end of the battery discharge is less than the lower limit of the maximum SOC usage; a second determination module, configured to determine the lower limit of the discharge SOC of the battery by using a fuzzy control algorithm according to the user driving behavior characterization coefficient; wherein, the lower limit of the discharge SOC of the battery is between the empty SOC of the battery and the lower limit of the maximum SOC usage.

[0029] In some embodiments, it further includes: a second acquisition module, configured to acquire the current temperature of the battery and the optimal temperature of the battery; a third determination module, configured to determine a temperature comfort characterization coefficient of the battery according to the current temperature of the battery and the optimal temperature of the battery.

[0030] The second determination module is specifically configured to determine the lower limit of the discharge SOC of the battery by using the fuzzy control algorithm according to the user driving behavior characterization coefficient and the temperature comfort characterization coefficient of the battery.

[0031] In a third aspect, the present application provides a device for determining the lower limit of battery SOC, including: a memory for storing a computer program; a processor for implementing the steps of the method for determining the lower limit of battery SOC according to any one of the above when executing the computer program.

[0032] In a fourth aspect, the present application provides a readable storage medium, in which a computer program is stored, and when the computer program is executed by a processor, the steps of the method for determining the lower limit of battery SOC according to any one of the above are implemented.

[0033] The above description is only an overview of the technical solution of the present application. In order to be able to understand the technical means of the present application more clearly, it can be implemented according to the content of the specification. And in order to make the above and other purposes, features and advantages of the present application more obvious and understandable, the following specifically illustrates the specific implementation manners of the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] By reading the detailed description of the preferred embodiments below, various other advantages and benefits will become clear to those of ordinary skill in the art. The drawings are only for the purpose of showing the preferred embodiments and are not considered to be a limitation of the present application. Moreover, in all the drawings, the same reference numerals are used to represent the same components. In the drawings:

[0035] Figure 1 is a flowchart of the method for determining the lower limit of battery SOC in some embodiments of the present application;

[0036] Figure 2 is a schematic diagram of the battery SOC distribution in some embodiments of the present application;

[0037] Figure 3 is a schematic diagram for determining the lower limit of battery SOC in some embodiments of the present application;

[0038] Figure 4 is a structural diagram of the fuzzy control algorithm in some embodiments of the present application;

[0039] Figure 5 is the membership function of the input quantity of the fuzzy controller in some embodiments of the present application;

[0040] Figure 6 is the membership function of the output quantity of the fuzzy controller in some embodiments of the present application;

[0041] Figure 7 is a schematic structural diagram of the device for determining the lower limit of battery SOC in some embodiments of the present application;

[0042] Figure 8 The figure is a schematic structural diagram of a device for determining the lower limit of the battery SOC in some embodiments of the present application. Detailed implementation manners

[0043] Hereinafter, embodiments of the technical solution of the present application will be described in detail with reference to the accompanying drawings. The following embodiments are only used to more clearly illustrate the technical solution of the present application, and thus are only examples and should not be used to limit the protection scope of the present application.

[0044] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the technical field to which this application belongs; the terms used herein are only for the purpose of describing specific embodiments and are not intended to limit this application; the terms "including" and "having" and any variations thereof in the specification and claims of this application and the above accompanying drawing descriptions are intended to cover non-exclusive inclusion.

[0045] In the description of the embodiments of this application, technical terms such as "first" and "second" are only used to distinguish different objects and cannot be understood as indicating or implying relative importance or implicitly indicating the quantity, specific order or primary-secondary relationship of the indicated technical features. In the description of the embodiments of this application, "a plurality of" means more than two unless otherwise specifically defined.

[0046] Referring to "embodiments" herein means that the specific features, structures or characteristics described in connection with the embodiments can be included in at least one embodiment of this application. The phrase appears in various places in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment mutually exclusive with other embodiments. Those skilled in the art explicitly and implicitly understand that the embodiments described herein can be combined with other embodiments.

[0047] The available cruising range estimation of electric vehicles and the SOC usage window of the batteries in electric vehicles have always been strongly correlated. Limited by the current industry development, SOC errors will exist for a long time. In order to avoid undervoltage faults caused by SOC errors at low battery levels, each vehicle manufacturer cannot open the true lower limit of SOC (i.e., 0%) for use. Generally, the maximum error of the system SOC under all working conditions is taken as the cut-off SOC during battery discharge, that is, the maximum SOC error in all working conditions and the entire life cycle is taken as the fixed SOC lower limit. However, the lower limit SOC set in this way is too large, resulting in the failure to release the true capacity of the battery and reducing the cruising range of the whole vehicle.

[0048] To this end, the applicant proposes a method for determining the lower limit of the battery SOC. The driving behavior characterization coefficient of the user is determined based on the number of discharges of the battery in the vehicle and the number of times that the SOC at the end of the battery discharge is less than the lower limit of the maximum SOC usage. According to the driving behavior characterization coefficient, a fuzzy control algorithm is used to adjust the lower limit of the discharge SOC of the battery between the empty SOC of the battery and the lower limit of the maximum SOC usage, so as to dynamically adjust the lower limit of the discharge SOC of the battery based on the driving behavior of the vehicle user, thereby improving the endurance of the whole vehicle, enhancing the competitiveness of the whole vehicle in the market, reducing the frequency of occurrence of terminal undervoltage faults caused by SOC errors, improving the safety of the battery and the vehicle, and prolonging the service life of the battery.

[0049] It should be noted that the method for determining the lower limit of the battery SOC provided in this application is applicable to all electric vehicles including batteries, such as pure electric vehicles, pure electric buses, etc.

[0050] See Figure 1 , which is a flowchart of the method for determining the lower limit of the battery SOC in some embodiments of this application, and may include the following steps:

[0051] S11: Obtain the number of discharges of the battery in the vehicle and the number of times that the SOC at the end of the battery discharge is less than the lower limit of the maximum SOC usage.

[0052] It should be noted that the execution subject of the method for determining the lower limit of the battery SOC provided in this application can be a cloud platform or a vehicle, etc. In this embodiment of the application, the execution subject is taken as an example of a cloud platform for description.

[0053] When determining the lower limit of the discharge SOC of the battery, the number of discharges N of the battery in the vehicle and the number of times M that the SOC at the end of the battery discharge is less than the lower limit of the maximum SOC usage can be obtained through big data statistics.

[0054] Specifically, the number of discharges N of the battery in the vehicle as of the current time (i.e., the total number of discharges of the battery in the vehicle from the first discharge to the current time) can be obtained in real time, at regular intervals, or according to an instruction for determining the lower limit of the battery discharge SOC received. Herein, each time the battery is plugged in for charging is counted as one discharge. At the same time, the discharge SOC of the battery in the vehicle can also be obtained, and the SOC at the end of each discharge of the battery (i.e., the end-of-discharge SOC of the battery) can be determined based on the discharge SOC of the battery. The number of times M that the end-of-discharge SOC of the battery is less than the lower limit of the maximum SOC usage can be statistically obtained based on the end-of-discharge SOC of the battery and the lower limit of the maximum SOC usage, that is, the number of times M in the number of discharges N of the battery that the end-of-discharge SOC is less than the lower limit of the maximum SOC usage is statistically obtained. The lower limit of the maximum SOC usage can be obtained by pre-calibration, and specifically, it can be the SOC error of the BMS (Battery Management System). The SOC error of the BMS is the maximum SOC error of the battery in the vehicle throughout its entire life cycle and under all operating conditions.

[0055] It should be noted that when the execution entity is the cloud platform, it can statistically obtain the number of discharges of the battery in the vehicle and the number of times that the end-of-discharge SOC of the battery is less than the lower limit of the maximum SOC usage through big data. When the execution entity is the vehicle, in order to save the resources of the vehicle, the cloud platform can statistically obtain the number of discharges of the battery in the vehicle and the number of times that the end-of-discharge SOC of the battery is less than the lower limit of the maximum SOC usage through big data, and send the number of discharges of the battery in the vehicle and the number of times that the end-of-discharge SOC of the battery is less than the lower limit of the maximum SOC usage obtained through big data statistics to the vehicle, and the vehicle determines the lower limit of the battery discharge SOC based on this information.

[0056] S12: Determine the user driving behavior characterization coefficient according to the number of discharges of the battery and the number of times that the end-of-discharge SOC of the battery is less than the lower limit of the maximum SOC usage.

[0057] After obtaining the number of discharges N of the battery and the number of times M that the end-of-discharge SOC of the battery is less than the lower limit of the maximum SOC usage, the user driving behavior characterization coefficient K can be calculated using K = M / N based on the number of discharges N of the battery and the number of times M that the end-of-discharge SOC of the battery is less than the lower limit of the maximum SOC usage, where 0 ≤ K ≤ 1.

[0058] The user driving behavior characterization coefficient K can characterize the user driving behavior of the vehicle. Specifically, the smaller K is, the lower the frequency that the SOC at the end of battery discharge is less than the lower limit of the maximum SOC usage, and the higher the frequency that the SOC at the end of battery discharge is not less than the lower limit of the maximum SOC usage. That is to say, the user driving behavior is relatively conservative. The larger K is, the higher the frequency that the SOC at the end of battery discharge is less than the lower limit of the maximum SOC usage, and the lower the frequency that the SOC at the end of battery discharge is not less than the lower limit of the maximum SOC usage. That is to say, the user driving behavior is relatively intense. In other words, the smaller K is, the better the user driving behavior is characterized, and the larger K is, the worse the user driving behavior is characterized. When the user driving behavior is good, the lower limit of the discharge SOC of the battery can be adjusted to the empty SOC of the battery (specifically, it can be 0%), so as to preferentially improve the endurance of the whole vehicle. When the user driving behavior is poor, the lower limit of the discharge SOC of the battery can be adjusted to the lower limit of the maximum SOC usage, so as to preferentially ensure the user's trust in the endurance of the whole vehicle and reduce the occurrence frequency of undervoltage faults caused by SOC errors during low SOC endurance.

[0059] Among them, each time the number of battery discharges N in the vehicle and the number of times M that the SOC at the end of battery discharge is less than the lower limit of the maximum SOC usage are obtained, the corresponding user driving behavior characterization coefficient K can be determined. That is, the user driving behavior characterization coefficient K changes with time (that is, the user driving behavior characterization coefficient K can be specifically expressed as K(t)).

[0060] Through the above process, not only can the normalization processing of the user driving behavior characterization coefficient K be realized, but also the driving behavior of the vehicle user can be identified through big data statistics, so as to dynamically adjust the lower limit of the discharge SOC of the battery in the vehicle according to the driving behavior of the vehicle user, and enable relevant personnel to understand the frequency of the SOC at the end of the whole vehicle discharge being less than the lower limit of the maximum SOC usage.

[0061] S13: Determine the lower limit of the discharge SOC of the battery by using a fuzzy control algorithm according to the user driving behavior characterization coefficient; among them, the lower limit of the discharge SOC of the battery is between the empty SOC of the battery and the lower limit of the maximum SOC usage.

[0062] After obtaining the user driving behavior characterization coefficient K, the lower limit of the battery discharge SOC can be determined using a fuzzy control algorithm based on the user driving behavior characterization coefficient K. Moreover, the determined lower limit of the battery discharge SOC lies between the battery empty SOC and the maximum SOC usage lower limit. That is to say, according to the user driving behavior characterization coefficient K at different times, the lower limit of the battery discharge SOC can be dynamically adjusted using a fuzzy control algorithm between the battery empty SOC and the maximum SOC usage lower limit (the boundary values can be taken). Fuzzy control is an intelligent control method based on fuzzy set theory, fuzzy linguistic variables, and fuzzy logic reasoning. It can imitate the fuzzy reasoning and decision-making process of humans in terms of behavior. Applying it to the determination of the lower limit of the battery discharge SOC can flexibly handle the fuzziness and uncertainty between user driving behavior and the lower limit of the battery discharge SOC, and achieve an adaptive adjustment of the lower limit of the battery discharge SOC, effectively improving the vehicle's endurance in some scenarios, reducing the frequency of battery end under-voltage faults caused by SOC errors, extending the battery life, enhancing the safety of the battery and the vehicle, and increasing users' trust in the vehicle's endurance.

[0063] Among them, the battery empty SOC is the true lower limit of the battery SOC, that is, the minimum SOC usage lower limit, which can specifically be 0%, and the battery empty SOC is less than the maximum SOC usage lower limit. Specifically, reference can be made to Figure 2 , which is a schematic diagram of the battery SOC distribution in some embodiments of this application. Among them, the abscissa is the battery SOC, and from left to right are the battery empty SOC, the maximum SOC usage lower limit, and the battery full charge SOC. Compared with the current situation where the BMS system SOC error is used as the lower limit of the battery discharge SOC (in this case, the battery SOC usage window is composed of the BMS system SOC error and the battery full charge SOC), in this application, according to the user driving behavior characterization coefficient, a fuzzy control algorithm is used to dynamically adjust the lower limit of the battery discharge SOC between the battery empty SOC and the maximum SOC usage lower limit. In this case, the battery SOC usage window is composed of the determined lower limit of the battery discharge SOC and the battery full charge SOC. Thus, through this application, the battery SOC usage window can be expanded, thereby improving the vehicle's endurance, reducing the frequency of end under-voltage faults, and increasing users' trust in the vehicle's endurance. Moreover, through the above process, relevant personnel can also timely understand and obtain this threshold value of the lower limit of the vehicle's discharge SOC.

[0064] The above technical solution disclosed in the embodiments of the present application determines the user driving behavior characterization coefficient according to the number of discharges of the battery in the vehicle and the number of times that the SOC at the end of battery discharge is less than the lower limit of the maximum SOC usage. According to the user driving behavior characterization coefficient, a fuzzy control algorithm is used to adjust the lower limit of the discharge SOC of the battery between the empty SOC of the battery and the lower limit of the maximum SOC usage, so as to dynamically adjust the lower limit of the discharge SOC of the battery based on the driving behavior of the vehicle user, thereby improving the cruising range of the whole vehicle, reducing the frequency of occurrence of end under-voltage faults caused by SOC errors, improving the safety of the battery and the vehicle, and extending the service life of the battery.

[0065] According to some embodiments of the present application, it may further include:

[0066] Obtain the current temperature of the battery and the optimal temperature of the battery;

[0067] According to the current temperature of the battery and the optimal temperature of the battery, determine the temperature comfort characterization coefficient of the battery;

[0068] According to the user driving behavior characterization coefficient, using a fuzzy control algorithm to determine the lower limit of the discharge SOC of the battery may include:

[0069] According to the user driving behavior characterization coefficient and the temperature comfort characterization coefficient of the battery, use a fuzzy control algorithm to determine the lower limit of the discharge SOC of the battery.

[0070] During the determination process of the lower limit of the discharge SOC of the battery, the current temperature of the battery in the vehicle and the optimal temperature of the battery may also be obtained. Specifically, the current temperature of the battery (i.e., the temperature of the battery at the current time) can be obtained simultaneously when obtaining the number of discharges of the battery in the vehicle and the number of times that the SOC at the end of battery discharge is less than the lower limit of the maximum SOC usage. For the optimal temperature of the battery, it can be obtained in advance or simultaneously obtain the optimal temperature of the battery when obtaining the current temperature of the battery for the first time. Among them, the optimal temperature of the battery can be specifically obtained by calibration in advance. The optimal temperature of the battery is the temperature at which the battery can exhibit the best performance and life (such as the highest discharge efficiency and the smallest life attenuation). Participating the optimal temperature of the battery in the determination of the temperature comfort characterization coefficient of the battery can facilitate determining whether the current temperature of the battery is comfortable for the battery, that is, it can facilitate determining whether the battery can exhibit the best performance, thereby facilitating improving the rationality and accuracy of the determination of the lower limit of the discharge SOC of the battery.

[0071] After obtaining the current temperature of the battery and the optimal temperature of the battery, the temperature comfort characterization coefficient P of the battery can be determined according to the current temperature of the battery and the optimal temperature of the battery, where 0 ≤ P ≤ 1. 0 indicates that the temperature difference (here the temperature difference is the absolute value of the difference between the aforementioned two temperatures) between the current temperature of the battery and the optimal temperature of the battery is small, and 1 indicates that the temperature difference between the current temperature of the battery and the optimal temperature of the battery is large. That is, the smaller P is, the smaller the temperature difference between the temperature of the battery and the optimal temperature of the battery is, which means the better the temperature comfort of the battery. The larger P is, the larger the temperature difference between the temperature of the battery and the optimal temperature of the battery is, which means the worse the temperature comfort of the battery. When the temperature comfort of the battery is good, the lower limit of the discharge SOC of the battery can be adjusted to the empty SOC of the battery to preferentially improve the endurance of the whole vehicle. When the temperature comfort of the battery is poor, the lower limit of the discharge SOC of the battery can be adjusted to the lower limit of the maximum SOC usage to preferentially ensure the user's trust in the endurance of the whole vehicle and reduce the occurrence frequency of undervoltage faults caused by SOC errors during low-SOC endurance.

[0072] Among them, each time the current temperature of the battery in the vehicle is obtained, the corresponding temperature comfort characterization coefficient P of the battery can be obtained. It can be seen from this that the temperature comfort characterization coefficient P of the battery also changes with time, that is, the temperature comfort characterization coefficient P of the battery can be expressed as P(t).

[0073] Through the above process, not only can the normalization processing of the battery temperature comfort characterization coefficient be realized, but also the battery temperature comfort situation can be obtained, so as to better and more accurately dynamically adjust the lower limit of the discharge SOC of the battery in the vehicle by integrating the driving behavior of vehicle users and the battery temperature comfort.

[0074] On this basis, when determining the lower limit of the discharge SOC of the battery by using the fuzzy control algorithm according to the user driving behavior characterization coefficient, specifically, according to the user driving behavior characterization coefficient and the temperature comfort characterization coefficient of the battery, the fuzzy control algorithm can be used to dynamically adjust the lower limit of the discharge SOC of the battery between the empty SOC of the battery and the lower limit of the maximum SOC usage, so as to more accurately adjust the lower limit of the discharge SOC of the battery, thereby better improving the endurance of the whole vehicle and better reducing the frequency of occurrence of terminal undervoltage faults caused by SOC errors, and improving the safety of the battery and the vehicle. That is to say, through the above process, big data can be used to identify the user's driving habits, and the current temperature comfort of the battery can be determined according to the battery information in the vehicle. Then, according to the user's driving habits and the current temperature comfort of the battery, the lower limit of the current discharge SOC of the whole vehicle can be output through the fuzzy control algorithm to improve the endurance of the whole vehicle in some scenarios and reduce the occurrence frequency of battery terminal undervoltage faults caused by SOC errors.

[0075] See Figure 3 and Figure 4 , whereFigure 3 Schematic diagram for determining the lower limit of battery SOC in some embodiments of the present application Figure 4 Structural diagram of the fuzzy control algorithm in some embodiments of the present application. According to some embodiments of the present application, to determine the lower limit of the discharge SOC of the battery using the fuzzy control algorithm based on the user driving behavior characterization coefficient and the battery temperature comfort characterization coefficient, it may include:

[0076] Taking the user driving behavior characterization coefficient K(t) and the battery temperature comfort characterization coefficient P(t) as the input variables of the fuzzy control algorithm, and taking the dynamic lower limit SOC characterization coefficient L(t) as the output variable of the fuzzy control algorithm;

[0077] Setting the fuzzy subsets corresponding to the input variables and the output variable to be {LE, ME, GE}, and setting the membership functions corresponding to the input variables and the membership functions corresponding to the output variable; where, LE is smaller, ME is medium, and GE is larger;

[0078] According to the fuzzy subsets corresponding to the input variables and the output variable, converting the first preset fuzzy rule into a first fuzzy control table, and inputting the first fuzzy control table, the membership functions corresponding to the input variables, and the membership functions corresponding to the output variable into the first fuzzy controller;

[0079] Inputting the user driving behavior characterization coefficient and the battery temperature comfort characterization coefficient into the first fuzzy controller to obtain the defuzzified dynamic lower limit SOC characterization coefficient;

[0080] Using the defuzzified dynamic lower limit SOC characterization coefficient, the maximum SOC usage lower limit, and the battery empty SOC to obtain the lower limit of the discharge SOC of the battery.

[0081] When determining the lower limit of the discharge SOC of the battery using the fuzzy control algorithm based on the user driving behavior characterization coefficient and the battery temperature comfort characterization coefficient, first, a first fuzzy controller can be constructed and designed, and then, the first fuzzy controller can be used for fuzzy inference to obtain the fuzzified output variable, and the first fuzzy controller can be used to defuzzify the fuzzified output variable, and the lower limit of the discharge SOC of the battery can be obtained based on the defuzzified output variable.

[0082] Specifically, the user driving behavior characterization coefficient K(t) and the battery temperature comfort characterization coefficient P(t) can be used as the input variables of the fuzzy control algorithm, and the dynamic lower limit SOC characterization coefficient L(t) can be used as the output variable of the fuzzy control algorithm. Among them, 0 ≤ L(t) ≤ 1. L(t) = 0 means that the lower limit of the battery discharge SOC is equal to the battery empty SOC, and L(t) = 1 means that the lower limit of the battery discharge SOC is equal to the lower limit of the maximum SOC usage, that is, L(t) can be used to characterize the proximity of the lower limit of the battery discharge SOC to the lower limit of the maximum SOC usage. The larger L(t) is, the closer the lower limit of the battery discharge SOC is to the lower limit of the maximum SOC usage, and the smaller L(t) is, the farther the lower limit of the battery discharge SOC is from the lower limit of the maximum SOC usage. Among them, using the dynamic lower limit SOC characterization coefficient L(t) as the output variable of the fuzzy control algorithm can eliminate the need for the lower limit of the maximum SOC usage of the battery in the vehicle and the battery empty SOC to participate in the fuzzy control, so as to improve the convenience of the fuzzy control and speed up the fuzzy control calculation process.

[0083] On the basis of setting the input and output variables of the fuzzy control algorithm, the fuzzy subsets corresponding to each input variable (i.e., the user driving behavior characterization coefficient K(t) and the battery temperature comfort characterization coefficient P(t)) of the fuzzy control algorithm can be set to {LE, ME, GE}, and furthermore, the fuzzy subset corresponding to the output variable (i.e., the dynamic lower limit SOC characterization coefficient L(t)) of the fuzzy control algorithm can also be set to {LE, ME, GE}, that is, the three fuzzy subsets LE, ME, and GE are set for both the input and output variables. Among them, LE is smaller, ME is medium, and GE is larger. In addition, the membership functions corresponding to each input variable of the fuzzy control algorithm and the membership function corresponding to the output variable of the fuzzy control algorithm can be set. Among them, the membership function is the transformation from a clear input to a fuzzy input, and it is the conversion from an exact value to a fuzzy language. The membership function corresponding to the input variable can fuzzify the input variable, and the membership function corresponding to the output variable can fuzzify the output variable and can defuzzify the output variable based on the membership function corresponding to the output variable.

[0084] Then, according to the fuzzy subsets corresponding to the input variables of the fuzzy control algorithm and the fuzzy subset corresponding to the output variable of the fuzzy control algorithm, the first preset fuzzy rule can be converted into a first fuzzy control table, that is, the first preset fuzzy rule can be described by using the fuzzy subsets corresponding to the input and output variables. Among them, the first preset fuzzy rule is set according to the user's driving behavior and the temperature difference between the current temperature of the battery and the optimal temperature. After the first fuzzy control table is obtained through conversion, the converted first fuzzy control table can be input into the first fuzzy controller (specifically, it can be input into the knowledge base of the first fuzzy controller). In addition, the membership functions corresponding to the input variables, the fuzzy subsets of the input variables, the membership functions corresponding to the output variable, and the fuzzy subset of the output variable can also be input into the first fuzzy controller (or into the knowledge base of the first fuzzy controller), so that the inference engine in the first fuzzy controller can perform fuzzy inference and defuzzification based on this information to obtain the defuzzified dynamic lower limit SOC characterization coefficient.

[0085] On this basis, the user driving behavior characterization coefficient and the battery temperature comfort characterization coefficient can be input into the first fuzzy controller, and the defuzzified dynamic lower limit SOC characterization coefficient can be obtained by using the first fuzzy controller. That is, the first fuzzy controller performs fuzzy inference according to the first fuzzy control table, the membership function corresponding to the input variable, and the membership function corresponding to the output variable to obtain the defuzzified dynamic lower limit SOC characterization coefficient. Specifically, the first fuzzy controller performs fuzzy processing on each input variable according to the membership function and fuzzy subset corresponding to each input variable, determines the fuzzy relationship according to the first fuzzy control table, etc., then makes a fuzzy decision to obtain the fuzzy output variable, and defuzzifies the fuzzy output variable to obtain the defuzzified dynamic lower limit SOC characterization coefficient. Among them, the defuzzification processing of the output variable can be specifically performed by using methods such as the maximum membership degree method and the median method.

[0086] After obtaining the de-fuzzified dynamic lower-limit SOC characterization coefficient, the lower-limit of the battery's discharge SOC can be calculated using the maximum SOC usage lower-limit, the battery's discharged SOC, and the de-fuzzified dynamic lower-limit SOC characterization coefficient. Moreover, the calculated lower-limit of the battery's discharge SOC lies between the battery's discharged SOC and the maximum SOC usage lower-limit. Among them, when the execution entity is the cloud platform, after calculating the lower-limit of the battery's discharge SOC, the cloud platform can send the lower-limit of the battery's discharge SOC to the corresponding vehicle, and the vehicle can perform corresponding control based on the lower-limit of the battery's discharge SOC. Alternatively, it can also be that after calculating the de-fuzzified dynamic lower-limit SOC characterization coefficient, the cloud platform sends the de-fuzzified dynamic lower-limit SOC characterization coefficient to the vehicle, and the vehicle calculates the lower-limit of the battery's discharge SOC using the maximum SOC usage lower-limit, the battery's discharged SOC, and the de-fuzzified dynamic lower-limit SOC characterization coefficient. When the execution entity is the vehicle, after calculating the lower-limit of the battery's discharge SOC, the vehicle can perform corresponding control based on the lower-limit of the battery's discharge SOC.

[0087] It should be noted that Figure 3 the user behavior statistical analysis refers to obtaining the number of times the battery in the vehicle discharges and the number of times the SOC at the end of the battery discharge is less than the maximum SOC usage lower-limit. The battery information refers to obtaining the current temperature of the battery and the optimal temperature of the battery. The input normalization refers to obtaining the user driving behavior characterization coefficient through normalization processing based on the user behavior statistical analysis results and obtaining the battery temperature comfort characterization coefficient through normalization processing based on the battery information. Then, the user driving behavior characterization coefficient and the battery temperature comfort characterization coefficient are used as input quantities to input into the fuzzy controller for fuzzification. The inference mechanism performs fuzzy inference based on the fuzzified input quantities and the relevant information in the knowledge base to obtain the fuzzified output quantity. Then, the fuzzified output quantity is de-fuzzified to obtain the de-fuzzified output quantity, and the lower-limit of the battery's discharge SOC is calculated using the de-fuzzified output quantity.

[0088] By using the user driving behavior characterization coefficient and the battery temperature comfort characterization coefficient as the input quantities of the fuzzy control and using the dynamic lower-limit SOC characterization coefficient as the output quantity of the fuzzy control, not only can the accuracy of determining the lower-limit of the battery's discharge SOC be improved, but also the convenience of determining the lower-limit of the battery's discharge SOC can be improved.

[0089] According to some embodiments of the present application, the first fuzzy control table may include:

[0090] K(t) = LE, P(t) = LE, L(t) = LE;

[0091] K(t) = LE, P(t) = ME, L(t) = LE;

[0092] K(t) = LE, P(t) = GE, L(t) = ME;

[0093] K(t) = ME, P(t) = LE, L(t) = LE;

[0094] K(t) = ME, P(t) = ME, L(t) = ME;

[0095] K(t) = ME, P(t) = GE, L(t) = ME;

[0096] K(t) = GE, P(t) = LE, L(t) = LE;

[0097] K(t) = GE, P(t) = ME, L(t) = ME;

[0098] K(t) = GE, P(t) = GE, L(t) = GE.

[0099] In the embodiments of the present application, the first preset fuzzy rule may specifically be: 1) When it is recognized that the user's driving behavior is relatively conservative (i.e., the SOC at the end of battery discharge is basically difficult to reach below the lower limit of the maximum SOC for use), the lower limit of the discharge SOC of the battery is adjusted to the empty SOC of the battery, giving priority to improving the endurance of the whole vehicle; 2) When it is recognized that the user's driving behavior is relatively intense (i.e., the lower limit of the SOC at the end of battery discharge is easily reached below the lower limit of the maximum SOC for use), the lower limit of the discharge SOC of the battery is adjusted to the lower limit of the maximum SOC for use of the battery, giving priority to ensuring the user's trust in the endurance of the whole vehicle and reducing the frequency of undervoltage faults caused by SOC errors during low-SOC endurance; 3) When it is recognized that the temperature difference between the current temperature of the battery and the optimal temperature of the battery is small, the lower limit of the discharge SOC of the battery is adjusted to the empty SOC of the battery, giving priority to improving the endurance of the whole vehicle; 4) When it is recognized that the temperature difference between the current temperature of the battery and the optimal temperature of the battery is large, the lower limit of the discharge SOC of the battery is adjusted to the lower limit of the maximum SOC for use of the battery, giving priority to ensuring the user's trust in the endurance of the whole vehicle and reducing the frequency of undervoltage faults caused by SOC errors during low-SOC endurance.

[0100] When the fuzzy subsets corresponding to the input and output quantities are set to {LE, ME, GE}, the first fuzzy control table obtained by converting the above first preset fuzzy rule is specifically as shown in Table 1 below:

[0101] Table 1 First Fuzzy Control Table

[0102]

[0103]

[0104] Through the above first fuzzy control table, the accurate determination of the lower limit of the battery discharge SOC can be achieved, so as to better improve the vehicle endurance and reduce the frequency of under-voltage faults at the end of discharge caused by SOC errors.

[0105] According to some embodiments of the present application, it may further include:

[0106] Pre-establish a table of temperature comfort characterization coefficients for the battery in the vehicle; the table of temperature comfort characterization coefficients may include the absolute value of the temperature difference between the battery temperature and the optimal temperature, and the temperature comfort characterization coefficient corresponding to the absolute value of the temperature difference;

[0107] Determining the temperature comfort characterization coefficient of the battery according to the current temperature of the battery and the optimal temperature of the battery may include:

[0108] Determine the absolute value of the temperature difference between the current temperature of the battery and the optimal temperature of the battery according to the current temperature of the battery and the optimal temperature of the battery;

[0109] Determine the temperature comfort characterization coefficient of the battery according to the absolute value of the temperature difference between the current temperature of the battery and the optimal temperature of the battery and the table of temperature comfort characterization coefficients.

[0110] Before determining the temperature comfort characterization coefficient of the battery according to the current temperature of the battery and the optimal temperature of the battery, a table of temperature comfort characterization coefficients for the battery in the vehicle may be pre-established, and the table of temperature comfort characterization coefficients includes the absolute value of the temperature difference between the battery temperature and the optimal temperature of the battery and the temperature comfort characterization coefficient corresponding to the absolute value of the temperature difference. Among them, the specific correspondence between the absolute value of the temperature difference and the temperature comfort characterization coefficient can be obtained by calibration in advance, or can be calculated by the following method: when the absolute value of the temperature difference is less than the battery comfort tolerance temperature difference, the temperature comfort characterization coefficient = the absolute value of the temperature difference / the battery comfort tolerance temperature difference; when the absolute value of the temperature difference is greater than or equal to the battery comfort tolerance temperature difference, the temperature comfort characterization coefficient = 1. Among them, the battery comfort tolerance temperature difference is the difference between the temperature corresponding to the worst reduction of the battery capacity and the optimal temperature, and this difference can be obtained by calibration in advance.

[0111] On the basis of pre-establishing the table of temperature comfort characterization coefficients, the absolute value of the temperature difference between the current temperature of the battery and the optimal temperature of the battery can be determined according to the current temperature of the battery and the optimal temperature of the battery. Then, look up the pre-established table of temperature comfort characterization coefficients according to the determined absolute value of the temperature difference to determine the temperature comfort characterization coefficient of the battery corresponding to the determined absolute value of the temperature difference.

[0112] Through the above method, the convenience and efficiency of determining the temperature comfort characterization coefficient of the battery can be improved, so as to improve the determination speed and efficiency of the lower limit of the battery discharge SOC.

[0113] Of course, it is also possible to pre-determine the relationship expression between the absolute value of the temperature difference between the battery temperature and the optimal temperature of the battery and the temperature comfort characterization coefficient, and then determine the temperature comfort characterization coefficient of the battery according to the relationship expression, the current temperature of the battery, and the absolute value of the temperature difference between the battery and the optimal temperature.

[0114] According to some embodiments of the present application, determining the lower limit of the discharge SOC of the battery by using a fuzzy control algorithm based on the user driving behavior characterization coefficient may include:

[0115] Taking the user driving behavior characterization coefficient K(t) as the input quantity of the fuzzy control algorithm, and taking the dynamic lower limit SOC characterization coefficient L(t) as the output quantity of the fuzzy control algorithm;

[0116] Setting the fuzzy subsets corresponding to the input quantity and the output quantity to be {LE, ME, GE}, and setting the membership function corresponding to the input quantity and the membership function corresponding to the output quantity; where LE is smaller, ME is medium, and GE is larger;

[0117] According to the fuzzy subset corresponding to the input quantity and the fuzzy subset corresponding to the output quantity, converting the second preset fuzzy rule into a second fuzzy control table, and inputting the second fuzzy control table, the membership function corresponding to the input quantity, and the membership function corresponding to the output quantity into the second fuzzy controller;

[0118] Inputting the user driving behavior characterization coefficient into the second fuzzy controller to obtain the defuzzified dynamic lower limit SOC characterization coefficient;

[0119] Using the defuzzified dynamic lower limit SOC characterization coefficient to obtain the lower limit of the discharge SOC of the battery.

[0120] During the determination of the lower limit of the discharge SOC of the battery, it is also possible to construct a fuzzy controller only based on the user driving behavior characterization coefficient, and perform fuzzy reasoning and defuzzification using the constructed fuzzy controller to obtain the defuzzified dynamic lower limit SOC characterization coefficient. Then, based on the defuzzified dynamic lower limit SOC characterization coefficient, determine the lower limit of the discharge SOC of the battery, so as to simplify the construction of the fuzzy controller, improve the determination efficiency of the output quantity, and realize the dynamic adjustment of the lower limit of the discharge SOC of the battery, so as to improve the vehicle endurance and reduce the occurrence frequency of under-voltage faults.

[0121] Specifically, the user driving behavior characterization coefficient K(t) can be used as the input of the fuzzy control algorithm, and the dynamic lower limit SOC characterization coefficient L(t) can be used as the output of the fuzzy control algorithm. Among them, 0≤L(t)≤1, L(t)=0 means that the lower limit of the discharge SOC of the battery is equal to the empty SOC of the battery, and L(t)=1 means that the lower limit of the discharge SOC of the battery is equal to the lower limit of the maximum SOC usage. Then, it is set that the fuzzy subsets corresponding to the input and output of the fuzzy control algorithm are both {LE, ME, GE}, where LE means smaller, ME means medium, and GE means larger. And the membership function corresponding to the input and the membership function corresponding to the output are also set.

[0122] After that, according to the fuzzy subset corresponding to the input of the fuzzy control algorithm and the fuzzy subset corresponding to the output of the fuzzy control algorithm, the second preset fuzzy rule can be converted into a second fuzzy control table, that is, the second preset fuzzy rule can be described by using the fuzzy subsets corresponding to the input and output. Among them, the second preset fuzzy rule is specifically set according to the user driving behavior. After obtaining the second fuzzy control table, it can be input into the second fuzzy controller (specifically, it can be input into the knowledge base of the second fuzzy controller). In addition, the fuzzy subset corresponding to the set input, the membership function, the fuzzy subset corresponding to the output, and the membership function can also be input into the second fuzzy controller (specifically, it can also be input into the knowledge base of the second fuzzy controller), so that the inference mechanism in the second fuzzy controller can perform fuzzy inference and defuzzification based on this information to obtain the defuzzified dynamic lower limit SOC characterization coefficient.

[0123] On this basis, the user driving behavior characterization coefficient can be input into the second fuzzy controller, and the defuzzified dynamic lower limit SOC characterization coefficient can be obtained by using the second fuzzy controller. Specifically, the second fuzzy controller performs fuzzy processing on the input according to the membership function and fuzzy subset corresponding to the input, determines the fuzzy relationship according to the second fuzzy control table, etc., then makes a fuzzy decision to obtain the fuzzy output, and defuzzifies the fuzzy output to obtain the defuzzified dynamic lower limit SOC characterization coefficient. Among them, the maximum membership degree method, the median method, etc. can be specifically used to defuzzify the output.

[0124] After obtaining the defuzzified dynamic lower limit SOC characterization coefficient, the lower limit of the discharge SOC of the battery can be calculated by using the lower limit of the maximum SOC usage, the empty SOC of the battery, and the defuzzified dynamic lower limit SOC characterization coefficient, and the calculated lower limit of the discharge SOC of the battery is between the empty SOC of the battery and the lower limit of the maximum SOC usage.

[0125] According to some embodiments of the present application, the second fuzzy control table may include:

[0126] K(t) = LE, L(t) = LE;

[0127] K(t) = ME, L(t) = ME;

[0128] K(t) = GE, L(t) = GE.

[0129] The second preset fuzzy rule can specifically be: 1) When it is recognized that the user's driving behavior is relatively conservative (that is, the SOC at the end of battery discharge is basically difficult to reach below the lower limit of the maximum SOC usage), adjust the lower limit of the battery's discharge SOC to the empty SOC of the battery, and give priority to improving the vehicle's endurance; 2) When it is recognized that the user's driving behavior is relatively intense (that is, the lower limit of the SOC at the end of battery discharge is easily reached below the lower limit of the maximum SOC usage), adjust the lower limit of the battery's discharge SOC to the lower limit of the maximum SOC usage of the battery, and give priority to ensuring the user's trust in the vehicle's endurance, and reduce the frequency of undervoltage faults caused by SOC errors during low-SOC endurance.

[0130] Based on the fuzzy subsets corresponding to the input quantity and the output quantity both being {LE, ME, GE}, the above second preset fuzzy rule can specifically be converted into the following second fuzzy control table:

[0131] Table 2 Second Fuzzy Control Table

[0132] K(t) = LE K(t) = ME K(t) = GE L(t) = LE L(t) = ME L(t) = GE

[0133] Through the above second fuzzy control table, both the determination efficiency of the output quantity can be improved, and the dynamic adjustment of the lower limit of the battery's discharge SOC can be realized, so as to improve the vehicle's endurance and reduce the occurrence frequency of undervoltage faults.

[0134] According to some embodiments of the present application, using the defuzzified dynamic lower limit SOC characterization coefficient, the lower limit of the maximum SOC usage, and the empty SOC of the battery to obtain the lower limit of the battery's discharge SOC may include:

[0135] Using the formula: the lower limit of the battery's discharge SOC = the defuzzified dynamic lower limit SOC characterization coefficient * (the lower limit of the maximum SOC usage - the empty SOC of the battery) + the empty SOC of the battery, calculate the lower limit of the battery's discharge SOC.

[0136] Wherein, when the empty SOC of the battery = 0%, the above formula is transformed into: the lower limit of the battery's discharge SOC = the defuzzified dynamic lower limit SOC characterization coefficient * the lower limit of the maximum SOC usage.

[0137] Through the above, the accuracy of determining the lower limit of the battery's discharge SOC can be improved, so as to better improve the vehicle's endurance and reduce the occurrence frequency of undervoltage faults.

[0138] According to some embodiments of the present application, setting the membership function corresponding to the input quantity and the membership function corresponding to the output quantity may include:

[0139] Set the membership function corresponding to the input quantity as a Gaussian function, and set the membership function corresponding to the output quantity as a Gaussian function.

[0140] When setting the membership function corresponding to the input quantity and the membership function corresponding to the output quantity of the fuzzy controller, specifically, the Gaussian function can be set as the membership function corresponding to the input quantity and the membership function corresponding to the output quantity. Specifically, reference can be made to Figure 5 and Figure 6 , where Figure 5 is the membership function of the input quantity of the fuzzy controller in some embodiments of the present application, Figure 6 is the membership function of the output quantity of the fuzzy controller in some embodiments of the present application. Among them, in Figure 5 , the membership functions corresponding to the fuzzy subsets LE, ME, and GE of the input quantity are all Gaussian functions.

[0141] By selecting the Gaussian function as the membership function of the input quantity and the output quantity in the fuzzy controller, the smoothing rate of the dynamic lower limit SOC characterization coefficient can be improved, thereby improving the smoothing rate of the discharge SOC lower limit of the battery. That is, selecting the Gaussian function as the membership function of the input quantity and the output quantity in the fuzzy controller can smoothly adjust and output the discharge SOC lower limit of the battery, so as to better improve the vehicle endurance in some scenarios, and better reduce the occurrence frequency of battery under-voltage faults at the end of discharge due to SOC errors, enhance the user's trust in the vehicle endurance, and improve the user's driving experience.

[0142] According to some embodiments of the present application, obtaining the number of discharges of the battery in the vehicle and the number of times that the SOC at the end of battery discharge is less than the maximum SOC usage lower limit may include:

[0143] Obtain the driving state of the vehicle, the charging state of the vehicle, and the SOC of the battery in the vehicle in real time;

[0144] According to the driving state of the vehicle, the charging state of the vehicle, and the SOC of the battery in the vehicle obtained in real time, obtain the number of discharges of the battery in the vehicle and the SOC at the end of battery discharge, and determine the number of times that the SOC at the end of battery discharge is less than the maximum SOC usage lower limit according to the SOC at the end of battery discharge and the maximum SOC usage lower limit.

[0145] In the embodiments of the present application, the big data information of the vehicle can be obtained in real time, and according to the big data information of the vehicle obtained in real time, the number of discharges of the battery in the vehicle and the number of times that the SOC at the end of the battery discharge is less than the maximum SOC usage lower limit can be obtained.

[0146] Specifically, the driving state of the vehicle, the charging state of the vehicle (i.e., the charge and discharge state of the battery in the vehicle), and the SOC of the battery in the vehicle can be obtained in real time. Based on the driving state of the vehicle, the charging state of the vehicle, and the SOC of the battery in the vehicle obtained in real time, the number of battery discharges and the end-of-discharge SOC of the battery in the vehicle can be obtained. Moreover, based on the end-of-discharge SOC of the battery and the lower limit of the maximum SOC usage of the battery, the number of times that the end-of-discharge SOC of the battery is less than the lower limit of the maximum SOC usage can be determined.

[0147] Among them, when the execution entity is the cloud platform, the above vehicle information can be sent to the cloud platform through the TBOX (Telematics BOX, remote communication terminal) in the vehicle. The cloud platform can obtain the above vehicle information in real time and, based on the information obtained in real time, obtain the number of battery discharges in the vehicle and the number of times that the end-of-discharge SOC of the battery is less than the lower limit of the maximum SOC usage. When the execution entity is the vehicle, the vehicle can obtain the above vehicle information in real time and send the above vehicle information obtained in real time to the cloud platform through the TBOX. The cloud platform can obtain the number of battery discharges in the vehicle and the number of times that the end-of-discharge SOC of the battery is less than the lower limit of the maximum SOC usage based on the obtained above vehicle information and send it to the vehicle to reduce the consumption of vehicle resources.

[0148] It should be noted that when the current temperature of the battery needs to be obtained, it can be obtained in real time together with the above vehicle information such as the driving state of the vehicle, the charging state of the vehicle, and the SOC of the battery in the vehicle.

[0149] Through the above method, the accuracy of obtaining the number of battery discharges in the vehicle and the number of times that the end-of-discharge SOC of the battery is less than the lower limit of the maximum SOC usage can be improved, so as to improve the accuracy of determining the lower limit of the discharge SOC of the battery.

[0150] The present application also provides a device for determining the lower limit of battery SOC. Refer to Figure 7 , which is a schematic structural diagram of the device for determining the lower limit of battery SOC in some embodiments of the present application. The device 70 for determining the lower limit of battery SOC may include: a first acquisition module 71, a first determination module 72, and a second determination module 73. Among them, the first acquisition module 71 is used to acquire the number of battery discharges in the vehicle and the number of times that the end-of-discharge SOC of the battery is less than the lower limit of the maximum SOC usage; the first determination module 72 is used to determine the user driving behavior characterization coefficient according to the number of battery discharges and the number of times that the end-of-discharge SOC of the battery is less than the lower limit of the maximum SOC usage; the second determination module 73 is used to determine the lower limit of the discharge SOC of the battery by using a fuzzy control algorithm according to the user driving behavior characterization coefficient; wherein, the lower limit of the discharge SOC of the battery is between the empty SOC of the battery and the lower limit of the maximum SOC usage.

[0151] According to some embodiments of the present application, the battery SOC lower limit determination device may further include a second acquisition module and a third determination module. The second acquisition module is configured to acquire the current temperature of the battery and the optimal temperature of the battery. The third determination module is configured to determine a temperature comfort characterization coefficient of the battery according to the current temperature of the battery and the optimal temperature of the battery.

[0152] The second determination module 73 determines the discharge SOC lower limit of the battery by using a fuzzy control algorithm according to the user driving behavior characterization coefficient, and is specifically configured to determine the discharge SOC lower limit of the battery by using a fuzzy control algorithm according to the user driving behavior characterization coefficient and the temperature comfort characterization coefficient of the battery.

[0153] According to some embodiments of the present application, the second determination module 73 determines the discharge SOC lower limit of the battery by using a fuzzy control algorithm according to the user driving behavior characterization coefficient and the temperature comfort characterization coefficient of the battery. Specifically, the user driving behavior characterization coefficient K(t) and the temperature comfort characterization coefficient P(t) of the battery are used as the input quantities of the fuzzy control algorithm, and the dynamic lower limit SOC characterization coefficient L(t) is used as the output quantity of the fuzzy control algorithm. The fuzzy subsets corresponding to the input quantity and the output quantity are both set to {LE, ME, GE}, and the membership functions corresponding to the input quantity and the output quantity are set. Wherein, LE is smaller, ME is medium, and GE is larger. According to the fuzzy subsets corresponding to the input quantity and the output quantity, the first preset fuzzy rule is converted into a first fuzzy control table, and the first fuzzy control table, the membership function corresponding to the input quantity, and the membership function corresponding to the output quantity are input into the first fuzzy controller. The user driving behavior characterization coefficient and the temperature comfort characterization coefficient of the battery are input into the first fuzzy controller to obtain the defuzzified dynamic lower limit SOC characterization coefficient. The discharge SOC lower limit of the battery is obtained by using the defuzzified dynamic lower limit SOC characterization coefficient, the maximum SOC usage lower limit, and the battery emptying SOC.

[0154] According to some embodiments of the present application, the first fuzzy control table may include: K(t)=LE, P(t)=LE, L(t)=LE; K(t)=LE, P(t)=ME, L(t)=LE; K(t)=LE, P(t)=GE, L(t)=ME; K(t)=ME, P(t)=LE, L(t)=LE; K(t)=ME, P(t)=ME, L(t)=ME; K(t)=ME, P(t)=GE, L(t)=ME; K(t)=GE, P(t)=LE, L(t)=LE; K(t)=GE, P(t)=ME, L(t)=ME; K(t)=GE, P(t)=GE, L(t)=GE.

[0155] According to some embodiments of the present application, the battery SOC lower limit determination device may further include a pre-built table module for pre-building a table of temperature comfort characterization coefficients of the battery in the vehicle; the table of temperature comfort characterization coefficients may include the absolute value of the temperature difference between the temperature of the battery and the optimal temperature, and the temperature comfort characterization coefficient corresponding to the absolute value of the temperature difference.

[0156] The third determination module determines the temperature comfort characterization coefficient of the battery according to the current temperature of the battery and the optimal temperature of the battery, specifically for determining the absolute value of the temperature difference between the current temperature of the battery and the optimal temperature of the battery according to the current temperature of the battery and the optimal temperature of the battery; and determining the temperature comfort characterization coefficient of the battery according to the absolute value of the temperature difference between the current temperature of the battery and the optimal temperature of the battery and the table of temperature comfort characterization coefficients.

[0157] According to some embodiments of the present application, the second determination module 73 determines the discharge SOC lower limit of the battery by using a fuzzy control algorithm according to the user driving behavior characterization coefficient, specifically for using the user driving behavior characterization coefficient K(t) as the input quantity of the fuzzy control algorithm and using the dynamic lower limit SOC characterization coefficient L(t) as the output quantity of the fuzzy control algorithm; setting the fuzzy subsets corresponding to the input quantity and the output quantity to be {LE, ME, GE}, and setting the membership function corresponding to the input quantity and the membership function corresponding to the output quantity; where LE is smaller, ME is medium, and GE is larger; converting the second preset fuzzy rule into a second fuzzy control table according to the fuzzy subset corresponding to the input quantity and the fuzzy subset corresponding to the output quantity, and inputting the second fuzzy control table, the membership function corresponding to the input quantity, and the membership function corresponding to the output quantity into the second fuzzy controller; inputting the user driving behavior characterization coefficient into the second fuzzy controller to obtain the defuzzified dynamic lower limit SOC characterization coefficient; and using the defuzzified dynamic lower limit SOC characterization coefficient to obtain the discharge SOC lower limit of the battery.

[0158] According to some embodiments of the present application, the second fuzzy control table may include: K(t)=LE, L(t)=LE; K(t)=ME, L(t)=ME; K(t)=GE, L(t)=GE.

[0159] According to some embodiments of the present application, the second determination module 73 uses the defuzzified dynamic lower limit SOC characterization coefficient, the maximum SOC usage lower limit, and the battery empty SOC to obtain the discharge SOC lower limit of the battery, specifically for using the formula: the discharge SOC lower limit of the battery = the defuzzified dynamic lower limit SOC characterization coefficient * (the maximum SOC usage lower limit - the battery empty SOC) + the battery empty SOC to calculate the discharge SOC lower limit of the battery.

[0160] According to some embodiments of the present application, the second determination module 73 sets the membership function corresponding to the input quantity and the membership function corresponding to the output quantity, specifically for setting the membership function corresponding to the input quantity as a Gaussian function and setting the membership function corresponding to the output quantity as a Gaussian function.

[0161] According to some embodiments of the present application, the first acquisition module 71 acquires the number of discharges of the battery in the vehicle and the number of times that the SOC at the end of battery discharge is less than the lower limit of the maximum SOC usage, specifically for acquiring the driving state of the vehicle, the charging state of the vehicle, and the SOC of the battery in the vehicle in real time; according to the driving state of the vehicle, the charging state of the vehicle, and the SOC of the battery in the vehicle acquired in real time, acquiring the number of discharges of the battery in the vehicle and the SOC at the end of battery discharge, and determining the number of times that the SOC at the end of battery discharge is less than the lower limit of the maximum SOC usage according to the SOC at the end of battery discharge and the lower limit of the maximum SOC usage.

[0162] The present application also provides a device for determining the lower limit of battery SOC. Refer to Figure 8 , which is a schematic structural diagram of the device for determining the lower limit of battery SOC in some embodiments of the present application. The device 80 for determining the lower limit of battery SOC may include: a memory 81 and a processor 82. Among them, the memory 81 is used to store a computer program; the processor 82, when executing the computer program stored in the memory, can implement the following steps:

[0163] Acquire the number of discharges of the battery in the vehicle and the number of times that the SOC at the end of battery discharge is less than the lower limit of the maximum SOC usage; determine the user driving behavior characterization coefficient according to the number of discharges of the battery and the number of times that the SOC at the end of battery discharge is less than the lower limit of the maximum SOC usage; determine the lower limit of the discharge SOC of the battery by using a fuzzy control algorithm according to the user driving behavior characterization coefficient; wherein, the lower limit of the discharge SOC of the battery is between the empty SOC of the battery and the lower limit of the maximum SOC usage.

[0164] The present application also provides a readable storage medium, in which a computer program is stored. When the computer program is executed by a processor, the following steps can be implemented:

[0165] Acquire the number of discharges of the battery in the vehicle and the number of times that the SOC at the end of battery discharge is less than the lower limit of the maximum SOC usage; determine the user driving behavior characterization coefficient according to the number of discharges of the battery and the number of times that the SOC at the end of battery discharge is less than the lower limit of the maximum SOC usage; determine the lower limit of the discharge SOC of the battery by using a fuzzy control algorithm according to the user driving behavior characterization coefficient; wherein, the lower limit of the discharge SOC of the battery is between the empty SOC of the battery and the lower limit of the maximum SOC usage.

[0166] For the description of the relevant parts in a battery SOC lower limit determination device, equipment, and readable storage medium provided by the embodiments of the present application, reference can be made to the corresponding detailed description in a battery SOC lower limit determination method provided by the present application, which will not be elaborated herein.

[0167] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them; although the present application has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions described in the foregoing embodiments, or perform equivalent replacements for some or all of the technical features; and these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present application, and they should all be covered within the scope of the claims and the description of the present application. In particular, as long as there is no structural conflict, the technical features mentioned in each embodiment can be combined in any way. The present application is not limited to the specific embodiments disclosed herein, but includes all technical solutions falling within the scope of the claims.

Claims

1. A method for determining the lower limit of battery SOC, characterized in that, it includes: Obtain the number of discharges of the battery in the vehicle and the number of times that the SOC at the end of the battery discharge is less than the maximum SOC usage lower limit; Determine the user driving behavior characterization coefficient according to the number of discharges of the battery and the number of times that the SOC at the end of the battery discharge is less than the maximum SOC usage lower limit; According to the user driving behavior characterization coefficient, use a fuzzy control algorithm to determine the lower limit of the battery discharge SOC; wherein, the lower limit of the battery discharge SOC is between the battery empty SOC and the maximum SOC usage lower limit.

2. The method for determining the lower limit of battery SOC according to claim 1, characterized in that, it further includes: Obtain the current temperature of the battery and the optimal temperature of the battery; Determine the temperature comfort characterization coefficient of the battery according to the current temperature of the battery and the optimal temperature of the battery; Using the fuzzy control algorithm to determine the lower limit of the battery discharge SOC according to the user driving behavior characterization coefficient includes: Using the fuzzy control algorithm to determine the lower limit of the battery discharge SOC according to the user driving behavior characterization coefficient and the temperature comfort characterization coefficient of the battery.

3. The method for determining the lower limit of battery SOC according to claim 2, characterized in that, Using the fuzzy control algorithm to determine the lower limit of the battery discharge SOC according to the user driving behavior characterization coefficient and the temperature comfort characterization coefficient of the battery includes: Take the user driving behavior characterization coefficient K(t) and the temperature comfort characterization coefficient P(t) of the battery as the input quantities of the fuzzy control algorithm, and take the dynamic lower limit SOC characterization coefficient L(t) as the output quantity of the fuzzy control algorithm; Set the fuzzy subsets corresponding to the input quantity and the output quantity to be {LE, ME, GE}, and set the membership function corresponding to the input quantity and the membership function corresponding to the output quantity; where LE is smaller, ME is medium, and GE is larger; According to the fuzzy subsets corresponding to the input quantity and the output quantity, convert the first preset fuzzy rule into a first fuzzy control table, and input the first fuzzy control table, the membership function corresponding to the input quantity and the membership function corresponding to the output quantity into the first fuzzy controller; Input the user driving behavior characterization coefficient and the temperature comfort characterization coefficient of the battery into the first fuzzy controller to obtain the defuzzified dynamic lower limit SOC characterization coefficient; Use the defuzzified dynamic lower limit SOC characterization coefficient, the maximum SOC usage lower limit and the battery empty SOC to obtain the lower limit of the battery discharge SOC.

4. The method for determining the lower limit of battery SOC according to claim 3, characterized in that, The first fuzzy control table includes: K(t)=LE, P(t)=LE, L(t)=LE; K(t)=LE, P(t)=ME, L(t)=LE; K(t)=LE, P(t)=GE, L(t)=ME; K(t)=ME, P(t)=LE, L(t)=LE; K(t) = ME, P(t) = ME, L(t) = ME; K(t) = ME, P(t) = GE, L(t) = ME; K(t) = GE, P(t) = LE, L(t) = LE; K(t) = GE, P(t) = ME, L(t) = ME; K(t) = GE, P(t) = GE, L(t) = GE.

5. The method for determining the lower limit of the battery SOC according to any one of claims 2 to 4, characterized in that, further comprising: pre - establishing a temperature comfort characterization coefficient table for the battery in the vehicle; the temperature comfort characterization coefficient table includes the absolute value of the temperature difference between the temperature of the battery and the optimal temperature, and the temperature comfort characterization coefficient corresponding to the absolute value of the temperature difference; determining the temperature comfort characterization coefficient of the battery according to the current temperature of the battery and the optimal temperature of the battery, including: determining the absolute value of the temperature difference between the current temperature of the battery and the optimal temperature of the battery according to the current temperature of the battery and the optimal temperature of the battery; determining the temperature comfort characterization coefficient of the battery according to the absolute value of the temperature difference between the current temperature of the battery and the optimal temperature of the battery and the temperature comfort characterization coefficient table.

6. The method for determining the lower limit of the battery SOC according to claim 1, characterized in that, determining the lower discharge SOC limit of the battery by using a fuzzy control algorithm according to the user driving behavior characterization coefficient, including: taking the user driving behavior characterization coefficient K(t) as the input of the fuzzy control algorithm, and taking the dynamic lower limit SOC characterization coefficient L(t) as the output of the fuzzy control algorithm; setting the fuzzy subsets corresponding to the input and the output to be {LE, ME, GE}, and setting the membership function corresponding to the input and the membership function corresponding to the output; where LE is smaller, ME is medium, and GE is larger; converting the second preset fuzzy rule into a second fuzzy control table according to the fuzzy subset corresponding to the input and the fuzzy subset corresponding to the output, and inputting the second fuzzy control table, the membership function corresponding to the input and the membership function corresponding to the output into a second fuzzy controller; inputting the user driving behavior characterization coefficient into the second fuzzy controller to obtain the defuzzified dynamic lower limit SOC characterization coefficient; using the defuzzified dynamic lower limit SOC characterization coefficient, the maximum SOC usage lower limit and the battery empty SOC to obtain the lower discharge SOC limit of the battery.

7. The method for determining the lower limit of the battery SOC according to claim 6, characterized in that, the second fuzzy control table includes: K(t) = LE, L(t) = LE; K(t) = ME, L(t) = ME; K(t) = GE, L(t) = GE.

8. The method for determining the lower limit of the battery SOC according to claim 3 or 6, characterized in that, using the defuzzified dynamic lower limit SOC characterization coefficient, the maximum SOC usage lower limit and the battery empty SOC to obtain the lower discharge SOC limit of the battery, including: Using the formula: the lower limit of the discharge SOC of the battery = the defuzzified dynamic lower limit SOC characterization coefficient * (the lower limit of the maximum SOC usage - the SOC at which the battery is emptied) + the SOC at which the battery is emptied, calculate the lower limit of the discharge SOC of the battery.

9. The method for determining the lower limit of the battery SOC according to claim 3 or 6, wherein, setting the membership function corresponding to the input quantity and the membership function corresponding to the output quantity includes: setting the membership function corresponding to the input quantity as a Gaussian function, and setting the membership function corresponding to the output quantity as a Gaussian function.

10. The method for determining the lower limit of the battery SOC according to claim 1, wherein, obtaining the number of discharges of the battery in the vehicle and the number of times that the SOC at the end of the battery discharge is less than the lower limit of the maximum SOC usage includes: obtaining in real time the driving state of the vehicle, the charging state of the vehicle, and the SOC of the battery in the vehicle; according to the driving state of the vehicle, the charging state of the vehicle, and the SOC of the battery in the vehicle obtained in real time, obtaining the number of discharges of the battery in the vehicle and the SOC at the end of the battery discharge, and determining the number of times that the SOC at the end of the battery discharge is less than the lower limit of the maximum SOC usage according to the SOC at the end of the battery discharge and the lower limit of the maximum SOC usage.

11. A device for determining the lower limit of the battery SOC, wherein, comprising: a first obtaining module, configured to obtain the number of discharges of the battery in the vehicle and the number of times that the SOC at the end of the battery discharge is less than the lower limit of the maximum SOC usage; a first determining module, configured to determine the user driving behavior characterization coefficient according to the number of discharges of the battery and the number of times that the SOC at the end of the battery discharge is less than the lower limit of the maximum SOC usage; a second determining module, configured to determine the lower limit of the discharge SOC of the battery by using a fuzzy control algorithm according to the user driving behavior characterization coefficient; wherein, the lower limit of the discharge SOC of the battery is between the SOC at which the battery is emptied and the lower limit of the maximum SOC usage.

12. The device for determining the lower limit of the battery SOC according to claim 11, wherein, further comprising: a second obtaining module, configured to obtain the current temperature of the battery and the optimal temperature of the battery; a third determining module, configured to determine the temperature comfort characterization coefficient of the battery according to the current temperature of the battery and the optimal temperature of the battery; the second determining module is specifically configured to determine the lower limit of the discharge SOC of the battery by using the fuzzy control algorithm according to the user driving behavior characterization coefficient and the temperature comfort characterization coefficient of the battery.

13. A device for determining the lower limit of the battery SOC, wherein, comprising: a memory, configured to store a computer program; a processor, configured to implement the steps of the method for determining the lower limit of the battery SOC according to any one of claims 1 to 10 when executing the computer program.

14. A readable storage medium, wherein, the readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the method for determining the lower limit of the battery SOC according to any one of claims 1 to 10 are implemented.

Images