Battery system management method and device

Abstract

The application discloses a kind of battery system management method and device, applied to the battery system management method of cloud platform includes: determining the self-discharge rate data of battery system in target vehicle and the reference self-discharge rate data corresponding to battery system;According to the self-discharge rate data of battery system and the reference self-discharge rate data corresponding to it, the correction coefficient corresponding to target vehicle is determined;The correction coefficient is sent to target vehicle, and the charging SOC upper limit of battery system is corrected according to the correction coefficient by target vehicle;The charging SOC upper limit after correction is less than charging SOC upper limit.The technical scheme disclosed in the application determines the correction coefficient according to the self-discharge rate data of battery system in target vehicle, and reduces the charging SOC upper limit of battery system in target vehicle according to the correction coefficient, so that the charging SOC upper limit of battery system in target vehicle can be reduced when charging, to reduce the side reaction in battery system, and then reduce the probability of battery system thermal runaway.

Description

Technical Field

[0001] This invention relates to the field of vehicle technology, and in particular to a battery system management method and apparatus. Background Technology

[0002] As a crucial component of electric vehicles, the safety of battery systems has become a major concern.

[0003] Thermal runaway is a key issue in battery system safety research. Severe thermal runaway can lead to spontaneous combustion and explosion of the battery, thereby threatening user safety.

[0004] In summary, reducing the probability of thermal runaway in battery systems and improving their safety are technical problems that urgently need to be solved by those skilled in the art. Summary of the Invention

[0005] In view of this, the purpose of the present invention is to provide a battery system management method and apparatus to reduce the probability of thermal runaway in the battery system and improve the safety of the battery system.

[0006] To achieve the above objectives, the present invention provides the following technical solution:

[0007] A battery system management method, applied to a cloud platform, includes:

[0008] Determine the self-discharge rate data of the battery system in the target vehicle and the corresponding baseline self-discharge rate data of the battery system;

[0009] Based on the self-discharge rate data of the battery system and the corresponding benchmark self-discharge rate data, the correction coefficient corresponding to the target vehicle is determined;

[0010] The correction coefficient is sent to the target vehicle, which then corrects the upper limit of the charging SOC of the battery system based on the correction coefficient, so that the battery system charges according to the corrected upper limit of the charging SOC; the corrected upper limit of the charging SOC is less than the upper limit of the charging SOC.

[0011] Optionally, when determining the self-discharge rate data of the battery system in the target vehicle, the method further includes:

[0012] Obtain the state of health (SOH) of the battery system in the target vehicle;

[0013] Determining the baseline self-discharge rate data corresponding to the battery system includes:

[0014] The self-discharge rate (SOH) of the battery systems in each vehicle using the same battery system is obtained at the corresponding data acquisition time, and the self-discharge rate data corresponding to the SOH of the battery system in the vehicle is determined.

[0015] Based on the State of Harm (SOH) and corresponding self-discharge rate data of the battery system in each vehicle, determine the baseline self-discharge rate data corresponding to different SOHs.

[0016] The baseline self-discharge rate data corresponding to the SOH of the battery system in the target vehicle is obtained from the baseline self-discharge rate data corresponding to different SOH values.

[0017] Optionally, the state of charge (SOH) of the battery system in each vehicle at the corresponding data acquisition time is obtained, and the self-discharge rate data corresponding to the SOH of the battery system in the vehicle is determined, including:

[0018] Obtain the cutoff SOC of the battery system in the vehicle during charging, and determine the SOC statistical range based on the cutoff SOC.

[0019] After the vehicle stops running and the SOC of the battery system in the vehicle is within the SOC statistical range, the SOH, cumulative equalization SOC, and SOC difference of the battery system in the vehicle are acquired at first preset time intervals; the SOC difference is the difference between the SOC of the highest SOC single cell and the SOC of the lowest SOC single cell in the battery system at the corresponding time.

[0020] Based on the cumulative balanced SOC and SOC difference of the battery system in the vehicle, the self-discharge rate of the battery system in the vehicle within the time from the previous data acquisition time to the current data acquisition time is calculated as the second preset time, and the self-discharge rate is used as the self-discharge rate corresponding to the current data acquisition time; the second preset time is longer than the first preset time.

[0021] Based on the self-discharge rate of the battery system in the vehicle at each data acquisition time within the second preset duration corresponding to the current data acquisition time, the maximum self-discharge rate and the average self-discharge rate of the battery system in the vehicle within the second preset duration corresponding to the current data acquisition time are determined, and the maximum self-discharge rate and the average self-discharge rate are used as the maximum self-discharge rate and the average self-discharge rate corresponding to the SOH at the current data acquisition time.

[0022] Optionally, based on the State of Harm (SOH) and corresponding self-discharge rate data of the battery system in each of the vehicles, a baseline self-discharge rate data corresponding to different SOHs is determined, including:

[0023] Obtain the maximum self-discharge rate and average self-discharge rate of the battery system in each of the vehicles corresponding to different SOHs;

[0024] The average maximum self-discharge rate of the battery system in each vehicle corresponding to each SOH is calculated to obtain the average maximum self-discharge rate corresponding to each SOH.

[0025] The average self-discharge rate of the battery system in each vehicle corresponding to each SOH is averaged to obtain the average value of the average self-discharge rate corresponding to each SOH.

[0026] Optionally, based on the self-discharge rate data of the battery system and the corresponding benchmark self-discharge rate data, a correction coefficient corresponding to the target vehicle is determined, including:

[0027] When the maximum self-discharge rate of the battery system in the target vehicle is greater than or equal to the sum of the corresponding average maximum self-discharge rate and the first calibration value, and the average self-discharge rate of the battery system in the target vehicle is greater than or equal to the sum of the corresponding average self-discharge rate and the second calibration value, then the correction coefficient corresponding to the target vehicle is determined based on the maximum self-discharge rate of the battery system in the target vehicle and the sum of the corresponding average maximum self-discharge rate and the first calibration value, and the average self-discharge rate of the battery system in the target vehicle and the sum of the corresponding average self-discharge rate and the second calibration value.

[0028] When the maximum self-discharge rate of the battery system in the target vehicle is greater than or equal to the sum of the average maximum self-discharge rate and the first calibration value, and the average self-discharge rate of the battery system in the target vehicle is less than the sum of the average average self-discharge rate and the second calibration value, then the correction coefficient corresponding to the target vehicle is determined based on the maximum self-discharge rate of the battery system in the target vehicle and the sum of the average maximum self-discharge rate and the first calibration value.

[0029] When the maximum self-discharge rate of the battery system in the target vehicle is less than the sum of the average maximum self-discharge rate and the first calibration value, and the average self-discharge rate of the battery system in the target vehicle is greater than or equal to the sum of the average average self-discharge rate and the second calibration value, then the correction coefficient corresponding to the target vehicle is determined based on the average self-discharge rate of the battery system in the target vehicle and the sum of the average average self-discharge rate and the second calibration value.

[0030] When the maximum self-discharge rate of the battery system in the target vehicle is less than the sum of the average maximum self-discharge rate and the first calibration value, and the average self-discharge rate of the battery system in the target vehicle is less than the sum of the average average self-discharge rate and the second calibration value, the correction coefficient corresponding to the target vehicle is determined to be equal to 0.

[0031] A battery system management method, applied to a target vehicle, includes:

[0032] The cloud platform receives the correction coefficient corresponding to the target vehicle sent by the cloud platform; the cloud platform obtains the correction coefficient corresponding to the target vehicle using the battery system management method applied to the cloud platform as described in any of the above-mentioned methods.

[0033] The upper limit of the charging SOC of the battery system in the target vehicle is corrected according to the correction coefficient, so that the battery system is charged according to the corrected upper limit of the charging SOC; the corrected upper limit of the charging SOC is less than the upper limit of the charging SOC.

[0034] Optionally, the upper limit of the charging SOC of the battery system is modified, including:

[0035] The percentage reduction in the charging limit is determined based on the correction coefficient; wherein, when the correction coefficient is equal to 0, the percentage reduction in the charging limit is equal to 0; when the correction coefficient is greater than 0 and less than a first preset value, the percentage reduction in the charging limit is positively correlated with the correction coefficient; when the correction coefficient is greater than or equal to the first preset value, the percentage reduction in the charging limit is fixed, and the current percentage reduction in the charging limit is greater than the percentage reduction in the charging limit when the correction coefficient is less than the first preset value;

[0036] The upper limit of the charging SOC of the battery system in the target vehicle is reduced by the percentage reduction of the charging upper limit to obtain the corrected upper limit of the charging SOC.

[0037] Optionally, it also includes:

[0038] When the correction coefficient is greater than or equal to the second preset value and less than the first preset value, vehicle-side online diagnostics are performed at a third preset time interval; the second preset value is less than the first preset value.

[0039] When the correction coefficient is greater than or equal to the first preset value, vehicle-side online diagnostics are performed every fourth preset time interval, and the cooling system is activated when the temperature of the battery system is greater than the first preset temperature. When the duration for which the temperature of the battery system is greater than the first preset temperature exceeds the fifth preset time interval, the charging and discharging power of the battery system in the target vehicle is reduced. The fourth preset time interval is less than the third preset time interval.

[0040] Optionally, perform online vehicle diagnostics, including:

[0041] Obtain the temperature of the battery system and / or the voltage of each individual cell in the battery system;

[0042] Based on the temperature of the battery system and / or the voltage of each individual cell in the battery system, determine whether any individual cell in the battery system has failed.

[0043] If so, a vehicle malfunction alarm is triggered, and the cooling system in the target vehicle is controlled to operate.

[0044] A battery system management device, applied to a cloud platform, includes:

[0045] The first determining module is used to determine the self-discharge rate data of the battery system in the target vehicle and the corresponding baseline self-discharge rate data of the battery system.

[0046] The second determining module is used to determine the correction coefficient corresponding to the target vehicle based on the self-discharge rate data of the battery system and the corresponding benchmark self-discharge rate data.

[0047] The sending module is used to send the correction coefficient to the target vehicle, so that the target vehicle corrects the upper limit of the charging SOC of the battery system, so that the battery system charges according to the corrected upper limit of the charging SOC; the corrected upper limit of the charging SOC is less than the upper limit of the charging SOC.

[0048] A battery system control device, characterized in that it comprises:

[0049] A receiving module is used to receive the correction coefficient corresponding to the target vehicle sent by the cloud platform; the cloud platform uses the battery system management method applied to the cloud platform as described in any of the above to obtain the correction coefficient corresponding to the target vehicle.

[0050] The correction module is used to correct the upper limit of the charging SOC of the battery system in the target vehicle according to the correction coefficient, so that the battery system is charged according to the corrected upper limit of the charging SOC; the corrected upper limit of the charging SOC is less than the upper limit of the charging SOC.

[0051] This invention provides a battery system management method and apparatus. The battery system management method applied to a cloud platform includes: determining the self-discharge rate data of the battery system in a target vehicle and the corresponding baseline self-discharge rate data; determining a correction coefficient for the target vehicle based on the battery system's self-discharge rate data and the corresponding baseline self-discharge rate data; sending the correction coefficient to the target vehicle, whereby the target vehicle corrects the upper limit of the battery system's charging SOC according to the correction coefficient, so that the battery system charges according to the corrected upper limit of the charging SOC; the corrected upper limit of the charging SOC is less than the upper limit of the charging SOC.

[0052] The technical solution disclosed in this invention determines the correction coefficient corresponding to the target vehicle based on the self-discharge rate data of the battery system in the target vehicle and the corresponding baseline self-discharge rate data of the battery system. Then, the correction coefficient corresponding to the target vehicle is sent to the target vehicle, and the target vehicle corrects the upper limit of the charging SOC of the battery system in the target vehicle according to the correction coefficient, so as to reduce the upper limit of the charging SOC of the battery system in the target vehicle. This allows the battery system in the target vehicle to reduce the upper limit of the charging SOC during subsequent charging, thereby reducing the side reactions in the battery system and reducing the probability of thermal runaway of the battery system, thus improving the safety of the battery system and the vehicle.

[0053] Additional aspects and advantages of the invention will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. Attached Figure Description

[0054] Figure 1 A flowchart illustrating a battery system management method applied to a cloud platform, provided as an embodiment of the present invention;

[0055] Figure 2 A flowchart illustrating a battery system management method for a target vehicle, provided as an embodiment of the present invention;

[0056] Figure 3 A schematic diagram of a battery system management device applied to a cloud platform, provided in an embodiment of the present invention;

[0057] Figure 4 This is a schematic diagram of a battery system management device for a target vehicle, provided as an embodiment of the present invention. Detailed Implementation

[0058] The core of this invention is to provide a battery system management method and apparatus to reduce the probability of thermal runaway in battery systems and improve the safety of battery systems.

[0059] Embodiments of the present invention are described in detail below, examples of which are illustrated in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and intended to explain the present invention, and should not be construed as limiting the present invention.

[0060] See Figure 1 This document illustrates a flowchart of a battery system management method applied to a cloud platform, provided by an embodiment of the present invention. The battery system management method provided by this embodiment, applied to a cloud platform, may include:

[0061] S11: Determine the self-discharge rate data of the battery system in the target vehicle and the corresponding baseline self-discharge rate data of the battery system.

[0062] It should be noted that the execution entity of the battery system management method provided in this embodiment of the invention can be a cloud platform connected to the vehicle.

[0063] When managing a vehicle's battery system, the cloud platform can determine the self-discharge rate data of the target vehicle's battery system. This real-time self-discharge rate data enables real-time monitoring and control of the battery system, thereby improving its safety and reliability. Alternatively, the cloud platform can determine the self-discharge rate data periodically (the interval can be set as needed). Furthermore, the cloud platform can also determine the baseline self-discharge data for the target vehicle's battery system.

[0064] The target vehicle is any vehicle connected to the cloud platform that requires battery system monitoring and management. Self-discharge rate data can specifically include the maximum self-discharge rate and / or average self-discharge rate over a period of time. The reference self-discharge rate data is of the same type as the self-discharge rate data of the battery system in the target vehicle, and it is standard self-discharge rate data—that is, normal and acceptable self-discharge rate data—used for comparison with the self-discharge rate data of the battery system in the target vehicle to determine whether the self-discharge status of the battery system in the target vehicle is normal.

[0065] S12: Determine the correction coefficient for the target vehicle based on the battery system's self-discharge rate data and the corresponding baseline self-discharge rate data.

[0066] Based on step S11, the correction coefficient corresponding to the target vehicle (specifically, the correction coefficient corresponding to the battery system in the target vehicle) can be determined according to the self-discharge rate data of the battery system in the target vehicle and the corresponding baseline self-discharge rate data of the battery system.

[0067] The aforementioned correction coefficient characterizes the degree of deviation between the self-discharge rate data of the battery system in the target vehicle and the corresponding baseline self-discharge rate data. Specifically, the correction coefficient can be a value greater than or equal to 0. A larger correction coefficient indicates a greater deviation between the self-discharge rate data of the battery system in the target vehicle and the corresponding baseline self-discharge rate data. In other words, it indicates a greater deviation from the normal self-discharge behavior of the battery system, and consequently, a higher risk of thermal runaway. To reduce the probability of thermal runaway in the battery system of the target vehicle, thereby improving the safety of the battery system and the safety of the vehicle and its users, the battery system in the target vehicle can be managed and controlled.

[0068] Once the cloud platform determines the self-discharge rate data of the battery system in the target vehicle in real time, it can determine the corresponding correction coefficient for the target vehicle in real time, so as to realize real-time control of the battery system in the target vehicle.

[0069] S13: Send the correction coefficient to the target vehicle, and the target vehicle corrects the upper limit of the charging SOC of the battery system according to the correction coefficient, so that the battery system is charged according to the corrected upper limit of the charging SOC; the corrected upper limit of the charging SOC is less than the upper limit of the charging SOC.

[0070] Specifically, after the cloud platform determines the correction coefficient corresponding to the target vehicle using step S12, during the operation of the target vehicle, the cloud platform can send the correction coefficient corresponding to the target vehicle to the target vehicle through vehicle-to-cloud interconnection technology. Specifically, the cloud platform can identify the target vehicle through vehicle identification features and accurately send the correction coefficient corresponding to the target vehicle to the target vehicle.

[0071] After receiving the correction coefficient from the cloud platform, the target vehicle can adjust the upper limit of the State of Charge (SOC) of its battery system (UP_SOC) based on the received coefficient. This means it can adjust the initial upper limit of the battery system's SOC (UP_SOC0) to reduce the upper limit of the battery system's SOC, resulting in a lower adjusted SOC than the initial SOC. The upper limit of the SOC is the maximum SOC value of the battery system during charging. After adjusting the upper limit of the battery system's SOC, the battery system can charge according to the adjusted SOC, ensuring that the maximum SOC at the end of charging does not exceed the adjusted upper limit. Specifically, after adjusting the upper limit of the battery system's SOC, the target vehicle can control the charging of the battery system to ensure that the maximum SOC at the end of charging does not exceed the adjusted upper limit.

[0072] By reducing the upper limit of the charging SOC of the battery system in the target vehicle according to the correction coefficient corresponding to the target vehicle, the maximum SOC of the battery system in the target vehicle can be reduced, thereby reducing the side reactions inside the battery system in the target vehicle, thus reducing the probability of thermal runaway of the battery system in the target vehicle, and improving the safety of the battery system in the target vehicle, thereby improving the safety of the target vehicle and the user.

[0073] The technical solution disclosed in this invention determines the correction coefficient corresponding to the target vehicle based on the self-discharge rate data of the battery system in the target vehicle and the corresponding baseline self-discharge rate data of the battery system. Then, the correction coefficient corresponding to the target vehicle is sent to the target vehicle, and the target vehicle corrects the upper limit of the charging SOC of the battery system in the target vehicle according to the correction coefficient, so as to reduce the upper limit of the charging SOC of the battery system in the target vehicle. This allows the battery system in the target vehicle to reduce the upper limit of the charging SOC during subsequent charging, thereby reducing the side reactions in the battery system and reducing the probability of thermal runaway of the battery system, thus improving the safety of the battery system and the vehicle.

[0074] The battery system management method provided in this embodiment of the invention may further include, when determining the self-discharge rate data of the battery system in the target vehicle:

[0075] Obtain the State of Health (SOH) of the battery system in the target vehicle;

[0076] Determining the baseline self-discharge rate data for the battery system may include:

[0077] Obtain the state of charge (SOH) of the battery systems in each vehicle using the same battery system at the corresponding data acquisition time, and determine the self-discharge rate data corresponding to the SOH of the battery systems in the vehicle.

[0078] Based on the State of Harm (SOH) and corresponding self-discharge rate data of the battery system in each vehicle, determine the baseline self-discharge rate data corresponding to different SOHs.

[0079] Obtain the baseline self-discharge rate data corresponding to the SOH of the battery system in the target vehicle from the baseline self-discharge rate data corresponding to different SOHs.

[0080] Considering that SOH (State of Health) reflects the health and lifespan of a battery system, and battery systems with different SOHs have different health and lifespans and performance, in order to improve the accuracy of battery system management, this embodiment of the invention incorporates the SOH of the battery system into the determination of self-discharge rate data and correction coefficient.

[0081] Specifically, when determining the self-discharge rate data of the battery system in the target vehicle, the SOH of the battery system in the target vehicle can also be obtained at the same time, that is, the SOH of the battery system in the target vehicle corresponds to the self-discharge rate data.

[0082] Furthermore, when determining the baseline self-discharge rate (SOH) data for the battery system, the baseline SOH data for the battery system at different State of Emergency (SOH) conditions can first be obtained. Then, the baseline SOH data for the target vehicle's battery system can be determined from this baseline SOH data. Specifically, the baseline SOH data can be determined using all vehicles employing the same battery system as the target vehicle (this "all vehicles" may include the aforementioned target vehicle). This improves the rationality and accuracy of the baseline SOH data determination and ensures that the determined baseline SOH data is applicable to any vehicle using the same battery system, thereby expanding the applicability of the baseline SOH data. Specifically, firstly, the State of Health (SOH) of the battery systems in each vehicle using the same battery system can be obtained at the corresponding data acquisition time (the data acquisition time mentioned here refers to the time when the cloud platform acquires the data; each vehicle can send its corresponding data to the cloud platform after collection, and the cloud platform can acquire the data; the time interval for data collection for each vehicle can be set according to its own conditions, etc.). Then, the self-discharge rate data of the battery system in each vehicle at the corresponding time can be determined, that is, the self-discharge rate data corresponding to the SOH of the battery system in the vehicle can be determined. Finally, based on the SOH of the battery systems in each vehicle and the corresponding self-discharge rate data, the baseline self-discharge rate data corresponding to different SOHs of the battery systems can be determined. Specifically, the baseline self-discharge rate data corresponding to different SOHs mentioned here can be stored in tabular form, with each table containing multiple SOHs and corresponding baseline self-discharge rate data, and the difference between two adjacent SOHs can be equal; or, the baseline self-discharge rate data corresponding to different SOHs can be stored in the form of expressions, which can contain two variables, one variable being SOH (which can be the independent variable) and the other variable being the baseline self-discharge rate data (which can be the dependent variable). The specific form of the expression can be obtained by fitting the SOH and its corresponding baseline self-discharge rate data, and the number of expressions can correspond to the number of types of baseline self-discharge rate data, with one expression corresponding to each type of baseline self-discharge rate data.

[0083] After determining the baseline self-discharge rate data corresponding to different SOH values ​​of the battery system, the baseline self-discharge rate data corresponding to the SOH of the battery system in the target vehicle can be obtained from this data. Subsequently, the correction factor corresponding to the target vehicle can be determined based on the self-discharge rate data corresponding to the SOH of the battery system in the target vehicle and the baseline self-discharge rate data corresponding to that SOH.

[0084] Of course, in addition to using all vehicles that use the same battery system as the target vehicle to determine the baseline self-discharge rate data corresponding to the battery system, it is also possible to use all vehicles that use the same battery system as the target vehicle and are of the same model as the target vehicle to determine the baseline self-discharge rate data corresponding to the battery system.

[0085] The above process fully leverages the advantages of cloud-based big data. By detecting and analyzing the historical self-discharge trend distribution of battery systems in various vehicles, it proactively determines the corresponding baseline self-discharge rate data for each battery system. Furthermore, this method allows the battery system's State of Charge (SOH) to be incorporated into the determination of the corresponding baseline self-discharge rate data, thereby improving the accuracy of determining the correction coefficient for the target vehicle's battery system. This, in turn, enhances the rationality and accuracy of the upper limit correction for the battery system's charging SOC, ultimately reducing the probability of thermal runaway in the vehicle's battery system.

[0086] This invention provides a battery system management method that acquires the state of harm (SOH) of the battery system in each vehicle at a corresponding data acquisition time and determines the self-discharge rate data corresponding to the SOH of the battery system in the vehicle. This method may include:

[0087] Obtain the cutoff SOC of the battery system in the vehicle during charging, and determine the SOC statistical range based on the cutoff SOC.

[0088] After the vehicle stops running and the SOC of the battery system in the vehicle is within the SOC statistical range, the SOH, cumulative equalized SOC and SOC difference of the battery system in the vehicle are acquired at first preset time intervals; the SOC difference is the difference between the SOC of the highest SOC single cell and the SOC of the lowest SOC single cell in the battery system at the corresponding time.

[0089] Based on the cumulative equalized SOC and SOC difference of the battery system in the vehicle, the self-discharge rate of the battery system in the vehicle within the second preset time period is calculated from the previous data acquisition time to the current data acquisition time, and the self-discharge rate is used as the self-discharge rate at the current data acquisition time; the second preset time period is longer than the first preset time period.

[0090] Based on the self-discharge rate of the battery system in the vehicle at each data acquisition time within the second preset duration corresponding to the current data acquisition time, determine the maximum self-discharge rate and average self-discharge rate of the battery system in the vehicle within the second preset duration corresponding to the current data acquisition time, and use the maximum self-discharge rate and average self-discharge rate as the maximum self-discharge rate and average self-discharge rate corresponding to the SOH at the current data acquisition time.

[0091] In this embodiment of the invention, the SOH of the battery systems in each vehicle using the same battery system at the corresponding data acquisition time can be obtained in the following manner, and the self-discharge rate data corresponding to the SOH of the battery systems in the vehicle can be determined:

[0092] 11) The cloud platform statistically obtains the SOC (State of Charge) of the battery system in each vehicle during a certain period (e.g., the last three months) of charging. The SOC mentioned here refers to the SOC at the end of charging. Then, for each vehicle, the SOC with the highest frequency of occurrence of the SOC cutoff is used as the ChrgStpSOC (charging cutoff SOC, which is the SOC statistical benchmark). Then, the ChrgStpSOC ± a preset value is used as the SOC statistical range. The preset value can be set according to the actual situation of the vehicle; for example, it can be 10%, meaning the ChrgStpSOC ± 10% can be used as the SOC statistical range. Determining the SOC statistical range based on the SOC with the highest frequency of occurrence of the SOC cutoff improves the effectiveness and reliability of subsequent data statistics and calculations.

[0093] 12) After the vehicle (referring to any of the aforementioned vehicles) stops operating and the SOC (specifically, the actual SOC) of the vehicle's battery system is within the SOC statistical range, the vehicle can automatically wake up once every first preset time interval (the size of the first preset time interval can be set according to needs, for example, 5 hours) to obtain the cumulative balanced SOC, SOC difference, and SOH of its own battery system after sufficient rest. Specifically, the aforementioned data can be obtained by the vehicle's BMS (Battery Management System). Among them, the cumulative balanced SOC is the balanced SOC of the individual cells in the battery system at the corresponding time, and this data can be directly read; the SOC difference is the difference between the SOC of the highest SOC individual cell and the lowest SOC individual cell in the battery system at the corresponding time, that is, the highest SOC and the lowest SOC of each individual cell in the battery system are read at the corresponding time, and the difference between the highest SOC and the lowest SOC is obtained to obtain the SOC difference. After acquiring the cumulative balanced SOC, SOC difference, and SOH of its own battery system at a given time, the vehicle can upload these values ​​to a cloud platform. This allows the cloud platform to acquire the cumulative balanced SOC, SOC difference, and SOH of the vehicle's battery system at predetermined intervals (the time it takes for the cloud platform to acquire this data is called the data acquisition time). In addition to acquiring the cumulative balanced SOC, SOC difference, and SOH of its own battery system, the vehicle can also acquire information such as the voltage of each individual cell in the battery system, the temperature of the battery system, and the minimum voltage number of each cell. This allows the cloud platform to obtain more information about the battery system in the target vehicle.

[0094] After acquiring the aforementioned data uploaded by the vehicles, the cloud platform can store this data, with each vehicle's data stored in a separate data table. This data table also includes the time the vehicle uploaded the data (which, for the cloud platform, is the data acquisition time). Specifically, data can be stored on the cloud platform throughout its entire lifecycle. Taking the first vehicle (represented by vehicle VIN1 in Table 1) as an example, see Table 1, which shows the corresponding data table for vehicle 1 on the cloud platform:

[0095] Table 1. Data table corresponding to vehicle 1 on the cloud platform.

[0096]

[0097] Here, Bal_SOC represents the cumulative balanced SOC of the battery system in the vehicle, and the data table for each vehicle is similar to that in Table 1. Furthermore, the cloud platform updates the data in the corresponding data table every time it receives data uploaded from a vehicle; the data acquisition time is in hours.

[0098] 13) After obtaining the cumulative balanced SOC and SOC difference of the battery system in the vehicle, the cloud platform can use Batt_K = ((△SOC) to calculate the cumulative balanced SOC and SOC difference of the battery system in the vehicle at each data acquisition time. n -△SOC m )+(Bal_SOC n -Bal_SOC m )) / (Ti n -Ti m )*(Ti n and Ti m The self-discharge rate Batt_K within the second preset time period (the number of days between the previous data acquisition time and the current data acquisition time) is calculated and used as the current data acquisition time (i.e., Ti). n The corresponding self-discharge rate, ΔSOC n Bal_SOC n Data acquisition time Ti n The corresponding SOC difference and cumulative equilibrium SOC, ΔSOC m Bal_SOC m Data acquisition time Ti m The corresponding SOC difference and cumulative equalized SOC are calculated. It should be noted that the calculated self-discharge rate Batt_K is at least 0%.

[0099] The second preset duration can be one month, one week, or other durations. This embodiment of the invention uses one month as an example. In this case, the self-discharge rate of the battery system within the second preset duration is the monthly self-discharge rate. That is, the monthly self-discharge rate Batt_K of the battery system can be calculated based on the battery system's uploaded data from the previous month at the current moment. Specifically, Batt_K... n =((△SOC) n -△SOC m )+(Bal_SOC n -Bal_SOC m )) / (Ti n -Ti m *30 days.

[0100] The self-discharge rate corresponding to each data acquisition time can be calculated by following the steps above.

[0101] 14) Based on step 13), the maximum self-discharge rate Batt_Kmax and the average self-discharge rate Batt_Kavrg of the battery system in the vehicle within the second preset time period (i.e., the second preset time corresponding to the current data acquisition time) can be determined according to the self-discharge rate corresponding to each data acquisition time included in the second preset time period, where Batt_Kmax = MAX(Batt_K... m , ..., Batt_K n Batt_Kavrg=∑(Batt_K) m , ..., Batt_K n (n-m+1). Furthermore, the maximum self-discharge rate (Batt_Kmax) and average self-discharge rate (Batt_Kavrg) of the vehicle's battery system within the second preset time period corresponding to the current data acquisition time can be recorded at the current data acquisition time, serving as the maximum self-discharge rate (Batt_Kmax) and average self-discharge rate (Batt_Kavrg) corresponding to the SOH at the current data acquisition time. The maximum self-discharge rate (Batt_Kmax) and average self-discharge rate (Batt_Kavrg) corresponding to each data acquisition time can be calculated using the above method.

[0102] Based on the calculations in steps 13) and 14) above, the data tables corresponding to each vehicle can be updated. Specifically, the self-discharge rate Batt_K, maximum self-discharge rate Batt_Kmax, and average self-discharge rate Batt_Kavrg corresponding to each data acquisition time can be updated to the data table. Specifically, in this embodiment of the invention, taking Table 1 above and the second preset duration of one month as an example, Table 1 is updated to obtain Table 2, which is the data table when recording the self-discharge rate data in Table 1.

[0103] Table 2 is a data table recording the self-discharge rate data from Table 1.

[0104]

[0105] In order to save cloud platform resources, the update time of Table 2 can be longer than that of Table 1. Table 1 is updated once every time data is received from a vehicle, while Table 2 can be updated weekly or monthly.

[0106] The above process enables accurate acquisition of self-discharge rate data of battery systems in various vehicles at different times, thereby improving the accuracy of determining the baseline self-discharge rate data corresponding to each SOH of the battery system, thus improving the accuracy of battery system management and control, and reducing the probability of thermal runaway of the battery system.

[0107] It should be noted that the self-discharge rate data of the battery system in the target vehicle can be determined in a similar way as described above. The difference is that the self-discharge rate data of the battery system in the target vehicle can be acquired in real time or at regular intervals. Specifically, the cutoff SOC of the battery system in the target vehicle during charging can be obtained, and the target SOC statistical range can be determined based on the cutoff SOC. After the target vehicle stops running and the SOC of the battery system in the target vehicle is within the target SOC statistical range, the SOH, cumulative equalization SOC, and SOC difference of the battery system in the target vehicle are obtained at first preset time intervals. The SOC difference is the difference between the SOC of the highest SOC single cell and the lowest SOC single cell in the battery system at the corresponding time. Based on the cumulative equalization SOC and SOC difference of the battery system in the target vehicle, the self-discharge rate of the battery system in the target vehicle from the previous time to the current time is calculated as the second preset time, and the self-discharge rate is used as the self-discharge rate at the current time. The second preset time is longer than the first preset time. Based on the self-discharge rate of the battery system in the target vehicle at each time within the second preset time corresponding to the current time, the maximum self-discharge rate and average self-discharge rate of the battery system in the target vehicle within the second preset time corresponding to the current time are determined, and the maximum self-discharge rate and average self-discharge rate are used as the maximum self-discharge rate and average self-discharge rate corresponding to the SOH at the current time.

[0108] This invention provides a battery system management method that determines baseline self-discharge rate data corresponding to different SOHs based on the State of Health (SOH) and corresponding self-discharge rate data of the battery systems in each vehicle. This method may include:

[0109] Obtain the maximum self-discharge rate and average self-discharge rate of the battery system in each vehicle for different SOH values;

[0110] The average maximum self-discharge rate of the battery system in each vehicle corresponding to each SOH is calculated to obtain the average maximum self-discharge rate for each SOH.

[0111] The average self-discharge rate of the battery system in each vehicle corresponding to each SOH is averaged to obtain the average self-discharge rate for each SOH.

[0112] In this embodiment of the invention, the baseline self-discharge rate data corresponding to different SOHs can be determined in the following manner:

[0113] 21) Determine different SOH values. The SOH mentioned here can be multiple values ​​between 0 and 1, and the difference between any two adjacent SOH values ​​can be the same. That is, multiple SOH values ​​can be obtained by gradually decreasing the SOH value by a fixed step size, starting from a maximum SOH value of 1. Of course, the different SOH values ​​mentioned here can also be all the SOH values ​​that appear in the battery systems of different vehicles using the same battery system.

[0114] 22) Obtain the maximum self-discharge rate and average self-discharge rate of the battery system in each vehicle corresponding to each SOH. Specifically, the SOH can be used as the horizontal axis, from vehicle VIN1, vehicle VIN2, ..., vehicle VIN M (Where M is the total number of vehicles using the same battery system) Extract the maximum self-discharge rate and average self-discharge rate corresponding to the SOH from the data tables respectively, so as to obtain the maximum self-discharge rate and average self-discharge rate of the battery system in each vehicle under each SOH.

[0115] Specifically, when a vehicle's battery system corresponds to multiple maximum self-discharge rates and average self-discharge rates under a given state of equilibrium (SOH), the average of these maximum self-discharge rates under the corresponding SOH can be used as the maximum self-discharge rate of the vehicle's battery system under that SOH. Similarly, the average of these average self-discharge rates under the corresponding SOH can be used as the average self-discharge rate of the vehicle's battery system under that SOH. When a vehicle's battery system has no maximum or average self-discharge rate under a given SOH, the maximum and average self-discharge rates under the corresponding SOH can be obtained using linear interpolation or other methods based on the maximum and average self-discharge rates of the vehicle's battery system under other SOHs.

[0116] After obtaining the maximum self-discharge rate and average self-discharge rate of the battery system in each vehicle corresponding to each SOH, a data table can be established showing the maximum self-discharge rate and average self-discharge rate of the battery system in each vehicle for different SOHs, as shown in Table 3:

[0117] Table 3 shows the data on SOH and the maximum and average self-discharge rates of battery systems in various vehicles.

[0118]

[0119] It should be noted that Table 3 uses a second preset duration of one month as an example, that is, the maximum self-discharge rate is the monthly maximum self-discharge rate and the average self-discharge rate is the monthly average self-discharge rate.

[0120] 23) Based on step 22), the maximum self-discharge rate of the battery system in each vehicle corresponding to each SOH can be averaged to obtain the average maximum self-discharge rate for each SOH. That is, the SOH can be used... x _BattKmax_Avrg=∑(VIN1_Batt_Kmax,...,VIN M The average maximum self-discharge rate corresponding to each SOH is calculated by _Batt_Kmax) / M, where SOH x _BattKmax_Avrg is SOH x The corresponding average maximum self-discharge rate, VIN1_Batt_Kmax, is the average value of vehicle 1 at SOH. x The maximum self-discharge rate at VIN M _Batt_Kmax represents vehicle M in SOH x The maximum self-discharge rate.

[0121] 24) Based on step 22), the average self-discharge rate of the battery system in each vehicle corresponding to each SOH can be averaged to obtain the average self-discharge rate corresponding to each SOH. That is, the average self-discharge rate can be calculated using the SOH. x _BattKavrg_Avrg=∑(VIN1_Batt_Kavrg,...,VIN M The average self-discharge rate corresponding to each SOH is calculated by _Batt_Kavrg) / M, where SOH x _BattKavrg_Avrg is SOH x The corresponding average self-discharge rate, VIN1_Batt_Kmax, is the average self-discharge rate of vehicle 1 at SOH. x The average self-discharge rate, VIN M _Batt_Kmax represents vehicle M in SOH x The average self-discharge rate under these conditions.

[0122] The above process allows us to determine the baseline self-discharge rate data for different SOHs (i.e., the baseline self-discharge rate data can specifically include the average maximum self-discharge rate and the average average self-discharge rate). After determining the average maximum self-discharge rate and the average average self-discharge rate for each SOH, a data table of the average maximum self-discharge rate and the average average self-discharge rate for each SOH can be created, as shown in Table 4.

[0123] Table 4 shows the average maximum self-discharge rate and average self-discharge rate for each SOH.

[0124]

[0125] It should be noted that Table 4 is based on the second preset duration of one month, i.e., the average maximum self-discharge rate is the average monthly maximum self-discharge rate and the average average self-discharge rate is the average monthly average self-discharge rate.

[0126] The above method can accurately determine the baseline self-discharge rate data corresponding to each SOH, thereby improving the accuracy of battery system management and reducing the probability of thermal runaway in the battery system.

[0127] This invention provides a battery system management method that determines a correction coefficient for a target vehicle based on the battery system's self-discharge rate data and corresponding baseline self-discharge rate data. The method may include:

[0128] When the maximum self-discharge rate of the battery system in the target vehicle is greater than or equal to the sum of the average maximum self-discharge rate and the first calibration value, and the average self-discharge rate of the battery system in the target vehicle is greater than or equal to the sum of the average average self-discharge rate and the second calibration value, the correction coefficient corresponding to the target vehicle is determined based on the maximum self-discharge rate of the battery system in the target vehicle and the sum of the average maximum self-discharge rate and the first calibration value, and the average self-discharge rate of the battery system in the target vehicle and the sum of the average average self-discharge rate and the second calibration value.

[0129] When the maximum self-discharge rate of the battery system in the target vehicle is greater than or equal to the sum of the average maximum self-discharge rate and the first calibration value, and the average self-discharge rate of the battery system in the target vehicle is less than the sum of the average average self-discharge rate and the second calibration value, the correction coefficient corresponding to the target vehicle is determined based on the maximum self-discharge rate of the battery system in the target vehicle and the sum of the average maximum self-discharge rate and the first calibration value.

[0130] When the maximum self-discharge rate of the battery system in the target vehicle is less than the sum of the average maximum self-discharge rate and the first calibration value, and the average self-discharge rate of the battery system in the target vehicle is greater than or equal to the sum of the average average self-discharge rate and the second calibration value, the correction coefficient corresponding to the target vehicle is determined based on the average self-discharge rate of the battery system in the target vehicle and the sum of the average average self-discharge rate and the second calibration value.

[0131] When the maximum self-discharge rate of the battery system in the target vehicle is less than the sum of the average maximum self-discharge rate and the first calibration value, and the average self-discharge rate of the battery system in the target vehicle is less than the sum of the average average self-discharge rate and the second calibration value, the correction coefficient corresponding to the target vehicle is determined to be equal to 0.

[0132] In this embodiment of the invention, the maximum self-discharge rate of the battery system in the target vehicle under a corresponding state of equilibrium (SOH) can be compared with the sum of the average maximum self-discharge rate under that SOH and a first calibration value. Furthermore, the average self-discharge rate of the battery system in the target vehicle under a corresponding SOH can be compared with the sum of the average average self-discharge rate under that SOH and a second calibration value to determine the correction coefficient corresponding to the target vehicle. The first and second calibration values ​​can be determined based on the cell composition (e.g., cell material) in the battery system.

[0133] Specifically, (1) when the maximum self-discharge rate of the battery system in the target vehicle is ≥ (the corresponding average maximum self-discharge rate + δ1) and the average self-discharge rate of the battery system in the target vehicle is ≥ (the corresponding average average self-discharge rate + δ2), the correction coefficient K corresponding to the target vehicle can be calculated using K = (the maximum self-discharge rate of the battery system in the target vehicle - (the corresponding average maximum self-discharge rate + δ1)) * first weight / δ1 + (the average self-discharge rate of the battery system in the target vehicle - (the corresponding average average self-discharge rate + δ2)) * second weight / δ2. Among them, the "corresponding" mentioned above refers to the same SOH, δ1 is the first calibration value, δ2 is the second calibration value, the first weight is the weight corresponding to the maximum self-discharge rate, and the second weight is the weight corresponding to the average self-discharge rate. The size of the first weight and the second weight can be set according to the actual situation. For example, the first weight and the second weight can both be 0.5.

[0134] (2) When the maximum self-discharge rate of the battery system in the target vehicle is greater than or equal to (the average maximum self-discharge rate + δ1) and the average self-discharge rate of the battery system in the target vehicle is less than (the average average self-discharge rate + δ2), the correction coefficient K corresponding to the target vehicle can be calculated by using K = (the maximum self-discharge rate of the battery system in the target vehicle - (the average maximum self-discharge rate + δ1)) * first weight / δ1.

[0135] (3) When the maximum self-discharge rate of the battery system in the target vehicle is less than (the corresponding average maximum self-discharge rate + δ1) and the average self-discharge rate of the battery system in the target vehicle is greater than or equal to (the corresponding average self-discharge rate + δ2), the correction coefficient K corresponding to the target vehicle can be calculated using K = (the average self-discharge rate of the battery system in the target vehicle - (the corresponding average self-discharge rate + δ2)) * the second weight / δ2.

[0136] (4) When the maximum self-discharge rate of the battery system in the target vehicle is less than (the corresponding average maximum self-discharge rate + δ1) and the average self-discharge rate of the battery system in the target vehicle is less than (the corresponding average self-discharge rate + δ2), the correction coefficient K corresponding to the target vehicle can be determined to be equal to 0. That is, the correction coefficient corresponding to the target vehicle is greater than or equal to 0.

[0137] When the second preset duration is one month, the maximum self-discharge rate is the monthly maximum self-discharge rate, the average maximum self-discharge rate is the monthly maximum self-discharge rate average, the average self-discharge rate is the monthly average self-discharge rate, and the average self-discharge rate average is the monthly average self-discharge rate average. Correspondingly, the calculation of the correction coefficient K for the target vehicle is as follows: 1) When the monthly maximum self-discharge rate ≥ (monthly maximum self-discharge rate average + δ1) and the monthly average self-discharge rate ≥ (monthly average self-discharge rate average + δ2), then K = (monthly maximum self-discharge rate - (monthly maximum self-discharge rate average + δ1)) * first weight / δ1 + (monthly average self-discharge rate - (monthly average self-discharge rate average + δ2)) * first weight / δ2; 2) When the monthly maximum self-discharge rate ≥ (monthly maximum self-discharge rate average + δ1) and the monthly average self-discharge rate < (monthly average self-discharge rate average + δ2), then K = (monthly maximum self-discharge rate - (monthly maximum self-discharge rate average + δ1)) * first weight / δ1 + (monthly average self-discharge rate - (monthly average self-discharge rate average + δ2)) * first weight / δ2; When the monthly maximum self-discharge rate is less than (average monthly maximum self-discharge rate + δ1) and the monthly average self-discharge rate is greater than or equal to (average monthly average self-discharge rate + δ2), then K = (average monthly self-discharge rate - (average monthly average self-discharge rate + δ2)) * first weight / δ2; 4) When the monthly maximum self-discharge rate is less than (average monthly maximum self-discharge rate + δ1) and the monthly average self-discharge rate is less than (average monthly average self-discharge rate + δ2), then K = 0.

[0138] The above methods can improve the accuracy of the correction coefficient calculation for the target vehicle, thereby improving the accuracy of the correction for the upper limit of the charging SOC of the target vehicle's battery system and reducing the probability of thermal runaway in the target vehicle's battery system.

[0139] This invention also provides a battery system management method for a target vehicle, which can be found in the following embodiments: Figure 2 The diagram illustrates a flowchart of a battery system management method for a target vehicle provided by an embodiment of the present invention, which may include:

[0140] S21: Receive the correction coefficient corresponding to the target vehicle sent by the cloud platform; the cloud platform uses any of the above-mentioned battery system management methods applied to the cloud platform to obtain the correction coefficient corresponding to the target vehicle;

[0141] S22: Correct the upper limit of the charging SOC of the battery system in the target vehicle according to the correction factor so that the battery system charges according to the corrected upper limit of the charging SOC; the corrected upper limit of the charging SOC is less than the upper limit of the charging SOC.

[0142] For details regarding steps S21 and S22, please refer to the detailed descriptions of the corresponding parts in the above-mentioned battery system management method applied to the cloud platform, which will not be repeated here.

[0143] This invention provides a battery system management method that corrects the upper limit of the charging SOC of a battery system, which may include:

[0144] The percentage reduction in the charging limit is determined based on the correction coefficient. When the correction coefficient is equal to 0, the percentage reduction in the charging limit is equal to 0. When the correction coefficient is greater than 0 and less than the first preset value, the percentage reduction in the charging limit is positively correlated with the correction coefficient. When the correction coefficient is greater than or equal to the first preset value, the percentage reduction in the charging limit is fixed, and the current percentage reduction in the charging limit is greater than the percentage reduction in the charging limit when the correction coefficient is less than the first preset value.

[0145] The upper limit of the charging SOC of the battery system in the target vehicle is reduced by a percentage to obtain the corrected upper limit of the charging SOC.

[0146] In this embodiment of the invention, after receiving the correction coefficient sent by the cloud platform, when correcting the upper limit of the charging SOC of the battery system in the target vehicle, the percentage reduction of the charging upper limit can first be determined based on the received correction coefficient. Specifically, when the correction coefficient is equal to 0, it indicates that the self-discharge of the battery system in the target vehicle is within the normal range. In this case, the percentage reduction of the charging upper limit can be determined to be 0, eliminating the need to correct the upper limit of the charging SOC of the battery system in the target vehicle, thereby reducing the impact on the charging of the battery system and the operation of the vehicle. When the correction coefficient is greater than 0 and less than a first preset value (the size of the first preset value can be set according to the battery system, vehicle conditions, etc., for example, it can be 2), it indicates that the self-discharge of the battery system in the target vehicle deviates from the normal range. Therefore, the percentage reduction of the charging upper limit can be greater than 0, and the percentage reduction of the charging upper limit can be positively correlated with the correction coefficient, that is, the larger the correction coefficient, the larger the percentage reduction of the charging upper limit. In this case, the percentage reduction of the charging upper limit can be determined by the correction coefficient. A larger correction coefficient (the greater the deviation of the battery system from the normal range) results in a larger percentage reduction in the charging limit, effectively correcting the charging SOC limit of the target vehicle's battery system to better reduce the probability of thermal runaway. When the correction coefficient is greater than or equal to a first preset value, to both reduce the probability of thermal runaway and ensure the battery system has sufficient charging SOC to improve its performance and power the vehicle, the percentage reduction in the charging limit can be kept fixed. This percentage reduction is greater than the percentage reduction when the correction coefficient is less than the first preset value; for example, when the correction coefficient is greater than or equal to the first preset value, the percentage reduction can be 20%. When the correction coefficient is greater than 0, the percentage reduction corresponding to each correction coefficient can be set according to the cell composition of the battery system.

[0147] After determining the percentage reduction of the charging upper limit, the target vehicle can reduce the charging upper limit of its own battery system (specifically the initial charging upper limit) by the determined percentage reduction. That is, the charging upper limit is subtracted from the charging upper limit reduction percentage to obtain the corrected charging upper limit.

[0148] For details, please refer to Table 5, which shows the correspondence between the correction factor, the percentage reduction in the charging upper limit, and the corrected charging SOC upper limit:

[0149] Table 5. Relationship between correction factor, percentage reduction in charging upper limit, and corrected charging SOC upper limit.

[0150]

[0151]

[0152] UP_SOC0 is the upper limit of the initial charging SOC.

[0153] The above approach can accurately correct the upper limit of the battery system's SOC in the target vehicle, thereby reducing the probability of thermal runaway in the battery system, and also reduce the impact of correcting the upper limit of the battery system's SOC on the battery system's ability to power the vehicle.

[0154] The battery system management method provided in this embodiment of the invention may further include:

[0155] When the correction coefficient is greater than or equal to the second preset value and less than the first preset value, vehicle-side online diagnostics are performed at a third preset time interval; the second preset value is less than the first preset value.

[0156] When the correction coefficient is greater than or equal to the first preset value, vehicle-side online diagnostics are performed every fourth preset time interval, and the cooling system is activated when the battery system temperature is greater than the first preset temperature. When the battery system temperature is greater than the first preset temperature for more than the fifth preset time interval, the charging and discharging power of the battery system in the target vehicle is reduced; the fourth preset time interval is less than the third preset time interval.

[0157] In this embodiment of the invention, when the correction coefficient is greater than or equal to the second preset value and less than the first preset value, it indicates that the self-discharge level of the battery system deviates from the normal range but is not particularly serious. Therefore, online diagnostics of the vehicle-side BMS can be triggered. If the target vehicle is in a dormant state at this time, the target vehicle should be actively woken up to perform online diagnostics, so as to promptly detect and notify fault information during vehicle operation. Specifically, when the target vehicle is in dormant state, the automatic wake-up time of the BMS is adjusted to a third preset duration, that is, the vehicle-side BMS is automatically activated every third preset duration to perform online diagnostics. The first preset value is greater than the second preset value, and the second preset value can also be set according to the battery system, vehicle conditions, etc. For example, the first preset value can be 2 and the second preset value can be 1. The third preset duration is less than the initial wake-up time interval corresponding to the BMS. For example, the initial wake-up time interval can be 5 hours and the third preset duration can be 60 minutes, thereby shortening the time interval for performing online diagnostics, so as to promptly detect vehicle-side faults and perform fault handling, thereby facilitating the safety of users and vehicles.

[0158] When the correction coefficient is greater than or equal to the first preset value, it indicates that the self-discharge level of the battery system deviates significantly from the normal range. Therefore, the vehicle-side online diagnostic time can be further shortened. Specifically, the target vehicle can undergo online diagnostics every fourth preset time interval, where the fourth preset time interval is less than the third preset time interval. Specifically, when the correction coefficient is greater than or equal to the first preset value, vehicle-side BMS online diagnostics can be triggered. If the target vehicle is in a dormant state at this time, it should be actively woken up to perform vehicle-side online diagnostics. Specifically, after the target vehicle goes into dormancy, the BMS automatic wake-up time is adjusted to the fourth preset time interval, meaning it automatically wakes up every fourth preset time interval to perform vehicle-side online diagnostics. The fourth preset time interval is less than the third preset time interval; for example, the third preset time interval could be 60 minutes, and the fourth preset time interval could be 30 minutes. This further shortens the time interval for vehicle-side online diagnostics, facilitating timely detection and handling of vehicle-side faults, thereby ensuring the safety of the user and the vehicle.

[0159] Specifically, when the correction coefficient is greater than or equal to a first preset value, the target vehicle can determine whether the temperature of its battery system exceeds the first preset temperature during online diagnostics. If the battery system temperature is not greater than the first preset temperature, no action needs to be taken. If the battery system temperature exceeds the first preset temperature, to improve battery system safety, the vehicle-side controller can activate the cooling system to quickly reduce the battery system temperature to below the first preset temperature. If the battery system temperature in the target vehicle remains above the first preset temperature for more than a fifth preset time (the fifth preset time can be set according to the battery system), that is, if the battery system temperature in the target vehicle remains above the first preset temperature during charging and discharging, the charging and discharging power of the battery system in the target vehicle can be reduced by the vehicle-side controller. When reducing the charging and discharging power of the battery system in a target vehicle, a first charging and discharging power corresponding to a first preset temperature and a second charging and discharging power corresponding to a second preset temperature (the second preset temperature is greater than the first preset temperature, for example, 50°C) can be preset. The first charging and discharging power corresponding to the first preset temperature and the second charging and discharging power corresponding to the second preset temperature are fitted (specifically, linear fitting) to determine the correspondence between temperature and charging and discharging power. Then, the target charging and discharging power can be determined based on the correspondence between temperature and charging and discharging power and the current temperature of the battery system. Finally, the charging and discharging power of the battery system can be reduced to the target charging and discharging power to achieve a smooth reduction in the charging and discharging power of the battery system.

[0160] Furthermore, when the vehicle's correction coefficient is greater than or equal to the second preset value and less than the first preset value, the vehicle can obtain and send the cumulative balanced SOC, SOC difference, and SOH of its battery system after sufficient rest at each first preset time interval to the cloud platform. This can be changed to obtaining and sending the cumulative balanced SOC, SOC difference, and SOH of its battery system after sufficient rest at each third preset time interval. And when the vehicle's correction coefficient is greater than or equal to the first preset value, the vehicle can change this to obtaining and sending the cumulative balanced SOC, SOC difference, and SOH of its battery system after sufficient rest at each fourth preset time interval to the cloud platform.

[0161] The battery system management method provided in this embodiment of the invention, which performs online diagnostics on the vehicle side, may further include:

[0162] Obtain the temperature of the battery system and / or the voltage of each individual cell in the battery system;

[0163] Based on the temperature of the battery system and / or the voltage of each individual cell in the battery system, determine whether any individual cell in the battery system has failed.

[0164] If so, a vehicle malfunction alarm will be triggered, and the cooling system in the target vehicle will be activated.

[0165] In this embodiment of the invention, when the target vehicle is undergoing online diagnostics, the temperature of the battery system and / or the voltage of each individual cell in the battery system can be obtained. Specifically, the BMS in the target vehicle can detect (or periodically detect) the temperature of the battery system and / or the voltage of each individual cell in the battery system. Then, the target vehicle can use a real-time online algorithm to determine whether there is a risk of thermal runaway. Specifically, based on the obtained temperature of the battery system and / or the voltage of each individual cell in the battery system, it is determined whether any individual cell in the battery system has failed. Specifically, when the target vehicle is operating under steady-state conditions, it can be determined whether the voltage drop of the individual cell in the battery system exceeds a first voltage value within a sixth preset time period; when the target vehicle is in sleep mode, it can be determined whether the voltage drop of the individual cell in the battery system exceeds a second voltage value within a seventh preset time period; and it can be determined whether the temperature of the battery system is greater than a third preset temperature. The third preset temperature is greater than the first preset temperature. The sixth preset duration, the first voltage value, the seventh preset duration, the second voltage value, and the third preset temperature can all be set according to the battery system. For example, the sixth preset duration is 10s, the first voltage value is 50mV, the seventh preset duration is 30min, the second voltage value is 10mV, and the third preset temperature is 75℃.

[0166] If the following conditions are met simultaneously: when the target vehicle is operating under steady-state conditions, the voltage drop of a single cell in the battery system within the sixth preset time period does not exceed the first voltage value; when the target vehicle is in hibernation, the voltage drop of a single cell in the battery system within the seventh preset time period does not exceed the second voltage value; and the temperature of the battery system is not greater than the third preset temperature, then it can be determined that no single cell in the battery system of the target vehicle has failed, that is, it is determined that the target vehicle does not have the risk of thermal runaway.

[0167] If at least one of the following conditions is met: the voltage drop of a single cell in the battery system exceeds a first voltage value within a sixth preset time period when the target vehicle is operating under steady-state conditions; the voltage drop of a single cell in the battery system exceeds a second voltage value within a seventh preset time period when the target vehicle is in sleep mode; or the temperature of the battery system is greater than a third preset temperature, then it is determined that a single cell in the target vehicle's battery system has failed, i.e., the target vehicle is at risk of thermal runaway. In this case, the target vehicle can trigger a vehicle fault alarm, specifically sending the alarm to a cloud platform so that after-sales service can intervene promptly to replace the battery pack, etc. The alarm can also be triggered within the target vehicle and sent to the mobile terminal carried by the target vehicle's user, allowing the user to be notified and stop using the vehicle. Furthermore, when a single cell failure is confirmed, the vehicle-side controller can also control the cooling system to operate at full power to cool and reduce the charge of the battery system, thereby minimizing the occurrence or severity of thermal runaway. The above methods enable real-time and accurate safety warnings for vehicle battery systems, thereby improving the safety of vehicle battery system operation.

[0168] This invention also provides a battery system management device applied to a cloud platform, see [link to relevant documentation]. Figure 3 The diagram illustrates a structural schematic of a battery system management device applied to a cloud platform according to an embodiment of the present invention, which may include:

[0169] The first determining module 31 is used to determine the self-discharge rate data of the battery system in the target vehicle and the corresponding baseline self-discharge rate data of the battery system.

[0170] The second determining module 32 is used to determine the correction coefficient corresponding to the target vehicle based on the self-discharge rate data of the battery system and the corresponding reference self-discharge rate data.

[0171] The sending module 33 is used to send the correction coefficient to the target vehicle, and the upper limit of the charging SOC of the battery system is corrected according to the correction coefficient so that the battery system is charged according to the corrected upper limit of the charging SOC; the corrected upper limit of the charging SOC is less than the upper limit of the charging SOC.

[0172] The battery system management device provided in this embodiment of the invention may further include:

[0173] The acquisition module is used to acquire the SOH of the battery system in the target vehicle when determining the self-discharge rate data of the battery system in the target vehicle;

[0174] The first determining module 31 may include:

[0175] The first acquisition unit is used to acquire the SOH of the battery system in each vehicle using the same battery system at the corresponding data acquisition time, and to determine the self-discharge rate data corresponding to the SOH of the battery system in the vehicle.

[0176] The first determining unit is used to determine the reference self-discharge rate data corresponding to different SOHs based on the SOH of the battery system in each vehicle and the corresponding self-discharge rate data.

[0177] The second acquisition unit is used to acquire the reference self-discharge rate data corresponding to the SOH of the battery system in the target vehicle from the reference self-discharge rate data corresponding to different SOHs.

[0178] The battery system management device provided in this embodiment of the invention may include a first acquisition unit that may include:

[0179] The first acquisition subunit is used to acquire the cutoff SOC of the battery system in the vehicle during charging, and to determine the SOC statistical range based on the cutoff SOC.

[0180] The second acquisition subunit is used to acquire the SOH, cumulative equalization SOC, and SOC difference of the battery system in the vehicle at first preset time intervals after the vehicle stops running and when the SOC of the battery system in the vehicle is within the SOC statistical range. The SOC difference is the difference between the SOC of the highest SOC single cell and the SOC of the lowest SOC single cell in the battery system at the corresponding time.

[0181] The calculation subunit is used to calculate the self-discharge rate of the battery system in the vehicle within a second preset time period from the previous data acquisition time to the current data acquisition time based on the cumulative equalized SOC and SOC difference of the battery system in the vehicle, and to use the self-discharge rate as the self-discharge rate at the current data acquisition time; the second preset time period is longer than the first preset time period.

[0182] The first determining subunit is used to determine the maximum self-discharge rate and average self-discharge rate of the battery system in the vehicle within the second preset time corresponding to the current data acquisition time, based on the self-discharge rate of the battery system in the vehicle within each data acquisition time within the second preset time corresponding to the current data acquisition time, and to use the maximum self-discharge rate and average self-discharge rate as the maximum self-discharge rate and average self-discharge rate corresponding to the SOH at the current data acquisition time.

[0183] The battery system management device provided in this embodiment of the invention may include a determining unit that includes:

[0184] The third acquisition subunit is used to acquire the maximum self-discharge rate and average self-discharge rate of the battery system in each vehicle corresponding to different SOH.

[0185] The first averaging subunit is used to perform averaging calculations on the maximum self-discharge rate of the battery system in each vehicle corresponding to each SOH, and obtain the average value of the maximum self-discharge rate corresponding to each SOH.

[0186] The second averaging subunit is used to average the average self-discharge rate of the battery system in each vehicle corresponding to each SOH, and obtain the average value of the average self-discharge rate corresponding to each SOH.

[0187] The battery system management device provided in this embodiment of the invention may include a second determining module 32, which may include:

[0188] The second determining unit is used to determine the correction coefficient corresponding to the target vehicle based on the maximum self-discharge rate of the battery system in the target vehicle and the sum of the corresponding average maximum self-discharge rate and the first calibration value, and the average self-discharge rate of the battery system in the target vehicle and the sum of the corresponding average self-discharge rate and the second calibration value when the maximum self-discharge rate of the battery system in the target vehicle is greater than or equal to the sum of the corresponding average self-discharge rate and the second calibration value.

[0189] The third determining unit is used to determine the correction coefficient corresponding to the target vehicle based on the maximum self-discharge rate of the battery system in the target vehicle and the sum of the corresponding average maximum self-discharge rate and the first calibration value when the maximum self-discharge rate of the battery system in the target vehicle is greater than or equal to the sum of the corresponding average self-discharge rate and the second calibration value.

[0190] The fourth determining unit is used to determine the correction coefficient corresponding to the target vehicle based on the average self-discharge rate of the battery system in the target vehicle and the sum of the average average self-discharge rate and the first calibration value when the maximum self-discharge rate of the battery system in the target vehicle is less than the sum of the corresponding average self-discharge rate and the first calibration value, and the average self-discharge rate of the battery system in the target vehicle is greater than or equal to the sum of the corresponding average self-discharge rate and the second calibration value.

[0191] The fifth determining unit is used to determine that the correction coefficient corresponding to the target vehicle is equal to 0 when the maximum self-discharge rate of the battery system in the target vehicle is less than the sum of the corresponding average maximum self-discharge rate and the first calibration value, and the average self-discharge rate of the battery system in the target vehicle is less than the sum of the corresponding average average self-discharge rate and the second calibration value.

[0192] For a description of the relevant parts of the battery system management device applied to a cloud platform provided in this embodiment of the invention, please refer to the detailed description of the corresponding parts of the battery system management method applied to a cloud platform provided in this embodiment of the invention, which will not be repeated here.

[0193] This invention also provides a battery system management device for a target vehicle, see [link to relevant documentation]. Figure 4 It shows a schematic diagram of a battery system management device for a target vehicle provided by an embodiment of the present invention, which may include:

[0194] The receiving module 41 is used to receive the correction coefficient corresponding to the target vehicle sent by the cloud platform; the cloud platform uses any of the above-mentioned battery system management methods applied to the cloud platform to obtain the correction coefficient corresponding to the target vehicle.

[0195] The correction module 42 is used to correct the upper limit of the charging SOC of the battery system in the target vehicle according to the correction coefficient, so that the battery system can charge according to the corrected upper limit of the charging SOC; the corrected upper limit of the charging SOC is less than the upper limit of the charging SOC.

[0196] The battery system management device provided in this embodiment of the invention includes a correction module 42 that may include:

[0197] The sixth determining unit is used to determine the percentage reduction of the charging upper limit based on the correction coefficient; wherein, when the correction coefficient is equal to 0, the percentage reduction of the charging upper limit is equal to 0; when the correction coefficient is greater than 0 and less than the first preset value, the percentage reduction of the charging upper limit is positively correlated with the correction coefficient; when the correction coefficient is greater than or equal to the first preset value, the percentage reduction of the charging upper limit is fixed, and the current percentage reduction of the charging upper limit is greater than the percentage reduction of the charging upper limit when the correction coefficient is less than the first preset value;

[0198] The reduction unit is used to reduce the upper limit of the charging SOC of the battery system in the target vehicle by a percentage to obtain the corrected upper limit of the charging SOC.

[0199] The battery system management device provided in this embodiment of the invention may further include:

[0200] The first online diagnostic module is used to perform vehicle-side online diagnostics every third preset time interval when the correction coefficient is greater than or equal to the second preset value and less than the first preset value; the second preset value is less than the first preset value.

[0201] The second online diagnostic module is used to perform vehicle-side online diagnostics every fourth preset time interval when the correction coefficient is greater than or equal to the first preset value, and to activate the cooling system when the battery system temperature is greater than the first preset temperature, and to reduce the charging and discharging power of the battery system in the target vehicle when the battery system temperature is greater than the first preset temperature for more than the fifth preset time interval; the fourth preset time interval is less than the third preset time interval.

[0202] The battery system management device provided in this embodiment of the invention may include, in both the first online diagnostic module and the second online diagnostic module, the following:

[0203] The third acquisition unit is used to acquire the temperature of the battery system and / or the voltage of each individual cell in the battery system;

[0204] The judgment unit is used to determine whether any individual cell in the battery system has failed, based on the temperature of the battery system and / or the voltage of each individual cell in the battery system.

[0205] The alarm unit is used to trigger a vehicle fault alarm and control the cooling system in the target vehicle to operate if a single cell in the battery system fails.

[0206] For a description of the relevant parts of the battery system management device for a target vehicle provided in this embodiment of the invention, please refer to the detailed description of the corresponding parts of the battery system management method for a target vehicle provided in this embodiment of the invention, which will not be repeated here.

[0207] It should be noted that the logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (such as a computer-based system, a processor-included system, or other system that can fetch and execute instructions from, an instruction execution system, apparatus, or device). For the purposes of this specification, "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transmit programs for use by, or in conjunction with, an instruction execution system, apparatus, or device. More specific examples (a non-exhaustive list) of computer-readable media include: an electrical connection having one or more wires (electronic device), a portable computer disk drive (magnetic device), random access memory (RAM), read-only memory (ROM), erasable and editable read-only memory (EPROM or flash memory), fiber optic devices, and portable optical disc read-only memory (CDROM). Alternatively, the computer-readable medium may be paper or other suitable media on which the program can be printed, since the program can be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, interpreting, or otherwise processing as necessary, and then stored in a computer memory.

[0208] It should be understood that various parts of the present invention can be implemented in hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented in software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, it can be implemented using any one or a combination of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0209] In the description of this specification, references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of the invention. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

[0210] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one of that feature. In the description of this invention, "a plurality of" means at least two, such as two, three, etc., unless otherwise explicitly specified.

[0211] In this invention, unless otherwise explicitly specified and limited, the terms "installation," "connection," "linking," and "fixing," etc., should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral part; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; they can refer to the internal communication of two components or the interaction between two components, unless otherwise explicitly limited. Those skilled in the art can understand the specific meaning of the above terms in this invention according to the specific circumstances.

[0212] Although embodiments of the present invention have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting the present invention. Those skilled in the art can make changes, modifications, substitutions and variations to the above embodiments within the scope of the present invention.

Claims

1. A battery system control method, characterized in that, Applied to cloud platforms, including: Determine the self-discharge rate data of the battery system in the target vehicle and the corresponding baseline self-discharge rate data of the battery system; Based on the self-discharge rate data of the battery system and the corresponding benchmark self-discharge rate data, the correction coefficient corresponding to the target vehicle is determined; The correction coefficient is sent to the target vehicle, which then corrects the upper limit of the battery system's charging SOC based on the correction coefficient, so that the battery system is charged according to the corrected upper limit of the charging SOC; the corrected upper limit of the charging SOC is less than the original upper limit of the charging SOC. When determining the self-discharge rate data of the battery system in the target vehicle, the following is also included: Obtain the state of health (SOH) of the battery system in the target vehicle; Determining the baseline self-discharge rate data corresponding to the battery system includes: The self-discharge rate (SOH) of the battery systems in each vehicle using the same battery system is obtained at the corresponding data acquisition time, and the self-discharge rate data corresponding to the SOH of the battery system in the vehicle is determined. Based on the State of Harm (SOH) and corresponding self-discharge rate data of the battery system in each vehicle, determine the baseline self-discharge rate data corresponding to different SOHs. Obtain the baseline self-discharge rate data corresponding to the SOH of the battery system in the target vehicle from the baseline self-discharge rate data corresponding to different SOHs; Acquire the state of harm (SOH) of the battery system in each vehicle at the corresponding data acquisition time, and determine the self-discharge rate data corresponding to the SOH of the battery system in the vehicle, including: Obtain the cutoff SOC of the battery system in the vehicle during charging, and determine the SOC statistical range based on the cutoff SOC. After the vehicle stops running and the SOC of the battery system in the vehicle is within the SOC statistical range, the SOH, cumulative equalization SOC, and SOC difference of the battery system in the vehicle are acquired at first preset time intervals; the SOC difference is the difference between the SOC of the highest SOC single cell and the SOC of the lowest SOC single cell in the battery system at the corresponding time. Based on the cumulative balanced SOC and SOC difference of the battery system in the vehicle, the self-discharge rate of the battery system in the vehicle within the time from the previous data acquisition time to the current data acquisition time is calculated as the second preset time, and the self-discharge rate is used as the self-discharge rate corresponding to the current data acquisition time; the second preset time is longer than the first preset time, wherein the cumulative balanced SOC is the balanced SOC of a single cell in the battery system at the corresponding time. Based on the self-discharge rate of the battery system in the vehicle at each data acquisition time within the second preset duration corresponding to the current data acquisition time, the maximum self-discharge rate and the average self-discharge rate of the battery system in the vehicle within the second preset duration corresponding to the current data acquisition time are determined, and the maximum self-discharge rate and the average self-discharge rate are used as the maximum self-discharge rate and the average self-discharge rate corresponding to the SOH at the current data acquisition time.

2. The battery system management method according to claim 1, characterized in that, Based on the State of Harm (SOH) and corresponding self-discharge rate data of the battery systems in each of the aforementioned vehicles, baseline self-discharge rate data corresponding to different SOHs are determined, including: Obtain the maximum self-discharge rate and average self-discharge rate of the battery system in each of the vehicles corresponding to different SOHs; The average maximum self-discharge rate of the battery system in each vehicle corresponding to each SOH is calculated to obtain the average maximum self-discharge rate corresponding to each SOH. The average self-discharge rate of the battery system in each vehicle corresponding to each SOH is averaged to obtain the average value of the average self-discharge rate corresponding to each SOH.

3. The battery system management method according to claim 2, characterized in that, Based on the self-discharge rate data of the battery system and the corresponding baseline self-discharge rate data, the correction coefficient corresponding to the target vehicle is determined, including: When the maximum self-discharge rate of the battery system in the target vehicle is greater than or equal to the sum of the corresponding average maximum self-discharge rate and the first calibration value, and the average self-discharge rate of the battery system in the target vehicle is greater than or equal to the sum of the corresponding average self-discharge rate and the second calibration value, then the correction coefficient corresponding to the target vehicle is determined based on the maximum self-discharge rate of the battery system in the target vehicle and the sum of the corresponding average maximum self-discharge rate and the first calibration value, and the average self-discharge rate of the battery system in the target vehicle and the sum of the corresponding average self-discharge rate and the second calibration value. The first calibration value and the second calibration value are determined based on the cell composition materials in the battery system. When the maximum self-discharge rate of the battery system in the target vehicle is greater than or equal to the sum of the average maximum self-discharge rate and the first calibration value, and the average self-discharge rate of the battery system in the target vehicle is less than the sum of the average average self-discharge rate and the second calibration value, then the correction coefficient corresponding to the target vehicle is determined based on the maximum self-discharge rate of the battery system in the target vehicle and the sum of the average maximum self-discharge rate and the first calibration value. When the maximum self-discharge rate of the battery system in the target vehicle is less than the sum of the average maximum self-discharge rate and the first calibration value, and the average self-discharge rate of the battery system in the target vehicle is greater than or equal to the sum of the average average self-discharge rate and the second calibration value, then the correction coefficient corresponding to the target vehicle is determined based on the average self-discharge rate of the battery system in the target vehicle and the sum of the average average self-discharge rate and the second calibration value. When the maximum self-discharge rate of the battery system in the target vehicle is less than the sum of the average maximum self-discharge rate and the first calibration value, and the average self-discharge rate of the battery system in the target vehicle is less than the sum of the average average self-discharge rate and the second calibration value, the correction coefficient corresponding to the target vehicle is determined to be equal to 0.

4. A battery system control method, characterized in that, Applied to target vehicles, including: The cloud platform receives the correction coefficient corresponding to the target vehicle sent by the cloud platform; the cloud platform obtains the correction coefficient corresponding to the target vehicle using the battery system management method as described in any one of claims 1 to 3. The upper limit of the charging SOC of the battery system in the target vehicle is corrected according to the correction coefficient, so that the battery system is charged according to the corrected upper limit of the charging SOC; the corrected upper limit of the charging SOC is less than the upper limit of the charging SOC.

5. The battery system management method according to claim 4, characterized in that, The upper limit of the charging SOC of the battery system is modified, including: The percentage reduction in the charging limit is determined based on the correction coefficient; wherein, when the correction coefficient is equal to 0, the percentage reduction in the charging limit is equal to 0; when the correction coefficient is greater than 0 and less than a first preset value, the percentage reduction in the charging limit is positively correlated with the correction coefficient; when the correction coefficient is greater than or equal to the first preset value, the percentage reduction in the charging limit is fixed, and the current percentage reduction in the charging limit is greater than the percentage reduction in the charging limit when the correction coefficient is less than the first preset value; The upper limit of the charging SOC of the battery system in the target vehicle is reduced by the percentage reduction of the charging upper limit to obtain the corrected upper limit of the charging SOC.

6. The battery system management method according to claim 5, characterized in that, Also includes: When the correction coefficient is greater than or equal to the second preset value and less than the first preset value, vehicle-side online diagnosis is performed at a third preset time interval. The second preset value is less than the first preset value; When the correction coefficient is greater than or equal to the first preset value, vehicle-side online diagnostics are performed every fourth preset time interval, and the cooling system is activated when the temperature of the battery system is greater than the first preset temperature. When the duration for which the temperature of the battery system is greater than the first preset temperature exceeds the fifth preset time interval, the charging and discharging power of the battery system in the target vehicle is reduced. The fourth preset time interval is less than the third preset time interval.

7. The battery system management method according to claim 6, characterized in that, Perform online vehicle diagnostics, including: Obtain the temperature of the battery system and / or the voltage of each individual cell in the battery system; Based on the temperature of the battery system and / or the voltage of each individual cell in the battery system, determine whether any individual cell in the battery system has failed. If so, a vehicle malfunction alarm is triggered, and the cooling system in the target vehicle is controlled to operate.

8. A battery system management device, suitable for control using the battery system management method according to any one of claims 1-3 or 4-7, characterized in that, Applied to cloud platforms, including: The first determining module is used to determine the self-discharge rate data of the battery system in the target vehicle and the corresponding baseline self-discharge rate data of the battery system. The second determining module is used to determine the correction coefficient corresponding to the target vehicle based on the self-discharge rate data of the battery system and the corresponding benchmark self-discharge rate data. The sending module is used to send the correction coefficient to the target vehicle, so that the target vehicle corrects the upper limit of the charging SOC of the battery system, so that the battery system charges according to the corrected upper limit of the charging SOC; the corrected upper limit of the charging SOC is less than the upper limit of the charging SOC.

9. A battery system control device, characterized in that, Applied to target vehicles, including: A receiving module is configured to receive the correction coefficient corresponding to the target vehicle sent by the cloud platform; the cloud platform obtains the correction coefficient corresponding to the target vehicle using the battery system management method as described in any one of claims 1 to 3. The correction module is used to correct the upper limit of the charging SOC of the battery system in the target vehicle according to the correction coefficient, so that the battery system is charged according to the corrected upper limit of the charging SOC; the corrected upper limit of the charging SOC is less than the upper limit of the charging SOC.

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