Methods, devices, terminal equipment, and readable storage media for identifying self-discharge anomalies

Abstract

This application relates to the field of battery technology and provides a method, apparatus, terminal device, and readable storage medium for identifying self-discharge anomalies. Specifically, the method for identifying self-discharge anomalies includes: acquiring target deviation matrix data of a battery pack to be tested, wherein the target deviation matrix data is used to characterize the mapping relationship between a first remaining charge, a first temperature, and a first voltage deviation of the battery pack to be tested; acquiring reference deviation matrix data of a reference battery pack, wherein the reference deviation matrix data is used to characterize the mapping relationship between a second remaining charge, a second temperature, and a second voltage deviation of the reference battery pack; determining the degree of degradation of the battery pack to be tested based on the reference deviation matrix data and the target deviation matrix data; and confirming that the battery pack to be tested has a self-discharge anomaly if the degree of degradation is greater than a preset degradation threshold. Embodiments of this application can improve the accuracy of self-discharge anomaly identification.

Description

Technical Field

[0001] This application belongs to the field of battery technology, and in particular relates to a method, apparatus, terminal device and readable storage medium for identifying self-discharge anomalies. Background Technology

[0002] The battery pack, as the main energy storage device in electric vehicles, is a key component of hybrid / electric vehicles, and its performance directly affects the performance of the vehicle. Abnormal self-discharge of individual cells within the battery pack is a significant issue concerning battery safety. Abnormal self-discharge refers to inconsistent self-discharge rates among the individual cells in the battery pack. Battery packs with abnormal self-discharge are prone to internal short circuits, inducing thermal runaway.

[0003] Self-discharge reactions in individual lithium-ion batteries are unavoidable. Their presence not only reduces the battery's capacity but also severely impacts battery pack assembly and cycle life. Once assembled into a battery pack, the characteristics of individual lithium-ion cells are not entirely consistent. Therefore, after each charge and discharge cycle, the terminal voltages of each cell cannot be completely uniform, leading to overcharged or over-discharged cells within the pack, resulting in deterioration of individual cell performance. This deterioration intensifies with each charge-discharge cycle, significantly reducing cycle life compared to unassembled cells. Therefore, in-depth research into the self-discharge anomalies of lithium-ion batteries is an urgent need for battery production.

[0004] The existing self-discharge anomaly identification schemes mainly measure the dispersion of the remaining capacity difference of individual cells by using quantile values, quantile interval values, and anomaly ratio values ​​to determine the self-discharge anomaly of individual cells. However, in practical applications, this method has been found to have low accuracy in identifying self-discharge anomalies, and it is prone to failing to identify battery packs with self-discharge anomalies in a timely manner. Summary of the Invention

[0005] This application provides a method, apparatus, terminal device, and readable storage medium for identifying self-discharge anomalies, which can improve the accuracy of self-discharge anomaly identification.

[0006] A first aspect of this application provides a method for identifying self-discharge anomalies, comprising: acquiring historical operating data of a battery pack to be tested; determining target deviation matrix data of the battery pack to be tested based on the historical operating data, wherein the target deviation matrix data is used to characterize the mapping relationship between a first remaining charge, a first temperature, and a first voltage deviation of the battery pack to be tested, wherein the first voltage deviation is used to characterize the voltage deviation between individual cells in the battery pack to be tested; acquiring reference deviation matrix data of a reference battery pack, wherein the reference deviation matrix data is used to characterize the mapping relationship between a second remaining charge, a second temperature, and a second voltage deviation of the reference battery pack, wherein the second voltage deviation is used to characterize the voltage deviation between individual cells in the reference battery pack; determining the degree of degradation of the battery pack to be tested based on the reference deviation matrix data and the target deviation matrix data; and confirming that the battery pack to be tested has a self-discharge anomaly if the degree of degradation is greater than a preset degradation threshold.

[0007] A second aspect of this application provides a self-discharge anomaly identification device, comprising: a historical operating data acquisition unit for acquiring historical operating data of a battery pack to be tested; a target deviation matrix data determination unit for determining target deviation matrix data of the battery pack to be tested based on the historical operating data, wherein the target deviation matrix data characterizes the mapping relationship between a first remaining charge, a first temperature, and a first voltage deviation of the battery pack to be tested, wherein the first voltage deviation characterizes the voltage deviation between individual cells in the battery pack to be tested; a reference deviation matrix data acquisition unit for acquiring reference deviation matrix data of a reference battery pack, wherein the reference deviation matrix data characterizes the mapping relationship between a second remaining charge, a second temperature, and a second voltage deviation of the reference battery pack, wherein the second voltage deviation characterizes the voltage deviation between individual cells in the reference battery pack; a deterioration degree determination unit for determining the deterioration degree of the battery pack to be tested based on the reference deviation matrix data and the target deviation matrix data; and a self-discharge anomaly identification unit for confirming that the battery pack to be tested has a self-discharge anomaly if the deterioration degree is greater than a preset deterioration degree threshold.

[0008] A third aspect of this application provides a terminal device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of the self-discharge abnormality identification method described above.

[0009] A fourth aspect of this application provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps of the self-discharge anomaly identification method described above.

[0010] The fifth aspect of this application provides a computer program product that, when run on a terminal device, causes the terminal device to execute the self-discharge anomaly identification method described in the first aspect above.

[0011] In the embodiments of this application, the degree of degradation of the battery pack under test is determined based on the reference deviation matrix data of the reference battery pack and the target deviation matrix data of the battery pack under test. If the degree of degradation exceeds a preset degradation threshold, it is confirmed that the battery pack under test has a self-discharge anomaly. The target deviation matrix data is used to characterize the mapping relationship between the first remaining charge, the first temperature, and the first voltage deviation of the battery pack under test. The first voltage deviation is used to characterize the voltage deviation between individual cells in the battery pack under test, enabling self-discharge anomaly identification in conjunction with the operating conditions of the battery pack under test. Since the self-discharge rate between individual cells may differ under different operating conditions, it is difficult to identify them using the same standard. This application combines operating conditions for self-discharge anomaly identification, which can improve the accuracy of anomaly identification and, to some extent, avoid the problem of untimely anomaly identification. Attached Figure Description

[0012] To more clearly illustrate the technical solutions in the embodiments of this application, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0013] Figure 1 This is a schematic diagram illustrating the implementation process of a self-discharge anomaly identification method provided in an embodiment of this application;

[0014] Figure 2 This is a schematic diagram of the vehicle control system provided in the embodiments of this application;

[0015] Figure 3 This is a schematic diagram illustrating the specific implementation process of obtaining the benchmark deviation matrix data provided in the embodiments of this application;

[0016] Figure 4 This is a schematic diagram illustrating the specific implementation process of obtaining target deviation matrix data provided in an embodiment of this application;

[0017] Figure 5 This is a schematic diagram of the deterioration degree chart provided in the embodiments of this application;

[0018] Figure 6 This is a schematic diagram of the structure of a self-discharge abnormality identification device provided in an embodiment of this application;

[0019] Figure 7 This is a schematic diagram of the structure of the terminal device provided in the embodiments of this application. Detailed Implementation

[0020] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application. All other embodiments obtained by those skilled in the art based on the embodiments of this application without inventive effort are protected by this application.

[0021] Relevant self-discharge anomaly identification schemes mainly measure the dispersion of the remaining capacity difference of individual cells based on quantile values, quantile interval values, and anomaly ratio values, thereby determining the self-discharge anomaly of individual cells. In practical applications, it has been found that the self-discharge rate of individual cells varies to some extent under different operating conditions, making it difficult to identify using the same standard. That is, when comparing the aforementioned dispersion with a threshold, using the same threshold under different operating conditions can easily lead to the identification of abnormal battery packs as normal. Therefore, this method has low accuracy in self-discharge anomaly identification and is prone to failing to identify battery packs with self-discharge anomalies in a timely manner.

[0022] In view of this, this application proposes a method for identifying self-discharge anomalies, which can identify self-discharge anomalies by combining the battery pack output conditions and the static voltage deviation of individual cells, thereby realizing the identification and early warning of battery self-discharge anomalies.

[0023] To illustrate the technical solution of this application, specific embodiments are described below.

[0024] Figure 1 This illustration shows a flowchart of a self-discharge anomaly identification method provided in an embodiment of this application. This method can be applied to terminal devices and is suitable for situations requiring improved accuracy in self-discharge anomaly identification. The terminal device can be a server, in-vehicle equipment, an industrial control computer, or other terminal devices that need to identify self-discharge anomalies in the battery pack.

[0025] As an example, please refer to Figure 2 , Figure 2 A vehicle management system is shown, which may include a vehicle mobile terminal, a Telematics Service Provider (TSP) cloud platform, and a big data cloud platform.

[0026] The vehicle mobile terminal may include a Head-Up Display (HU), a Telematics-Box (TBOX), and a Controller Area Network (CAN) bus. The TBOX can send heartbeat packets and CAN vehicle data to the TSP cloud platform according to the acquisition signal files configured on the TSP cloud platform. The provided data includes battery pack operating data. Furthermore, it can receive push service commands from the TSP cloud platform, display push service information on the HU according to the commands, and upload the results back to the TSP cloud platform via the TBOX.

[0027] The TSP cloud platform can be used to send configuration signal files to the TBOX and send the data uploaded by the TBOX to the big data cloud platform. The TSP cloud platform can also receive information output by the big data cloud platform to determine whether to push vehicle daily trip services to the vehicle mobile terminal.

[0028] The big data cloud platform can receive data uploaded from the TSP cloud platform, collect, store, and analyze the data, and output the model calculation results to the TSP cloud platform based on the design and deployment of various algorithm models. The self-discharge anomaly identification method proposed in this application can be implemented on the aforementioned big data cloud platform.

[0029] Specifically, the above-mentioned method for identifying self-discharge anomalies may include the following steps S101 to S105.

[0030] Step S101: Obtain historical operating data of the battery pack to be tested.

[0031] Among them, the battery pack to be tested refers to the battery pack that needs to be tested for self-discharge abnormality. For example, it can refer to the battery pack installed on the target vehicle to supply power to the target vehicle and / or the components of the target vehicle.

[0032] The aforementioned historical operating data can be data recorded during the normal operation of the battery pack under test, such as the operating data of the battery pack under test recorded by the target vehicle during operation.

[0033] Specifically, the aforementioned historical operating data can be the historical operating data of the battery pack under test at multiple sampling times within a preset duration. Both the preset duration and sampling times can be selected according to actual conditions; for example, the preset duration can be one day, 12 hours, etc., and the sampling times can be every minute, every second, etc. The aforementioned historical operating data can include the first voltage of each individual cell in the battery pack under test at each sampling time, the first remaining charge (SOC) of the battery pack under test at each sampling time, and the first temperature. In some embodiments, it may also include the timestamp of the sampling time, the vehicle identification number of the target vehicle, the charging status of the target vehicle, the speed of the target vehicle, the rotational speed of the drive motor in the target vehicle, the output voltage of the battery pack under test, and the output current, etc.

[0034] The first remaining charge of the battery pack under test can refer to the proportion of usable charge to nominal capacity, which can be collected and uploaded by the aforementioned vehicle mobile terminal. The first temperature of the battery pack under test can refer to the real-time temperature of the battery pack, which can be collected by the temperature sensor installed on the target vehicle where the battery pack is located. Based on the first voltage of each battery cell, the terminal device can calculate the first voltage deviation of each battery cell in the battery pack under test, which can characterize the voltage deviation between the individual batteries in the battery pack under test.

[0035] Step S102: Determine the target deviation matrix data of the battery pack to be tested based on historical operating data.

[0036] In the embodiments of this application, the terminal device can generate a mapping relationship between the first remaining power of the battery pack to be tested, the first temperature of the battery pack to be tested, and the first voltage deviation between individual cells in the battery pack to be tested, based on the first remaining power of the battery pack to be tested, the first temperature of the battery pack to be tested, and the first voltage deviation between individual cells in the battery pack to be tested, based on historical operating data, and represent the mapping relationship in the form of a matrix to obtain target deviation matrix data.

[0037] In other words, the target deviation matrix data can characterize the mapping relationship between the remaining capacity of the battery pack under test, the first temperature of the battery pack under test, and the first voltage deviation of the individual cells in the battery pack under test.

[0038] Step S103: Obtain the reference deviation matrix data of the reference battery pack.

[0039] The reference battery pack refers to the battery pack used as a benchmark for comparison with the battery pack under test, that is, a battery pack that does not have self-discharge abnormalities.

[0040] Similarly, the terminal device can generate a mapping relationship between the second remaining capacity of the reference battery pack, the second temperature of the reference battery pack, and the second voltage deviation of the individual cells in the reference battery pack, based on the second remaining capacity of the reference battery pack, the second temperature of the reference battery pack, and the second voltage deviation of the individual cells in the reference battery pack. This mapping relationship can then be represented as a matrix to obtain reference deviation matrix data. The second voltage deviation can be used to characterize the voltage deviation between the individual cells in the reference battery pack.

[0041] In other words, the reference deviation matrix data can characterize the mapping relationship between the second remaining capacity of the reference battery pack, the second temperature of the reference battery pack, and the second voltage deviation of the individual cells in the reference battery pack.

[0042] Step S104: Determine the degree of deterioration of the battery pack to be tested based on the baseline deviation matrix data and the target deviation matrix data.

[0043] In the embodiments of this application, after obtaining the reference deviation matrix data and the target deviation matrix data, the terminal device can compare the reference deviation matrix data and the target deviation matrix data. Since the reference deviation matrix data is the deviation matrix data of a reference battery pack without self-discharge abnormalities, the degree of degradation of the battery pack under test can be determined based on the deviation between the target deviation matrix data and the reference deviation matrix data. The degree of degradation is also the difference between the current performance of the battery pack under test and its initial performance. The initial performance can refer to the performance of the battery pack under test when it left the factory, that is, the performance when there is no self-discharge abnormality.

[0044] Step S105: If the degree of deterioration is greater than the preset deterioration threshold, it is confirmed that the battery pack under test has an abnormal self-discharge.

[0045] In the embodiments of this application, the specific value of the degradation threshold can be adjusted according to the actual situation, for example, it can be set to 1.2 or other values. If the degradation degree is less than or equal to the degradation degree threshold, it indicates that the current performance of the battery pack under test is relatively close to its initial performance, that is, the self-discharge rate of each individual cell is relatively consistent. In this case, it can be confirmed that the battery pack under test does not have a self-discharge abnormality. If the degradation degree is greater than the degradation degree threshold, it indicates that the current performance of the battery pack under test is significantly different from its initial performance, that is, the self-discharge rate of each individual cell is inconsistent. In this case, it can be confirmed that the battery pack under test has a self-discharge abnormality.

[0046] In some implementations, if the battery pack under test is confirmed to have an abnormal self-discharge, the terminal device can feed the result back to the vehicle mobile terminal through the aforementioned TSP cloud platform to provide push service in the HU and remind the user to perform timely maintenance on the battery pack.

[0047] In the embodiments of this application, the degree of degradation of the battery pack under test is determined based on the reference deviation matrix data of the reference battery pack and the target deviation matrix data of the battery pack under test. If the degree of degradation exceeds a preset degradation threshold, it is confirmed that the battery pack under test has a self-discharge anomaly. The target deviation matrix data is used to characterize the mapping relationship between the first remaining charge, the first temperature, and the first voltage deviation of the battery pack under test. The first voltage deviation is used to characterize the voltage deviation between individual cells in the battery pack under test, enabling self-discharge anomaly identification in conjunction with the battery pack's operating conditions. Since the self-discharge rate between individual cells may differ under different operating conditions, it is difficult to identify them using the same standard. This application combines operating conditions for self-discharge anomaly identification, which can improve the accuracy of anomaly identification and, to some extent, avoid the problem of untimely anomaly identification.

[0048] The improved self-discharge anomaly identification method of this application will be described below with reference to specific embodiments.

[0049] To eliminate the influence of vehicle and battery specifications on anomaly detection, the terminal device can categorize vehicles according to vehicle attributes and battery pack attributes, and model each category separately. Vehicle attributes include, but are not limited to, vehicle brand and model. Battery pack attributes include, but are not limited to, battery pack brand, range, and model.

[0050] Specifically, the terminal device acquires multiple candidate deviation matrix data. For example, the terminal device can model based on different vehicle attributes and battery pack attributes to obtain multiple candidate deviation matrix data, each corresponding to a reference battery pack attribute and a reference vehicle attribute. More specifically, by classifying the baseline operating data recorded by different reference vehicles during operation according to the reference vehicle attributes and the reference battery pack attributes of the reference battery panels on the reference vehicles, a single modeling is performed on the baseline operating data with the same reference vehicle attributes and reference battery pack attributes to obtain one candidate deviation matrix data. Modeling the baseline operating data with different vehicle attributes or battery pack attributes separately can yield multiple candidate deviation matrix data.

[0051] Accordingly, the terminal device can obtain the target vehicle attributes of the target vehicle and the target battery pack attributes of the battery pack to be detected. Then, it uses the candidate deviation matrix data, which has the same reference vehicle attributes as the target vehicle attributes and the same reference battery pack attributes as the target battery pack attributes, as the benchmark deviation matrix data to eliminate the influence of vehicle specifications and battery specifications on anomaly identification.

[0052] Please refer to Figure 3 The modeling process of the benchmark deviation matrix data may include the following steps S301 to S302.

[0053] Step 301: Obtain the baseline operating data of the reference battery pack.

[0054] The reference operating data can be reference operating data recorded when the reference vehicle is running in an experimental environment or on a real road surface, or it can be theoretical data based on empirical values. This application does not impose any restrictions on this.

[0055] Specifically, the benchmark operating data can be the operating data of the reference battery pack at multiple benchmark sampling times within a benchmark duration. Both the benchmark duration and the benchmark sampling times can be selected according to actual conditions; for example, the benchmark duration could be one month, 15 days, etc., and the benchmark times could be every minute, every second, etc. The benchmark operating data can include the second voltage of each individual cell in the reference battery pack at each benchmark sampling time, the second remaining charge of the reference battery pack at each benchmark sampling time, and the second temperature. In some embodiments, it may also include the timestamp of the benchmark sampling time, the vehicle identification number of the reference vehicle, the charging status of the reference vehicle, the vehicle speed of the reference vehicle, the rotational speed of the drive motor in the reference vehicle, the output voltage of the reference battery pack, and the output current, etc.

[0056] To make the obtained baseline deviation matrix data more accurate, the terminal device can clean the baseline running data. The cleaning strategy can include data filling and removal of abnormal data.

[0057] Specifically, the data filling process is as follows: The terminal device can classify the baseline operating data according to the vehicle identification number (VIN) and sort the baseline operating data for the same VIN in ascending order of timestamps. Then, outliers and invalid values ​​in the baseline operating data are filled in according to the forward filling rule. The forward filling rule is based on the timestamps of outliers and invalid values, selecting the value of the previous timestamp from the baseline operating data for the same VIN as the replacement.

[0058] Abnormal data removal is performed as follows: The terminal device can remove abnormal data from the baseline operating data. Abnormal data may include one or more of the following: the reference vehicle is in an abnormal operating state at the corresponding baseline sampling time; the reference vehicle is in a charging state at the corresponding baseline sampling time; the reference battery pack has a second temperature that is less than a temperature threshold at the corresponding baseline sampling time; and the reference battery pack has a second remaining charge that is less than a remaining charge threshold at the corresponding baseline sampling time.

[0059] Specifically, if the vehicle speed of the reference vehicle is greater than or equal to the vehicle speed threshold at the corresponding reference sampling time, or if the speed of the drive motor is less than or equal to the speed threshold at the corresponding reference sampling time, then the reference vehicle can be confirmed to be in an abnormal operating state. Otherwise, the reference vehicle can be confirmed to be in a normal operating state.

[0060] The vehicle speed threshold, engine speed threshold, temperature threshold, and remaining battery power threshold can all be adjusted according to actual conditions. For example, the vehicle speed threshold can be set to 0.5 km / h, the engine speed threshold can be set to 0, the temperature threshold can be set to 20℃, and the remaining battery power threshold can be set to 40%.

[0061] In other words, because the battery pack operates differently in charging and non-charging states, the terminal device can filter benchmark operating data based on vehicle status, specifically filtering benchmark operating data from non-charging states. Since abnormal operating conditions can easily affect the output power of the reference battery pack, the terminal device can filter benchmark operating data based on vehicle speed and drive motor speed to ensure the vehicle is in normal operating condition. Because data from low remaining battery charge is prone to significant errors, the terminal device can filter benchmark operating data based on the remaining charge of the reference battery pack to avoid using benchmark operating data from low remaining charge conditions. Furthermore, the terminal device can filter benchmark operating data based on the temperature of the reference battery pack to ensure the reference battery pack temperature is within the normal operating range, eliminating data from the reference vehicle's start-up process where the reference battery pack heats up, ensuring the reference vehicle and target vehicle operate within the same temperature range.

[0062] Step 302: Based on the reference operating data at each reference sampling time, calculate the second voltage deviation of the individual cells of the reference battery pack at the corresponding second remaining charge and the corresponding second temperature, and obtain the reference deviation matrix data.

[0063] Specifically, the terminal device can divide the reference operating data into multiple data groups based on the remaining second charge and second temperature of the reference battery pack at each reference sampling time. Each data group contains the reference operating data where the second remaining charge is in the same remaining charge range and the second temperature is in the same temperature range.

[0064] For example, based on the remaining battery power, the following multiple remaining battery power intervals can be obtained: [40, 45), [45, 50), [50, 55), [55, 60), [60, 65), [65, 70), [70, 75), [75, 80), [80, 85), [85, 90), [90, 95), [95, 100). Based on the temperature, the following multiple intervals can be obtained: [20, 25), [25, 30), [30, 35), [35, 40), [40, 45), [45, 50), [50, 55), [55, 60), [60, 65), [65, 70]. Therefore, all the baseline operating data where the remaining power is in the remaining power range [40, 45) and the temperature is in the temperature range [20, 25) can form the first data group, all the baseline operating data where the remaining power is in the remaining power range [40, 45) and the temperature is in the temperature range [25, 30) can form the second data group, and so on, until a total of 90 data groups are obtained.

[0065] Based on the baseline operating data contained in each data set, the terminal device can determine the second voltage deviation of each individual cell in the reference battery pack within the corresponding operating parameter range of the data set. The operating parameter range may include the remaining charge range and temperature range corresponding to the data set.

[0066] Specifically, for one data set, the average voltage of each individual cell in the reference battery pack at the same reference sampling time can be determined from the reference operating data at the same reference sampling time. Then, based on the average voltage at the same reference sampling time and the second voltage of each individual cell in the reference battery pack at the same reference sampling time, the second voltage deviation at that reference sampling time can be determined from the reference operating data at the same reference sampling time. For example, the maximum voltage deviation between the second voltage and the average voltage can be used as the second voltage deviation at that reference sampling time.

[0067] After obtaining the second voltage deviation at each reference sampling time, the second voltage deviation of the reference battery pack within the operating parameter range of the data set can be determined based on the second voltage deviation at the reference sampling time corresponding to each reference operating data in the data set. For example, the average of the second voltage deviations at all reference sampling times can be calculated, and the obtained average value can be used as the second voltage deviation of a single cell in the reference battery pack within the operating parameter range of the data set.

[0068] For example, taking a data set with the remaining power in the range of [85, 90) and the temperature in the range of [35, 40) as an example, the following shows the baseline operating data of this data set. Each row in the table above represents the baseline operating data at a reference sampling time. The average of the second voltages 1 to n of the n individual cells is calculated row by row and recorded as the voltage mean. The difference between the average second voltage and the second voltages 1 to n of each individual cell is calculated to obtain the voltage deviation of the n individual cells. The largest voltage deviation is recorded as the second voltage deviation at that reference sampling time. The resulting data set is shown in the table below:

[0069]

[0070] For the above data set, the mean value of the second voltage deviation at each reference sampling time in the data set is calculated column by column, and used as the second voltage deviation of the reference battery pack when the remaining power is in the remaining power range [85, 90) and the temperature is in the temperature range [35, 40).

[0071] Using the same calculation method, the second voltage deviation of the reference battery pack in other operating parameter ranges can be obtained. Furthermore, a three-dimensional matrix of second remaining capacity – second temperature – second voltage deviation can be generated, yielding the baseline deviation matrix data. An example of the baseline deviation matrix data is shown below, which can be named BaseMax.

[0072]

[0073] Similarly, please refer to Figure 4 The specific process of obtaining the target deviation matrix data may include the following steps S401 to S402.

[0074] Step S401: Obtain historical operating data of the battery pack under test at multiple sampling times within a preset time period.

[0075] To compare with the baseline deviation matrix data, the terminal device can clean the historical operating data in the same way. The cleaning strategy can include data filling and the removal of abnormal data.

[0076] Specifically, the data filling process is as follows: The terminal device can categorize historical operation data according to the vehicle identification number (VIN) and sort the historical operation data for the same VIN in ascending order of timestamp. Then, outliers and invalid values ​​in the historical operation data are filled in according to the forward filling rule.

[0077] The removal of abnormal data is as follows: The terminal device can remove abnormal data from the historical operation data. Abnormal data may include one or more of the following: historical operation data of the target vehicle in an abnormal operation state at the corresponding sampling time; historical operation data of the target vehicle in a charging state at the corresponding sampling time; historical operation data of the battery pack under test at a first temperature less than a temperature threshold at the corresponding sampling time; and historical operation data of the battery pack under test at a first remaining charge less than a remaining charge threshold at the corresponding sampling time.

[0078] The specific process of cleaning historical operating data can be referred to the aforementioned process of cleaning baseline operating data, and will not be repeated here.

[0079] Step S402: Based on the historical operating data at each sampling time, calculate the first voltage deviation of the individual cells of the battery pack under test at the corresponding first remaining charge and the corresponding first temperature, and obtain the target deviation matrix data.

[0080] Specifically, the terminal device can divide the historical operating data into multiple data groups based on the first remaining power and first temperature of the battery pack under test at each sampling time. Each data group contains historical operating data where the first remaining power is in the same remaining power range and the first temperature is in the same temperature range.

[0081] Then, based on the historical operating data contained in each data group, the first voltage deviation of each individual cell in the battery pack under test is determined under the corresponding operating parameter range of the data group. The operating parameter range may include the remaining charge range and temperature range corresponding to the data group.

[0082] For the same data set, the terminal device can determine the average voltage of each individual cell in the battery pack under test at the same sampling time based on historical operating data at the same sampling time. Then, based on the average voltage at that sampling time and the first voltage of each individual cell in the battery pack under test at that sampling time, it determines the first voltage deviation at that sampling time. For example, the maximum voltage deviation from the average voltage can be used as the first voltage deviation at that sampling time.

[0083] After obtaining the first voltage deviation at each sampling moment, for the same data set, the first voltage deviation of a single cell in the battery pack under test can be determined based on the first voltage deviation at the sampling moment corresponding to each historical operating data set within the operating parameter range of that data set.

[0084] Finally, target deviation matrix data is generated based on the first voltage deviation of the individual cells in each operating parameter range of the battery pack under test.

[0085] The specific process for generating the target deviation matrix data can be referred to the aforementioned process for generating the benchmark deviation matrix data, and will not be elaborated upon here.

[0086] The target deviation matrix data is shown in the example below, which can be named Daymax.

[0087]

[0088]

[0089] After obtaining the baseline deviation matrix data and the target deviation matrix data, the terminal device can divide the second voltage deviation of a single cell in the baseline deviation matrix data for each operating parameter interval by the first voltage deviation of a single cell in the target deviation matrix data for the same operating parameter interval, thus obtaining the offset matrix. This offset matrix can be used to characterize the degree of degradation of the battery pack under test in each operating parameter interval.

[0090] Specifically, the intersection of the baseline deviation matrix data BaseMax and the target deviation matrix data DayMax is taken, and the values ​​at the same positions in the two matrices are divided (that is, the second voltage deviation of a single cell in the baseline deviation matrix data for each operating parameter interval is divided by the first voltage deviation of a single cell in the target deviation matrix data for the same operating parameter interval). This yields the degree of degradation of the single cell in each operating parameter interval, and generates the offset matrix BiasMax. The value at each position in the offset matrix BiasMax represents the degree of degradation in the corresponding operating parameter interval.

[0091] An example of the BiasMax offset matrix is ​​shown below:

[0092]

[0093] Based on the aforementioned offset matrix BiasMax, the terminal device can determine the degree of degradation of the battery pack under test.

[0094] For example, the mean deterioration level of the battery pack under test can be calculated based on the deterioration level of the battery pack under test in each operating parameter range in the offset matrix BiasMax, and this mean deterioration level can be used as the deterioration level of the battery pack under test. Specifically, based on the offset matrix BiasMax, the row and column mean values ​​can be obtained, that is, the mean value of all deterioration levels in the matrix can be obtained to obtain the deterioration level of the battery pack under test.

[0095] After obtaining the degree of degradation of the battery pack under test, the degradation degree can be compared with a degradation degree threshold. If the degradation degree is greater than the preset degradation degree threshold, the terminal device can confirm that the battery pack under test has an abnormal self-discharge.

[0096] In some implementations, the terminal device can also acquire the degree of degradation of the battery pack under test within each of multiple preset time periods. For example, the degree of degradation of the battery pack under test can be calculated once a day, generating chart data of the degree of degradation.

[0097] For example, the following table and Figure 5 The data shows the degradation level of the tested battery packs from January 1, 2021 to January 10, 2021, according to BiasDayMax.

[0098] BiasDayMax 2021 / 1 / 1 0.97 2021 / 1 / 2 0.92 2021 / 1 / 3 0.96 2021 / 1 / 4 0.96 2021 / 1 / 5 0.89 2021 / 1 / 6 0.94 2021 / 1 / 7 0.83 2021 / 1 / 8 0.92 2021 / 1 / 9 0.88 2021 / 1 / 10 0.92

[0099] Of course, the degree of degradation of the battery pack under test can also be calculated every 12 hours, or the degree of degradation can be calculated for a longer or shorter preset time period. This application does not limit this.

[0100] Based on the self-discharge anomaly identification method provided in the embodiments of this application, it is possible to automatically measure whether the self-discharge capability of the battery pack in an electric vehicle has deteriorated, accurately monitor self-discharge anomalies for users, promptly remind users to perform battery pack maintenance, and reduce damage caused by battery pack failure.

[0101] It should be noted that, for the sake of simplicity, the aforementioned method embodiments are all described as a series of actions. However, those skilled in the art should understand that this application is not limited to the described order of actions, because according to this application, some steps can be performed in other orders.

[0102] like Figure 6 The diagram shown is a structural schematic of a self-discharge abnormality identification device 600 provided in an embodiment of this application. The self-discharge abnormality identification device 600 is configured on a terminal device.

[0103] Specifically, the self-discharge anomaly identification device 600 may include:

[0104] Historical operating data acquisition unit 601 is used to acquire historical operating data of the battery pack to be tested;

[0105] The target deviation matrix data determination unit 602 is used to determine the target deviation matrix data of the battery pack to be tested based on the historical operating data. The target deviation matrix data is used to characterize the mapping relationship between the first remaining charge, the first temperature, and the first voltage deviation of the battery pack to be tested. The first voltage deviation is used to characterize the voltage deviation between individual cells in the battery pack to be tested.

[0106] The reference deviation matrix data acquisition unit 603 is used to acquire the reference deviation matrix data of the reference battery pack. The reference deviation matrix data is used to characterize the mapping relationship between the second remaining charge, the second temperature, and the second voltage deviation of the reference battery pack. The second voltage deviation is used to characterize the voltage deviation between individual cells in the reference battery pack.

[0107] The degradation degree determination unit 604 is used to determine the degradation degree of the battery pack to be tested based on the reference deviation matrix data and the target deviation matrix data.

[0108] The self-discharge anomaly identification unit 605 is used to confirm that the battery pack under test has a self-discharge anomaly if the degree of deterioration is greater than a preset deterioration threshold.

[0109] In some embodiments of this application, the aforementioned historical operating data may include historical operating data of the battery pack under test at multiple sampling times within a preset time period. The historical operating data at each sampling time may include the first voltage of each individual cell of the battery pack under test at the corresponding sampling time, the first remaining charge of the battery pack under test at the corresponding sampling time, and the first temperature. The aforementioned target deviation matrix data determination unit 602 may be specifically used to: calculate the first voltage deviation of the individual cells of the battery pack under test at the corresponding first remaining charge and the corresponding first temperature based on the historical operating data at each sampling time, and obtain the target deviation matrix data.

[0110] In some embodiments of this application, the target deviation matrix data determination unit 602 can be specifically used to: divide the historical operating data into multiple data groups based on the first remaining charge and first temperature of the battery pack under test at each sampling time, wherein each data group contains historical operating data where the first remaining charge is within the same remaining charge range and the first temperature is within the same temperature range; based on the historical operating data contained in each data group, determine the first voltage deviation of a single cell of the battery pack under test in the corresponding operating parameter range of the data group, wherein the operating parameter range includes the remaining charge range and temperature range corresponding to the data group; and generate the target deviation matrix data based on the first voltage deviation of a single cell of the battery pack under test in each operating parameter range.

[0111] In some embodiments of this application, the target deviation matrix data determination unit 602 described above can be specifically used to: determine the average voltage of each individual cell in the battery pack to be tested at the same sampling time based on the historical operating data at the same sampling time; determine the first voltage deviation at the same sampling time based on the average voltage at the same sampling time and the first voltage of each individual cell in the battery pack to be tested at the same sampling time based on the historical operating data at the same sampling time; and determine the first voltage deviation of each individual cell in the battery pack to be tested within the operating parameter range of the same data group based on the first voltage deviation at each sampling time.

[0112] In some embodiments of this application, the aforementioned degradation degree determination unit 604 may be specifically used to: divide the second voltage deviation of a single cell in each of the operating parameter intervals of the reference deviation matrix data by the first voltage deviation of a single cell in the same operating parameter interval in the target deviation matrix data to obtain an offset matrix, wherein the offset matrix is ​​used to characterize the degradation degree of the battery pack under test in each of the operating parameter intervals; and determine the degradation degree of the battery pack under test based on the offset matrix.

[0113] In some embodiments of this application, the aforementioned degradation degree determination unit 604 may be specifically used to: calculate the average degradation degree based on the degradation degree of the battery pack under test in each of the operating parameter ranges; and use the average degradation degree as the degradation degree of the battery pack under test.

[0114] In some embodiments of this application, the aforementioned battery pack to be tested can be used to power a target vehicle and / or components of the target vehicle; the aforementioned reference deviation matrix data acquisition unit 603 can be specifically used to: acquire the target vehicle attributes of the target vehicle and the target battery pack attributes of the battery pack to be tested; acquire candidate deviation matrix data, each of the candidate deviation matrix data corresponding to a reference battery pack attribute and a reference vehicle attribute; and use the candidate deviation matrix data whose reference vehicle attributes are the same as the target vehicle attributes and whose reference battery pack attributes are the same as the target battery pack attributes as the reference deviation matrix data.

[0115] It should be noted that, for the sake of convenience and brevity, the specific working process of the self-discharge anomaly identification device 600 described above can be found in the following reference: Figures 1 to 5 The corresponding process of the method will not be described in detail here.

[0116] like Figure 7The diagram shown is a schematic of a terminal device provided in an embodiment of this application. The terminal device 7 may include: a processor 70, a memory 71, and a computer program 72 stored in the memory 71 and executable on the processor 70, such as a self-discharge anomaly identification program. When the processor 70 executes the computer program 72, it implements the steps in the various self-discharge anomaly identification method embodiments described above, for example... Figure 1 The steps S101 to S105 are shown. Alternatively, when the processor 70 executes the computer program 72, it implements the functions of each module / unit in the above-described device embodiments, for example... Figure 6 The historical operation data acquisition unit 601, target deviation matrix data determination unit 602, benchmark deviation matrix data acquisition unit 603, deterioration degree determination unit 604, and self-discharge anomaly identification unit 605 are shown.

[0117] The computer program can be divided into one or more modules / units, which are stored in the memory 71 and executed by the processor 70 to complete this application. The one or more modules / units can be a series of computer program instruction segments capable of performing specific functions, which describe the execution process of the computer program in the terminal device.

[0118] For example, the computer program can be divided into: a historical operation data acquisition unit, a target deviation matrix data determination unit, a benchmark deviation matrix data acquisition unit, a deterioration degree determination unit, and a self-discharge anomaly identification unit.

[0119] The specific functions of each unit are as follows: A historical operation data acquisition unit, used to acquire historical operation data of the battery pack to be tested; a target deviation matrix data determination unit, used to determine the target deviation matrix data of the battery pack to be tested based on the historical operation data. The target deviation matrix data is used to characterize the mapping relationship between the first remaining charge, the first temperature, and the first voltage deviation of the battery pack to be tested. The first voltage deviation is used to characterize the voltage deviation between individual cells in the battery pack to be tested; a reference deviation matrix data acquisition unit, used to acquire reference deviation matrix data of a reference battery pack. The reference deviation matrix data is used to characterize the mapping relationship between the second remaining charge, the second temperature, and the second voltage deviation of the reference battery pack. The second voltage deviation is used to characterize the voltage deviation between individual cells in the reference battery pack; a degradation degree determination unit, used to determine the degradation degree of the battery pack to be tested based on the reference deviation matrix data and the target deviation matrix data; and a self-discharge anomaly identification unit, used to confirm that the battery pack to be tested has a self-discharge anomaly if the degradation degree is greater than a preset degradation degree threshold.

[0120] The terminal device may include, but is not limited to, a processor 70 and a memory 71. Those skilled in the art will understand that... Figure 7 This is merely an example of a terminal device and does not constitute a limitation on the terminal device. It may include more or fewer components than shown, or combine certain components, or different components. For example, the terminal device may also include input / output devices, network access devices, buses, etc.

[0121] The processor 70 may be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor.

[0122] The memory 71 can be an internal storage unit of the terminal device, such as a hard drive or memory. The memory 71 can also be an external storage device of the terminal device, such as a plug-in hard drive, Smart Media Card (SMC), Secure Digital (SD) card, or Flash Card. Furthermore, the memory 71 can include both internal and external storage units. The memory 71 is used to store the computer program and other programs and data required by the terminal device. The memory 71 can also be used to temporarily store data that has been output or will be output.

[0123] It should be noted that, for the sake of convenience and brevity, the structure of the terminal device described above can also be referred to the specific description of the structure in the method embodiment, which will not be repeated here.

[0124] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the above-described division of functional units and modules is merely an example. In practical applications, the above functions can be assigned to different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiments can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit. Furthermore, the specific names of the functional units and modules are only for easy differentiation and are not intended to limit the scope of protection of this application. The specific working process of the units and modules in the above system can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0125] In the above embodiments, the description of each embodiment has its own focus. For parts that are not described or recorded in detail in a certain embodiment, reference can be made to the relevant description of other embodiments.

[0126] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for various specific applications, but such implementations should not be considered beyond the scope of this application.

[0127] In the embodiments provided in this application, it should be understood that the disclosed devices / terminal equipment and methods can be implemented in other ways. For example, the device / terminal equipment embodiments described above are merely illustrative. For instance, the division of modules or units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the displayed or discussed mutual coupling or direct coupling or communication connection may be through some interfaces; the indirect coupling or communication connection between devices or units may be electrical, mechanical, or other forms.

[0128] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0129] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0130] If the integrated module / unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the methods of the above embodiments can also be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. The computer-readable medium can include: any entity or device capable of carrying the computer program code, recording media, USB flash drives, portable hard drives, magnetic disks, optical disks, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signals, telecommunication signals, and software distribution media, etc. It should be noted that the content included in the computer-readable medium can be appropriately added or removed according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, computer-readable media do not include electrical carrier signals and telecommunication signals.

[0131] The above-described embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application, and should all be included within the protection scope of this application.

Claims

1. A method for identifying self-discharge anomalies, characterized in that, include: Obtain historical operating data of the battery pack under test; Based on the historical operating data, the target deviation matrix data of the battery pack to be tested is determined. The target deviation matrix data is used to characterize the mapping relationship between the first remaining charge, the first temperature, and the first voltage deviation of the battery pack to be tested. The first voltage deviation is used to characterize the voltage deviation between individual cells in the battery pack to be tested. Obtain reference deviation matrix data of a reference battery pack. The reference deviation matrix data is used to characterize the mapping relationship between the second remaining charge, the second temperature, and the second voltage deviation of the reference battery pack. The second voltage deviation is used to characterize the voltage deviation between individual cells in the reference battery pack. The second voltage deviation of a single cell in each operating parameter interval of the baseline deviation matrix data is divided by the first voltage deviation of a single cell in the same operating parameter interval of the target deviation matrix data to obtain an offset matrix. The offset matrix is ​​used to characterize the degree of deterioration of the battery pack under test in each operating parameter interval. The operating parameter range includes the corresponding remaining power range and temperature range; Calculate the average degree of degradation based on the degree of degradation of the battery pack under test in each of the operating parameter ranges; The average degree of degradation is taken as the degree of degradation of the battery pack under test; If the degree of deterioration is greater than a preset deterioration threshold, then the battery pack under test is confirmed to have an abnormal self-discharge.

2. The method for identifying self-discharge anomalies as described in claim 1, characterized in that, The historical operating data includes the historical operating data of the battery pack under test at multiple sampling times within a preset time period. The historical operating data at each sampling time includes the first voltage of each individual cell of the battery pack under test at the corresponding sampling time, the first remaining charge of the battery pack under test at the corresponding sampling time, and the first temperature. The step of determining the target deviation matrix data of the battery pack to be tested based on the historical operating data includes: Based on the historical operating data at each sampling time, the first voltage deviation of a single cell in the battery pack under test is calculated at the corresponding first remaining charge and the corresponding first temperature, thus obtaining the target deviation matrix data.

3. The method for identifying self-discharge anomalies as described in claim 2, characterized in that, The step of calculating the first voltage deviation of a single cell in the battery pack under test at the corresponding first remaining charge and corresponding first temperature based on the historical operating data at each sampling time, to obtain the target deviation matrix data, includes: Based on the first remaining charge and first temperature of the battery pack under test at each sampling time, the historical operating data is divided into multiple data groups. Each data group contains historical operating data where the first remaining charge is within the same remaining charge range and the first temperature is within the same temperature range. Based on the historical operating data contained in each data group, the first voltage deviation of a single cell in the battery pack to be tested is determined under the operating parameter range of the corresponding data group. The operating parameter range includes the remaining charge range and temperature range corresponding to the data group. The target deviation matrix data is generated based on the first voltage deviation of the individual cells of the battery pack under test in each of the operating parameter ranges.

4. The method for identifying self-discharge anomalies as described in claim 3, characterized in that, The step of determining the first voltage deviation of a single cell in the battery pack under test within the operating parameter range of the corresponding data group, based on the historical operating data contained in each data group, includes: For the historical operating data at the same sampling time, determine the average voltage of each individual cell in the battery pack to be tested at that sampling time; For the historical operating data at the same sampling time, the first voltage deviation at that sampling time is determined based on the average voltage at that sampling time and the first voltage of each individual cell in the battery pack to be tested at that sampling time. For the same data set, the first voltage deviation of a single cell in the battery pack under test is determined based on the first voltage deviation at each sampling time within the operating parameter range of the data set.

5. The method for identifying self-discharge anomalies as described in any one of claims 1 to 4, characterized in that, The battery pack to be tested is used to power the target vehicle and / or components of the target vehicle; The acquisition of the reference deviation matrix data of the reference battery pack includes: Obtain the target vehicle attributes of the target vehicle and the target battery pack attributes of the battery pack to be detected; Acquire candidate deviation matrix data, where each candidate deviation matrix data corresponds to a reference battery pack attribute and a reference vehicle attribute; The candidate deviation matrix data, in which the reference vehicle attributes are the same as the target vehicle attributes and the reference battery pack attributes are the same as the target battery pack attributes, are used as the baseline deviation matrix data.

6. A device for identifying self-discharge anomalies, characterized in that, include: The historical operation data acquisition unit is used to acquire the historical operation data of the battery pack under test. The target deviation matrix data determination unit is used to determine the target deviation matrix data of the battery pack to be tested based on the historical operating data. The target deviation matrix data is used to characterize the mapping relationship between the first remaining charge, the first temperature, and the first voltage deviation of the battery pack to be tested. The first voltage deviation is used to characterize the voltage deviation between individual cells in the battery pack to be tested. A reference deviation matrix data acquisition unit is used to acquire reference deviation matrix data of a reference battery pack. The reference deviation matrix data is used to characterize the mapping relationship between the second remaining charge, the second temperature, and the second voltage deviation of the reference battery pack. The second voltage deviation is used to characterize the voltage deviation between individual cells in the reference battery pack. The degradation degree determination unit is used to divide the second voltage deviation of the single cell in each operating parameter interval of the reference deviation matrix data by the first voltage deviation of the single cell in the same operating parameter interval of the target deviation matrix data to obtain an offset matrix. The offset matrix is ​​used to characterize the degradation degree of the battery pack under test in each operating parameter interval. The operating parameter range includes the corresponding remaining power range and temperature range; based on the degree of deterioration of the battery pack under test in each operating parameter range, the average degree of deterioration is calculated; the average degree of deterioration is taken as the degree of deterioration of the battery pack under test. The self-discharge anomaly identification unit is used to confirm that the battery pack under test has a self-discharge anomaly if the degree of deterioration is greater than a preset deterioration threshold.

7. A terminal device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the steps of the self-discharge anomaly identification method as described in any one of claims 1 to 4.

8. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, it implements the steps of the self-discharge anomaly identification method as described in any one of claims 1 to 4.

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