Battery self-discharge fault diagnosis method and system

Abstract

The invention discloses a battery self-discharge fault diagnosis method and system, and relates to the technical field of battery safety. According to the diagnosis method, actual battery data is obtained, the obtained actual battery data is divided by using a preset division rule to obtain a plurality of data segments with specific features, and then feature extraction is performed on each divided data segment by using a preset algorithm to obtain a plurality of battery data; and finally, according to the extracted voltage characteristics, carrying out comprehensive evaluation on each single battery in the battery so as to screen out the single battery with a self-discharge fault. According to the diagnosis method, data generated in the actual use process of the battery are fully utilized, accurate recognition and fault diagnosis work of the self-discharge fault of the battery can be completed in a short time, and then the safety and economical efficiency of battery operation are remarkably improved.

Description

Technical Field

[0001] The present invention relates to the technical field of battery safety, and in particular to a battery self-discharge fault diagnosis method and system. Background Art

[0002] With the vigorous development of related industries such as electrified vehicles (such as electric vehicles, rail vehicles, electric ships, electric aircraft, etc.) and energy storage power stations, batteries (such as lithium-ion batteries, sodium-ion batteries, quasi-solid-state batteries, all-solid-state batteries, etc.) as key components in the above industries, their safety and reliability issues are increasingly receiving widespread attention. Among them, abnormal self-discharge, as a relatively common battery failure phenomenon, has a significant impact on battery performance and life. Specifically, the battery self-discharge phenomenon refers to the spontaneous decrease in capacity of the battery after it is in an open circuit state and has been left stationary for a period of time. When there are differences in the degree of self-discharge between battery cells, it will lead to large inconsistencies between battery cells, and this inconsistency will seriously shorten the overall life of the battery.

[0003] The existing diagnostic methods for battery self-discharge failures mainly focus on studying the self-discharge characteristics of batteries through experiments or modeling. The experimental method generally requires the battery to be left stationary and monitored for a long time under specific experimental conditions to obtain battery self-discharge data. However, this method is not only time-consuming and labor-intensive, but also difficult to achieve real-time and accurate diagnosis of battery self-discharge failures in practical applications. Although the modeling method can predict the self-discharge behavior of the battery by establishing a mathematical model of the battery, the accuracy and reliability of the model are easily affected by various factors, such as the degree of battery aging and the operating environment. Therefore, the study of a simple and fast battery self-discharge failure diagnosis method that can accurately identify and eliminate battery cells with self-discharge anomalies in a timely manner is of great significance for improving the safety, reliability and service life of battery systems in application scenarios such as electrified vehicles and energy storage power stations. Summary of the Invention

[0004] The purpose of the present invention is to provide a battery self-discharge fault diagnosis method and system. The method and system have wide applicability and can be widely used in various types of electrified vehicles, including electric vehicles, rail vehicles, electric ships, electric aircraft, and other different types. It is also applicable to scenarios such as energy storage power stations. In the present invention, electric vehicles among electrified vehicles are focused on as the research object. The diagnostic method is based on the actual battery data of electric vehicles, deeply mines and extracts multiple key voltage features that can accurately characterize the battery self-discharge characteristics, and uses the extracted voltage features to achieve accurate identification and diagnosis of battery self-discharge faults, effectively solving the problems of poor accuracy and insufficient reliability in the prior art when diagnosing battery self-discharge faults using experiments or modeling methods.

[0005] To solve the above technical problems, the present invention is achieved through the following technical solutions:

[0006] The present invention provides a battery self-discharge fault diagnosis method, which includes:

[0007] Get actual battery data;

[0008] The acquired actual battery data is divided using a preset division rule to obtain multiple data segments with specific characteristics;

[0009] Using a preset algorithm to extract features from each divided data segment to obtain multiple voltage features that can characterize the self-discharge characteristics of the battery;

[0010] A comprehensive evaluation is performed on each battery cell in the battery based on the extracted voltage characteristics to screen out battery cells with self-discharge faults.

[0011] In one embodiment of the present invention, the obtaining of actual battery data includes:

[0012] The actual battery data generated by the electric vehicle during actual operation is obtained. The actual battery data includes multiple information about the battery in actual usage scenarios, which can reflect the comprehensive impact of user behavior habits, vehicle operating status and environmental factors on battery performance.

[0013] In one embodiment of the present invention, the method of dividing the acquired actual battery data using a preset division rule to obtain a plurality of data segments with specific characteristics includes:

[0014] Extracting battery parameters from the actual battery data, the battery parameters including current, vehicle speed, vehicle charging state, and state of charge, wherein the vehicle charging state includes a parked charging state, a driving charging state, an uncharged state, and a charging complete state;

[0015] According to the extracted battery parameters, the actual battery data is divided using a preset division rule to obtain a plurality of data segments with specific characteristics.

[0016] In one embodiment of the present invention, the actual battery data is divided according to the extracted battery parameters using a preset division rule to obtain a plurality of data segments with specific characteristics, including:

[0017] The actual battery data is divided according to the division rules that the current is 0, the vehicle speed is 0, the vehicle charging state is uncharged, the charge state is constant, and the segment time exceeds a preset time to obtain multiple data segments with specific characteristics.

[0018] In one embodiment of the present invention, in the step of extracting features from each divided data segment using a preset algorithm to obtain a plurality of voltage features capable of characterizing the self-discharge characteristics of the battery, the voltage features include an average voltage value, a voltage standard deviation, and a voltage change rate.

[0019] In one embodiment of the present invention, the feature extraction of each divided data segment using a preset algorithm to obtain multiple voltage features capable of characterizing the self-discharge characteristics of the battery includes:

[0020] Calculate the average voltage value of each battery cell in each data segment using the mean calculation formula based on the actual voltage value of each battery cell at different time points in each data segment;

[0021] Calculate the voltage standard deviation of each battery cell in each data segment using a standard deviation calculation formula based on the average voltage value of each battery cell in each data segment and the actual voltage value at different time points in the corresponding data segment;

[0022] The voltage change rates of the battery as a whole and each battery cell in each data segment are calculated based on the maximum value of the battery voltage as a whole, the maximum value of each battery cell voltage and the time difference between the maximum and minimum values in the corresponding data segment.

[0023] In one embodiment of the present invention, the comprehensive evaluation of each battery cell in the battery according to the extracted voltage characteristics to screen out battery cells with self-discharge faults includes:

[0024] Using the triple standard deviation rule to set a threshold condition for each extracted voltage feature, the threshold condition is used to determine whether the voltage feature of the battery cell is within a normal range;

[0025] Based on the extracted voltage characteristics and the set threshold conditions, a comprehensive evaluation is performed on each battery cell in the battery to screen out battery cells with self-discharge faults.

[0026] In one embodiment of the present invention, the step of setting a threshold condition for each extracted voltage feature using the triple standard deviation rule includes:

[0027] Obtain the mean and standard deviation of the average voltage values of all battery cells in each data segment, and use the triple standard deviation rule to set the threshold conditions for the average voltage values of battery cells in different data segments;

[0028] The voltage standard deviation and voltage change rate of each battery cell in each data segment are standardized to obtain the standardized voltage standard deviation and voltage change rate. Based on the standardized voltage standard deviation and voltage change rate, the triple standard deviation rule is used to set the threshold conditions for the voltage standard deviation and voltage change rate of the battery cells in different data segments.

[0029] In one embodiment of the present invention, the step of comprehensively evaluating each battery cell in the battery based on the extracted voltage characteristics and the set threshold conditions to screen out battery cells with self-discharge faults includes:

[0030] When any voltage feature extracted from any data segment does not meet the corresponding set threshold condition, the corresponding data segment is determined to be an abnormal data segment;

[0031] When the number of abnormal data segments determined for any battery cell based on any voltage feature is greater than or equal to a preset number, the corresponding battery cell is determined to be a battery cell with a self-discharge fault.

[0032] Based on the same inventive concept, another embodiment of the present invention further provides a battery self-discharge fault diagnosis system, which is implemented using the battery self-discharge fault diagnosis method described in any of the above embodiments, including:

[0033] Data acquisition module, used to obtain actual battery data;

[0034] A data segmentation module is used to segment the acquired actual battery data using a preset segmentation rule to obtain multiple data segments with specific characteristics;

[0035] A voltage feature extraction module is used to extract features from each divided data segment using a preset algorithm to obtain multiple voltage features that can characterize the self-discharge characteristics of the battery;

[0036] The fault diagnosis module is used to comprehensively evaluate each battery cell in the battery based on the extracted voltage characteristics to screen out battery cells with self-discharge faults.

[0037] As described above, the present invention provides a battery self-discharge fault diagnosis method. This method obtains actual battery data that comprehensively covers various types of information about the battery in actual usage scenarios and can comprehensively reflect the impact of user behavior, vehicle operating status, and environmental factors on battery performance. The acquired actual battery data is segmented using preset segmentation rules to obtain multiple data segments with specific characteristics. The preset segmentation rules fully consider the inherent correlation between battery self-discharge and battery operating parameters. A preset algorithm is used to extract features from each segmented data segment to obtain multiple voltage signatures that can characterize the battery self-discharge characteristics. These voltage signatures can accurately reflect the voltage changes of battery cells during the self-discharge process. Based on the extracted voltage signatures, a comprehensive assessment is performed on each battery cell to screen out those with self-discharge faults. This diagnostic method has the ability to quickly identify and accurately locate electric vehicles with self-discharge faults. Based on the extracted multiple key voltage signatures, batteries with varying degrees of fault severity can be efficiently and accurately identified. Compared to traditional methods of measuring battery self-discharge rates based on experiments or modeling, the diagnostic method described in this invention fully utilizes the data generated by the battery during actual use. It can quickly identify and diagnose battery self-discharge faults, significantly improving the safety and economic efficiency of battery operation. Of course, any product implementing this invention does not necessarily need to achieve all of the advantages described above. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for describing the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0039] Figure 1 A flowchart of a battery self-discharge fault diagnosis method provided by an exemplary embodiment of the present application.

[0040] Figure 2 A system block diagram of a battery self-discharge fault diagnosis method provided by an exemplary embodiment of the present application.

[0041] Figure 3 A flowchart of setting threshold conditions for voltage characteristics is provided in accordance with an exemplary embodiment of the present application.

[0042] Figure 4 A schematic structural diagram of a battery self-discharge fault diagnosis system provided by another exemplary embodiment of the present application. DETAILED DESCRIPTION

[0043] The following describes the embodiments of the present invention through specific examples. Those skilled in the art can easily understand other advantages and effects of the present invention from the content disclosed in this specification. The present invention can also be implemented or applied through other different specific embodiments. The details in this specification can also be modified or changed based on different viewpoints and applications without departing from the spirit of the present invention. It should be noted that the following embodiments and features in the embodiments can be combined with each other unless they conflict.

[0044] It should be noted that the illustrations provided in the following embodiments are merely schematic illustrations of the basic concept of the present invention. Therefore, the illustrations only show components related to the present invention and are not drawn according to the number, shape, and size of components in actual implementation. In actual implementation, the type, quantity, and proportion of each component may be changed arbitrarily, and the component layout may also be more complex.

[0045] In the following description, numerous details are discussed to provide a more thorough explanation of the embodiments of the present invention. However, it is obvious to those skilled in the art that the embodiments of the present invention can be implemented without these specific details. In other embodiments, publicly known structures and devices are shown in block diagram form rather than in detail to avoid making the embodiments of the present invention difficult to understand.

[0046] Taking the electric vehicle sector as an example, current research on battery self-discharge is largely based on data collected in laboratory environments. Compared to laboratory data, battery data generated during actual driving can more accurately and comprehensively reflect the combined impact of user behavior (such as driving style, charging frequency, and depth of discharge), vehicle operating conditions (such as load, mileage, and operating conditions), and environmental factors (such as temperature, humidity, and altitude) on battery performance. Therefore, using actual electric vehicle battery data for self-discharge fault detection is accurate and rapid, and has high engineering application value.

[0047] In order to solve the problems of poor accuracy and insufficient reliability in the existing technology when diagnosing battery self-discharge faults using experiments or modeling, the present invention provides a battery self-discharge fault diagnosis method. Based on the actual battery data of electric vehicles, the diagnostic method deeply mines and extracts multiple key voltage features that can accurately characterize the battery self-discharge characteristics, and uses the extracted voltage features to accurately identify and diagnose battery self-discharge faults.

[0048] See also Figure 1 As shown, in an exemplary embodiment of the present application, the battery self-discharge fault diagnosis method includes the following steps:

[0049] S100: Acquire actual battery data;

[0050] S200: Dividing the acquired actual battery data using a preset division rule to obtain a plurality of data segments with specific characteristics;

[0051] S300: extracting features from each divided data segment using a preset algorithm to obtain multiple voltage features that can characterize battery self-discharge characteristics;

[0052] S400: Performing a comprehensive evaluation on each battery cell in the battery according to the extracted voltage characteristics to screen out battery cells with self-discharge faults.

[0053] The steps of the above-mentioned battery self-discharge fault diagnosis method will be discussed in detail below.

[0054] First, see Figure 1 As shown, step S100 is executed to obtain actual battery data.

[0055] In an exemplary embodiment of the present application, in step S100, obtaining actual battery data specifically includes: obtaining actual battery data generated by the electric vehicle during actual operation, wherein the actual battery data includes multivariate information of the battery in actual usage scenarios, which can reflect the comprehensive impact of user behavior habits, vehicle operating status and environmental factors on battery performance.

[0056] It should be noted that in this embodiment, the selected research object is an electric vehicle. Therefore, the actual battery data obtained in step S100 is the real battery data generated by the electric vehicle during actual operation. However, in other embodiments, if the selected research object is changed to other electrified vehicles (such as rail vehicles, electric ships, electric aircraft, etc.) or energy storage power stations, the actual battery data obtained in step S100 is correspondingly adjusted to the actual battery data corresponding to the research object to ensure the applicability and accuracy of the diagnostic method.

[0057] Specifically, the actual battery data is derived from real-time recordings of the electric vehicle during actual operation, rather than from battery data collected in a laboratory simulation environment. While laboratory environments can study specific battery performance by controlling variables, they cannot fully reflect the complex and changing conditions faced by batteries in actual use. In this embodiment, the actual battery data includes a variety of information about the battery in actual usage scenarios, including but not limited to user behavior, vehicle operating conditions, and environmental factors. It should be noted that user behavior includes but is not limited to charging frequency and driving style, which can affect the battery's charging and discharging processes and, in turn, its performance. The vehicle operating conditions include vehicle load, mileage, driving speed, and road conditions. It is worth noting that the vehicle operating conditions determine the battery's workload and discharge pattern, significantly affecting battery wear and performance variations. Environmental factors include ambient temperature and humidity, and battery performance can vary significantly under different environmental conditions.

[0058] Next, please continue to read Figure 1 As shown, step S200 is executed, that is, the acquired actual battery data is divided using a preset division rule to obtain a plurality of data segments with specific characteristics.

[0059] In an exemplary embodiment of the present application, step S200 further includes the following steps:

[0060] S210: extracting battery parameters from the actual battery data, the battery parameters including current, vehicle speed, vehicle charging state, and state of charge, wherein the vehicle charging state includes a parked charging state, a driving charging state, an uncharged state, and a charging complete state;

[0061] S220: Divide the actual battery data according to the extracted battery parameters using a preset division rule to obtain a plurality of data segments with specific characteristics.

[0062] It should be noted that in step S200, the operation of extracting battery parameters and dividing the actual battery data using a preset division rule is intended to ensure that the data segments obtained by this division method can, to a certain extent, simulate the static state of the battery. When the battery is in a static state for a long time, its voltage can be approximately equivalent to the open circuit voltage, which is of great significance for accurately analyzing battery characteristics. Since the research object selected in this embodiment is an electric vehicle, the battery parameters extracted from the actual battery data include current, vehicle speed, vehicle charging state, and state of charge. These parameters can fully reflect the working state of the battery during the actual operation of the electric vehicle and provide a reliable basis for data division. However, in other embodiments, if the selected research object is changed to other electrified vehicles (such as rail vehicles, electric ships, electric aircraft, etc.) or energy storage power stations, the battery parameters extracted in step S200 need to be adjusted accordingly to battery parameters closely related to the research object to ensure that the actual battery data obtained can be reasonably divided into multiple segments based on the extracted battery parameters, thereby effectively simulating the static state of the battery and laying a solid foundation for subsequent battery self-discharge fault diagnosis and other operations.

[0063] Specifically, since the self-discharge phenomenon of the battery is closely related to the voltage, the open circuit voltage is often used to describe the self-discharge rate of the battery. However, accurate measurement of the self-discharge rate generally requires complex experiments. In order to obtain a battery self-discharge scenario that is close to the real and effective, please refer to Figure 2 As shown, in this embodiment, battery parameters such as current, vehicle speed, vehicle state of charge, and state of charge (SOC) are used for segmentation. It should be noted that before segmentation, key battery parameters must be extracted from the actual battery data. These key battery parameters include, but are not limited to, current, vehicle speed, vehicle state of charge, and SOC. The vehicle charge state describes the battery's charging status at a specific moment, including the parked charging state, the driving charging state, the uncharged state, and the charging complete state. More specifically, the parked charging state indicates that the vehicle is parked and charging is in progress. The driving charging state indicates that the vehicle is driving, converting some of its kinetic energy into electrical energy through the energy recovery system, thereby charging the battery. The uncharged state indicates that the vehicle is neither in the parked charging state nor in the driving charging state, and the battery is either naturally discharging or supplying power for normal vehicle operation. The charging complete state indicates that the battery is fully charged and the charging process has completed. Furthermore, the SOC indicates the percentage of the battery's current remaining charge as a percentage of its total capacity and is an important indicator of battery charge level.

[0064] After extracting key battery parameters, the actual battery data is segmented according to preset segmentation rules to obtain multiple data segments with specific characteristics. In this embodiment, the actual battery data is segmented according to the segmentation rules of: the current is 0, the vehicle speed is 0, the vehicle charging state is uncharged, and the state of charge is constant and the segment duration exceeds a preset time, thereby obtaining multiple data segments with specific characteristics. It should be noted that when the current is 0, it indicates that the battery is neither charging nor discharging and is in a relatively stable state. When the vehicle speed is 0, it indicates that the vehicle is stationary. In addition, when the vehicle is stationary and uncharged, the battery charging and discharging conditions are relatively simple, and the data characteristics are more obvious. Therefore, based on the above three conditions being met, if the battery state of charge remains constant for a period of time, and this period of time exceeds the preset time, the actual battery data during this period is segmented into a separate data segment. It is worth noting that the preset time can be set according to actual application requirements. In this embodiment, the preset time is set to 3 hours. By extracting key battery parameters and segmenting the actual battery data according to the preset segmentation rules, a data foundation is provided for subsequent battery self-discharge fault diagnosis.

[0065] Next, please continue to read Figure 1 As shown, step S300 is executed, that is, a preset algorithm is used to extract features from each divided data segment to obtain a plurality of voltage features that can characterize the self-discharge characteristics of the battery.

[0066] Please note that Figure 2 As shown, in this embodiment, the voltage characteristics include an average voltage value, a voltage standard deviation, and a voltage change rate.

[0067] In an exemplary embodiment of the present application, step S300 further includes the following steps:

[0068] S310: Calculating the average voltage value of each battery cell in each data segment using an average calculation formula based on the actual voltage value of each battery cell at different time points in each data segment;

[0069] S320: Calculating the voltage standard deviation of each battery cell in each data segment using a standard deviation calculation formula based on the average voltage value of each battery cell in each data segment and the actual voltage values at different time points in the corresponding data segment;

[0070] S330: Calculate the voltage change rate of the battery as a whole and each battery cell in each data segment based on the maximum value of the battery as a whole voltage, the maximum value of each battery cell voltage, and the time difference between the maximum value and the minimum value in the corresponding data segment.

[0071] Specifically, when left at rest, the voltage of a battery cell will spontaneously drop. However, the voltage of a battery cell with self-discharge failure will drop more than that of a normal battery cell, so its average voltage value will also be lower than that of a normal battery cell. By comparing the average voltage values of the battery cells, it is possible to preliminarily determine which battery cell has self-discharge failure. Based on the above data segment division method, the average voltage value of each battery cell in each data segment can be calculated:

[0072]

[0073] in, is the average voltage of the battery cells, is the actual voltage of the battery cell, and n is the number of valid data points in each data segment.

[0074] When the initial states of battery cells vary, using average voltage values to identify self-discharge failures is not a very clear method. Because the voltage variation of battery cells with self-discharge failures is more severe than that of normal battery cells, the dispersion of their voltage variation is also greater than that of normal battery cells, and therefore their voltage standard deviation is also greater than that of normal battery cells. More accurate self-discharge diagnosis can be achieved by comparing the voltage standard deviations between different battery cells. The voltage standard deviation of each battery cell in each data segment is:

[0075]

[0076] Where S is the voltage standard deviation.

[0077] Over the same period of time, the battery voltage change is closely related to the voltage change of the battery cell. The voltage change of a battery cell with self-discharge fault is faster than that of a normal battery cell. By using the voltage change method, the influence of the initial state of the battery cell itself is eliminated. Therefore, the self-discharge fault phenomenon reflected by the voltage change rate is more obvious and accurate than the average voltage value. Its calculation formula is:

[0078]

[0079] Among them, K line is the voltage change rate of the entire battery, and K is the voltage change rate of the battery cell. num is the number of battery cells, δ t1 is the time difference between the maximum and minimum values of the battery's overall voltage, δ t2 It is the time difference between the maximum and minimum values of the battery cell voltage. max and V min are the maximum and minimum values of the battery's overall voltage, and They are the maximum and minimum values of the battery cell voltage respectively.

[0080] Finally, please continue to see Figure 1 As shown, step S400 is executed, that is, a comprehensive evaluation is performed on each battery cell in the battery according to the extracted voltage characteristics to screen out battery cells with self-discharge faults.

[0081] In an exemplary embodiment of the present application, step S400 further includes the following steps:

[0082] S410: setting a threshold condition for each extracted voltage feature using the triple standard deviation rule, wherein the threshold condition is used to determine whether the voltage feature of the battery cell is within a normal range;

[0083] S420: Perform a comprehensive evaluation on each battery cell in the battery according to the extracted voltage characteristics and the set threshold conditions to screen out battery cells with self-discharge faults.

[0084] In an exemplary embodiment of the present application, step 410 further includes the following steps:

[0085] S411: Obtain the mean and standard deviation of the average voltage values of all battery cells in each data segment, and use the triple standard deviation rule to set the threshold conditions of the average voltage values of battery cells in different data segments;

[0086] S412: Standardize the voltage standard deviation and voltage change rate of each battery cell in each data segment to obtain the standardized voltage standard deviation and voltage change rate, and use the triple standard deviation rule to set threshold conditions for the voltage standard deviation and voltage change rate of the battery cells in different data segments based on the standardized voltage standard deviation and voltage change rate.

[0087] In an exemplary embodiment of the present application, step 420 further includes the following steps:

[0088] S421: When any voltage feature extracted from any data segment does not meet a corresponding set threshold condition, the corresponding data segment is determined to be an abnormal data segment;

[0089] S422: When the number of abnormal data segments of any battery cell determined based on any voltage feature is greater than or equal to a preset number, the corresponding battery cell is determined to be a battery cell with a self-discharge fault.

[0090] Specifically, see Figure 3As shown, in this embodiment, the battery self-discharge fault diagnosis method is to set corresponding threshold conditions for each voltage feature. Most battery cells have high voltage consistency, so the average voltage values of similar battery cells conform to the normal distribution. The mean and standard deviation of the average voltage values of all battery cells in each data segment are calculated, and the threshold conditions are determined using the triple standard deviation (3σ) rule:

[0091]

[0092] in, and are the upper and lower limits of the threshold, and are the mean and standard deviation of the average voltage values of all cells in a data segment. When the average voltage value of the battery cells in a data segment exceeds the threshold limit, the data segment is determined to be an abnormal data segment.

[0093] At the same time, considering the possible differences in the initial states of different battery cells, as well as the influence of different SOC and static time, it is necessary to standardize the two voltage characteristic segments of voltage standard deviation and voltage change rate:

[0094]

[0095] in, and They are the standardized voltage standard deviation and voltage change rate, S ave and S std are the mean and standard deviation of the voltage standard deviation of all battery cells in a certain data segment, K ave and K std are the mean and standard deviation of the voltage change rate of all battery cells in a certain data segment. and The mean and variance of are 0 and 1 respectively. Therefore, for these two voltage characteristics, the threshold condition can be set as follows according to the 3σ rule:

[0096]

[0097] in, and When the voltage standard deviation or voltage change rate of a battery cell in a data segment exceeds the threshold limit, the data segment is determined to be an abnormal data segment.

[0098] It should be noted that in order to exclude the situation where abnormal values exceed the threshold range, the number of abnormal data segments needs to be set. In this embodiment, a battery cell with a number of abnormal data segments greater than or equal to 7 can be diagnosed as a battery cell with self-discharge fault.

[0099] In summary, the present invention provides a battery self-discharge fault diagnosis method. This method obtains actual battery data, which comprehensively covers various types of information about the battery in actual usage scenarios and can comprehensively reflect the impact of user behavior habits, vehicle operating status, and environmental factors on battery performance. The acquired actual battery data is divided using preset segmentation rules to obtain multiple data segments with specific characteristics. The preset segmentation rules fully consider the inherent correlation between battery self-discharge and battery operating parameters. A preset algorithm is used to extract features from each divided data segment to obtain multiple voltage features that can characterize the battery self-discharge characteristics. These voltage features can accurately reflect the voltage changes of battery cells during the self-discharge process. Based on the extracted voltage features, each battery cell in the battery is comprehensively evaluated to screen out battery cells with self-discharge faults. This diagnostic method has the ability to quickly identify and accurately locate electric vehicles with self-discharge faults. Based on the extracted multiple key voltage features, batteries with different fault levels can be efficiently and accurately identified. Compared with the traditional method of measuring battery self-discharge rate based on experiments or modeling, the diagnostic method described in the present invention makes full use of the data generated by the battery during actual use, and can complete the identification and diagnosis of battery self-discharge faults in a short period of time, thereby significantly improving the safety and economy of battery operation.

[0100] Based on the same inventive concept, please refer to Figure 4 As shown, another embodiment of the present invention further provides a battery self-discharge fault diagnosis system 100. The system 100 is implemented by the battery self-discharge fault diagnosis method as described in any of the above embodiments, including:

[0101] A data acquisition module 110 is used to acquire actual battery data;

[0102] A data segmentation module 120 is configured to segment the acquired actual battery data using a preset segmentation rule to obtain a plurality of data segments having specific characteristics;

[0103] The voltage feature extraction module 130 is used to extract features from each divided data segment using a preset algorithm to obtain multiple voltage features that can characterize the self-discharge characteristics of the battery;

[0104] The fault diagnosis module 140 is used to perform a comprehensive evaluation on each battery cell in the battery according to the extracted voltage characteristics, so as to screen out battery cells with self-discharge faults.

[0105] It should be noted that the battery self-discharge fault diagnosis system 100 includes the battery self-discharge fault diagnosis method described in any of the above embodiments. Since the battery self-discharge fault diagnosis system 100 provided in this embodiment and the battery self-discharge fault diagnosis method provided in any of the above embodiments are based on the same inventive concept and therefore have at least the same beneficial effects, they will not be described in detail here.

[0106] The above embodiments are merely illustrative of the principles and effects of the present invention and are not intended to limit the present invention. Anyone skilled in the art may modify or alter the above embodiments without departing from the spirit and scope of the present invention. Therefore, all equivalent modifications or alterations made by one of ordinary skill in the art without departing from the spirit and technical principles disclosed herein are intended to be covered by the claims of the present invention.

Claims

1. A battery self-discharge fault diagnosis method, characterized in that: include: Get actual battery data; The acquired actual battery data is divided using a preset division rule to obtain multiple data segments with specific characteristics; Using a preset algorithm to extract features from each divided data segment to obtain multiple voltage features that can characterize the self-discharge characteristics of the battery; A comprehensive evaluation is performed on each battery cell in the battery based on the extracted voltage characteristics to screen out battery cells with self-discharge faults.

2. The battery self-discharge fault diagnosis method according to claim 1, characterized in that: The obtaining of actual battery data includes: The actual battery data generated by the electric vehicle during actual operation is obtained. The actual battery data includes multiple information about the battery in actual usage scenarios, which can reflect the comprehensive impact of user behavior habits, vehicle operating status and environmental factors on battery performance.

3. The battery self-discharge fault diagnosis method according to claim 1, characterized in that: The method of dividing the acquired actual battery data using a preset division rule to obtain a plurality of data segments with specific characteristics includes: Extracting battery parameters from the actual battery data, the battery parameters including current, vehicle speed, vehicle charging state, and state of charge, wherein the vehicle charging state includes a parked charging state, a driving charging state, an uncharged state, and a charging complete state; According to the extracted battery parameters, the actual battery data is divided using a preset division rule to obtain a plurality of data segments with specific characteristics.

4. The battery self-discharge fault diagnosis method according to claim 3, characterized in that: The actual battery data is divided according to the extracted battery parameters using a preset division rule to obtain a plurality of data segments with specific characteristics, including: The actual battery data is divided according to the division rules that the current is 0, the vehicle speed is 0, the vehicle charging state is uncharged, the charge state is constant, and the segment time exceeds a preset time to obtain multiple data segments with specific characteristics.

5. The battery self-discharge fault diagnosis method according to claim 1, characterized in that: In the step of extracting features from each divided data segment using a preset algorithm to obtain a plurality of voltage features capable of characterizing the self-discharge characteristics of the battery, the voltage features include an average voltage value, a voltage standard deviation, and a voltage change rate.

6. The battery self-discharge fault diagnosis method according to claim 5, characterized in that: The method uses a preset algorithm to extract features from each divided data segment to obtain multiple voltage features that can characterize the self-discharge characteristics of the battery, including: Calculate the average voltage value of each battery cell in each data segment using the mean calculation formula based on the actual voltage value of each battery cell at different time points in each data segment; Calculate the voltage standard deviation of each battery cell in each data segment using a standard deviation calculation formula based on the average voltage value of each battery cell in each data segment and the actual voltage value at different time points in the corresponding data segment; The voltage change rates of the battery as a whole and each battery cell in each data segment are calculated based on the maximum value of the battery voltage as a whole, the maximum value of each battery cell voltage and the time difference between the maximum and minimum values in the corresponding data segment.

7. The battery self-discharge fault diagnosis method according to claim 5, characterized in that: The comprehensive evaluation of each battery cell in the battery according to the extracted voltage characteristics to screen out battery cells with self-discharge faults includes: Using the triple standard deviation rule to set a threshold condition for each extracted voltage feature, the threshold condition is used to determine whether the voltage feature of the battery cell is within a normal range; Based on the extracted voltage characteristics and the set threshold conditions, a comprehensive evaluation is performed on each battery cell in the battery to screen out battery cells with self-discharge faults.

8. The battery self-discharge fault diagnosis method according to claim 7, characterized in that: The method of setting a threshold condition for each extracted voltage feature using the triple standard deviation rule includes: Obtain the mean and standard deviation of the average voltage values of all battery cells in each data segment, and use the triple standard deviation rule to set the threshold conditions for the average voltage values of battery cells in different data segments; The voltage standard deviation and voltage change rate of each battery cell in each data segment are standardized to obtain the standardized voltage standard deviation and voltage change rate. Based on the standardized voltage standard deviation and voltage change rate, the triple standard deviation rule is used to set the threshold conditions for the voltage standard deviation and voltage change rate of the battery cells in different data segments.

9. The battery self-discharge fault diagnosis method according to claim 7, characterized in that: The method of comprehensively evaluating each battery cell in the battery based on the extracted voltage characteristics and the set threshold conditions to screen out battery cells with self-discharge faults includes: When any voltage feature extracted from any data segment does not meet the corresponding set threshold condition, the corresponding data segment is determined to be an abnormal data segment; When the number of abnormal data segments determined for any battery cell based on any voltage feature is greater than or equal to a preset number, the corresponding battery cell is determined to be a battery cell with a self-discharge fault.

10. A battery self-discharge fault diagnosis system, characterized in that: The system is implemented by the battery self-discharge fault diagnosis method according to any one of claims 1 to 9, comprising: Data acquisition module, used to obtain actual battery data; A data segmentation module is used to segment the acquired actual battery data using a preset segmentation rule to obtain multiple data segments with specific characteristics; A voltage feature extraction module is used to extract features from each divided data segment using a preset algorithm to obtain multiple voltage features that can characterize the self-discharge characteristics of the battery; The fault diagnosis module is used to comprehensively evaluate each battery cell in the battery based on the extracted voltage characteristics to screen out battery cells with self-discharge faults.

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