Battery anomaly detection method and device, storage medium and program product

Abstract

The invention provides a battery anomaly detection method and device, a storage medium and a program product. Relates to the technical field of battery detection. The method comprises the following steps: acquiring battery parameters of a to-be-detected battery in charging and discharging processes; performing outlier detection based on the battery parameters to judge whether a battery cell module with parameter outlier exists in the to-be-detected battery; and if the cell module with the parameter outlier exists, determining the cell module with the parameter outlier as an abnormal cell module. According to the embodiment of the invention, the outlier detection is carried out through the battery parameters, so that the abnormal battery cell module in the to-be-detected battery is found through balance judgment, and the accuracy of battery abnormality detection is improved.

Description

Technical Field

[0001] The present application relates to the technical field of battery detection, and in particular to a battery abnormality detection method, device, storage medium and program product. Background Art

[0002] At present, the threshold judgment method is mostly used for battery anomaly detection, for example: through the battery safety monitoring module (Energy Storage Management System, ESMS) in the battery management system (Battery Management System, BMS). By monitoring and recording the charge and discharge current and voltage of the battery cell, battery module, battery cluster and / or battery stack. When the collected data exceeds the preset threshold, the system will use an alarm or shut down the battery charge and discharge function to handle the anomaly. This detection method has a large error. Summary of the invention

[0003] The purpose of the embodiments of the present application is to provide a battery abnormality detection method, device, storage medium and program product to improve the accuracy of battery abnormality detection.

[0004] In a first aspect, an embodiment of the present application provides a battery abnormality detection method, comprising: Obtaining battery parameters of the battery to be tested during the charging and discharging process; Perform outlier detection based on battery parameters to determine whether there are cell modules with outlier parameters in the battery to be tested; If there is a cell module with outlier parameters, the cell module with outlier parameters is determined as an abnormal cell module; The battery parameters include the cell voltage and / or the cell temperature; outlier detection is performed based on the battery parameters to determine whether there is a cell module with outlier parameters in the battery to be detected, including: Acquire a first battery parameter of each battery cell in the battery to be detected at a charging end, and acquire a second battery parameter of each battery cell in the battery to be detected at a discharging end; For each battery cell, a first outlier result is obtained according to a first battery parameter corresponding to the battery cell and a first average battery parameter of the battery to be detected at the charging end; a second outlier result is obtained according to a second battery parameter corresponding to the battery cell and a second average battery parameter of the battery to be detected at the discharging end; Determine whether the battery cell is a battery cell with outliers in parameters according to the first outlier result and / or the second outlier result; Obtaining a first outlier result according to a first battery parameter corresponding to the battery cell and a first average battery parameter of the battery to be detected at a charging terminal includes: An outlier detection function is used to perform an outlier analysis on the first battery parameter and the first average battery parameter. If it is determined that the battery cell parameter is an outlier and the first battery parameter of the battery cell is greater than the first average battery parameter, the first outlier result of the battery is determined to be a high-charge outlier; if it is determined that the battery cell parameter is an outlier and the first battery parameter of the battery cell is less than the first average battery parameter, the first outlier result of the battery is determined to be a low-charge outlier; Obtaining a second outlier result according to a second battery parameter corresponding to the battery cell and a second average battery parameter of the battery to be detected at the end of discharge, including: An outlier detection function is used to perform an outlier analysis on the second battery parameter and the second average battery parameter. If it is determined that the battery cell parameter is an outlier and the second battery parameter of the battery cell is less than the second average battery parameter, then the first outlier result of the battery is determined to be a low outlier; if it is determined that the battery cell parameter is an outlier and the second battery parameter of the battery cell is greater than the second average battery parameter, then the first outlier result of the battery is determined to be a high outlier.

[0005] The embodiment of the present application performs outlier detection through a first battery parameter at the charging end and a second battery parameter at the discharging end. Since the battery parameters at the charging end and the discharging end can reflect the battery performance and health status of the battery during the charging and discharging processes, outlier detection is performed through the battery parameters of the charging end and the discharging module. This can improve the accuracy and reliability of abnormality detection and help to promptly discover and handle potential battery failures.

[0006] In any embodiment, determining whether the battery cell is a battery cell with outliers in parameters according to the first outlier result and the second outlier result includes: If the first outlier result is a high charge outlier and the second outlier result is a low charge outlier, the battery is determined to be a cell with parameter outliers.

[0007] In the embodiment of the present application, if the voltage and / or battery temperature of a battery cell at the end of charging is significantly higher than those of other battery cells, and at the end of discharging, its voltage and / or battery temperature is significantly lower than those of other battery cells, it means that the battery cell is abnormal. Therefore, this feature can be used to more accurately detect abnormal batteries.

[0008] In any embodiment, determining whether the battery cell is a battery cell with outliers in parameters according to the first outlier result and the second outlier result includes: If the first outlier result is a low charge outlier and the second outlier result is a low discharge outlier, the battery is determined to be a cell with parameter outliers.

[0009] In the embodiment of the present application, if the voltage and / or battery temperature of a battery cell at the end of charging is significantly lower than that of other battery cells, and its voltage and / or battery temperature is also significantly lower than that of other battery cells at the end of discharging, it is determined that the battery cell may have abnormalities such as insufficient power, and the battery cell is an abnormal battery cell.

[0010] In any embodiment, determining whether the battery cell is a battery cell with outliers in parameters according to the first outlier result and the second outlier result includes: If the first outlier result is a low charge outlier or a high charge outlier, and / or the second outlier result is a low discharge outlier or a high discharge outlier, then the battery is determined to be a cell with outliers in parameters.

[0011] In the embodiment of the present application, the outlier cell may be determined by the first outlier result and / or the second outlier result, and the cell is determined to be an abnormal cell.

[0012] In any embodiment, the battery parameters include the battery module voltage and / or the battery module temperature; and outlier detection is performed based on the battery parameters to determine whether there is a cell module with outlier parameters in the battery to be detected, including: Acquire a third battery parameter of each battery module in the battery to be detected at the charging end, and acquire a fourth battery parameter of each battery module in the battery to be detected at the discharging end; For each battery module, a third outlier result is obtained according to a third battery parameter corresponding to the battery module and a third average battery parameter of the battery to be detected at the charging end; a fourth outlier result is obtained according to a fourth battery parameter at the discharge end corresponding to the battery module and a fourth average battery parameter of the battery to be detected at the discharge end; If at least one of the third outlier result and the fourth outlier result indicates that the battery module is outlier, it is determined that there is a cell module with outlier parameters in the battery to be detected.

[0013] The present application performs outlier detection on the voltage and / or temperature of the battery module. When there is a battery module with a large difference in battery parameters from other battery modules, the battery module is determined to be an abnormal battery cell module. The abnormal battery module can be identified quickly and accurately through outlier detection.

[0014] In any embodiment, the battery parameters include a battery cell voltage and / or a battery cell temperature; after obtaining the battery parameters of the battery to be tested during the charging and discharging process, the method further includes: Extracting battery parameters between adjacent cells in the battery to be tested from the acquired battery parameters; Calculating a first difference degree of battery parameters between adjacent battery cells; If the first difference is greater than a first preset difference threshold, it is determined that the battery to be detected is abnormal.

[0015] In the embodiment of the present application, under normal circumstances, the battery parameters of adjacent battery cells will not differ too much. If the difference is large, it means that the battery is abnormal. Therefore, it is possible to reasonably and accurately determine whether the battery is abnormal by calculating the difference in voltage and / or battery temperature between adjacent battery cells.

[0016] In any embodiment, the battery parameters include the battery stack voltage and the battery stack current; after obtaining the battery parameters of the battery to be tested during the charging and discharging process, the method further includes: If the battery stack current is less than the preset current threshold, and the battery stack voltage is increasing, and the battery stack voltage change rate is less than the preset change rate threshold, and the duration is greater than the preset duration, the battery stack is determined to be abnormal.

[0017] The embodiment of the present application can more accurately detect whether the battery is abnormal by monitoring the battery stack current and the battery stack voltage and combining it with the judgment of the duration.

[0018] In any embodiment, the battery parameters include the state of charge of the battery cluster; after obtaining the battery parameters of the battery to be detected during the charging and discharging process, the method further includes: Performing a difference analysis according to the charge states of multiple battery clusters in the battery stack to obtain a second difference; If the second difference is greater than the second preset difference threshold, it is determined that the battery cluster is abnormal.

[0019] The embodiment of the present application can quantify the SOC differences between the battery clusters by analyzing the differences in the charge states of multiple battery clusters in the battery stack. When the SOC of a battery cluster is significantly different from that of other battery clusters, it may mean that the battery cluster has an internal fault or performance degradation. This judgment method based on the difference is more accurate than a single threshold judgment, because the abnormality of a battery cluster may manifest as a significant deviation in the SOC, rather than just an abnormality in the absolute value.

[0020] In any embodiment, after determining that the cell module with outlier parameters is an abnormal cell module, the method further includes: The battery parameters are input into a pre-trained abnormality analysis model to obtain the abnormal cause output by the abnormality analysis model; wherein the abnormality analysis model is generated based on a machine learning model, combined with the SHAP theory and the information gain IG evaluation method.

[0021] The embodiment of the present application uses a machine learning model to obtain abnormal cause information, and can use a large amount of battery parameter data for training and learning, and dig out the potential relationship between data features and battery abnormalities in the data, so as to achieve more accurate abnormality identification. In the battery abnormality analysis scenario, the SHAP method can help identify which battery parameters have the greatest impact on abnormal judgment, thereby providing valuable insights and enhancing the transparency and credibility of the model.

[0022] In a second aspect, an embodiment of the present application provides a battery abnormality detection device, comprising: A parameter acquisition module is used to obtain the battery parameters of the battery to be tested during the charging and discharging process; A detection module, used for performing outlier detection based on battery parameters to determine whether there is a cell module with outlier parameters in the battery to be detected; an abnormality determination module, for determining the cell module with outlier parameters as an abnormal cell module if there is one; The battery parameters include the cell voltage and / or the cell temperature; the detection module is specifically used for: Acquire a first battery parameter of each battery cell in the battery to be detected at a charging end, and acquire a second battery parameter of each battery cell in the battery to be detected at a discharging end; For each battery cell, a first outlier result is obtained according to a first battery parameter corresponding to the battery cell and a first average battery parameter of the battery to be detected at the charging end; a second outlier result is obtained according to a second battery parameter corresponding to the battery cell and a second average battery parameter of the battery to be detected at the discharging end; Determine whether the battery cell is a battery cell with outliers in parameters according to the first outlier result and / or the second outlier result; Obtaining a first outlier result according to a first battery parameter corresponding to the battery cell and a first average battery parameter of the battery to be detected at a charging terminal includes: An outlier detection function is used to perform an outlier analysis on the first battery parameter and the first average battery parameter. If it is determined that the battery cell parameter is an outlier and the first battery parameter of the battery cell is greater than the first average battery parameter, the first outlier result of the battery is determined to be a high-charge outlier; if it is determined that the battery cell parameter is an outlier and the first battery parameter of the battery cell is less than the first average battery parameter, the first outlier result of the battery is determined to be a low-charge outlier; Obtaining a second outlier result according to a second battery parameter corresponding to the battery cell and a second average battery parameter of the battery to be detected at the end of discharge, including: An outlier detection function is used to perform an outlier analysis on the second battery parameter and the second average battery parameter. If it is determined that the battery cell parameter is an outlier and the second battery parameter of the battery cell is less than the second average battery parameter, then the first outlier result of the battery is determined to be a low outlier; if it is determined that the battery cell parameter is an outlier and the second battery parameter of the battery cell is greater than the second average battery parameter, then the first outlier result of the battery is determined to be a high outlier.

[0023] In a third aspect, an embodiment of the present application provides an electronic device, comprising: a processor, a memory and a bus, wherein the processor and the memory communicate with each other through the bus; the memory stores program instructions that can be executed by the processor, and the processor calls the program instructions to execute the method of the first aspect.

[0024] In a fourth aspect, an embodiment of the present application provides a non-transitory computer-readable storage medium, including: The non-transitory computer-readable storage medium stores computer instructions that cause the computer to execute the method of the first aspect.

[0025] In a fifth aspect, an embodiment of the present application provides a computer program product, comprising computer program instructions, wherein when the computer program instructions are read and executed by a processor, the method of the first aspect is executed.

[0026] Other features and advantages of the present application will be described in the following description, and partly become apparent from the description, or be understood by practicing the embodiments of the present application. The purpose and other advantages of the present application can be realized and obtained by the structures specifically pointed out in the written description, claims, and drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings required for use in the embodiments of the present application will be briefly introduced below. It should be understood that the following drawings only show certain embodiments of the present application and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without paying creative work.

[0028] Figure 1 A flowchart of a battery abnormality detection method provided in an embodiment of the present application; Figure 2 A schematic diagram of another battery abnormality detection method provided in an embodiment of the present application; Figure 3 A schematic diagram of the structure of a battery abnormality detection device provided in an embodiment of the present application; Figure 4 A schematic diagram of the physical structure of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0029] The following embodiments of the technical solution of the present application are described in detail in conjunction with the accompanying drawings. The following embodiments are only used to more clearly illustrate the technical solution of the present application, and are therefore only used as examples, and cannot be used to limit the scope of protection of the present application.

[0030] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by technicians in the technical field to which this application belongs; the terms used herein are only for the purpose of describing specific embodiments and are not intended to limit this application; the terms "including" and "having" in the specification and claims of this application and the above-mentioned figure descriptions and any variations thereof are intended to cover non-exclusive inclusions.

[0031] In the description of the embodiments of the present application, the technical terms "first", "second", etc. are only used to distinguish different objects, and cannot be understood as indicating or implying relative importance or implicitly indicating the number, specific order or primary and secondary relationship of the indicated technical features. In the description of the embodiments of the present application, the meaning of "multiple" is more than two, unless otherwise clearly and specifically defined.

[0032] Reference to "embodiments" herein means that a particular feature, structure, or characteristic described in conjunction with the embodiments may be included in at least one embodiment of the present application. The appearance of the phrase in various locations in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment that is mutually exclusive with other embodiments. It is explicitly and implicitly understood by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0033] In the description of the embodiments of the present application, the term "and / or" is only a description of the association relationship of the associated objects, indicating that there may be three relationships. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. In addition, the character " / " in this article generally indicates that the associated objects before and after are in an "or" relationship.

[0034] In the description of the embodiments of the present application, the term "multiple" refers to more than two (including two). Similarly, "multiple groups" refers to more than two groups (including two groups), and "multiple pieces" refers to more than two pieces (including two pieces).

[0035] In the description of the embodiments of the present application, unless otherwise clearly specified and limited, technical terms such as "installed", "connected", "connected", "fixed" and the like should be understood in a broad sense. For example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection or an indirect connection through an intermediate medium, and it can be the internal connection of two elements or the interaction relationship between two elements. For those of ordinary skill in the art, the specific meanings of the above terms in the embodiments of the present application can be understood according to the specific circumstances.

[0036] Battery abnormality refers to abnormal conditions or performance degradation of the battery during charging, discharging or storage. Abnormal scenarios include but are not limited to the following: Battery heat: The battery temperature is too high during use or charging, and may exceed the normal operating temperature range.

[0037] Battery short circuit: Direct contact between the positive and negative poles inside the battery causes excessive current, which may cause fire or explosion.

[0038] Battery expansion or deformation: The battery casing expands or deforms due to increased internal pressure, which may be accompanied by leakage.

[0039] The battery power decreases too quickly: Under normal use conditions, the battery power decreases rapidly and is far less than the expected usage time.

[0040] Unable to charge: The battery cannot receive charging current, or the charging speed is too slow to be fully charged.

[0041] Battery aging: After long-term use or multiple charge and discharge cycles, the battery's performance gradually declines and its capacity decreases.

[0042] Battery anomalies may lead to serious safety accidents such as fire and explosion. Through timely anomaly detection, problems can be discovered and solved before accidents occur, so that the battery can operate more safely and stably. The existing anomaly detection method is to set thresholds for some key parameters of the battery, such as temperature threshold, voltage threshold, current threshold, etc. When the parameters of the battery cell are detected to exceed the set parameter threshold, it is determined that the battery cell is abnormal. The detection results obtained in this way are not accurate enough, because the threshold device will be affected by factors such as battery aging and temperature fluctuations. These factors may cause changes in the threshold, thereby increasing the risk of misjudgment.

[0043] In order to solve the above technical problems, the embodiments of the present application provide a battery abnormality detection method, device, storage medium and program product. Among them, the subject executing the method can be a battery management system, or a battery cloud server, etc. If it is a battery cloud server, the battery cloud server can communicate with the battery to obtain the battery parameters during the charging and discharging process.

[0044] Furthermore, the battery may be a power battery or an energy storage battery. One structure of the battery may include multiple battery stacks, and the multiple battery stacks are connected in series, parallel or mixed. Each battery stack includes multiple battery clusters, and the multiple battery clusters are connected in series, parallel or mixed. Each battery cluster includes multiple battery modules connected in series, parallel or mixed. Each battery module includes multiple battery cells connected in series, parallel or mixed. Another structure of the battery may include multiple battery cells connected in series, parallel or mixed. Yet another structure of the battery may include multiple battery stacks, each battery stack including multiple battery cells. The structure of the battery may also be other, which is not specifically limited in the embodiments of the present application.

[0045] Figure 1 A flowchart of a battery abnormality detection method provided in an embodiment of the present application is shown in FIG. Figure 1 As shown, the method includes: Step 101: Obtaining battery parameters of the battery to be tested during the charging and discharging process; Step 102: performing outlier detection based on battery parameters to determine whether there is a cell module with outlier parameters in the battery to be detected; Step 103: If there is a cell module with outlier parameters, the cell module with outlier parameters is determined as an abnormal cell module.

[0046] In the specific implementation process, the battery parameters can be collected by the battery management system to obtain the relevant parameters of the battery to be tested during the charging and discharging process, and these parameters can reflect the performance and health status of the battery. The battery parameters can be the voltage, current, battery temperature and other parameters of each battery cell contained in the battery to be tested, and can also be the voltage, current, battery temperature and other parameters of the battery module, and can also be the voltage, current, battery temperature and other parameters of the battery cluster. Battery parameters can also include battery capacity, battery internal resistance, etc. Therefore, which parameters the battery parameters specifically include can be set according to actual conditions. It should be noted that before obtaining the battery parameters, the document configuration parsing template of the original charging and discharging data can be exported from the host computer. The document configuration parsing template determines what the battery parameters specifically include. Then, the original charging and discharging data document within the detection time period is exported from the host computer, and the battery parameters in the original charging and discharging data document are obtained according to the document configuration parsing template.

[0047] When obtaining battery parameters, professional testing equipment can be used, such as battery management systems, data acquisition devices, etc. These devices can accurately measure the various parameters of the battery during the charging and discharging process, and record them for subsequent analysis.

[0048] Outlier detection is used to identify points in the collected data set that are significantly different from the majority of data points. After obtaining the battery parameters, the battery parameters can be preprocessed, including steps such as data cleaning, missing value filling, and data standardization. This helps with data accuracy and consistency, laying the foundation for subsequent analysis.

[0049] After obtaining the battery parameters, outlier detection is performed based on the battery parameters to determine whether there is a cell module with outlier parameters in the battery to be detected. It should be noted that a cell module can be a single cell, a battery module composed of multiple cells, or a battery cluster composed of multiple battery modules. A cell module with outlier parameters refers to a cell module whose battery parameters are significantly different from those of other cell modules. Whether there is a significant difference can be determined by some outlier algorithms, such as simple statistical analysis, 3D ... The battery parameters are analyzed using the principle of sparse analysis, box plot analysis, or machine learning algorithms to identify outliers that are significantly different from the majority of data points. These outliers may represent battery modules with abnormal performance.

[0050] After outlier detection, if a cell module with outlier parameters is found in the battery, the cell module with outlier parameters is determined as an abnormal cell module.

[0051] It should be noted that if it is determined that there is an abnormal cell module in the battery to be tested, the abnormal information will be displayed for the staff to review and determine whether professional personnel are needed to analyze the abnormal details. If professional personnel are required to analyze, the analysis report will be output by the professional personnel.

[0052] The embodiment of the present application uses outlier detection to identify anomalies. Compared with the traditional method of using thresholds to identify anomalies, the advantage of this method is that it improves the accuracy of detection. For example, for temperature detection, if a normally operating battery is in a high temperature environment, the temperature of each battery cell is relatively high as a whole during the charging and discharging process. If a temperature threshold is used, it is likely that the battery will be considered abnormal. However, using the outlier detection method of the present application, if the temperature of the battery cells is generally high, but does not reach the temperature of thermal runaway, it can be considered normal.

[0053] The embodiment of the present application obtains the battery parameters of the battery to be tested during the charging and discharging process, and performs outlier detection based on these parameters, so as to accurately determine whether there are cell modules with outlier parameters in the battery pack, and take corresponding treatment measures. This is of great significance for ensuring the performance and safety of the battery pack.

[0054] The battery parameters include the cell voltage and / or the cell temperature; outlier detection is performed based on the battery parameters to determine whether there is a cell module with outlier parameters in the battery to be detected, including: Acquire a first battery parameter of each battery cell in the battery to be detected at a charging end, and acquire a second battery parameter of each battery cell in the battery to be detected at a discharging end; For each battery cell, a first outlier result is obtained according to a first battery parameter corresponding to the battery cell and a first average battery parameter of the battery to be detected at the charging end; a second outlier result is obtained according to a second battery parameter corresponding to the battery cell and a second average battery parameter of the battery to be detected at the discharging end; It is determined whether the battery cell is a battery cell with outliers in parameters according to the first outlier result and the second outlier result.

[0055] In a specific implementation process, the battery parameters include the cell voltage and / or the cell temperature. Therefore, the battery parameters may include only the cell voltage, only the cell temperature, or both the cell voltage and the cell temperature. The following describes the above three situations respectively: (1) Battery parameters only include cell voltage: When obtaining the cell voltage of the battery to be tested, the cell voltage of the battery to be tested at the charging end (referred to as the first battery parameter in this embodiment of the application) and the cell voltage of the battery to be tested at the discharging end (referred to as the second battery parameter in this embodiment of the application) can be obtained.

[0056] For each cell included in the battery to be detected, outlier detection is performed based on the cell voltage at the charging terminal corresponding to the cell and the average cell voltage at the charging terminal corresponding to the battery to be detected, to obtain a first outlier result of the cell. The first outlier result is used to characterize whether the cell voltage of the cell at the charging terminal is outlier.

[0057] Similarly, outlier detection is performed based on the cell voltage at the discharge end corresponding to the cell and the average cell voltage at the discharge end corresponding to the battery to be detected to obtain a second outlier result for the cell. The second outlier result is used to characterize whether the cell voltage at the discharge end of the cell is outlier.

[0058] After the first outlier result and the second outlier result are obtained, it can be determined whether the battery cell is a battery cell with outlier parameters according to the first outlier result and / or the second outlier result.

[0059] It should be noted that the charging end refers to the stage when the battery is close to being fully charged during the charging process. It can be determined specifically by the SOC value, voltage value, etc. of the battery during the charging process. For example, when the SOC value reaches 95%, it can be considered to have reached the charging end. It can also be considered to have reached the charging end when the battery voltage reaches 3.4V. Among them, the SOC value and voltage value for judging the charging end can be set according to actual conditions, and the embodiments of the present application do not specifically limit this.

[0060] During the charging process, when the battery reaches the charging end, the charging will continue until the charging cutoff is reached. Therefore, in the process from entering the charging end to the charging cutoff, the cell voltages at multiple time points can be collected, and then the average value can be taken as the cell voltage at the charging end. Of course, the first cell voltage collected after entering the charging end can also be used as the cell voltage at the charging end.

[0061] (2) Battery parameters only include cell temperature: When obtaining the cell temperature of the battery to be tested, the cell temperature of the battery to be tested at the charging end (referred to as the first battery parameter in the embodiment of the present application) and the cell temperature of the battery to be tested at the discharging end (referred to as the second battery parameter in the embodiment of the present application) can be obtained.

[0062] For each battery cell included in the battery to be detected, an outlier detection is performed based on the battery cell temperature at the charging terminal corresponding to the battery cell and the average battery cell temperature at the charging terminal corresponding to the battery to be detected, to obtain a first outlier result of the battery cell. The first outlier result is used to characterize whether the battery cell temperature at the charging terminal of the battery cell is outlier.

[0063] Similarly, outlier detection is performed based on the cell temperature at the discharge end corresponding to the cell and the average cell temperature at the discharge end corresponding to the battery to be detected to obtain a second outlier result for the cell. The second outlier result is used to characterize whether the cell temperature at the discharge end of the cell is outlier.

[0064] After the first outlier result and the second outlier result are obtained, it can be determined whether the battery cell is a battery cell with outlier parameters according to the first outlier result and / or the second outlier result.

[0065] It should be noted that the charging end refers to the stage when the battery is close to being fully charged during the charging process. It can be determined specifically by the SOC value, voltage value, etc. of the battery during the charging process. For example, when the SOC value reaches 95%, it can be considered to have reached the charging end. It can also be considered to have reached the charging end when the battery voltage reaches 3.4V. Among them, the SOC value and voltage value for judging the charging end can be set according to actual conditions, and the embodiments of the present application do not specifically limit this.

[0066] During the charging process, when the battery reaches the charging end, the charging will continue until the charging cutoff is reached. Therefore, in the process from entering the charging end to the charging cutoff, the battery cell temperature at multiple time points can be collected, and then the average value can be taken as the battery cell temperature at the charging end. Of course, the first battery cell temperature collected after entering the charging end can also be used as the battery cell temperature at the charging end.

[0067] (3) Battery parameters include cell voltage and cell temperature: In the case where the battery parameters include the cell voltage and the cell temperature, the above method can be used to perform outlier detection on the cell voltage and the cell temperature, respectively. Thus, the first outlier result and the second outlier result of the cell voltage can be obtained; and the first outlier result and the second outlier result of the cell temperature can be obtained.

[0068] When judging whether a cell is a cell with outliers in parameters, the first outlier result and the second outlier result of the cell voltage, as well as the first outlier result and the second outlier result of the cell temperature can be comprehensively determined. For example, when it is determined that both the cell voltage and the cell temperature are outliers, the cell is determined to be an outlier; or when either the cell voltage or the cell temperature is an outlier, the cell is considered to be an outlier. The outlier judgment can also be performed by weighted averaging.

[0069] The embodiment of the present application performs outlier detection through a first battery parameter at the charging end and a second battery parameter at the discharging end. Since the battery parameters at the charging end and the discharging end can reflect the battery performance and health status of the battery during the charging and discharging processes, outlier detection is performed through the battery parameters of the charging end and the discharging module. This can improve the accuracy and reliability of abnormality detection and help to promptly discover and handle potential battery failures.

[0070] The step of obtaining a first outlier result according to a first battery parameter corresponding to the battery cell and a first average battery parameter of the battery to be detected at the charging terminal includes: An outlier detection function is used to perform an outlier analysis on the first battery parameter and the first average battery parameter. If it is determined that the battery cell parameter is an outlier and the first battery parameter of the battery cell is greater than the first average battery parameter, the first outlier result of the battery is determined to be a high-charge outlier; if it is determined that the battery cell parameter is an outlier and the first battery parameter of the battery cell is less than the first average battery parameter, the first outlier result of the battery is determined to be a low-charge outlier; Obtaining a second outlier result according to a second battery parameter corresponding to the battery cell and a second average battery parameter of the battery to be detected at the end of discharge, including: An outlier detection function is used to perform an outlier analysis on the second battery parameter and the second average battery parameter. If it is determined that the battery cell parameter is an outlier and the second battery parameter of the battery cell is less than the second average battery parameter, then the first outlier result of the battery is determined to be a low outlier; if it is determined that the battery cell parameter is an outlier and the second battery parameter of the battery cell is greater than the second average battery parameter, then the first outlier result of the battery is determined to be a high outlier.

[0071] In the specific implementation process, for the outlier detection of the battery at the end of charging, taking the cell voltage as an example, the cell voltage of the cell with the outlier parameter may be significantly higher than the cell voltage of other cells, or may be significantly lower than the cell voltage of other cells. Therefore, the first outlier result includes high-charge outliers and low-charge outliers. High-charge outliers refer to a cell whose battery parameters are significantly higher than most other cells at the end of charging. Charging outliers refer to a battery whose battery parameters are significantly lower than most other cells at the end of charging.

[0072] Correspondingly, for outlier detection of a battery at the end of discharge, the second outlier result may include a high outlier and a low outlier. A high outlier means that at the end of discharge, the battery parameters of a certain battery cell are significantly higher than those of most other batteries. A charging outlier means that at the end of charging, the battery parameters of a certain battery cell are significantly lower than those of most other batteries.

[0073] The basis for judging whether the high-charge outlier is: using the outlier detection function to perform outlier analysis on the first battery parameter and the first average battery parameter, if it is determined that the battery cell parameter is outlier, and the first battery parameter of the battery cell is greater than the first average battery parameter.

[0074] The judgment basis of low-charge outlier is: using the outlier detection function to perform outlier analysis on the first battery parameter and the first average battery parameter, if it is determined that the battery cell parameter is outlier, and the first battery parameter of the battery cell is less than the first average battery parameter.

[0075] The judgment basis of the high outlier is: using the outlier detection function to perform outlier analysis on the second battery parameter and the second average battery parameter, if it is determined that the battery cell parameter is outlier, and the second battery parameter of the battery cell is greater than the second average battery parameter.

[0076] The judgment basis for lowering the outlier is: using the outlier detection function to perform outlier analysis on the second battery parameter and the second average battery parameter, if it is determined that the battery cell parameter is outlier, and the second battery parameter of the battery cell is less than the second average battery parameter.

[0077] The outlier detection parameters are pre-set, for example, they may be: .in, It is the scenario coefficient. Different anomaly detection scenarios have different scenario coefficients. This value can be preset. is a degree constant, which is also pre-set according to different anomaly detection scenarios. The larger the value, the stricter the detection; is an outlier; is the battery parameter of the i-th battery cell; is the average parameter of the battery cell (which may be the first average battery parameter, the second average battery parameter, etc.). The outlier value can be calculated by the above formula, and whether the battery cell is outlier can be further determined according to the outlier value.

[0078] In addition, the outlier detection parameters can also be based on 3 In principle, the standard deviation is calculated based on the first battery parameter and the first average battery parameter μ , then based on the first average battery parameter and standard deviation Determine the standard parameter interval, that is, (μ-3 , μ+3 ), if the first battery parameter is within the standard parameter range, it means that there is no outlier, otherwise it means that the battery parameter of the battery cell is outlier.

[0079] It should be noted that other outlier detection methods may also be used, and the embodiments of the present application do not specifically limit this.

[0080] The embodiments of the present application judge high-charge outliers, low-charge outliers, high-discharge outliers and low-discharge outliers, and these features provide a basis for subsequent abnormal detection of battery cell modules.

[0081] On the basis of the above embodiment, determining whether the battery cell is a battery cell with outliers in parameters according to the first outlier result and the second outlier result includes: If the first outlier result is a high charge outlier and the second outlier result is a low charge outlier, the battery is determined to be a cell with parameter outliers.

[0082] In a specific implementation process, if the battery voltage and / or battery temperature of a cell at the end of charging is higher than that of most other cells, and at the end of discharging, the battery voltage and / or battery temperature of a cell is lower than that of most other cells, it may be because the cell capacity of the cell is low, or the cell is mixed, or the cell is aged. Therefore, the cell is determined as a cell with outliers.

[0083] In the embodiment of the present application, if the voltage and / or battery temperature of a battery cell at the end of charging is significantly higher than those of other battery cells, and at the end of discharging, its voltage and / or battery temperature is significantly lower than those of other battery cells, it means that the battery cell is abnormal. Therefore, this feature can be used to more accurately detect abnormal batteries.

[0084] On the basis of the above embodiment, determining whether the battery cell is a battery cell with outliers in parameters according to the first outlier result and the second outlier result includes: If the first outlier result is a low charge outlier and the second outlier result is a low discharge outlier, the battery is determined to be a cell with parameter outliers.

[0085] In a specific implementation process, if the battery voltage and / or battery temperature of a cell at the end of charging is lower than that of most other cells, and at the end of discharging, its battery voltage and / or battery temperature is lower than that of most other cells, it may be due to insufficient power of the cell (excessive self-discharge / internal micro short circuit), or cell mixing, or aging of the cell. Therefore, the cell is determined as a cell with outliers. Therefore, the cell is determined as a cell with outliers.

[0086] In the embodiment of the present application, if the voltage and / or battery temperature of a battery cell at the end of charging is significantly lower than that of other battery cells, and its voltage and / or battery temperature is also significantly lower than that of other battery cells at the end of discharging, it is determined that the battery cell may have abnormalities such as insufficient power, and the battery cell is an abnormal battery cell.

[0087] On the basis of the above embodiment, determining whether the battery cell is a battery cell with outliers in parameters according to the first outlier result and the second outlier result includes: If the first outlier result is a low charge outlier or a high charge outlier, and / or the second outlier result is a low discharge outlier or a high discharge outlier, then the battery is determined to be a cell with outliers in parameters.

[0088] In the specific implementation process, for the single cell outlier determination, when at least one of the following occurs: high charge outlier, low charge outlier, high discharge outlier, and low discharge outlier, it can be determined as a single cell outlier. When determining outliers, the outlier determination rules are relatively strict. For example: When making a judgment, k can be 1.25, a can be 1.5, and of course, other values ​​are also possible.

[0089] In the embodiment of the present application, the outlier cell may be determined by the first outlier result and / or the second outlier result, and the cell is determined to be an abnormal cell.

[0090] On the basis of the above embodiment, the battery parameters include the battery module voltage and / or the battery module temperature; outlier detection is performed based on the battery parameters to determine whether there is a cell module with outlier parameters in the battery to be detected, including: Acquire a third battery parameter of each battery module in the battery to be detected at the charging end, and acquire a fourth battery parameter of each battery module in the battery to be detected at the discharging end; For each battery module, a third outlier result is obtained according to a third battery parameter corresponding to the battery module and a third average battery parameter of the battery to be detected at the charging end; a fourth outlier result is obtained according to a fourth battery parameter at the discharge end corresponding to the battery module and a fourth average battery parameter of the battery to be detected at the discharge end; If at least one of the third outlier result and the fourth outlier result indicates that the battery module is outlier, it is determined that there is a cell module with outlier parameters in the battery to be detected.

[0091] In the specific implementation process, the abnormal detection of the battery module in the battery cluster mainly involves horizontal comparison between the battery modules. Therefore, the battery parameters include the battery module voltage and / or the battery module temperature. The battery module includes multiple battery cells, and the battery module voltage is the average value of the voltages of all the battery cells in the battery module. The battery module temperature is the average value of the temperatures of all the battery cells in the battery module.

[0092] For a certain battery module, obtain the third battery parameter (battery module voltage and / or battery module temperature) of the battery module at the charging terminal, and the third average battery parameter corresponding to the battery cluster where the battery module is located at the charging terminal. Perform outlier analysis on the battery module using the third battery parameter and the third average battery parameter to obtain a third outlier result. The third outlier result can be a battery module high charging outlier or a battery module low charging outlier.

[0093] Similarly, the fourth battery parameter (battery module voltage and / or battery module temperature) of the battery module at the end of discharge and the fourth average battery parameter corresponding to the battery cluster where the battery module is located at the end of discharge are obtained. The fourth battery parameter and the fourth average battery parameter are used to perform an outlier analysis on the battery module to obtain a fourth outlier result. The fourth outlier result can be a high outlier for the battery module or a low outlier for the battery module.

[0094] It should be noted that the method for obtaining the third outlier result and the fourth outlier result can refer to the method for obtaining the first outlier result and the second outlier result in the above embodiment, and the embodiment of the present application does not specifically limit this.

[0095] During the charging process, when the battery reaches the charging end, the charging will continue until the charging cutoff is reached. Therefore, in the process from entering the charging end to the charging cutoff, for each battery module, the battery module voltage at multiple time points can be calculated, and then the average value can be taken as the battery module voltage at the charging end. Of course, the first battery module voltage calculated after entering the charging end can also be used as the battery module voltage at the charging end. The method for obtaining the battery module temperature is similar and will not be repeated here.

[0096] During the discharge process, when the battery reaches the end of discharge, the discharge will continue until the discharge is cut off. Therefore, in the process from entering the end of discharge to the end of discharge, for each battery module, the battery module voltage at multiple time points can be calculated, and then the average value can be taken as the battery module voltage at the end of discharge. Of course, the first battery module voltage calculated after entering the end of discharge can also be used as the battery module voltage at the end of discharge. The method for obtaining the battery module temperature is similar and will not be repeated here.

[0097] The present application performs outlier detection on the voltage and / or temperature of the battery module. When there is a battery module with a large difference in battery parameters from other battery modules, the battery module is determined to be an abnormal battery cell module. The abnormal battery module can be identified quickly and accurately through outlier detection.

[0098] On the basis of the above embodiment, the battery parameters include the battery cell voltage and / or the battery cell temperature; after obtaining the battery parameters of the battery to be tested during the charging and discharging process, the method further includes: Extracting battery parameters between adjacent cells in the battery to be tested from the acquired battery parameters; Calculating a first difference degree of battery parameters between adjacent battery cells; If the first difference is greater than a first preset difference threshold, it is determined that the battery to be detected is abnormal.

[0099] In a specific implementation process, the first difference refers to the degree of difference in battery parameters between adjacent battery cells. The difference can be obtained by calculating the absolute difference or relative difference of the battery parameters of adjacent battery cells. Among them, the absolute difference method directly calculates the absolute value of the difference between the parameters of adjacent battery cells, that is, the first difference = |battery parameter of battery cell A - battery parameter of battery cell B|. The relative difference method refers to the ratio of the difference between two data to their average value, usually expressed in the form of a percentage, and the specific formula is: First difference = , where A is the battery parameter of cell A, and B is the battery parameter of cell B.

[0100] The first preset difference threshold is a preset value, and its specific value can be set according to the normal operating range, design rules and safety standards of the battery to be detected. It is intended to capture significant differences that may indicate abnormalities inside the battery. If the first difference is greater than the first preset difference threshold, it means that the battery parameters (battery voltage / battery temperature) between two adjacent cells are significantly different, so it can be determined that the battery to be detected is abnormal.

[0101] In the embodiment of the present application, under normal circumstances, the battery parameters of adjacent battery cells will not differ too much. If the difference is large, it means that the battery is abnormal. Therefore, it is possible to reasonably and accurately determine whether the battery is abnormal by calculating the difference in voltage and / or battery temperature between adjacent battery cells.

[0102] On the basis of the above embodiment, the battery parameters include the battery stack voltage and the battery stack current; after obtaining the battery parameters of the battery to be detected during the charging and discharging process, the method further includes: If the battery stack current is less than the preset current threshold, and the battery stack voltage is increasing, and the battery stack voltage change rate is less than the preset change rate threshold, and the duration is greater than the preset duration, the battery stack is determined to be abnormal.

[0103] Based on the above embodiments, the battery stack is usually composed of multiple cells, which can be the same type or different types of batteries. In a series connection, the voltages of the cells are added to obtain the total voltage of the battery stack; in a parallel connection, the currents of the cells are added, but ideally, the voltage remains unchanged. The battery stack current refers to the total current flowing through the battery stack, which is generated by the battery stack composed of multiple cells connected in series or in parallel during the charging and discharging process.

[0104] For abnormal scenarios of long-term low-current charging, the following conditions can be used to determine the battery stack voltage and battery stack current: (1) Whether the battery stack current is less than the preset current threshold; (2) Whether the battery stack voltage is increasing; (3) Whether the battery stack voltage change rate is less than the preset change rate threshold; (4) Whether the duration of the above phenomena (1)-(3) is greater than the preset duration.

[0105] If (1) to (4) are all judged to be yes, it means that the battery stack is in an abnormal situation of continuous charging with a small current, which may be caused by an abnormality in the management logic of the power conversion system (PCS).

[0106] The specific values ​​of the preset current threshold, the preset change rate threshold and the preset duration can be set according to the design situation.

[0107] The embodiment of the present application can more accurately detect whether the battery is abnormal by monitoring the battery stack current and the battery stack voltage and combining it with the judgment of the duration.

[0108] On the basis of the above embodiment, the battery parameters include the state of charge of the battery cluster; after obtaining the battery parameters of the battery to be detected during the charging and discharging process, the method further includes: Performing a difference analysis according to the charge states of multiple battery clusters in the battery stack to obtain a second difference; If the second difference is greater than the second preset difference threshold, it is determined that the battery cluster is abnormal.

[0109] In the specific implementation process, the state of charge of the battery cluster refers to the percentage of the current remaining power of the battery cluster to its rated capacity. The state of charge SOC of the battery cluster = (current power of the battery cluster - lower limit of battery capacity) / (upper limit of battery capacity - lower limit of battery capacity) × 100%.

[0110] After obtaining the state of charge of multiple battery clusters in a battery stack, an analysis is performed based on the SOC inconsistency between the battery clusters. Specifically, the second difference in the state of charge between any two battery clusters in the multiple battery clusters can be analyzed. Among them, the calculation of the second difference can be obtained by subtracting the state of charge of the two battery clusters. In addition, the calculated second difference can also be normalized so that the difference length of the SOC between the two battery clusters can be more intuitively represented. The normalization method is to divide the difference value by a reference value (such as the rated capacity of the battery cluster or the SOC value when fully charged) to obtain a difference index between 0 and 1 (or adjust the range according to the specific situation). However, in this scenario, since the SOC itself is a percentage value, and the rated capacities of the two battery clusters may be the same or similar, the difference value can be directly used as the difference, or it can be divided by 100 to obtain a more intuitive percentage form.

[0111] After calculating and obtaining the second difference, it can be determined whether the second difference is greater than a second preset difference threshold. If so, it indicates that there are battery clusters with large differences in charge state in the battery stack. This may be due to the different degrees of cell aging, resulting in obvious differences between different battery clusters.

[0112] The embodiment of the present application can quantify the SOC differences between the battery clusters by analyzing the differences in the charge states of multiple battery clusters in the battery stack. When the SOC of a battery cluster is significantly different from that of other battery clusters, it may mean that the battery cluster has an internal fault or performance degradation. This judgment method based on the difference is more accurate than a single threshold judgment, because the abnormality of a battery cluster may manifest as a significant deviation in the SOC, rather than just an abnormality in the absolute value.

[0113] On the basis of the above embodiment, after determining the cell module with outlier parameters as an abnormal cell module, the method further includes: The battery parameters are input into a pre-trained abnormality analysis model to obtain the abnormal cause output by the abnormality analysis model; wherein the abnormality analysis model is generated based on a machine learning model, combined with the SHAP theory and the information gain IG evaluation method.

[0114] In the specific implementation process, the embodiment of the present application relies on a machine learning solution and is based on a decision tree (DecisionTree) machine learning model. Through machine learning, the manufacturer outlier function of the corresponding scenario is fitted. The implementation method of this function is to form a feature engineering, use the SHAP theory combined with the information gain IG evaluation method, extract and clean the original data documents with business annotations, summarize the scene performance characteristics of the same manufacturer and the same battery cell positive electrode material, and extract and screen the feature subset that has the greatest influence on battery cell anomaly detection.

[0115] SHAP (SHapley Additive exPlanations) value is a method based on cooperative game theory to explain the contribution of each feature in a single prediction value. SHAP value can provide a more fine-grained feature importance assessment and is suitable for complex machine learning models. In order to analyze the cause of battery failure, the embodiment of the present application infers the cause of the failure through the SHAP theory, providing effective guidance measures for the next step of battery safety maintenance.

[0116] The calculation formula of SHAP value is as follows:

[0117] Represents the kth subset. The subset here means selecting some features from all the features, and the selected features are taken as a subset. Represents the SHAP value of the ith feature for a given model f and input instance x. The SHAP value indicates how much the marginal contribution is to that particular prediction when only the ith feature is considered. Σ represents the sum over all possible subsets S. S is a subset selected from the set of all features [N] that does not contain feature i. N is the set of all features, and Represents the remaining feature set after removing the i-th feature.

[0118] represents the total number of all possible subsets S. Since there are M features, and each feature can choose to be included in or not included in the subset, the total number of subsets is However, since the case containing feature i is excluded here, the actual number of subsets is . Represents the number of elements in the kth subset.

[0119] This part is the weight factor, which means that among all possible subsets, the size is The probability that the kth subset of appears in a set of size M. It is derived from the combinatorial formula in combinatorial mathematics.

[0120] represents the result predicted by model f when only the subset S and the value of feature i are present. In other words, this is the predicted output corresponding to the input vector that only includes the features in S and feature i. represents the result predicted by model f when only the feature values ​​in subset S are present. In other words, this is the predicted output corresponding to an input vector consisting only of features in S. It indicates the change in the prediction result after adding feature i. It reflects the marginal contribution of feature i on subset S.

[0121] Information Gain (IG) is a method based on information theory that measures the reduction in uncertainty of a data set after the introduction of a certain feature. The model f can be calculated using information gain as follows:

[0122]

[0123] in, The value of 𝑌 is The probability of 𝑌 is 𝑌, and N is the number of possible values ​​of 𝑌.

[0124]

[0125] in, The value of X is The probability of M is the number of possible values ​​of X. is The probability of 𝑌 under the condition.

[0126] In SHAP theory, The comparison is between the difference between the model output Y containing feature i and the model output Y without feature i. This method of judging the difference by distance has limited accuracy. Therefore, the embodiment of the present application considers the information gain of the model output Y containing feature i and the model output Y without feature i. It does not simply use the difference between the two output Y values ​​as the basis for judgment, but makes judgment by calculating the entropy and then making the difference (information gain). At the same time, it takes advantage of the SHAP theory, that is, considering the contribution of feature i to the whole relative to each subset to evaluate the contribution of the feature, and finally completes the evaluation of the importance of all features.

[0127]

[0128] During the model training process, cross-validation technology is used to ensure the stability of the model on different data sets, and pruning technology is used to avoid overfitting.

[0129] Based on the fitting results, the obtained function is used to construct multiple decision tree combinations (to improve robustness) using the random forest ensemble learning method.

[0130]

[0131] The weight dictionary for all models is w={'model 1': w 1 , 'Model 2': w 2 , …, 'Model N': w n}.

[0132] It should be noted that the data used to train the model are battery parameters with known abnormal causes collected historically.

[0133] Through the above process, an anomaly analysis model for anomaly analysis can be obtained, which can improve the accuracy of anomaly analysis performed in the embodiment of the present application.

[0134] After the abnormality analysis model is obtained, if it is determined through the above embodiment that there is an abnormal cell module in the battery to be detected, the obtained battery parameters are input into the abnormality analysis model, and the abnormality analysis model outputs the abnormality cause of the battery to be detected.

[0135] The embodiment of the present application uses a machine learning model to obtain abnormal cause information, and can use a large amount of battery parameter data for training and learning, and dig out the potential relationship between data features and battery abnormalities in the data, so as to achieve more accurate abnormality identification. In the battery abnormality analysis scenario, the SHAP method can help identify which battery parameters have the greatest impact on abnormal judgment, thereby providing valuable insights and enhancing the transparency and credibility of the model.

[0136] Figure 2 A flowchart of another battery abnormality detection method provided in an embodiment of the present application is shown as follows: Figure 2 As shown, the method includes: Step 201: Obtain battery parameters: The battery parameters of the battery to be tested can be obtained through the BMS, or other battery testing devices. The battery parameters may include the voltage and temperature of a single cell, the voltage and temperature of a battery module, the battery state of charge, voltage, temperature of a battery cluster, or the voltage, current, temperature of a battery stack.

[0137] Step 202: Obtain battery parameters at the charging end and the discharging end; step 201 may be the battery parameters for the entire time period, and this step may extract the battery parameters at the charging end and the battery parameters at the discharging end from step 201.

[0138] Step 203: Data cleaning: Clean the acquired battery parameters, for example, remove outliers, fill in missing values, etc.

[0139] Step 204: Single cell voltage outlier analysis; The outlier analysis of single cell voltage may include outlier analysis of the cell voltage at the charging end and the discharging end; difference analysis of the cell voltage at the charging end and the discharging end; high charge and low discharge analysis of the cell voltage at the charging end and the discharging end. The specific analysis method can be found in the above embodiment and will not be described here. Each analysis method can obtain a corresponding analysis result, that is, it can be obtained whether the battery to be tested includes single cells with outliers in parameters. Then, step 209 is executed.

[0140] Step 205: Single cell temperature outlier analysis; The single cell temperature outlier analysis may include: outlier analysis of the cell temperature at the charging end and the discharging end; difference detection of the cell temperature at the charging end and the discharging end; high charging and low discharging analysis of the cell temperature at the charging end and the discharging end. The specific analysis method can be found in the above embodiment and will not be described here. Each analysis method can obtain a corresponding analysis result, that is, it can be obtained whether the battery to be tested includes single cells with outliers in parameters. Then, step 209 is executed.

[0141] Step 206: Module outlier analysis; the module outlier analysis may include: analyzing the temperature outliers in the module at the charging end and the discharging end and analyzing the voltage outliers in the module at the charging end and the discharging end. The specific analysis method can be found in the above embodiment and will not be described here. Each analysis method can obtain a corresponding analysis result, that is, it can be obtained whether the battery to be tested includes a battery module with parameter outliers. Then, step 209 is executed.

[0142] Step 207: Analysis of low current continuous charging: the analysis method of low current continuous charging can be found in the above embodiment, which will not be described in detail here. Then, step 209 is executed.

[0143] Step 208: Perform outlier analysis on the SOC of the cluster within the battery stack; determine the difference between the SOCs of the battery clusters within the battery stack, and perform outlier analysis based on the difference.

[0144] Step 209: record the cell components with outlier parameters; since the battery to be detected cannot have more than one abnormal single cell, more than one abnormal battery module, or more than one abnormal battery cluster, all abnormal cell modules can be counted.

[0145] Step 210: Record all abnormal battery cell modules and abnormal scenarios. According to steps 203 to 206, all abnormal battery cell modules and the scenarios corresponding to the abnormalities can be obtained.

[0146] Step 211: Summarize the error information and display it on the front-end page.

[0147] The embodiment of the present application can recognize the original data formats of the host computer (such as xlsx, db, csv, etc.) in the current market mainstream, and can be configured through a custom import configuration template to support the system to recognize the battery cell voltage, temperature and other data in the original files of different manufacturers for diagnosis.

[0148] Unlike the mainstream solutions on the market that use the BMS system to make simple threshold judgments on the battery cells, this solution uses independent equipment to read the original charge and discharge data of the power station host computer as the data source for analysis. The export file format, data storage location, and naming of the same field of each manufacturer are different. Through custom configuration import templates, business personnel can customize the system to identify the imported value format to read the original charge and discharge data of different manufacturers. Since the mainstream energy storage power station manufacturers on the market can export the original charge and discharge data from their energy management system (EMS) host computers, this solution supports the analysis of mainstream power station data on the market, and the value scheme is universal.

[0149] This solution can be integrated into an installation package and used on a Windows system. Business personnel can use the integrated software of this solution during factory testing / project implementation / inspection to test the battery cells anytime and anywhere. The operation steps are simple. For abnormal data, the charging and discharging data can be visualized for analysis by business personnel. This solution is easy to use.

[0150] Figure 3 The schematic diagram of the structure of a battery abnormality detection device provided in an embodiment of the present application is shown in FIG. 1 , and the device may be a module, program segment or code on an electronic device. It should be understood that the device is similar to the above-mentioned Figure 1 The method embodiment corresponds to and can be executed Figure 1 The various steps involved in the method embodiment and the specific functions of the device can be found in the above description. To avoid repetition, detailed description is appropriately omitted here. The device includes: a parameter acquisition module 301, a detection module 302 and an abnormality determination module 303, wherein: The parameter acquisition module 301 is used to obtain the battery parameters of the battery to be tested during the charging and discharging process; The detection module 302 is used to perform outlier detection based on battery parameters to determine whether there is a cell module with outlier parameters in the battery to be detected; The abnormality determination module 303 is used to determine the cell module with outlier parameters as an abnormal cell module if there is one; The battery parameters include battery cell voltage and / or battery cell temperature; the detection module 302 is specifically used for: Acquire a first battery parameter of each battery cell in the battery to be detected at a charging end, and acquire a second battery parameter of each battery cell in the battery to be detected at a discharging end; For each battery cell, a first outlier result is obtained according to the first battery parameter corresponding to the battery cell and a first average battery parameter of the battery to be detected at a charging end; a second outlier result is obtained according to the second battery parameter corresponding to the battery cell and a second average battery parameter of the battery to be detected at a discharging end; Determining whether the battery cell is a battery cell with an outlier parameter according to the first outlier result and / or the second outlier result; The detection module 302 is specifically used for: An outlier detection function is used to perform an outlier analysis on the first battery parameter and the first average battery parameter. If it is determined that the battery cell parameter is an outlier and the first battery parameter of the battery cell is greater than the first average battery parameter, the first outlier result of the battery is determined to be a high-charge outlier; if it is determined that the battery cell parameter is an outlier and the first battery parameter of the battery cell is less than the first average battery parameter, the first outlier result of the battery is determined to be a low-charge outlier; The obtaining a second outlier result according to the second battery parameter corresponding to the battery cell and a second average battery parameter of the battery to be detected at the end of discharge includes: The outlier detection function is used to perform an outlier analysis on the second battery parameter and the second average battery parameter. If it is determined that the battery cell parameter is an outlier and the second battery parameter of the battery cell is less than the second average battery parameter, the first outlier result of the battery is determined to be a low outlier; if it is determined that the battery cell parameter is an outlier and the second battery parameter of the battery cell is greater than the second average battery parameter, the first outlier result of the battery is determined to be an high outlier.

[0151] Based on the above embodiment, the detection module 302 is specifically used for: If the first outlier result is a high-charge outlier and the second outlier result is a low-charge outlier, the battery is determined to be a cell with parameter outliers.

[0152] Based on the above embodiment, the detection module 302 is specifically used for: If the first outlier result is a low-charge outlier and the second outlier result is a low-discharge outlier, the battery is determined to be a cell with parameter outliers.

[0153] Based on the above embodiment, the detection module 302 is specifically used for: If the first outlier result is a low-charging outlier or a high-charging outlier, or the second outlier result is a low-charging outlier or a high-charging outlier, the battery is determined to be a cell with parameter outliers.

[0154] Based on the above embodiment, the battery parameters include the battery module voltage and / or the battery module temperature; the detection module 302 is specifically used for: Acquire a third battery parameter of each battery module in the battery to be detected at a charging end, and acquire a fourth battery parameter of each battery module in the battery to be detected at a discharging end; For each battery module, a third outlier result is obtained according to the third battery parameter corresponding to the battery module and the third average battery parameter of the battery to be tested at the charging end; a fourth outlier result is obtained according to the fourth battery parameter at the discharge end corresponding to the battery module and the fourth average battery parameter of the battery to be tested at the discharge end; If at least one of the third outlier result and the fourth outlier result indicates that the battery module is outlier, it is determined that there is a cell module with outlier parameters in the battery to be detected.

[0155] Based on the above embodiment, the battery parameters include the battery cell voltage and / or the battery cell temperature; the detection module 302 is further used for: Extracting battery parameters between adjacent cells in the battery to be tested from the acquired battery parameters; Calculating a first difference degree of battery parameters between adjacent battery cells; If the first difference is greater than a first preset difference threshold, it is determined that the battery to be tested is abnormal.

[0156] Based on the above embodiment, the battery parameters include the battery stack voltage and the battery stack current; the detection module 302 is also used for: If the battery stack current is less than a preset current threshold, and the battery stack voltage is increasing, and the battery stack voltage change rate is less than a preset change rate threshold, and the duration is greater than a preset duration, then the battery stack is determined to be abnormal.

[0157] Based on the above embodiment, the battery parameter includes the state of charge of the battery cluster; the detection module 302 is further used for: Performing a difference analysis according to the charge states of multiple battery clusters in the battery stack to obtain a second difference; If the second difference is greater than a second preset difference threshold, it is determined that the battery cluster is abnormal.

[0158] Based on the above embodiment, the device further includes an abnormality analysis module, which is used to: The battery parameters are input into a pre-trained abnormality analysis model to obtain the abnormality cause output by the abnormality analysis model; wherein the abnormality analysis model is generated based on a machine learning model in combination with the SHAP theory and the information gain IG evaluation method.

[0159] Figure 4 A schematic diagram of the physical structure of an electronic device provided in an embodiment of the present application, such as Figure 4As shown, the electronic device includes: a processor (processor) 401, a memory (memory) 402 and a bus 403; wherein the processor 401 and the memory 402 communicate with each other through the bus 403; the processor 401 is used to call the program instructions in the memory 402 to execute the methods provided by the above-mentioned method embodiments, for example, including: obtaining the battery parameters of the battery to be detected during the charging and discharging process; performing outlier detection based on the battery parameters to determine whether there is a battery cell module with outlier parameters in the battery to be detected; if there is a battery cell module with outlier parameters, determining the battery cell module with outlier parameters as The abnormal cell module; the battery parameters include cell voltage and / or cell temperature; the cell module that performs outlier detection based on the battery parameters to determine whether there is a parameter outlier in the battery to be detected, including: obtaining a first battery parameter of each cell in the battery to be detected at the charging end, and obtaining a second battery parameter of each cell in the battery to be detected at the discharging end; for each cell, obtaining a first outlier result according to the first battery parameter corresponding to the cell and the first average battery parameter of the battery to be detected at the charging end; obtaining a second outlier result according to the second battery parameter corresponding to the cell and the second average battery parameter of the battery to be detected at the discharging end result; determining whether the battery cell is a battery cell with parameter outliers according to the first outlier result and / or the second outlier result; obtaining the first outlier result according to the first battery parameter corresponding to the battery cell and the first average battery parameter of the battery to be detected at the charging terminal, including: using an outlier detection function to perform outlier analysis on the first battery parameter and the first average battery parameter, if it is determined that the battery cell parameter is outliers, and the first battery parameter of the battery cell is greater than the first average battery parameter, then determining that the first outlier result of the battery is a high-charge outlier; if it is determined that the battery cell parameter is outliers, and the first battery parameter of the battery cell is less than the first average battery parameter, Then it is determined that the first outlier result of the battery is a low-charge outlier; the second outlier result is obtained according to the second battery parameter corresponding to the battery cell and the second average battery parameter of the battery to be detected at the end of discharge, including: using the outlier detection function to perform outlier analysis on the second battery parameter and the second average battery parameter, if it is determined that the battery cell parameter is an outlier, and the second battery parameter of the battery cell is less than the second average battery parameter, then the first outlier result of the battery is determined to be a low-charge outlier; if it is determined that the battery cell parameter is an outlier, and the second battery parameter of the battery cell is greater than the second average battery parameter, then the first outlier result of the battery is determined to be a high-charge outlier.

[0160] The processor 401 may be an integrated circuit chip with signal processing capability. The processor 401 may be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc.; it may also be a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field programmable gate array (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components. It may implement or execute various methods, steps and logic block diagrams disclosed in the embodiments of the present application. A general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc.

[0161] The memory 402 may include, but is not limited to, random access memory (RAM), read only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), etc.

[0162] The present embodiment discloses a computer program product, which includes a computer program stored on a non-transitory computer-readable storage medium, and the computer program includes program instructions. When the program instructions are executed by a computer, the computer can execute the methods provided by the above-mentioned method embodiments, for example, including: obtaining battery parameters of a battery to be detected during the charging and discharging process; performing outlier detection based on the battery parameters to determine whether there is a cell module with outlier parameters in the battery to be detected; if there is a cell module with outlier parameters, determining the cell module with outlier parameters as an abnormal cell module; the battery parameters include cell voltage and / or voltage; core temperature; the cell module for performing outlier detection based on the battery parameters to determine whether there is a parameter outlier in the battery to be detected, comprising: obtaining a first battery parameter of each cell in the battery to be detected at a charging terminal, and obtaining a second battery parameter of each cell in the battery to be detected at a discharging terminal; for each cell, obtaining a first outlier result according to the first battery parameter corresponding to the cell and a first average battery parameter of the battery to be detected at a charging terminal; obtaining a second outlier result according to the second battery parameter corresponding to the cell and a second average battery parameter of the battery to be detected at a discharging terminal; obtaining a second outlier result according to the first outlier result and / or The second outlier result determines whether the battery cell is a battery cell with an outlier parameter; the first outlier result is obtained according to the first battery parameter corresponding to the battery cell and the first average battery parameter of the battery to be detected at the charging terminal, including: using an outlier detection function to perform an outlier analysis on the first battery parameter and the first average battery parameter, if it is determined that the battery cell parameter is an outlier, and the first battery parameter of the battery cell is greater than the first average battery parameter, then determining that the first outlier result of the battery is a high-charge outlier; if it is determined that the battery cell parameter is an outlier, and the first battery parameter of the battery cell is less than the first average battery parameter, then determining that the battery The first outlier result is a low-charge outlier; the second outlier result is obtained according to the second battery parameter corresponding to the battery cell and the second average battery parameter of the battery to be detected at the end of discharge, including: using the outlier detection function to perform outlier analysis on the second battery parameter and the second average battery parameter, if it is determined that the battery cell parameter is an outlier, and the second battery parameter of the battery cell is less than the second average battery parameter, then the first outlier result of the battery is determined to be a low-charge outlier; if it is determined that the battery cell parameter is an outlier, and the second battery parameter of the battery cell is greater than the second average battery parameter, then the first outlier result of the battery is determined to be a high-charge outlier.

[0163] The present embodiment provides a non-transitory computer-readable storage medium, wherein the non-transitory computer-readable storage medium stores computer instructions, wherein the computer instructions enable the computer to execute the methods provided by the above-mentioned method embodiments, for example, including: obtaining battery parameters of a battery to be detected during the charging and discharging process; performing outlier detection based on the battery parameters to determine whether there is a cell module with outlier parameters in the battery to be detected; if there is a cell module with outlier parameters, determining the cell module with outlier parameters as an abnormal cell module; the battery parameters include cell voltage and / or cell temperature; performing outlier detection based on the battery parameters , to determine whether there is a cell module with outliers in the battery to be detected, including: obtaining a first battery parameter of each cell in the battery to be detected at the charging end, and obtaining a second battery parameter of each cell in the battery to be detected at the discharging end; for each cell, obtaining a first outlier result according to the first battery parameter corresponding to the cell and the first average battery parameter of the battery to be detected at the charging end; obtaining a second outlier result according to the second battery parameter corresponding to the cell and the second average battery parameter of the battery to be detected at the discharging end; determining the cell according to the first outlier result and / or the second outlier result. whether it is a battery cell with outliers in parameters; obtaining a first outlier result according to the first battery parameter corresponding to the battery cell and the first average battery parameter of the battery to be detected at the charging terminal, including: using an outlier detection function to perform outlier analysis on the first battery parameter and the first average battery parameter, if it is determined that the battery cell parameter is outliers, and the first battery parameter of the battery cell is greater than the first average battery parameter, then determining that the first outlier result of the battery is a high-charge outlier; if it is determined that the battery cell parameter is outliers, and the first battery parameter of the battery cell is less than the first average battery parameter, then determining that the first outlier result of the battery is Low charge outlier; the second outlier result is obtained according to the second battery parameter corresponding to the battery cell and the second average battery parameter of the battery to be detected at the end of discharge, including: using the outlier detection function to perform outlier analysis on the second battery parameter and the second average battery parameter, if it is determined that the battery cell parameter is an outlier, and the second battery parameter of the battery cell is less than the second average battery parameter, then the first outlier result of the battery is determined to be a low charge outlier; if it is determined that the battery cell parameter is an outlier, and the second battery parameter of the battery cell is greater than the second average battery parameter, then the first outlier result of the battery is determined to be a high charge outlier.

[0164] In the embodiments provided in the present application, it should be understood that the disclosed devices and methods can be implemented in other ways. The device embodiments described above are merely schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some communication interfaces, and the indirect coupling or communication connection of the devices or units can be electrical, mechanical or other forms.

[0165] In addition, the units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0166] Furthermore, the functional modules in the various embodiments of the present application may be integrated together to form an independent part, or each module may exist separately, or two or more modules may be integrated to form an independent part.

[0167] In this document, relational terms such as first and second, etc. are used merely to distinguish one entity or operation from another entity or operation, but do not necessarily require or imply any such actual relationship or order between these entities or operations.

[0168] The above description is only an embodiment of the present application and is not intended to limit the protection scope of the present application. For those skilled in the art, the present application may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application shall be included in the protection scope of the present application.

Claims

1. A battery abnormality detection method, characterized in that: include: Obtaining battery parameters of the battery to be tested during the charging and discharging process; Performing outlier detection based on the battery parameters to determine whether there is a cell module with outlier parameters in the battery to be detected; If there is a cell module with outlier parameters, the cell module with outlier parameters is determined as an abnormal cell module; The battery parameters include a cell voltage and / or a cell temperature; and the outlier detection based on the battery parameters to determine whether there is a cell module with outlier parameters in the battery to be detected includes: Acquire a first battery parameter of each battery cell in the battery to be detected at a charging end, and acquire a second battery parameter of each battery cell in the battery to be detected at a discharging end; For each battery cell, a first outlier result is obtained according to the first battery parameter corresponding to the battery cell and a first average battery parameter of the battery to be detected at a charging end; a second outlier result is obtained according to the second battery parameter corresponding to the battery cell and a second average battery parameter of the battery to be detected at a discharging end; Determining whether the battery cell is a battery cell with an outlier parameter according to the first outlier result and / or the second outlier result; The obtaining a first outlier result according to the first battery parameter corresponding to the battery cell and a first average battery parameter of the battery to be detected at the charging terminal includes: An outlier detection function is used to perform an outlier analysis on the first battery parameter and the first average battery parameter. If it is determined that the battery cell parameter is an outlier and the first battery parameter of the battery cell is greater than the first average battery parameter, the first outlier result of the battery is determined to be a high-charge outlier; if it is determined that the battery cell parameter is an outlier and the first battery parameter of the battery cell is less than the first average battery parameter, the first outlier result of the battery is determined to be a low-charge outlier; The obtaining a second outlier result according to the second battery parameter corresponding to the battery cell and a second average battery parameter of the battery to be detected at the end of discharge includes: The outlier detection function is used to perform an outlier analysis on the second battery parameter and the second average battery parameter. If it is determined that the battery cell parameter is an outlier and the second battery parameter of the battery cell is less than the second average battery parameter, the first outlier result of the battery is determined to be a low outlier; if it is determined that the battery cell parameter is an outlier and the second battery parameter of the battery cell is greater than the second average battery parameter, the first outlier result of the battery is determined to be an high outlier.

2. The method according to claim 1, characterized in that The determining whether the battery cell is a battery cell with an outlier parameter according to the first outlier result and / or the second outlier result includes: If the first outlier result is a high-charge outlier and the second outlier result is a low-charge outlier, the battery is determined to be a cell with parameter outliers.

3. The method according to claim 1, characterized in that The determining whether the battery cell is a battery cell with an outlier parameter according to the first outlier result and / or the second outlier result includes: If the first outlier result is a low-charge outlier and the second outlier result is a low-discharge outlier, the battery is determined to be a cell with parameter outliers.

4. The method according to claim 1, characterized in that: The determining whether the battery cell is a battery cell with an outlier parameter according to the first outlier result and / or the second outlier result includes: If the first outlier result is a low-charging outlier or a high-charging outlier, or the second outlier result is a low-charging outlier or a high-charging outlier, the battery is determined to be a cell with parameter outliers.

5. The method according to claim 1, characterized in that: The battery parameters include battery module voltage and / or battery module temperature; the outlier detection based on the battery parameters to determine whether there is a cell module with outlier parameters in the battery to be detected includes: Acquire a third battery parameter of each battery module in the battery to be detected at a charging end, and acquire a fourth battery parameter of each battery module in the battery to be detected at a discharging end; For each battery module, a third outlier result is obtained according to the third battery parameter corresponding to the battery module and the third average battery parameter of the battery to be detected at the charging end; a fourth outlier result is obtained according to the fourth battery parameter at the discharge end corresponding to the battery module and the fourth average battery parameter of the battery to be detected at the discharge end; If at least one of the third outlier result and the fourth outlier result indicates that the battery module is outlier, it is determined that there is a cell module with outlier parameters in the battery to be detected.

6. The method according to claim 1, characterized in that The battery parameters include battery cell voltage and / or battery cell temperature; after obtaining the battery parameters of the battery to be tested during the charging and discharging process, the method further includes: Extracting battery parameters between adjacent cells in the battery to be tested from the acquired battery parameters; Calculating a first difference degree of battery parameters between adjacent battery cells; If the first difference is greater than a first preset difference threshold, it is determined that the battery to be detected is abnormal.

7. The method according to claim 1, characterized in that The battery parameters include battery stack voltage and battery stack current; after obtaining the battery parameters of the battery to be detected during the charging and discharging process, the method further includes: If the battery stack current is less than a preset current threshold, and the battery stack voltage is increasing, and the battery stack voltage change rate is less than a preset change rate threshold, and the duration is greater than a preset duration, then the battery stack is determined to be abnormal.

8. The method according to claim 1, characterized in that: The battery parameters include the state of charge of the battery cluster; after obtaining the battery parameters of the battery to be detected during the charging and discharging process, the method further includes: Performing a difference analysis according to the charge states of multiple battery clusters in the battery stack to obtain a second difference; If the second difference is greater than a second preset difference threshold, it is determined that the battery cluster is abnormal.

9. The method according to claim 1, characterized in that: After determining the cell module with outlier parameters as an abnormal cell module, the method further includes: The battery parameters are input into a pre-trained abnormality analysis model to obtain the abnormality cause output by the abnormality analysis model; wherein the abnormality analysis model is generated based on a machine learning model in combination with the SHAP theory and the information gain IG evaluation method.

10. An electronic device, characterized in that: include: processor, memory and bus, wherein, The processor and the memory communicate with each other via the bus; The memory stores program instructions that can be executed by the processor, and the processor calls the program instructions to execute the method according to any one of claims 1 to 9.

11. A non-transitory computer-readable storage medium, characterized in that: The non-transitory computer-readable storage medium stores computer instructions, which, when executed by a computer, cause the computer to perform the method according to any one of claims 1 to 9.

12. A computer program product, characterized in that The method comprises computer program instructions, and when the computer program instructions are read and executed by a processor, the method according to any one of claims 1 to 9 is executed.

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