Battery cell anomaly detection method and device, computer equipment and storage medium

Abstract

The invention relates to a battery cell anomaly detection method and device, computer equipment and a storage medium. The method comprises the following steps: acquiring a charging characteristic voltage and a discharging characteristic voltage of each to-be-detected battery cell in a plurality of to-be-detected battery cells; determining a charging outlier characteristic parameter and a discharging outlier characteristic parameter according to the charging characteristic voltage and the discharging characteristic voltage of each battery cell to be detected; and according to the charging outlier characteristic parameters, the discharging outlier characteristic parameters, and the charging characteristic voltage and the discharging characteristic voltage of each to-be-detected cell, carrying out anomaly detection on the plurality of to-be-detected cells. By comprehensively considering the charging characteristic voltage and the discharging characteristic voltage, the abnormal battery cell can be detected as early as possible in advance, so that the safety of the battery cell is improved, and threat to personnel safety is further avoided.

Description

Technical Field

[0001] The present application relates to the field of lithium battery technology, and in particular to a method, device, computer equipment, and storage medium for detecting abnormalities in battery cells. Background Art

[0002] With the rapid development of new energy vehicles, portable electronic devices, and energy storage systems, lithium batteries have become the mainstream power source due to their high energy density, long cycle life, and environmentally friendly features. In the new energy vehicle sector, the high energy density of lithium batteries enables vehicles to achieve longer driving ranges. Furthermore, the long cycle life of lithium batteries ensures that vehicles maintain good performance after repeated charge and discharge cycles. However, lithium batteries pose safety risks during use, such as overcharging, over-discharging, short circuits, and thermal runaway, which can lead to safety risks such as fire or explosion. These issues not only threaten user safety but also pose challenges to the sustainable development of the entire industry.

[0003] Current technologies for lithium battery safety testing primarily include voltage monitoring, temperature monitoring, and internal resistance monitoring. However, these safety testing methods are unable to provide early diagnosis of battery cells. This means that by the time a fault is detected, a safety incident may have already occurred, posing a threat to personnel safety. Summary of the Invention

[0004] Based on this, it is necessary to provide a battery cell abnormality detection method, device, computer equipment and storage medium to address the above technical problems.

[0005] In a first aspect, the present application provides a method for detecting abnormalities in battery cells, the method comprising: obtaining a charging characteristic voltage and a discharging characteristic voltage of each of a plurality of battery cells to be detected; determining a charging outlier characteristic parameter and a discharging outlier characteristic parameter based on the charging characteristic voltage and the discharging characteristic voltage of each of the battery cells to be detected; and performing abnormality detection on the plurality of battery cells to be detected based on the charging outlier characteristic parameter, the discharging outlier characteristic parameter, and the charging characteristic voltage and the discharging characteristic voltage of each of the battery cells to be detected.

[0006] In one embodiment, before obtaining the charging characteristic voltage and the discharging characteristic voltage of each of the multiple battery cells to be tested, the method also includes: during the charging process of the target battery cell, collecting the charging voltage and the charging current at preset time intervals, and updating the charging characteristic voltage of the target battery cell according to the charging voltage and the charging current at the current time point; the target battery cell is any battery cell among the multiple battery cells to be tested; during the discharging process of the target battery cell, collecting the discharge voltage and the discharge current at preset time intervals, and updating the discharge characteristic voltage of the target battery cell according to the discharge voltage and the discharge current at the current time point.

[0007] In one embodiment, updating the charging characteristic voltage of the target battery cell based on the charging voltage and charging current at the current time point includes: setting the charging characteristic voltage of the target battery cell to the initial charging voltage; obtaining the battery cell state of charge in real time during the charging process of the target battery cell; if the battery cell state of charge meets the preset state of charge range, obtaining the charging voltage and charging current at the current time point; and updating the charging characteristic voltage of the target battery cell based on the charging voltage and charging current.

[0008] In one embodiment, updating the charging characteristic voltage of the target battery cell based on the charging voltage and the charging current includes: determining a current weight based on the charging current and the preset time interval; and updating the charging characteristic voltage of the target battery cell based on the current weight, the charging voltage, and the standard current weight.

[0009] In one embodiment, updating the discharge characteristic voltage of the target battery cell based on the discharge voltage and discharge current at the current time point includes: setting the discharge characteristic voltage of the target battery cell to the initial discharge voltage; obtaining the battery cell state of charge in real time during the discharge of the target battery cell; if the battery cell state of charge meets the preset state of charge range, obtaining the discharge voltage and discharge current at the current time point; and updating the discharge characteristic voltage of the target battery cell based on the discharge voltage and discharge current.

[0010] In one embodiment, determining the charging outlier characteristic parameter and the discharging outlier characteristic parameter based on the charging characteristic voltage and the discharging characteristic voltage of each battery cell to be detected includes: calculating the charging voltage standard deviation and the charging voltage mean based on the charging characteristic voltage of each battery cell to be detected; calculating the discharge voltage standard deviation and the discharge voltage mean based on the discharge characteristic voltage of each battery cell to be detected; determining a first charging outlier parameter and a second charging outlier parameter based on the charging voltage standard deviation, the charging voltage mean and a preset redundancy value; the first charging outlier parameter is greater than the second charging outlier parameter; determining a first discharging outlier parameter and a second discharging outlier parameter based on the discharge voltage standard deviation, the discharge voltage mean and the preset redundancy value; the first discharging outlier parameter is greater than the second discharging outlier parameter.

[0011] In one embodiment, the abnormality detection of multiple cells to be detected based on the charging outlier characteristic parameter, the discharging outlier characteristic parameter, the charging characteristic voltage and the discharging characteristic voltage of each cell to be detected includes: if the charging characteristic voltage of the cell to be detected is greater than the first charging outlier parameter, and the discharging characteristic voltage is greater than the first discharging outlier parameter; then the abnormal state of the cell to be detected is a high platform voltage abnormality or a collection abnormality; if the charging characteristic voltage of the cell to be detected is less than the second charging outlier parameter, and the discharging characteristic voltage is less than the second discharging outlier parameter; then the abnormal state of the cell to be detected is a low platform voltage abnormality or a collection abnormality; if the charging characteristic voltage of the cell to be detected is less than the second charging outlier parameter, and the discharging characteristic voltage is greater than the first discharging outlier parameter; then the abnormal state of the cell to be detected is a small internal resistance abnormality; if the charging characteristic voltage of the cell to be detected is greater than the first charging outlier parameter, and the discharging characteristic voltage is less than the second discharging outlier parameter; then the abnormal state of the cell to be detected is a large internal resistance abnormality.

[0012] In one embodiment, the method further includes: calculating a charging voltage average based on the charging characteristic voltage of each of the battery cells to be tested; calculating a discharge voltage average based on the discharge characteristic voltage of each of the battery cells to be tested; and calculating the cell efficiency of multiple battery cells to be tested based on the charging voltage average and the discharge voltage average.

[0013] In the second aspect, the present application also provides a battery cell abnormality detection device, which includes: an acquisition module for acquiring the charging characteristic voltage and the discharging characteristic voltage of each of the multiple battery cells to be detected; an outlier feature calculation module for determining the charging outlier feature parameters and the discharging outlier feature parameters based on the charging characteristic voltage and the discharging characteristic voltage of each of the battery cells to be detected; and an abnormality detection module for performing abnormality detection on the multiple battery cells to be detected based on the charging outlier feature parameters, the discharging outlier feature parameters, and the charging characteristic voltage and the discharging characteristic voltage of each of the battery cells to be detected.

[0014] In a third aspect, the present application further provides a computer device comprising a memory and a processor, wherein the memory stores a computer program, and the processor implements any one of the battery cell abnormality detection methods described in the first aspect when executing the computer program.

[0015] In a fourth aspect, the present application further provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements any one of the battery cell abnormality detection methods described in the first aspect.

[0016] The above-mentioned battery cell abnormality detection method, device, computer equipment and storage medium obtain the charging characteristic voltage and discharge characteristic voltage of each battery cell to be detected among multiple battery cells to be detected. Based on the charging characteristic voltage and discharge characteristic voltage of each battery cell to be detected, the charging outlier characteristic parameters and discharge outlier characteristic parameters are determined. Based on the charging outlier characteristic parameters, the discharge outlier characteristic parameters, and the charging characteristic voltage and discharge characteristic voltage of each battery cell to be detected, abnormality detection is performed on multiple battery cells to be detected. By comprehensively considering the charging characteristic voltage and the discharge characteristic voltage, abnormal batteries can be detected as early as possible, thereby improving the safety of the battery cells and further avoiding threats to personnel safety. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 1 is a charge and discharge curve of a lithium battery in one embodiment;

[0018] Figure 2 1 is a flow chart of a method for detecting abnormality of a battery cell according to an embodiment;

[0019] Figure 3 1 is a flow chart of a method for updating a charging characteristic voltage according to an embodiment;

[0020] Figure 4 1 is a flow chart of a method for updating a discharge characteristic voltage according to an embodiment;

[0021] Figure 5 is a structural block diagram of a battery cell abnormality detection device in one embodiment;

[0022] Figure 6 FIG. 1 is a diagram showing the internal structure of a computer device in one embodiment. DETAILED DESCRIPTION

[0023] In order to make the purpose, technical solutions and advantages of this application more clear, the following further describes this application in detail with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain this application and are not intended to limit this application.

[0024] With the rapid development of electric vehicles, portable electronic devices, and energy storage systems, lithium batteries have become the mainstream power source due to their high energy density, long cycle life, and environmentally friendly characteristics. However, lithium batteries have safety hazards during use, such as overcharging, over-discharging, short circuiting, thermal runaway, and other problems that may cause fire or explosion. Currently, lithium battery safety diagnosis in related technologies mainly includes voltage monitoring, temperature monitoring, and internal resistance measurement, but the following problems still exist: when lithium battery safety detection detects an anomaly, the accident is already underway and it is impossible to prevent the accident from happening, making it difficult to respond to abnormal situations quickly. Some complex algorithms claim to be able to detect faults early, such as voltage entropy and large battery models, but their deployment is complex and requires a lot of computing costs, with poor interpretability, making them difficult to be widely used.

[0025] like Figure 1 As shown, Figure 1 It is the charge and discharge curve of the lithium battery. Among them, the solid line is the curve of the voltage changing with the state of charge SOC during the discharge process; the dotted line is the curve of the voltage changing with the state of charge SOC during the charging process. Based on this curve, the lithium battery has a platform characteristic during the charge and discharge process. The platform characteristic shows that within the corresponding SOC range, the voltage basically does not change, that is, the voltage does not change significantly with the SOC. At this time, the voltage is greatly affected by the internal resistance, temperature, and acquisition conditions. If the voltage characteristics change at this time, the battery will have an abnormal risk. In the embodiment of the present application, based on the above-mentioned platform characteristics, the characteristic voltage of the battery charging platform and the characteristic voltage of the discharge platform are calculated, and the battery abnormality is detected through mathematical statistics. The abnormality may be caused by factors such as internal resistance, temperature inconsistency, and acquisition.

[0026] In one embodiment, Figure 2 As shown, a method for detecting abnormality of a battery cell is provided, comprising the following steps:

[0027] Step 201 : Obtain a charge characteristic voltage and a discharge characteristic voltage of each battery cell to be detected among a plurality of battery cells to be detected.

[0028] The battery to be tested is in compliance with Figure 1Any type of battery cell of the charge and discharge curve, that is, it is sufficient that the battery cell to be tested has a platform characteristic. This embodiment does not specifically limit the type of battery cell to be tested. Among them, the battery cell to be tested can be a battery cell of a new energy vehicle, a battery cell of various electronic devices, or a battery cell of an energy storage system. This embodiment does not specifically limit the purpose of the battery cell to be tested. Taking the battery cell to be tested as a battery cell of an energy storage system as an example, the battery of the energy storage system includes multiple battery cells. At this time, the battery cell that needs to be monitored for abnormalities is used as the battery cell to be tested, and the charging characteristic voltage and discharge characteristic voltage of each battery cell to be tested are obtained. Among them, the charging characteristic voltage is the characteristic voltage of the battery cell to be tested in the charging platform period during the charging process, which is used to characterize the charging platform characteristics of the battery cell to be tested. The discharge characteristic voltage is the characteristic voltage of the battery cell to be tested in the discharge platform period during the discharge process, which is used to characterize the discharge platform characteristics of the battery cell to be tested. The charging characteristic voltage of the battery cell to be tested is determined and stored after real-time updates based on the charging voltage and charging current detected during the charging process. The discharge characteristic voltage of the battery cell to be tested is determined and stored after real-time updates based on the discharge voltage and discharge current detected during the discharge process. When abnormality detection is required for multiple battery cells to be tested, the latest charging characteristic voltage and discharge characteristic voltage of each battery cell to be tested are obtained.

[0029] Step 202 : determining a charge outlier characteristic parameter and a discharge outlier characteristic parameter according to the charge characteristic voltage and the discharge characteristic voltage of each battery cell to be detected.

[0030] The charging outlier characteristic parameters are calculated based on the charging characteristic voltage of each battery cell to be tested. For example, the charging statistical parameters are calculated based on the charging characteristic voltage of each battery cell to be tested, wherein the charging statistical parameters may include standard deviation, mean, variance, etc. Then, based on the charging statistical parameters, the charging outlier characteristic parameters are calculated. The charging outlier characteristic parameters may be a threshold range, which is used to characterize the threshold range in which the charging characteristic voltage is not abnormal. The discharge outlier characteristic parameters are calculated based on the discharge characteristic voltage of each battery cell to be tested. For example, the discharge statistical parameters are calculated based on the discharge characteristic voltage of each battery cell to be tested, wherein the discharge statistical parameters may include standard deviation, mean, variance, etc. Then, based on the discharge statistical parameters, the discharge outlier characteristic parameters are calculated. The discharge outlier characteristic parameters may be a threshold range, which is used to characterize the threshold range in which the discharge characteristic voltage is not abnormal.

[0031] Step 203 : performing abnormality detection on the multiple cells to be detected based on the charging outlier characteristic parameter, the discharging outlier characteristic parameter, the charging characteristic voltage, and the discharging characteristic voltage of each cell to be detected.

[0032] After calculating the charging outlier characteristic parameters and the discharging outlier characteristic parameters, each battery cell to be detected is detected according to the charging outlier characteristic parameters and the discharging outlier characteristic parameters. Specifically, the charging characteristic voltage of the battery cell to be detected is compared with the charging outlier characteristic parameters, and the discharging characteristic voltage is compared with the discharging outlier characteristic parameters. If the charging characteristic voltage of the battery cell to be detected does not meet the charging outlier characteristic parameters, and the discharging characteristic voltage does not meet the discharging outlier characteristic parameters, then the corresponding battery cell to be detected is abnormal, and at this time, an alarm is issued to the user. If the charging characteristic voltage of the battery cell to be detected does not meet the charging outlier characteristic parameters, and the discharging characteristic voltage meets the discharging outlier characteristic parameters; or if the charging characteristic voltage of the battery cell to be detected meets the charging outlier characteristic parameters, and the discharging characteristic voltage does not meet the discharging outlier characteristic parameters; or if the charging characteristic voltage of the battery cell to be detected meets the charging outlier characteristic parameters, and the discharging characteristic voltage meets the discharging outlier characteristic parameters; then the corresponding battery cell to be detected is normal. Among them, the alarm can be issued through voice broadcast, flashing lights or text display. This embodiment does not specifically limit the alarm method, as long as it can prompt the user.

[0033] This embodiment obtains the charging characteristic voltage and the discharging characteristic voltage of each battery cell to be detected among multiple battery cells to be detected. According to the charging characteristic voltage and the discharging characteristic voltage of each battery cell to be detected, the charging outlier characteristic parameters and the discharging outlier characteristic parameters are determined. According to the charging outlier characteristic parameters, the discharging outlier characteristic parameters, the charging characteristic voltage and the discharging characteristic voltage of each battery cell to be detected, the multiple battery cells to be detected are detected for abnormalities. By comprehensively considering the charging characteristic voltage and the discharging characteristic voltage, the abnormal battery cells can be detected as early as possible, thereby improving the safety of the battery cells and further avoiding threats to personnel safety. In addition, the outlier characteristics of the charging characteristic voltage and the discharging characteristic voltage are used for abnormality detection, and the amount of calculation is small. On the basis of accurate abnormality detection, the amount of calculation is further reduced and the cost is reduced.

[0034] Due to the platform characteristics of lithium batteries during the charge and discharge process, within the SOC range corresponding to these platform characteristics, the lithium battery voltage does not change significantly with SOC. The charge characteristic voltage and discharge characteristic voltage of the battery cell to be tested characterize the charging platform characteristics and discharge platform characteristics of the battery cell to be tested. At this time, if the internal resistance and open circuit voltage (OCV) of multiple battery cells to be tested are consistent, the charge characteristic voltages and discharge characteristic voltages of multiple battery cells to be tested will be relatively close. Conversely, if the charge characteristic voltage and discharge characteristic voltage of multiple battery cells to be tested exhibit outliers, the internal resistance, OCV, or voltage acquisition of the corresponding battery cells to be tested is abnormal. In other words, by performing outlier analysis on the charge characteristic voltage and discharge characteristic voltage of the battery cell to be tested, it can be determined whether the battery cell to be tested has an abnormality. Statistical analysis based on the plateau characteristic voltage can achieve early fault warning for the battery cell to be tested, reduce subjective judgment errors, and improve detection accuracy.

[0035] In one embodiment, during the lithium battery charging process, the charging characteristic voltage of each cell is calculated, updated, and stored in real time; during the lithium battery discharging process, the discharge characteristic voltage of each cell is calculated, updated, and stored in real time. When abnormality detection is required for the cell to be tested, the charging characteristic voltage and discharge characteristic voltage of each cell to be tested are obtained.

[0036] During the charging process of a target cell, the charging voltage and charging current are collected at preset time intervals, and the charging characteristic voltage of the target cell is updated based on the charging voltage and charging current at the current time point. The target cell is any one of multiple cells to be tested, and the charging characteristic voltage is calculated and updated for each cell to be tested. During the charging process of the target cell, the charging voltage and charging current of the target cell are sampled at preset time intervals. The preset time interval is a pre-set sampling period that can be set based on actual usage requirements and is not specifically limited in this embodiment. For example, the preset time interval can be 1 second, 5 seconds, 10 seconds, etc. First, the charging characteristic voltage is set as the initial charging voltage. This initial charging voltage can be set based on experience, or the charging voltage collected in real time can be used as the initial charging voltage. Subsequently, after each collection of the charging voltage and charging current, the charging characteristic voltage is updated in real time based on the charging voltage and charging current. Since the charging characteristic voltage represents the charging platform characteristics of the target cell, it is only necessary to sample the charging voltage and charging current within the SOC range corresponding to the charging platform characteristics.

[0037] During the discharge of a target cell, the discharge voltage and discharge current are collected at preset time intervals, and the discharge characteristic voltage of the target cell is updated based on the discharge voltage and discharge current at the current time point. The target cell is any cell among multiple cells to be tested, and the discharge characteristic voltage is calculated and updated for each cell to be tested. During the discharge of the target cell, the discharge voltage and discharge current of the target cell are sampled at preset time intervals. The preset time interval is a pre-set sampling period that can be set based on actual usage requirements and is not specifically limited in this embodiment. For example, the preset time interval can be 1 second, 5 seconds, 10 seconds, etc. The sampling period for the charging voltage and charging current can be the same as or different from the sampling period for the discharge voltage and discharge current and is not specifically limited in this embodiment. First, the discharge characteristic voltage is set to an initial discharge voltage. This initial discharge voltage can be set based on experience, or the discharge voltage collected in real time can be used as the initial discharge voltage. Subsequently, after each discharge voltage and discharge current are collected, the discharge characteristic voltage is updated in real time based on the discharge voltage and discharge current. Since the discharge characteristic voltage represents the discharge platform characteristics of the target battery cell, it is only necessary to sample the discharge voltage and discharge current within the SOC range corresponding to the discharge platform characteristics.

[0038] This embodiment determines the discharge characteristic voltage or the charge characteristic voltage by collaboratively analyzing the current and voltage, thereby enhancing the anti-interference capability, improving the accuracy of characteristic voltage extraction, and providing more reliable reference parameters for subsequent abnormality detection.

[0039] In one embodiment, Figure 3 As shown, a charging characteristic voltage updating method is provided, comprising the following steps:

[0040] Step 301: Set the charging characteristic voltage of the target battery cell as the initial charging voltage.

[0041] When the target cell is first detected for abnormality using the method of this embodiment, the target cell does not yet have a charging characteristic voltage parameter. The charging characteristic voltage of the target cell needs to be initialized, that is, set to the initial charging voltage. For example, the charging voltage collected during the initialization can be used as the initial charging voltage.

[0042] Step 302 : obtaining the state of charge of the target battery cell in real time during the charging process of the target battery cell.

[0043] After initializing the charging characteristic voltage, the target cell's state of charge (SOC) must be acquired in real time during the charging process. The SOC represents the ratio of the cell's remaining capacity to its fully charged capacity. Because the charging characteristic voltage represents the target cell's charging platform characteristics, the charging voltage and current are only collected when the cell's SOC falls within the SOC range corresponding to the charging platform characteristics.

[0044] Step 303 : If the state of charge of the battery cell meets the preset state of charge range, the charging voltage and charging current at the current time point are obtained.

[0045] After obtaining the cell's state of charge (SOC), if the cell's SOC falls within a preset SOC range, the target cell is currently in the charging platform period, and the charging voltage and charging current at the current time point need to be obtained. The preset SOC range is the SOC range of the target cell's charging platform characteristics. The preset SOC range includes: a first boundary SOC and a second boundary SOC. The range between the first boundary SOC and the second boundary SOC is the preset SOC range. The first boundary SOC is greater than the second boundary SOC. The first boundary SOC and the second boundary SOC can be set based on the charging platform characteristics of the target cell. Preferably, the first boundary SOC can be 70%-90%; the second boundary SOC can be 20%-40%. If the cell's SOC does not fall within the preset SOC range, the target cell is not currently in the charging platform period, and the target cell's charging characteristic voltage does not need to be updated. In other words, the charging voltage and charging current do not need to be obtained at this time. Understandably, there's no need to obtain the charging voltage and current, meaning the algorithm for updating the charging characteristic voltage doesn't need to do so. Actual devices still collect the charging voltage and current at preset intervals for other parameter calculations or monitoring and early warning.

[0046] Step 304 : updating the charging characteristic voltage of the target battery cell according to the charging voltage and the charging current.

[0047] After obtaining the charging voltage and charging current, the charging characteristic voltage of the target battery cell can be updated. Specifically, the current weight is determined based on the charging current and the preset time interval. The current weight can be expressed by the charging amount corresponding to the charging current within the preset time interval. For example, the charging current is integrated within the preset time interval to determine the current weight. After determining the current weight, the charging characteristic voltage of the target battery cell is updated based on the current weight, the charging voltage and the standard current weight. The standard current weight can be expressed by the nominal capacity of the target battery cell. For example, the standard current weight is 1-20 times the nominal capacity. The specific value of the standard current weight can be set according to the actual usage scenario. When the fluctuation of the charging current in the usage scenario is large, the standard current weight can be set larger, for example, 18 times the nominal capacity; when the fluctuation of the charging current in the usage scenario is small, the standard current weight can be set smaller, for example, 2 times the nominal capacitance. The specific formula for updating the charging characteristic voltage of the target battery cell is as follows:

[0048]

[0049] in, Indicates the updated charging characteristic voltage; Indicates the charging characteristic voltage before updating; Q indicates the standard current weight; represents the current weight; Indicates the charging current; t indicates the preset time interval; Indicates the charging voltage.

[0050] In one of the specific embodiments, when calculating the charging characteristic voltage of each battery cell, the charging characteristic voltage of the battery cell is first initially set to the current collected voltage. During charging, if SocH>Current Soc>SocL, the charging voltage and charging current are obtained in real time, and the charging characteristic voltage is updated in real time with the ampere-hour at the preset time interval as the weight. Specifically, the charging characteristic voltage is updated in real time with the average charging voltage with the ampere-hour as the weight. If SocH>Current Soc>SocL is not satisfied, the charging characteristic voltage is not updated. Among them, SocH is the high SOC threshold of the charging platform characteristic, which is generally 70%~90%; SocL is the low SOC threshold of the charging platform characteristic, which is generally 20%~40%. The purpose of limiting SOC is to ensure that the data for calculating the charging characteristic voltage are within the range of the charging platform characteristic.

[0051] This embodiment initializes the charging characteristic voltage and detects the cell state of charge in real time. When the cell state of charge meets preset requirements, the charging voltage and charging current at the current time point are obtained and the charging characteristic voltage is updated. This allows for precise location of the charging plateau, significantly improving the calculation accuracy of the charging characteristic voltage, providing a more reliable data foundation for subsequent anomaly detection, and effectively reducing the risk of misjudgment.

[0052] In one embodiment, Figure 4 As shown, a method for updating a discharge characteristic voltage is provided, comprising the following steps:

[0053] Step 401: Set the discharge characteristic voltage of the target battery cell as the initial discharge voltage.

[0054] When the target cell is first detected for abnormality using the method of this embodiment, the target cell does not yet have a discharge characteristic voltage parameter. The discharge characteristic voltage of the target cell needs to be initialized, that is, the discharge characteristic voltage of the target cell is set to the initial discharge voltage. For example, the discharge voltage collected during the initialization can be used as the initial discharge voltage.

[0055] Step 402 : obtaining the state of charge of the target cell in real time during the discharge process of the target cell.

[0056] After initializing the discharge characteristic voltage, the target cell's state of charge (SOC) needs to be acquired in real time during the discharge process. Since the discharge characteristic voltage represents the discharge platform characteristics of the target cell, the discharge voltage and current are only collected when the cell's SOC falls within the SOC range corresponding to the discharge platform characteristics.

[0057] Step 403 : If the state of charge of the battery cell meets the preset state of charge range, the discharge voltage and discharge current at the current time point are obtained.

[0058] After obtaining the cell state of charge (SOC), if the cell state of charge (SOC) falls within a preset SOC range, the target cell is currently in the discharge plateau period, and the discharge voltage and discharge current at the current time point need to be obtained. The preset SOC range is the SOC range of the target cell's discharge plateau characteristics. The preset SOC range includes a third boundary SOC and a fourth boundary SOC. The range between the third boundary SOC and the fourth boundary SOC is the preset SOC range. The third boundary SOC is greater than the fourth boundary SOC. The third boundary SOC and the fourth boundary SOC can be set based on the discharge plateau characteristics of the target cell. Preferably, the third boundary SOC can be 70%-90%; the fourth boundary SOC can be 20%-40%. If the cell state of charge does not fall within the preset SOC range, the target cell is not currently in the discharge plateau period, and the target cell's discharge characteristic voltage does not need to be updated. In other words, the discharge voltage and discharge current do not need to be obtained at this time. Understandably, the discharge voltage and current do not need to be acquired, meaning that the algorithm for updating the discharge characteristic voltage does not need to acquire these values. In actual equipment, the discharge voltage and current are still acquired at preset intervals for use in other parameter calculations or monitoring and early warning.

[0059] Step 404 : updating the discharge characteristic voltage of the target battery cell according to the discharge voltage and the discharge current.

[0060] After obtaining the discharge voltage and discharge current, the discharge characteristic voltage of the target battery cell can be updated. Specifically, the current weight is determined according to the discharge current and the preset time interval. Among them, the current weight can be expressed by the discharge amount corresponding to the discharge current within the preset time interval. For example, the discharge current is integrated within the preset time interval to determine the current weight. After determining the current weight, the discharge characteristic voltage of the target battery cell is updated according to the current weight, the discharge voltage and the standard current weight. Among them, the standard current weight can be expressed by the nominal capacity of the target battery cell. For example, the standard current weight is 1-20 times the nominal capacity. The specific value of the standard current weight can be set according to the actual usage scenario. When the fluctuation of the discharge current in the usage scenario is large, the standard current weight can be set larger, for example, 18 times the nominal capacity; when the fluctuation of the discharge current in the usage scenario is small, the standard current weight can be set smaller, for example, 2 times the nominal capacitance. The specific formula for updating the discharge characteristic voltage of the target battery cell is as follows:

[0061]

[0062] in, Indicates the updated discharge characteristic voltage; represents the discharge characteristic voltage before updating; Q represents the standard current weight; represents the current weight; Indicates the discharge current; t indicates the preset time interval; Indicates the discharge voltage.

[0063] In one of the specific embodiments, when calculating the discharge characteristic voltage of each battery cell, the discharge characteristic voltage of the battery cell is first initially set to the current collected voltage. During discharge, if SocH>Current Soc>SocL, the discharge voltage and discharge current are obtained in real time, and the discharge characteristic voltage is updated in real time with the ampere-hour at the preset time interval as the weight. Specifically, the discharge characteristic voltage is updated in real time with the average discharge voltage with the ampere-hour as the weight. If SocH>Current Soc>SocL is not satisfied, the discharge characteristic voltage is not updated. Among them, SocH is the high SOC threshold of the discharge platform characteristic, which is generally 70%~90%; SocL is the low SOC threshold of the discharge platform characteristic, which is generally 20%~40%. The purpose of limiting SOC is to ensure that the data for calculating the discharge characteristic voltage are within the range of the discharge platform characteristic.

[0064] This embodiment initializes the discharge characteristic voltage and detects the cell state of charge in real time. When the cell state of charge meets preset requirements, the discharge voltage and discharge current at the current time point are obtained and the discharge characteristic voltage is updated. This allows for precise location of the discharge plateau, significantly improving the calculation accuracy of the discharge characteristic voltage, providing a more reliable data foundation for subsequent anomaly detection, and effectively reducing the risk of misjudgment.

[0065] In one embodiment, when performing abnormality detection on multiple cells to be detected, after obtaining the charging characteristic voltages and the discharging characteristic voltages of the multiple cells to be detected, it is necessary to calculate the charging outlier characteristic parameters and the discharging outlier characteristic parameters, which specifically includes the following steps:

[0066] After obtaining the charge and discharge characteristic voltages of multiple cells to be tested, outlier detection is required for these voltages. A common method for outlier detection is the Raida criterion. This criterion assumes that a set of test data contains only random errors. The standard deviation of this set of test data is calculated and processed, and then an interval is determined based on a certain probability. If a data point in a set of test data exceeds this interval, it is considered to be a gross error rather than a random error, indicating an anomaly. Accordingly, the data points with gross errors are removed from this set of test data. Outlier detection is typically performed using the 3σ rule, which states that under a normal distribution, 99.7% of the data fall within the range of μ±3σ, where μ represents the mean and σ represents the standard deviation. In other words, if a data point in a set of test data exceeds the range of μ±3σ, it indicates an anomaly. For example, in a set of detection features, when an individual feature is greater than the feature mean + 3 × standard deviation or less than the feature mean - 3 × standard deviation, the individual feature is determined to be an abnormal feature or an outlier feature. Based on this theory, the charging outlier feature parameters and the discharging outlier feature parameters are calculated in the following manner.

[0067] Step 1: Calculate the charging voltage standard deviation and the charging voltage mean according to the charging characteristic voltage of each battery cell to be tested.

[0068] After obtaining the characteristic charging voltage of each battery cell to be tested, a statistical analysis is performed on the characteristic charging voltage of each battery cell to calculate the standard deviation and mean charging voltage. The mean charging voltage is the average value calculated based on the multiple characteristic charging voltages. The standard deviation charging voltage is the standard deviation calculated based on the multiple characteristic charging voltages. The calculation methods for the mean and standard deviation are well-known statistical methods and are not further described here.

[0069] Step 2: Calculate the discharge voltage standard deviation and discharge voltage mean based on the discharge characteristic voltage of each battery cell to be tested.

[0070] After obtaining the discharge characteristic voltage of each battery cell to be tested, a statistical analysis is performed on the discharge characteristic voltage of each battery cell to calculate the discharge voltage standard deviation and discharge voltage mean. The discharge voltage mean is the average value calculated based on the multiple discharge characteristic voltages. The discharge voltage standard deviation is the standard deviation calculated based on the multiple discharge characteristic voltages. The calculation methods of the mean and standard deviation are statistically well-known and will not be repeated here.

[0071] Step 3: Determine a first charging outlier parameter and a second charging outlier parameter based on the charging voltage standard deviation, the charging voltage mean, and a preset redundancy value; the first charging outlier parameter is greater than the second charging outlier parameter.

[0072] Considering the importance of cell fault diagnosis and the occasional errors caused by omitting some cell data, a preset redundancy value can be introduced. By setting the preset redundancy value, the calculation of the charging outlier characteristic parameters and the discharging outlier characteristic parameters is more closely aligned with the cell detection application scenario, preventing misjudgments and improving the accuracy of anomaly detection. The preset redundancy value can be set according to actual usage requirements and is not specifically limited in this embodiment. For example, the preset redundancy value can range from 3 to 30mV.

[0073] The charging outlier characteristic parameter includes a first charging outlier parameter being greater than a second charging outlier parameter, wherein the first charging outlier parameter = a charging voltage mean + 3× a charging voltage standard deviation + a preset redundancy value. The second charging outlier parameter = a charging voltage mean - 3× a charging voltage standard deviation - a preset redundancy value.

[0074] Step 4: determining a first discharge outlier parameter and a second discharge outlier parameter according to the discharge voltage standard deviation, the discharge voltage mean, and a preset redundancy value; the first discharge outlier parameter is greater than the second discharge outlier parameter.

[0075] The discharge outlier characteristic parameters include a first discharge outlier parameter and a second discharge outlier parameter. The first discharge outlier parameter = discharge voltage mean + 3 × discharge voltage standard deviation + preset redundancy value. The second discharge outlier parameter = discharge voltage mean - 3 × discharge voltage standard deviation - preset redundancy value.

[0076] This embodiment calculates the first and second charging outlier parameters corresponding to the charging characteristic voltage, and the first and second discharging outlier parameters corresponding to the discharging characteristic voltage. Preset redundancy values are introduced into the calculation process, making it more suitable for battery cell detection applications, further preventing misjudgments and improving the accuracy of anomaly detection.

[0077] In one embodiment, after determining the charging outlier characteristic parameter, the discharging outlier characteristic parameter, the charging characteristic voltage, and the discharging characteristic voltage of each battery cell to be detected, it is necessary to perform abnormality detection on the multiple battery cells to be detected using the above parameters, as follows:

[0078] It is understandable that the charging characteristic voltage and the discharging characteristic voltage are not independent of each other. Lithium battery model based on constant current:

[0079] Vol = OCV(SOC)+I×R+voltage acquisition error

[0080] Among them, VOL is the charging voltage or the discharging voltage, OCV is the open circuit voltage, I is the charging current or the discharging current, R is the internal resistance of the battery cell, and SOC is the state of charge of the battery cell. When the OCV or the collected voltage is abnormal, the charging characteristic voltage and the discharging characteristic voltage will be outliers in the same direction. For example, the charging characteristic voltage is greater than the first charging outlier parameter, and the discharging characteristic voltage is greater than the first discharging outlier parameter; or the charging characteristic voltage is less than the second charging outlier parameter, and the discharging characteristic voltage is less than the second discharging outlier parameter. When the internal resistance of the lithium battery is abnormal, because the current signs are opposite during charging and discharging, the charging characteristic voltage and the discharging characteristic voltage will be outliers in different directions. For example, the charging characteristic voltage is greater than the first charging outlier parameter, and the discharging characteristic voltage is less than the second discharging outlier parameter; or the charging characteristic voltage is less than the second charging outlier parameter, and the discharging characteristic voltage is greater than the first discharging outlier parameter. The specific methods for determining the abnormality are as follows:

[0081] If the charging characteristic voltage of the battery cell to be detected is greater than the first charging outlier parameter, and the discharging characteristic voltage is greater than the first discharging outlier parameter, then the abnormal state of the battery cell to be detected is abnormal high platform voltage or abnormal collection.

[0082] If the charging characteristic voltage of the battery cell to be tested is less than the second charging outlier parameter, and the discharging characteristic voltage is less than the second discharging outlier parameter, then the abnormal state of the battery cell to be tested is abnormal low platform voltage or abnormal acquisition;

[0083] If the charging characteristic voltage of the battery cell to be tested is less than the second charging outlier parameter, and the discharging characteristic voltage is greater than the first discharging outlier parameter, then the abnormal state of the battery cell to be tested is an abnormally small internal resistance;

[0084] If the charging characteristic voltage of the battery cell to be detected is greater than the first charging outlier parameter, and the discharging characteristic voltage is less than the second discharging outlier parameter, then the abnormal state of the battery cell to be detected is an abnormally large internal resistance.

[0085] If the charging characteristic voltage of the battery cell to be tested is less than or equal to the first charging outlier parameter and greater than or equal to the second charging outlier parameter; and the discharge characteristic voltage is less than or equal to the first discharge outlier parameter and greater than or equal to the second discharge outlier parameter; then the battery cell to be tested is not abnormal.

[0086] If the charging characteristic voltage of the battery cell to be tested is greater than the first charging outlier parameter, or the charging characteristic voltage is less than the second charging outlier parameter; and the discharge characteristic voltage is less than or equal to the first discharge outlier parameter, and greater than or equal to the second discharge outlier parameter; then the battery cell to be tested is not abnormal.

[0087] If the charging characteristic voltage of the battery cell to be detected is less than or equal to the first charging outlier parameter and greater than or equal to the second charging outlier parameter; and the discharging characteristic voltage is greater than the first discharging outlier parameter or the discharging characteristic voltage is less than the second discharging outlier parameter; then there is no abnormality in the battery cell to be detected.

[0088] In one specific embodiment, when performing abnormality detection on each battery cell, first summarize the charging characteristic voltages [V_chg1, V_chg2, …, V_chgN] of each battery cell and the discharging characteristic voltages [V_dis1, V_dis2, …, V_disN] of each battery cell. Here, V_chg1 represents the charging characteristic voltage of the first battery cell, V_chg2 represents the charging characteristic voltage of the second battery cell, and so on. V_dis1 represents the discharging characteristic voltage of the first battery cell, V_dis2 represents the discharging characteristic voltage of the second battery cell, and so on. Then, according to the charging characteristic voltages of each battery cell, calculate the charging voltage standard deviation chgStdVol and the charging voltage average value chgAvgVol of the charging characteristic voltage; according to the discharging characteristic voltages of each battery cell, calculate the discharging voltage standard deviation disStdVol and the discharging voltage average value disAvgVol of the discharging characteristic voltage. Assume that the charging characteristic voltage of a certain individual battery cell is V_chg and the discharging characteristic voltage is V_dis, and the preset redundancy value is volTd.

[0089] If the charging characteristic voltage V_chg of a certain individual battery cell > chgAvgVol + 3×chgStdVol + volTd and the discharging characteristic voltage V_dis > disAvgVol + 3×disStdVol + volTd, then warn the user that there is an abnormality in the platform voltage being too high or an acquisition abnormality for this battery cell.

[0090] If the charging characteristic voltage V_chg of a certain individual battery cell < chgAvgVol - 3×chgStdVol - volTd and the discharging characteristic voltage V_dis < disAvgVol - 3×disStdVol - volTd, then warn the user that there is an abnormality in the platform voltage being too low or an acquisition abnormality for this battery cell.

[0091] If the charging characteristic voltage V_chg of a certain individual battery cell < chgAvgVol - 3×chgStdVol - volTd and the discharging characteristic voltage V_dis > disAvgVol + 3×disStdVol + volTd, then warn the user that there may be an abnormality in the internal resistance of this battery cell and it is too small.

[0092] If the charging characteristic voltage V_chg of a certain individual battery cell > chgAvgVol + 3 × chgStdVol + volTd, and the discharging characteristic voltage V_dis < disAvgVol - 3 × disStdVol - volTd, then warn the user that the internal resistance of this battery cell may be abnormally large.

[0093] In one embodiment, according to the charging characteristic voltages and discharging characteristic voltages of multiple battery cells to be detected, the battery cell efficiency of the multiple battery cells to be detected can also be calculated. Here, the battery cell efficiency refers to the efficiency of the battery cell during the energy conversion process. Specifically, according to the charging characteristic voltage of each battery cell to be detected, calculate the average charging voltage; according to the discharging characteristic voltage of each battery cell to be detected, calculate the average discharging voltage; according to the average charging voltage and the average discharging voltage, calculate the battery cell efficiency of the multiple battery cells to be detected. After calculating the average charging voltage and the average discharging voltage, take the ratio of the average discharging voltage to the average charging voltage as the battery cell efficiency of the multiple battery cells to be detected. The specific formula is as follows:

[0094]

[0095] Among them, efficiency represents the battery cell efficiency of the multiple battery cells to be detected; disAvgVol represents the average discharging voltage; chgAvgVol represents the average charging voltage.

[0096] In this embodiment, the battery cell efficiency is calculated through the charging characteristic voltage and the discharging characteristic voltage. It is not necessary to fully charge and discharge the multiple battery cells to be detected for calculation, and the calculation is simple and convenient.

[0097] The battery cell abnormality detection method provided in the above embodiment can discover potential risks of the battery cell in advance and give early warnings, thereby improving the safety, reliability, and service life of the lithium battery cell. Moreover, the calculation method of the embodiment of the present application requires little computing resources, and the corresponding algorithm can be deployed in various automotive or energy storage scenarios such as MCU, CPU, smart terminals, and the cloud.

[0098] It should be understood that, although the various steps in the flowcharts involved in the various embodiments described above are displayed in sequence according to the instructions of the arrows, these steps are not necessarily executed in sequence in the order indicated by the arrows. Unless otherwise specified herein, there is no strict order restriction on the execution of these steps, and these steps can be executed in other orders. Moreover, at least a portion of the steps in the flowcharts involved in the various embodiments described above can include multiple steps or multiple stages, and these steps or stages are not necessarily executed and completed at the same time, but can be executed at different times, and the execution order of these steps or stages is not necessarily to be carried out in sequence, but can be executed in turn or alternately with other steps or at least a portion of steps or stages in other steps.

[0099] Based on the same inventive concept, the present application also provides a battery cell anomaly detection device for implementing the aforementioned battery cell anomaly detection method. The solution provided by this device is similar to the solution described in the aforementioned method. Therefore, the specific limitations in one or more battery cell anomaly detection device embodiments provided below can be found in the limitations of the battery cell anomaly detection method described above and will not be further elaborated here.

[0100] In one embodiment, Figure 5 As shown, a battery cell anomaly detection device is provided, comprising: an acquisition module 100, an outlier feature calculation module 200 and an anomaly detection module 300, wherein:

[0101] An acquisition module 100 is configured to acquire a charge characteristic voltage and a discharge characteristic voltage of each of a plurality of cells to be detected;

[0102] An outlier feature calculation module 200 is configured to determine a charge outlier feature parameter and a discharge outlier feature parameter based on the charge feature voltage and the discharge feature voltage of each battery cell to be detected;

[0103] The abnormality detection module 300 is configured to perform abnormality detection on the plurality of cells to be detected based on the charging outlier characteristic parameter, the discharging outlier characteristic parameter, the charging characteristic voltage, and the discharging characteristic voltage of each cell to be detected.

[0104] The battery cell anomaly detection device further includes: an update module;

[0105] An update module is used to collect the charging voltage and charging current at preset time intervals during the charging process of the target battery cell, and update the charging characteristic voltage of the target battery cell according to the charging voltage and charging current at the current time point; the target battery cell is any one of the multiple battery cells to be detected.

[0106] The updating module is further used to collect the discharge voltage and discharge current at preset time intervals during the discharge process of the target battery cell, and update the discharge characteristic voltage of the target battery cell according to the discharge voltage and discharge current at the current time point.

[0107] The update module is also used to set the charging characteristic voltage of the target battery cell as the initial charging voltage; during the charging process of the target battery cell, obtain the battery cell charge state in real time; if the battery cell charge state meets the preset charge state range, obtain the charging voltage and charging current at the current time point; and update the charging characteristic voltage of the target battery cell according to the charging voltage and charging current.

[0108] The updating module is further used to determine the current weight according to the charging current and the preset time interval; and update the charging characteristic voltage of the target battery cell according to the current weight, the charging voltage and the standard current weight.

[0109] The update module is also used to set the discharge characteristic voltage of the target battery cell as the initial discharge voltage; during the discharge process of the target battery cell, obtain the battery cell charge state in real time; if the battery cell charge state meets the preset charge state range, obtain the discharge voltage and discharge current at the current time point; and update the discharge characteristic voltage of the target battery cell according to the discharge voltage and discharge current.

[0110] The outlier feature calculation module 200 is further used to calculate the charging voltage standard deviation and the charging voltage mean based on the charging characteristic voltage of each battery cell to be detected; calculate the discharge voltage standard deviation and the discharge voltage mean based on the discharge characteristic voltage of each battery cell to be detected; determine a first charging outlier parameter and a second charging outlier parameter based on the charging voltage standard deviation, the charging voltage mean and a preset redundancy value; the first charging outlier parameter is greater than the second charging outlier parameter; determine a first discharge outlier parameter and a second discharge outlier parameter based on the discharge voltage standard deviation, the discharge voltage mean and the preset redundancy value; the first discharge outlier parameter is greater than the second discharge outlier parameter.

[0111] The abnormality detection module 300 is also used to: if the charging characteristic voltage of the battery cell to be detected is greater than the first charging outlier parameter, and the discharge characteristic voltage is greater than the first discharge outlier parameter; then the abnormal state of the battery cell to be detected is a high platform voltage abnormality or a collection abnormality; if the charging characteristic voltage of the battery cell to be detected is less than the second charging outlier parameter, and the discharge characteristic voltage is less than the second discharge outlier parameter; then the abnormal state of the battery cell to be detected is a low platform voltage abnormality or a collection abnormality; if the charging characteristic voltage of the battery cell to be detected is less than the second charging outlier parameter, and the discharge characteristic voltage is greater than the first discharge outlier parameter; then the abnormal state of the battery cell to be detected is a small internal resistance abnormality; if the charging characteristic voltage of the battery cell to be detected is greater than the first charging outlier parameter, and the discharge characteristic voltage is less than the second discharge outlier parameter; then the abnormal state of the battery cell to be detected is a large internal resistance abnormality.

[0112] The abnormality detection module 300 is further used to calculate the charging voltage mean based on the charging characteristic voltage of each of the battery cells to be detected; calculate the discharge voltage mean based on the discharge characteristic voltage of each of the battery cells to be detected; and calculate the cell efficiency of multiple battery cells to be detected based on the charging voltage mean and the discharge voltage mean.

[0113] Each module in the battery cell anomaly detection device can be implemented in whole or in part through software, hardware, or a combination thereof. Each module can be embedded in or independent of a processor in a computer device in hardware form, or stored in a computer device memory in software form, so that the processor can call and execute the corresponding operations of each module.

[0114] In one embodiment, a computer device is provided. The computer device may be a terminal, and its internal structure diagram may be as follows: Figure 6 As shown. The computer device includes a processor, memory, communication interface, display screen and input device connected via a system bus. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and computer program in the non-volatile storage medium. The communication interface of the computer device is used to communicate with an external terminal in a wired or wireless manner. The wireless manner can be achieved through WIFI, a mobile cellular network, NFC (near field communication) or other technologies. When the computer program is executed by the processor, a method for detecting battery cell abnormalities is implemented.

[0115] Those skilled in the art will understand that Figure 6The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than shown in the figure, or combine certain components, or have a different component arrangement.

[0116] In one embodiment, a computer device is provided, including a memory and a processor. The memory stores a computer program, and when the processor executes the computer program, any one of the battery cell abnormality detection methods in the above embodiments is implemented.

[0117] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, any one of the battery cell abnormality detection methods in the above embodiments is implemented.

[0118] Those skilled in the art will appreciate that all or part of the processes in the above-mentioned embodiments can be implemented by instructing the relevant hardware through a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the above-mentioned embodiments. In particular, any reference to memory, database, or other media used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM). The databases involved in the various embodiments provided herein may include at least one of a relational database and a non-relational database. Non-relational databases may include, but are not limited to, distributed databases based on blockchains. The processors involved in the various embodiments provided herein may be, but are not limited to, general-purpose processors, central processing units (CPUs), graphics processing units (GPUs), digital signal processors (DSPs), programmable logic devices (PLDs), data processing logic devices based on quantum computing, and the like.

[0119] The technical features of the above embodiments can be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0120] The above-described embodiments merely represent several implementation methods of the present application. While the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the present application. It should be noted that a person of ordinary skill in the art may make various modifications and improvements without departing from the spirit of the present application, and these modifications and improvements fall within the scope of protection of the present application. Therefore, the scope of protection of the present application shall be determined by the appended claims.

Claims

1. A method for detecting abnormality of a battery cell, characterized in that: The method comprises: Obtaining a charge characteristic voltage and a discharge characteristic voltage of each of the plurality of battery cells to be tested; Determining a charging outlier characteristic parameter and a discharging outlier characteristic parameter according to the charging characteristic voltage and the discharging characteristic voltage of each of the battery cells to be tested; Abnormality detection is performed on the plurality of battery cells to be detected according to the charging outlier characteristic parameter, the discharging outlier characteristic parameter, the charging characteristic voltage, and the discharging characteristic voltage of each of the battery cells to be detected.

2. The method according to claim 1, characterized in that Before obtaining the charging characteristic voltage and the discharging characteristic voltage of each of the plurality of cells to be detected, the method further includes: During the charging process of the target battery cell, the charging voltage and the charging current are collected at preset time intervals, and the charging characteristic voltage of the target battery cell is updated according to the charging voltage and the charging current at the current time point; the target battery cell is any one of the multiple battery cells to be tested; During the discharge process of the target battery cell, the discharge voltage and the discharge current are collected at preset time intervals, and the discharge characteristic voltage of the target battery cell is updated according to the discharge voltage and the discharge current at a current time point.

3. The method according to claim 2, characterized in that The updating of the charging characteristic voltage of the target battery cell according to the charging voltage and charging current at the current time point includes: Setting the charging characteristic voltage of the target battery cell as an initial charging voltage; During the charging process of the target battery cell, obtaining the charge state of the battery cell in real time; If the state of charge of the battery cell meets the preset state of charge range, obtaining the charging voltage and charging current at the current time point; The charging characteristic voltage of the target battery cell is updated according to the charging voltage and the charging current.

4. The method according to claim 3, characterized in that Updating the charging characteristic voltage of the target battery cell according to the charging voltage and the charging current includes: Determining a current weight according to the charging current and the preset time interval; The charging characteristic voltage of the target battery cell is updated according to the current weight, the charging voltage, and the standard current weight.

5. The method according to claim 2, characterized in that The updating of the discharge characteristic voltage of the target battery cell according to the discharge voltage and discharge current at the current time point includes: Setting the discharge characteristic voltage of the target battery cell as an initial discharge voltage; During the discharge process of the target battery cell, obtaining the charge state of the battery cell in real time; If the state of charge of the battery cell meets the preset state of charge range, obtaining the discharge voltage and discharge current at the current time point; The discharge characteristic voltage of the target battery cell is updated according to the discharge voltage and the discharge current.

6. The method according to claim 2, characterized in that Determining a charging outlier characteristic parameter and a discharging outlier characteristic parameter according to the charging characteristic voltage and the discharging characteristic voltage of each of the battery cells to be detected includes: Calculating a charging voltage standard deviation and a charging voltage mean according to the charging characteristic voltage of each battery cell to be tested; Calculating a discharge voltage standard deviation and a discharge voltage mean according to the discharge characteristic voltage of each of the battery cells to be tested; determining a first charging outlier parameter and a second charging outlier parameter according to the charging voltage standard deviation, the charging voltage mean, and a preset redundancy value; wherein the first charging outlier parameter is greater than the second charging outlier parameter; A first discharge outlier parameter and a second discharge outlier parameter are determined according to the discharge voltage standard deviation, the discharge voltage mean, and a preset redundancy value; the first discharge outlier parameter is greater than the second discharge outlier parameter.

7. The method according to claim 6, characterized in that The performing abnormality detection on the plurality of cells to be detected according to the charging outlier characteristic parameter, the discharging outlier characteristic parameter, the charging characteristic voltage, and the discharging characteristic voltage of each cell to be detected comprises: If the charging characteristic voltage of the battery cell to be detected is greater than the first charging outlier parameter, and the discharging characteristic voltage is greater than the first discharging outlier parameter; the abnormal state of the battery cell to be detected is abnormal high platform voltage or abnormal acquisition; If the charging characteristic voltage of the battery cell to be detected is less than the second charging outlier parameter, and the discharging characteristic voltage is less than the second discharging outlier parameter, then the abnormal state of the battery cell to be detected is abnormal low platform voltage or abnormal acquisition; If the charging characteristic voltage of the battery cell to be detected is less than the second charging outlier parameter, and the discharging characteristic voltage is greater than the first discharging outlier parameter, then the abnormal state of the battery cell to be detected is an abnormally small internal resistance; If the charging characteristic voltage of the battery cell to be detected is greater than the first charging outlier parameter, and the discharging characteristic voltage is less than the second discharging outlier parameter, then the abnormal state of the battery cell to be detected is an abnormal large internal resistance.

8. The method according to claim 1, characterized in that The method further comprises: Calculating a charging voltage mean value according to the charging characteristic voltage of each of the battery cells to be tested; Calculating a discharge voltage mean value according to the discharge characteristic voltage of each of the battery cells to be tested; The cell efficiency of the plurality of cells to be tested is calculated according to the average charging voltage and the average discharging voltage.

9. A battery cell abnormality detection device, characterized in that: The device comprises: An acquisition module, configured to acquire a charge characteristic voltage and a discharge characteristic voltage of each of a plurality of cells to be detected; an outlier feature calculation module, configured to determine a charge outlier feature parameter and a discharge outlier feature parameter according to the charge feature voltage and the discharge feature voltage of each battery cell to be detected; An anomaly detection module is used to perform anomaly detection on the multiple cells to be detected based on the charging outlier characteristic parameter, the discharging outlier characteristic parameter, the charging characteristic voltage and the discharging characteristic voltage of each cell to be detected.

10. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the method according to any one of claims 1 to 8 is implemented.

11. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the method according to any one of claims 1 to 8 is implemented.

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