Method and device for identifying abnormal self-discharge of battery cell, electronic device and medium

Abstract

The application provides a method and device for identifying abnormal self-discharge of an electric core, an electronic device and a medium. The method comprises: obtaining feature values of each electric core in each interval of a first time period, and determining a feature point of each electric core according to the feature values of the electric core in each interval; clustering the feature points of each electric core to obtain outliers in each interval; determining an abnormal outlier electric core according to the number of outliers of each electric core; and determining an abnormal self-discharge electric core from the abnormal outlier electric core according to a first average voltage of the abnormal outlier electric core in each interval and a second average voltage of a non-abnormal outlier electric core in the corresponding interval. The method can identify an abnormal self-discharge electric core in a battery pack.

Description

TECHNICAL FIELD

[0001] The application relates to the technical field of batteries, and in particular to a method and device for identifying abnormal self-discharge of a battery cell, an electronic device and a medium. BACKGROUND

[0002] A battery pack is a green energy source and is widely used in various industries, such as a power source of an electric vehicle or an energy storage container.

[0003] A plurality of battery cells are connected in series in the battery pack, and in actual application, individual battery cells may have abnormal self-discharge, which may result in a large voltage difference between the battery cells, and thus the service life and efficiency of the battery pack are low.

[0004] Therefore, it is necessary to provide a method for identifying abnormal self-discharge of a battery cell. SUMMARY

[0005] The application provides a method and device for identifying abnormal self-discharge of a battery cell, an electronic device and a medium, which can identify abnormal self-discharge of a battery cell and improve the service life and efficiency of a battery pack.

[0006] In a first aspect, the application provides a method for identifying abnormal self-discharge of a battery cell, which comprises: obtaining feature values of each battery cell in each interval in a first time period for each battery cell in a battery pack; the battery cell is in a static state in the first time period, and the feature values represent the voltage of the battery cell in the interval; for each interval, determining a feature point of the battery cell according to the feature values of the battery cell, clustering the feature points of the battery cells to obtain outliers in each interval; obtaining the number of outliers of each battery cell according to the outliers in the intervals, and determining an abnormal outlier battery cell according to the number of outliers of the battery cell; and determining an abnormal self-discharge battery cell from the abnormal outlier battery cell based on a first change rate of the voltage of the abnormal outlier battery cell in the first time period and a second change rate of a voltage difference between the voltage of the abnormal outlier battery cell and the voltage of a non-abnormal outlier battery cell in the battery pack except the abnormal outlier battery cell in the first time period.

[0007] Optionally, determining the abnormal self-discharge cell from the abnormal outlier cells based on the first change rate of the voltage of the abnormal outlier cell in the first time period and the second change rate of the voltage difference between the voltage of the non-abnormal outlier cell and the voltage of the abnormal outlier cell in the first time period, comprises: for each abnormal outlier cell, obtaining the first change rate corresponding to the abnormal outlier cell; if the first change rate is less than a first threshold, obtaining the second change rate corresponding to the abnormal outlier cell, and detecting whether the second change rate corresponding to the abnormal outlier cell is positive; if the second change rate corresponding to the abnormal outlier cell is positive, determining that the abnormal outlier cell is an abnormal self-discharge cell.

[0008] Optionally, the obtaining of the first change rate of the abnormal outlier cell comprises: for each abnormal outlier cell, obtaining the voltage of the abnormal outlier cell in each interval; performing linear fitting on the voltage of the abnormal outlier cell in each interval, and taking the slope of the fitted straight line as the first change rate corresponding to the abnormal outlier cell.

[0009] Optionally, the obtaining of the second change rate corresponding to the abnormal outlier cell comprises: for each abnormal outlier cell, subtracting the voltage of the abnormal outlier cell in each interval from the voltage of the non-abnormal outlier cell in the corresponding interval to obtain the first voltage difference of the abnormal outlier cell in each interval; performing linear fitting on the first voltage difference of the abnormal outlier cell in each interval, and taking the slope of the fitted straight line as the second change rate corresponding to the abnormal outlier cell.

[0010] Optionally, the obtaining of the feature value of each cell in the battery pack in each interval in the first time period comprises: for each cell, obtaining the quartile of the voltage of the cell in each interval; taking the voltage at any two quartiles of the quartile of the voltage in each interval as the feature value of the cell in each interval; and the feature values of each cell in each interval are voltages at the same quartile.

[0011] Optionally, the determining of the abnormal outlier cell according to the number of outlier points of the cell comprises: dividing the number of outlier points of the cell by the number of feature points of the cell to obtain a first result; if the first result exceeds a second threshold, taking the cell corresponding to the first result as the abnormal outlier cell.

[0012] Optionally, the obtaining, for each battery cell in the battery pack, the feature value of the battery cell in each interval of the first time period further includes: obtaining operation data of the battery pack; obtaining, based on the operation data of the battery pack, the battery pack with a duration of static state not less than the first time period; and obtaining, for each battery cell in the battery pack with the duration of static state not less than the first time period, the feature value of the battery cell in each interval of the first time period.

[0013] Optionally, the obtaining, for each battery cell in the battery pack, the feature value of the battery cell in each interval of the first time period further includes: obtaining operation data of the battery pack; obtaining, based on the operation data of the battery pack, the battery pack with a duration of static state not less than the first time period; and obtaining, for each battery cell in the battery pack with the duration of static state not less than the first time period, the feature value of the battery cell in each interval of the first time period.

[0014] In a second aspect, the present application provides a device for identifying abnormal self-discharge of battery cells, comprising: a first obtaining module configured to obtain, for each battery cell in a battery pack, a feature value of the battery cell in each interval of a first time period; the battery cell is in a static state in the first time period, and the feature value represents a voltage of the battery cell in the interval; a second obtaining module configured to determine, for each interval, a feature point of the battery cell according to the feature value of the battery cell, and obtain, by clustering the feature points of the battery cells, an outlier point in each interval; a first identifying module configured to obtain a number of outlier points of each battery cell according to the outlier points in the intervals, and determine an abnormal outlier battery cell according to the number of outlier points of the battery cell; and a second identifying module configured to determine, from the abnormal outlier battery cell, an abnormal self-discharge battery cell based on a first change rate of the voltage of the abnormal outlier battery cell in the first time period and a second change rate of a voltage difference between the voltage of the abnormal outlier battery cell and the voltage of a non-abnormal outlier battery cell in the battery pack other than the abnormal outlier battery cell in the first time period.

[0015] Optionally, the second identifying module is specifically configured to obtain, for each abnormal outlier battery cell, a first change rate corresponding to the abnormal outlier battery cell; if the first change rate is less than a first threshold value, obtain a second change rate corresponding to the abnormal outlier battery cell, and detect whether the second change rate corresponding to the abnormal outlier battery cell is positive; and the second identifying module is specifically further configured to determine that the abnormal outlier battery cell is an abnormal self-discharge battery cell if the second change rate corresponding to the abnormal outlier battery cell is positive.

[0016] Optionally, the second identifying module is specifically configured to obtain, for each abnormal outlier battery cell, a voltage of the abnormal outlier battery cell in each interval; and the second identifying module is specifically further configured to perform linear fitting on the voltage of the abnormal outlier battery cell in the intervals, and take a slope of the fitted straight line as the first change rate corresponding to the abnormal outlier.

[0017] Optionally, the second change rate corresponding to the abnormal outlier battery cell is obtained by: the second identification module is specifically configured to subtract the voltage of the non-abnormal outlier battery cell in each interval from the voltage of the abnormal outlier battery cell in the corresponding interval to obtain a first voltage difference of the abnormal outlier battery cell in each interval; and the second identification module is specifically further configured to perform linear fitting on the first voltage difference of the abnormal outlier battery cell in each interval, and take the slope of the fitted straight line as the second change rate corresponding to the abnormal outlier battery cell.

[0018] Optionally, the first acquisition module is specifically configured to acquire the quartile of the voltage of each battery cell in each interval; and the first acquisition module is specifically further configured to take the voltage at any two quartiles of the quartiles of the voltage in each interval as the feature value of the battery cell in each interval, wherein the feature values of each battery cell in each interval are voltages at the same quartile.

[0019] Optionally, the first identification module is specifically configured to divide the number of outlier points of the battery cell by the number of feature points of the battery cell to obtain a first result; and the first identification module is specifically further configured to take the battery cell corresponding to the first result as the abnormal outlier battery cell if the first result exceeds a second threshold.

[0020] Optionally, the second acquisition module is specifically configured to cluster the feature points of each battery cell based on a density clustering algorithm to obtain the outlier points in each interval.

[0021] Optionally, the first acquisition module is specifically configured to acquire the quartile of the voltage of each battery cell in each interval; and the first acquisition module is specifically further configured to take the voltage at any two quartiles of the quartiles of the voltage in each interval as the feature value of the battery cell in each interval, wherein the feature values of each battery cell in each interval are voltages at the same quartile.

[0022] Optionally, the first identification module is specifically configured to divide the number of outlier points of the battery cell by the number of feature points of the battery cell to obtain a first result; and the first identification module is specifically further configured to take the battery cell corresponding to the first result as the abnormal outlier battery cell if the first result exceeds a second threshold.

[0023] Optionally, the first obtaining module is specifically configured to obtain the running data of the battery pack; the first obtaining module is specifically further configured to obtain the battery pack with a duration of the stationary state not less than the first time period based on the running data of the battery pack; and the first obtaining module is specifically configured to obtain the feature value of each interval of the first time period for each battery cell in the battery pack with the duration of the stationary state not less than the first time period.

[0024] Optionally, the second obtaining module is specifically configured to cluster the feature points of the battery cells based on a density clustering algorithm to obtain the outlier points in each interval.

[0025] In a third aspect, the present application provides an electronic device, comprising a processor and a memory connected with the processor in communication; the memory stores computer execution instructions; and the processor executes the computer execution instructions stored in the memory to implement the method as described above.

[0026] In a fourth aspect, the present application provides a computer readable storage medium, wherein the computer readable storage medium stores computer execution instructions, and the computer execution instructions are executed by a processor to implement the method as described above.

[0027] In the method, device, electronic device and medium for identifying abnormal self-discharge of battery cells provided by the present application, the feature value of each interval of the first time period for each battery cell in the battery pack is obtained, and for each interval, the feature point of the battery cell is determined according to the feature value of the battery cell. The outlier points in each interval are obtained by clustering the feature points of the battery cells. Then, the abnormal outlier battery cell is determined according to the number of outlier points of the battery cell, and the abnormal self-discharge battery cell is determined from the abnormal outlier battery cell through the first change rate and the second change rate corresponding to the voltage of the abnormal outlier battery cell. In the present application, the abnormal outlier battery cell in the battery pack is determined by the clustering algorithm, and the abnormal outlier battery cell is further determined through the first change rate and the second change rate corresponding to the abnormal outlier battery cell, so as to avoid the influence of some interference factors, thereby accurately and quickly identifying the abnormal self-discharge battery cell. BRIEF DESCRIPTION OF DRAWINGS

[0028] The accompanying drawings, which are incorporated herein and form part of the specification, illustrate embodiments consistent with the present application and, together with the description, further serve to explain the principles behind the embodiments of the present application.

[0029] The above drawings have shown the specific embodiments of the present application, and more detailed descriptions will be given hereinafter. These drawings and the written description are not intended to limit the scope of the present application in any way by any means, but to illustrate the concept of the present application to those skilled in the art by referring to specific embodiments.

[0030] Figure 1 a structural schematic diagram of an example electric vehicle;

[0031] Figure 2 a flowchart of an identification method of abnormal self-discharge of an electric core provided by Embodiment One of the present application;

[0032] Figure 3 a flowchart of another identification method of abnormal self-discharge of an electric core provided by Embodiment One of the present application;

[0033] Figure 4 a schematic diagram of voltage change of an abnormal outlier electric core in each interval;

[0034] Figure 5 a structural schematic diagram of an identification device of abnormal self-discharge of an electric core provided by Embodiment Two of the present application;

[0035] Figure 6 a structural schematic diagram of an electronic device provided by Embodiment Three of the present application.

[0036] The specific embodiments of the present application have been shown in the above-described drawings, and will be described in more detail hereinafter. These drawings and textual descriptions are not intended to limit the scope of the concept of the present application by any means, but to illustrate the concept of the present application to those skilled in the art by referring to specific embodiments. DETAILED DESCRIPTION

[0037] The exemplary embodiments will be described in detail herein with reference to the accompanying drawings. The following description is presented with reference to the drawings, wherein the same reference numerals are used to refer to like or similar elements throughout the several exemplary embodiments and / or drawings. The following description is not intended to represent all embodiments in accordance with the present application. Rather, they merely represent typical embodiments of devices and methods consistent with certain aspects of the present application, as detailed in the appended claims.

[0038] Figure 1 a structural schematic diagram of an example electric vehicle, as Figure 1 shown, the electric vehicle includes a battery pack 12 and a power device 11, each battery pack 12 includes a plurality of electric cores 121, the electric cores 121 are charged by a commercial power supply 13 and store electric quantity, when the electric vehicle is not started, the battery pack 12 is in a static state; when the electric vehicle is started, the battery pack 12 discharges the power device 11 to provide power.

[0039] Continuing to refer to Figure 1In the battery pack 12, a plurality of battery cells 121 are arranged in series. In some cases, an individual battery cell 1 may abnormally self-discharge, i.e., discharge a large amount of electricity, which may result in a large voltage difference between the battery cells. In actual applications, the charging strategy of the battery pack is to stop charging when a battery cell reaches an upper limit, and to stop discharging when a battery cell reaches a lower limit. Therefore, a larger voltage difference may result in a lower service life and efficiency of the battery pack. Thus, it is necessary to provide a method capable of identifying an abnormal self-discharge battery cell.

[0040] The technical solutions of the present application and the technical solutions of the present application will be described in detail below with specific embodiments. The following specific embodiments can be combined with each other, and the same or similar concepts or processes may not be described again in some embodiments. In the description of the present application, unless otherwise explicitly specified and limited, each term should be understood in a broad sense within the art. The embodiments of the present application will be described below with reference to the accompanying drawings.

[0041] Embodiment one

[0042] Figure 2 A flowchart of a method for identifying abnormal self-discharge of a battery cell provided by Embodiment One of the present application is shown in FIG. 1. The method provided by the present embodiment includes the following steps. Figure 2

[0043] S201, for each battery cell in the battery pack, obtaining the feature value of the battery cell in each interval in the first time period;

[0044] S202, for each interval, determining the feature point of the battery cell according to the feature value of the battery cell, and clustering the feature points of the battery cells to obtain the outlier point under each interval;

[0045]

[0046] S203, obtaining the number of outlier points of each battery cell according to the outlier points under each interval, and determining an abnormal outlier battery cell according to the number of outlier points of the battery cell;

[0047]

[0048] 0S204, determining an abnormal self-discharge battery cell from the abnormal outlier battery cell based on the first change rate of the voltage of the abnormal outlier battery cell in the first time period and the second change rate of the voltage difference between the voltage of the abnormal outlier battery cell and the voltage of a non-abnormal outlier battery cell other than the abnormal outlier battery cell in the first time period. In actual applications, the execution subject of the present embodiment can be a battery cell abnormal self-discharge identification device, which can

[0049]

[0050] ​​​​The identification device can be a drive program, program software, a medium storing a related computer program, such as a U disk, or an entity device integrating or installing a related computer program, such as a chip, a smart terminal, a computer, etc.

[0051] For example, in actual application, the battery pack has three states of the battery cells: a charging state, a static state and a discharging state, and the states of the battery cells are consistent. Under normal circumstances, the battery cells will also have self-discharge

[0052] However, the self-discharge is in a small amount, and the amount of electricity discharged by each battery cell in the same time is similar, and a high voltage difference will not be generated. The method provided in the embodiment mainly identifies the battery cell with abnormal self-discharge. Since the voltage of the battery cell is unstable in the charging and discharging states, the battery cell with abnormal self-discharge is identified in the static state in the embodiment. When the state of the battery cell is determined, the running data of the battery pack is acquired, and the battery pack with a static state lasting for a time not less than the first time period is acquired based on the running data of the battery pack, and the battery pack is identified.

[0053]

[0054] In step S201, the feature value of each battery cell in each interval of the first time period is acquired, wherein the battery cell is in a static state in the first time period, that is, the identification is performed in the static state. In actual application, in order to accurately identify the battery cell with abnormal self-discharge, the first time period needs to be set to be relatively long, for example, the first time period can be set to be more than 2 hours. The first time period is divided into multiple intervals, for example, the first time period is 120 minutes, and each 10 minutes is divided into an interval, so that there are 12 intervals. The feature value represents the voltage of the battery cell in the interval. In actual application, the feature value can be the average voltage in each interval or the voltage at a certain quantile in the interval. As an implementation manner, S201 can include:

[0055] For each battery cell, the four quantiles of the voltage of the battery cell in each interval are acquired;

[0056] The voltage at any two quantiles of the four quantiles of the voltage in each interval is taken as the feature value of the battery cell in the corresponding interval; and the feature values of the battery cell in each interval are the voltages at the same quantile.

[0057]

[0058]

[0059] ​​​With the example of the scenario, in actual application, the battery pack is generally managed by a battery management system (BMS), and the BMS acquires the voltage of each cell in the battery pack at a fixed frequency, for example, once every 1 minute. The execution subject of the embodiment can acquire the voltage acquired by the BMS as the voltage in each interval, but to improve the subsequent calculation efficiency, the voltage acquired by the BMS can be down-sampled, for example, once every 2 minutes. That is, the voltage of each cell is acquired 5 times in an interval of 10 minutes. For each cell, there are 5 voltages at different time points in each interval, and the quartiles of these voltages are acquired, such as (V1, V2, V3, V4, V5), wherein the first quartile Q1 is V2, the second quartile Q2 is V3, and the third quartile Q3 is V4. The voltage at any two quartiles is selected as a feature value, for example, the voltages V3 and V4 at Q3 and Q4 are selected as the feature value. The voltages at the same quartiles are selected as the feature value for each cell in each interval, that is, the voltages at Q3 and Q4 are also selected as the feature value.

[0060] In step S202, the feature points are determined according to the feature values. For example, the feature points can be determined according to the feature values (V3, V4) acquired above. Each cell corresponds to a feature point in each interval. Then, the outliers in each interval are obtained by clustering the feature points of each cell. That is, for each node, the feature points of each cell need to be clustered, and since the feature points are determined by the feature values representing the voltage in the interval, the cell corresponding to the outlier can be understood as the cell that has a larger voltage difference from other cells in the interval. Of course, this difference may be caused by abnormal self-discharge of the cell, or may be caused by detection or acquisition error. It can be understood that in the embodiment, the abnormal outlier cell is obtained by clustering the feature points of each cell in each node, and only the further identification of each abnormal outlier cell is required subsequently, which avoids identifying each cell, and thus the embodiment can improve the identification speed of the abnormal self-discharge cell in the battery pack.

[0061] In actual application, a density clustering algorithm (Density-Based Spatial Clustering of Applications with Noise, referred to as DBSCAN) can be used for clustering, wherein the DBSCAN algorithm is a density-based spatial clustering algorithm. The algorithm divides a region with sufficient density into a cluster, and can discover clusters with arbitrary shapes in a spatial database with noise. The DBSCAN algorithm can automatically determine the number of categories without human definition, and can identify noise points, and has strong noise resistance. Therefore, through DBSCAN clustering, outliers, that is, noise points, can be accurately identified.

[0062] In step S203, the number of outliers of each battery cell is obtained to determine an abnormal outlier battery cell according to the number of outliers. It can be understood that the abnormal outlier battery cell is a battery cell with a relatively large possibility of abnormal self-discharge. In actual application, the abnormal self-discharge battery cell is usually processed by replacement and rejection. Therefore, to improve the accuracy of identification, further judgment needs to be made on the abnormal outlier battery cell to avoid the loss of the battery cell.

[0063] In step S204, the first change rate represents the change of the voltage of the abnormal outlier battery cell in the first time period; and the second change rate represents the change of the difference between the voltage of the abnormal outlier battery cell and the voltage of the non-abnormal outlier battery cell in the first time period.

[0064] It can be understood that the smaller the first change rate is, the more the abnormal outlier battery cell discharges to the outside in the first time period. The second change rate of the abnormal outlier battery cell can indicate the voltage change gap between the abnormal outlier battery cell and other non-abnormal outlier battery cells. The larger the second change rate is, the larger the voltage gap between the abnormal outlier battery cell and other non-abnormal outlier battery cells is.

[0065] In the scheme, the first change rate and the second change rate are used to further determine the abnormal outlier battery cell, so as to accurately identify the abnormal self-discharge battery cell.

[0066] To further improve the accuracy of identification of the abnormal self-discharge battery cell, Figure 3 A flowchart of another method for identifying abnormal self-discharge of a battery cell provided by an embodiment of the present application is shown in FIG. 2B. As shown in FIG. 2B, on the basis of the above embodiment, S204 includes: Figure 3

[0067] S2041, for each abnormal outlier battery cell, obtaining a first change rate corresponding to the abnormal outlier battery cell; and

[0068] S2042, if the first change rate is less than a first threshold, obtaining a second change rate corresponding to the abnormal outlier battery cell, and detecting whether the second change rate corresponding to the abnormal outlier battery cell is positive.​

[0069] S2043: If the second change rate corresponding to the abnormal outlier cell is a positive value, determine that the abnormal outlier cell is an abnormal self-discharging cell.

[0070] In this example, when the battery cell is in a static state, the voltage of the battery cell will only remain unchanged or gradually decrease, but will not increase. Therefore, the first rate of change is not greater than zero, so the first threshold is also set to a value not greater than zero. The first threshold can be

[0071] The first threshold is set based on the characteristics of the battery cells. It is understood that a smaller first threshold value means fewer abnormal outlier cells require further screening, resulting in higher identification efficiency. A larger first threshold value means more abnormal outlier cells require further screening, but the probability of missing abnormal self-discharging cells is lower. For example, the first threshold value can be zero or another predetermined value.

[0072] 5 Taking actual scenarios as an example, in actual applications, when the first change rate of the abnormal outlier cell exceeds the first threshold,

[0073] This indicates that the abnormal outlier cell has a relatively large discharge phenomenon, but there are many reasons for this. One possibility is that the cell is discharged abnormally. Another possibility is that the first threshold is set too large (i.e., close to zero), screening out the cells with normal discharge. Of course, another possibility is that all the cells in the battery pack are performing a certain discharge.

[0074] Electrical processing, uniformly in a state of large-scale discharge. As for the latter two reasons, although there is a discharge phenomenon in the battery cells, the voltages of the batteries in the battery pack are in a consistent state, so these two situations belong to the normal state of the battery pack. The first reason is what we need to identify. Because when the first change rate of the abnormal outlier battery cell is less than the first threshold, by detecting whether the second change rate corresponding to the abnormal outlier battery cell is a positive value, it is further judged whether the change in the voltage of the abnormal outlier battery cell is consistent with that of the non-abnormal outlier battery cell. It can be understood that if the second change rate is a positive value, it indicates that the difference between the voltage of the non-abnormal outlier battery cell and the voltage of the abnormal outlier battery cell is gradually increasing, then the changes in the two voltages are inconsistent, that is, the abnormal outlier battery cell is an abnormal self-discharging battery cell. In this way, the discharge phenomenon of the abnormal outlier battery cell caused by the first two situations mentioned above is eliminated, and the abnormal self-discharging battery cell is accurately identified.

[0075] In this example, the abnormal self-discharging cells in the abnormal outlier cells are determined by detecting whether the second change rate corresponding to the abnormal outlier cells whose first change rate is greater than the first threshold is positive. This solution eliminates the interference of other factors and can accurately identify the abnormal self-discharging cells.

[0076] In practical applications, the accuracy of obtaining the first change rate and the second change rate affects the accuracy of identifying the abnormal self-discharge battery cell. Therefore, in one example, obtaining the first change rate of the abnormal outlier battery cell in S2041 includes:

[0077] For each abnormal outlier battery cell, obtain the voltage of the abnormal outlier battery cell in each interval;

[0078] Linearly fit the voltage of the abnormal outlier battery cell in each interval, and take the slope of the fitted straight line as the first change rate.

[0079] In this example, the voltage of the abnormal outlier battery cell in each interval can be the average voltage of the battery cell in each interval, or the voltage at a certain quantile, but the way of selecting the voltage of all abnormal outlier battery cells in each interval is the same, that is, all select the average voltage, or all select the 25% quantile voltage.

[0080] Taking the average voltage as an example, in combination with the above example, the voltage in an interval is obtained in turn as V1, V2, V3, V4, V5, then the average voltage V = (V1+V2+V3+V4+V5) / 5, and the average voltage of each interval is obtained in this way. Figure 4 For an example of voltage change of the abnormal outlier battery cell in each interval, as shown in Figure 4 The first time period is from 0 to t9, then t1, t2,..., t9 divide the first time period into 10 intervals, such as 0-t1, the average voltage of the outlier voltage in each interval is linearly fitted, and the slope of the fitted straight line is the first change rate.

[0081] In this example, by linearly fitting the voltage in multiple intervals and taking the slope of the fitted straight line as the first change rate, the example obtains the first change rate based on the voltage reference of multiple intervals, which can avoid the influence of inaccurate individual voltage, and further improve the accuracy of the first change rate.

[0082] In another example, obtaining the second change rate corresponding to the abnormal outlier battery cell in S2041 includes:

[0083] For each abnormal outlier battery cell, subtract the voltage of the non-abnormal outlier battery cell in each interval from the voltage of the abnormal outlier battery cell in the corresponding interval to obtain the first voltage difference of the abnormal outlier battery cell in each interval;

[0084] Linearly fit the first difference of the abnormal outlier battery cell in each interval, and take the slope of the fitted straight line as the second change rate.

[0085] The present example illustrates the acquisition method of the second change rate. In the present example, the voltage of the non-anomalous outlier battery cell in each interval can be the average voltage of all non-anomalous outlier battery cells in each interval. For example, the voltage of each non-anomalous outlier battery cell in the interval is acquired in the same way as the voltage of the anomalous outlier battery cell in each interval in the above example, and will not be repeated here. Then the average voltage of all non-anomalous outlier battery cells in the interval is taken as the voltage of the non-anomalous outlier battery cell in the interval. Of course, the voltage of the non-anomalous outlier battery cell in each interval can also randomly select the voltage of a non-anomalous outlier battery cell in the interval.

[0086] Taking a scenario as an example, the voltage of the non-anomalous outlier battery cell in each interval is acquired. Taking one of the intervals as an example, the voltage of the non-anomalous outlier battery cell in the interval is U1, and the voltage of the anomalous outlier battery cell in the interval is U2. Then the first pressure difference ΔU = U1-U2. The ΔU under each interval is linearly fitted, and the slope of the fitted straight line is taken as the second change rate corresponding to the anomalous outlier battery cell.

[0087] In the present example, the voltages in multiple intervals are linearly fitted, and the slope of the fitted straight line is taken as the first change rate. In the present example, the voltage reference of multiple intervals is used to acquire the second change rate, which can avoid the influence of inaccurate individual voltage, and further improve the accuracy of the second change rate.

[0088] To further improve the accuracy of the identification of the anomalous self-discharge battery cell, in one example, the determination of the anomalous outlier battery cell according to the number of outliers of the battery cell comprises:

[0089] Divide the number of outliers of the battery cell by the number of feature points of the battery cell to obtain a first result.

[0090] If the first result exceeds a second threshold, the battery cell corresponding to the first result is taken as the anomalous outlier battery cell.

[0091] In actual application, each battery cell corresponds to a feature point in each interval, and thus the number of feature points of the battery cell is the same as the number of intervals in the first time period. The second threshold is a value set according to actual requirements. After the number of outliers of the battery cell is obtained, the number of outliers of the battery cell is divided by the number of feature points of the battery cell. If the first result is greater than the second threshold, it indicates that the voltage of the battery cell has a larger fluctuation range in the first time period compared with other battery cells, and thus the battery cell is regarded as an abnormal outlier battery cell. The accuracy of determining the abnormal outlier battery cell affects the accuracy and rate of subsequent identification of the abnormal self-discharge battery cell. In the example, the first result obtained by dividing the number of outliers of the battery cell by the number of feature points of the battery cell is compared with the second threshold to determine the abnormal outlier battery cell. In this way, the influence of the length of the first time period and the division of the intervals on the determination result is considered, and thus the accuracy of identification of the abnormal self-discharge battery cell can be further improved.

[0092] In the method for identifying abnormal self-discharge of a battery cell provided in the embodiment, for each battery cell in a battery pack, a feature value of the battery cell in each interval in a first time period is obtained, and for each interval, a feature point of the battery cell is determined according to the feature value of the battery cell. Outliers in each interval are obtained by clustering the feature points of the battery cells. Then, an abnormal outlier battery cell is determined according to the number of outliers of the battery cell, and an abnormal self-discharge battery cell is determined from the abnormal outlier battery cell according to the first change rate and the second change rate corresponding to the voltage of the abnormal outlier battery cell. In the scheme, the abnormal outlier battery cell in the battery pack is determined by a clustering algorithm, and the abnormal outlier battery cell is further judged according to the first change rate and the second change rate corresponding to the abnormal outlier battery cell, so as to avoid the influence of some interference factors, thereby accurately and quickly identifying the abnormal self-discharge battery cell.

[0093] Embodiment Two

[0094] Figure 5 A structural schematic diagram of a device for identifying abnormal self-discharge of a battery cell provided in Embodiment Two of the present application is shown in Figure 5 The device provided in the embodiment includes:

[0095] A first obtaining module 51 is configured to obtain, for each battery cell in a battery pack, a feature value of the battery cell in each interval in a first time period. The battery cell is in a static state in the first time period, and the feature value represents the voltage of the battery cell in the interval.

[0096] A second obtaining module 52 is configured to determine, for each interval, a feature point of the battery cell according to the feature value of the battery cell, and obtain outliers in each interval by clustering the feature points of the battery cells.

[0097] The first identification module 53 is configured to acquire the number of outliers of each battery cell according to the outliers in each interval, and determine an abnormal outlier battery cell according to the number of outliers of the battery cell.

[0098] The second identification module 54 is configured to determine an abnormal self-discharge battery cell from the abnormal outlier battery cell based on a first change rate of the voltage of the abnormal outlier battery cell in a first time period and a second change rate of a voltage difference between the voltage of the abnormal outlier battery cell and the voltage of a non-abnormal outlier battery cell in the battery pack except the abnormal outlier battery cell in the first time period.

[0099] In actual application, the battery cell abnormal self-discharge identification device provided by the embodiment can be a driving program, a program software, or a medium storing a related computer program, such as a U disk, etc. Alternatively, the identification device can also be an entity device integrated or installed with a related computer program, such as a chip, a smart terminal, a computer, etc.

[0100] Taking an actual scene as an example: in actual application, the battery cells in the battery pack have three states: a charging state, a static state and a discharge state, and the states of these battery cells are consistent. Under normal circumstances, the battery cells will also self-discharge, but the self-discharge is in trace amount, and the electric quantity discharged by each battery cell in the same time is similar, and will not cause a high voltage difference. The method provided by the embodiment mainly identifies the abnormal self-discharge battery cell. Since the voltage of the battery cell is unstable in the charging and discharging states, the identification of the abnormal self-discharge battery cell in the battery pack is performed when the battery cell is in the static state. When the state of the battery cell is determined, the running data of the battery pack is acquired, and the battery pack with a static state lasting for a time not less than the first time period is acquired based on the running data of the battery pack, and the battery pack is identified.

[0101] The first acquisition module 51 acquires the characteristic value of each battery cell in each interval in a first time period, wherein the battery cell is in a static state in the first time period, that is, the identification is performed in the static state. As an implementation manner,

[0102] The first acquisition module 51 is specifically configured to acquire the quartile of the voltage of each battery cell in each interval;

[0103] The first acquisition module 51 is specifically further configured to take the voltage at any two quartiles of the quartiles of the voltage in each interval as the characteristic value of the battery cell in each interval; and the characteristic value of each battery cell in each interval is the voltage at the same quartile.

[0104] The second acquisition module 52 determines the feature points according to the feature values. Each battery cell corresponds to a feature point in each interval. Then, the feature points of each battery cell are clustered to obtain the outlier points in each interval. That is, for each node, the feature points of each battery cell need to be clustered, and since the feature points are determined by the feature values representing the voltage in the interval, the battery cell corresponding to the outlier point can be understood as the battery cell with a larger voltage difference from other battery cells in the interval. Of course, this difference may be caused by abnormal self-discharge of the battery cell, or may be caused by detection or acquisition error.

[0105] In actual application, the second acquisition module 52 is specifically configured to cluster the feature points of each battery cell based on a density-based spatial clustering of applications with noise (DBSCAN) algorithm to obtain the outlier points in each interval. The DBSCAN algorithm is a density-based spatial clustering algorithm. The algorithm divides regions with sufficient density into clusters and can discover clusters of arbitrary shape in a spatial database with noise. The DBSCAN algorithm can automatically determine the number of categories without human definition, and can identify noise points, and has strong noise resistance. Therefore, clustering by DBSCAN can accurately identify outlier points, i.e., noise points.

[0106] The first identification module 53 acquires the number of outlier points of each battery cell to determine the abnormal outlier battery cell according to the number of outlier points. It can be understood that the abnormal outlier battery cell is a battery cell with a relatively large possibility of abnormal self-discharge. In actual application, the abnormal self-discharge battery cell is usually replaced or discarded, so to improve the accuracy of identification, further judgment needs to be made on the abnormal outlier battery cell to avoid waste of the battery cell.

[0107] The first change rate represents the change of the voltage of the abnormal outlier battery cell in the first time period, and the second change rate represents the change of the difference between the voltage of the abnormal outlier battery cell and the voltage of the non-abnormal outlier battery cell in the first time period. It can be understood that the smaller the first change rate is, the more the abnormal outlier battery cell discharges. The second change rate of the abnormal outlier battery cell can indicate the voltage difference between the abnormal outlier battery cell and other non-abnormal outlier battery cells. The larger the second change rate is, the larger the voltage difference between the abnormal outlier battery cell and other non-abnormal outlier battery cells is. In the present scheme, the second identification module 54 further determines the abnormal outlier battery cell by the first change rate and the second change rate to accurately identify the abnormal self-discharge battery cell.

[0108] To further improve the accuracy of identification of the abnormal self-discharge battery cell, on the basis of the above embodiments, in one example,

[0109] The second identification module 54 is specifically configured to acquire a first change rate corresponding to each abnormal outlier battery cell; if the first change rate is less than a first threshold, acquire a second change rate corresponding to the abnormal outlier battery cell, and detect whether the second change rate corresponding to the abnormal outlier battery cell is a positive value;

[0110] The second identification module 54 is specifically configured to determine that the abnormal outlier battery cell is an abnormal self-discharge battery cell if the second change rate corresponding to the abnormal outlier battery cell is a positive value.

[0111] In this example, the voltage of the battery cell in the static state only remains unchanged or gradually decreases, and does not increase, so the first change rate is not greater than zero, and therefore the first threshold is also set to a value not greater than zero. The first threshold can be set according to the characteristics of the battery cell. It can be understood that the smaller the first threshold is set, the fewer abnormal outlier battery cells that need to be further screened, and therefore the recognition efficiency is higher. The larger the first threshold is set, the more abnormal outlier battery cells that need to be further screened, but the probability of missing abnormal self-discharge battery cells is smaller. For example, the first threshold can be zero or other predetermined values.

[0112] In combination with an actual scenario, in actual application, when the first change rate of the abnormal outlier battery cell exceeds the first threshold, it indicates that the abnormal outlier battery cell has a relatively large discharge phenomenon, but there are many reasons for this situation. One possibility is that the battery cell abnormally discharges, and another possibility is that the first threshold is set too small, and the normal discharging battery cell is screened out. Of course, there is also a possibility that all battery cells in the battery pack are performing some discharge processing and are uniformly in a large discharge state. For the latter two reasons, although the battery cells have a discharge phenomenon, the voltages of the battery cells in the battery pack are in a consistent state, and therefore these two situations belong to the normal state of the battery pack. The first reason is what we need to identify. Because when the first change rate of the abnormal outlier battery cell exceeds the first threshold, whether the second change rate corresponding to the abnormal outlier battery cell is a positive value is detected to further determine whether the voltage of the abnormal outlier battery cell and the voltage of the non-abnormal outlier battery cell change consistently. It can be understood that if the second change rate is a positive value, it indicates that the difference between the voltage of the non-abnormal outlier battery cell and the voltage of the abnormal outlier battery cell is gradually increasing, and therefore the voltages of the two do not change consistently, that is, the abnormal outlier battery cell is an abnormal self-discharge battery cell. In this way, the discharge phenomenon of the abnormal outlier battery cell caused by the above-mentioned first two situations is excluded, and the abnormal self-discharge battery cell is accurately identified.

[0113] In this example, the second identification module determines the abnormal self-discharge battery cell in the abnormal outlier battery cell by detecting whether the second change rate corresponding to the abnormal outlier battery cell whose first change rate is greater than the first threshold is a positive value. The present scheme excludes the interference of other factors, and therefore the abnormal self-discharge battery cell can be accurately identified.

[0114] In actual application, the accuracy of obtaining the first change rate and the second change rate affects the accuracy of identifying the abnormal self-discharge battery cell. Therefore, in one example,

[0115] The second identification module 54 is specifically configured to obtain the voltage of each abnormal outlier battery cell in each interval.

[0116] The second identification module 54 is specifically configured to further perform linear fitting on the voltage of the abnormal outlier battery cell in each interval, and take the slope of the fitted straight line as the first change rate corresponding to the abnormal outlier.

[0117] In this example, the second identification module 54 obtains the voltage of the abnormal outlier battery cell in each interval. This voltage can be the average voltage of the battery cell in each interval, or the voltage at a certain quantile, but the voltage selection method of all abnormal outlier battery cells in each interval is the same, that is, the average voltage is selected or the 25% quantile voltage is selected.

[0118] In this example, the second identification module performs linear fitting on the voltage in multiple intervals, and takes the slope of the fitted straight line as the first change rate. In this example, the first change rate is obtained based on the voltage reference of multiple intervals, which can avoid the influence of inaccurate individual voltage, and further improve the accuracy of the first change rate.

[0119] In another example, the second identification module 54 is specifically configured to, for each abnormal outlier battery cell, subtract the voltage of the non-abnormal outlier battery cell in each interval from the voltage of the abnormal outlier battery cell in the corresponding interval to obtain the first voltage difference of the abnormal outlier battery cell in each interval.

[0120] The second identification module 54 is specifically configured to further perform linear fitting on the first difference of the abnormal outlier battery cell in each interval, and take the slope of the fitted straight line as the second change rate corresponding to the abnormal outlier battery cell.

[0121] This example exemplarily introduces the method for obtaining the second change rate. In this example, the voltage of the non-abnormal outlier battery cell in each interval can be the average voltage of all non-abnormal outlier battery cells in each interval. For example, the voltage of each non-abnormal outlier battery cell in the interval is obtained in the same way as the voltage of the abnormal outlier battery cell in each interval in the above example, which will not be described again. Then, the average voltage of all non-abnormal outlier battery cells in the interval is taken as the voltage of the non-abnormal outlier battery cell in the interval. Of course, the voltage of the non-abnormal outlier battery cell in each interval can also randomly select the voltage of a non-abnormal outlier battery cell in the interval.

[0122] In this example, by linearly fitting the voltages in multiple intervals and taking the slope of the fitted straight line as the first rate of change, the second rate of change is obtained based on the voltage reference of multiple intervals, which can avoid the influence of inaccurate individual voltages, and further improve the accuracy of the second rate of change.

[0123] To further improve the accuracy of identifying abnormal self-discharge battery cells, in one example,

[0124] The first identification module 53 is specifically configured to divide the number of outliers of the battery cell by the number of feature points of the battery cell to obtain a first result.

[0125] The first identification module 53 is specifically further configured to, if the first result exceeds a second threshold, take the battery cell corresponding to the first result as the abnormal outlier battery cell.

[0126] In actual application, each battery cell corresponds to a feature point in each interval, so the number of feature points of the battery cell is the same as the number of intervals in the first time period. The second threshold is a value set according to actual demand. After obtaining the number of outliers of the battery cell, if the first result obtained by dividing the number of outliers of the battery cell by the number of feature points of the battery cell is greater than the second threshold, it indicates that the fluctuation range of the voltage of the battery cell in the first time period is larger than that of other battery cells, and thus the battery cell is taken as the abnormal outlier battery cell. The accuracy of determining the abnormal outlier battery cell affects the accuracy and speed of identifying the abnormal self-discharge battery cell.

[0127] In this example, the first result obtained by dividing the number of outliers of the battery cell by the number of feature points of the battery cell is compared with the second threshold to determine the abnormal outlier battery cell, which takes into account the influence of the length of the first time period and the division of the intervals on the determination result, and thus this example can further improve the accuracy of identifying the abnormal self-discharge battery cell.

[0128] In the battery cell abnormal self-discharge identification device provided in this embodiment, the first acquisition module acquires the feature values of the battery cell in each interval in the first time period for each battery cell in the battery pack, the second acquisition module determines the feature point of the battery cell according to the feature value of the battery cell for each interval, and obtains the outlier in each interval by clustering the feature points of each battery cell; the first identification module determines the abnormal outlier battery cell according to the number of outliers of the battery cell, and the second identification module determines the abnormal self-discharge battery cell from the abnormal outlier battery cell by the first rate of change and the second rate of change corresponding to the voltage of the abnormal outlier battery cell. In this scheme, the abnormal outlier battery cell in the battery pack is determined by a clustering algorithm, and the abnormal outlier battery cell is further judged by the first rate of change and the second rate of change corresponding to the abnormal outlier battery cell, so as to avoid the influence of some interference factors, thereby accurately and quickly identifying the abnormal self-discharge battery cell.

[0129] Embodiment Three

[0130] Figure 6 A structural schematic diagram of an electronic device provided in Embodiment Three of the present application is shown in FIG. 3, which includes: Figure 6

[0131] A processor 291, the electronic device further includes a memory 292; and can further include a communication interface 293 and a bus 294. The processor 291, the memory 292, the communication interface 293 can communicate with each other through the bus 294. The communication interface 293 can be used for information transmission. The processor 291 can invoke the logic instructions in the memory 292 to execute the method of the above-mentioned embodiments.

[0132] In addition, the logic instructions in the memory 292 described above can be implemented in the form of a software function unit and sold or used as an independent product, which can be stored in a computer readable storage medium.

[0133] The memory 292 as a computer readable storage medium can be used to store software programs, computer executable programs, such as program instructions / modules corresponding to the method in the embodiments of the present application. The processor 291 executes the functions and data processing by running the software programs, instructions and modules stored in the memory 292, that is, implements the method in the above-mentioned method embodiments.

[0134] The memory 292 can include a program storage area and a data storage area, wherein the program storage area can store an operating system and at least one application required by a function; the data storage area can store data created according to the use of the terminal device, etc. In addition, the memory 292 can include a high-speed random access memory, and can also include a non-volatile memory.

[0135] The embodiments of the present application also provide a computer readable storage medium, the computer readable storage medium stores computer execution instructions, and the computer execution instructions are executed by the processor to implement the method in any embodiment.

[0136] Other embodiments of the application will be apparent to those skilled in the art from consideration of the specification and practice of the application disclosed herein. The specification and examples given are exemplary only and the true scope and spirit of the application is indicated by the claims. The true scope and spirit of the application are indicated by the claims.

[0137] ​It is to be understood that the application is not limited to the precise construction already described above and shown in the drawings, and that various modifications and changes can be made by those skilled in the art without departing from the scope of the application. The scope of the application should only be limited by the claims appended hereto.

Claims

1. A method for identifying abnormal self-discharge of an electric core, characterized by, The method comprises: obtaining feature values of each battery cell in each interval of a first time period; the battery cell is in a static state in the first time period, and the feature values represent the voltage of the battery cell in the interval; for each interval, determining a feature point of the battery cell according to the feature value of the battery cell, and obtaining an outlier point in each interval by clustering the feature points of each battery cell; obtaining the number of outlier points of each battery cell according to the outlier points in each interval, and determining an abnormal outlier battery cell according to the number of outlier points of the battery cell; determining an abnormal self-discharge battery cell from the abnormal outlier battery cell based on a first change rate of the voltage of the abnormal outlier battery cell in the first time period and a second change rate of a voltage difference between the voltage of the non-abnormal outlier battery cell and the voltage of the abnormal outlier battery cell in the first time period; wherein the first change rate is the slope of the fitted straight line of the voltage of the abnormal outlier battery cell in the first time period.

2. The method of claim 1, wherein, determining an abnormal self-discharge battery cell from the abnormal outlier battery cell based on a first change rate of the voltage of the abnormal outlier battery cell in the first time period and a second change rate of a voltage difference between the voltage of the non-abnormal outlier battery cell and the voltage of the abnormal outlier battery cell in the first time period, comprises: for each abnormal outlier battery cell, obtaining a corresponding first change rate of the abnormal outlier battery cell; if the first change rate is less than a first threshold, obtaining a corresponding second change rate of the abnormal outlier battery cell, and detecting whether the corresponding second change rate of the abnormal outlier battery cell is positive; if the corresponding second change rate of the abnormal outlier battery cell is positive, determining that the abnormal outlier battery cell is an abnormal self-discharge battery cell.

3. The method of claim 2, wherein, The method comprises: for each abnormal outlier battery cell, obtaining the voltage of the abnormal outlier battery cell in each interval; performing linear fitting on the voltage of the abnormal outlier battery cell in each interval, and taking the slope of the fitted straight line as the corresponding first change rate of the abnormal outlier battery cell.

4. The method of claim 2, wherein, The method comprises: for each abnormal outlier battery cell, subtracting the voltage of the abnormal outlier battery cell in the corresponding interval from the voltage of the non-abnormal outlier battery cell in each interval to obtain a first voltage difference of the abnormal outlier battery cell in each interval; performing linear fitting on the first voltage difference of the abnormal outlier battery cell in each interval, and taking the slope of the fitted straight line as the corresponding second change rate of the abnormal outlier battery cell.

5. The method of claim 1, wherein, The method comprises: for each battery cell, obtaining the fourth quantile of the voltage in each interval; taking the voltage at any two quantiles of the fourth quantile of the voltage in each interval as the feature value of the battery cell in each interval; the feature value of each battery cell in each interval is the voltage at the same quantile.

6. The method according to any one of claims 1 to 5, characterized in that, The method comprises: Divide the number of outliers of the battery cell by the number of feature points of the battery cell to obtain a first result; If the first result exceeds a second threshold, the battery cell corresponding to the first result is determined as the abnormal outlier battery cell.

7. The method according to any one of claims 1 to 5, characterized in that, The obtaining of the outlier in each interval by clustering the feature points of each battery cell comprises: The feature points of each battery cell are clustered based on a density clustering algorithm to obtain the outlier in each interval.

8. A device for identifying abnormal self-discharge of a battery cell, characterized in that: It comprises: The first acquisition module is configured to acquire, for each battery cell in the battery pack, a feature value of the battery cell in each interval in a first time period; The battery cell is in a static state in the first time period, and the feature value represents the voltage of the battery cell in the interval; The second acquisition module is configured to determine, for each interval, a feature point of the battery cell according to the feature value of the battery cell, and obtain the outlier in each interval by clustering the feature points of each battery cell; The first identification module is configured to acquire the number of outliers of each battery cell according to the outliers in each interval, and determine an abnormal outlier battery cell according to the number of outliers of the battery cell. The second identification module is configured to determine an abnormal self-discharge battery cell from the abnormal outlier battery cell based on a first change rate of the voltage of the abnormal outlier battery cell in the first time period and a second change rate of a voltage difference between the voltage of the abnormal outlier battery cell and the voltage of a non-abnormal outlier battery cell in the battery pack other than the abnormal outlier battery cell in the first time period. The first change rate is the slope of a fitting straight line of the voltage of the abnormal outlier battery cell in the first time period.

9. The apparatus of claim 8, wherein The second identification module is specifically configured to acquire, for each abnormal outlier battery cell, a first change rate corresponding to the abnormal outlier battery cell; if the first change rate is less than a first threshold, the second identification module is configured to acquire a second change rate corresponding to the abnormal outlier battery cell, and detect whether the second change rate corresponding to the abnormal outlier battery cell is positive. The second identification module is specifically further configured to determine that the abnormal outlier battery cell is an abnormal self-discharge battery cell if the second change rate corresponding to the abnormal outlier battery cell is positive.

10. The apparatus of claim 9, wherein The second identification module is specifically configured to acquire, for each abnormal outlier battery cell, the voltage of the abnormal outlier battery cell in each interval. The second identification module is specifically further configured to perform linear fitting on the voltage of the abnormal outlier battery cell in each interval, and take the slope of the fitted straight line as the first change rate corresponding to the abnormal outlier.

11. The apparatus of claim 9, wherein The second identification module is specifically configured to, for each abnormal outlier battery cell, subtract the voltage of the non-abnormal outlier battery cell in each interval from the voltage of the abnormal outlier battery cell in the corresponding interval to obtain a first voltage difference of the abnormal outlier battery cell in each interval. The second identification module is specifically further configured to perform linear fitting on the first difference of the abnormal outlier battery cell in each interval, and take the slope of the fitted straight line as the second change rate corresponding to the abnormal outlier battery cell.

12. The apparatus of claim 8, wherein, the first obtaining module is specifically configured to obtain, for each battery cell, quartiles of voltages of the battery cell in each interval; the first obtaining module is further configured to take voltages at any two quartiles of the quartiles of voltages in each interval as characteristic values of the battery cell in each interval; and the characteristic values of each battery cell in each interval are voltages at the same quartile.

13. The apparatus of any one of claims 8-12, wherein, the first identifying module is specifically configured to divide the number of outliers of the battery cell by the number of characteristic points of the battery cell to obtain a first result; and the first identifying module is further configured to take the battery cell corresponding to the first result as the abnormal outlier battery cell if the first result exceeds a second threshold.

14. The apparatus of any one of claims 8-12, wherein, the second obtaining module is specifically configured to cluster the characteristic points of each battery cell based on a density clustering algorithm to obtain outliers in each interval.

15. An electronic device, comprising: comprising: a processor, and a memory connected to the processor in communication; the memory stores computer-executable instructions; the processor executes the computer-executable instructions stored in the memory to implement the method of any one of claims 1-7.

16. A computer-readable storage medium, characterized in that, the computer-readable storage medium stores computer-executable instructions, and the computer-executable instructions are executed by the processor to implement the method of any one of claims 1-7.

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