A method, device, and electronic device for screening defective battery cells

Abstract

The present invention discloses a method, device and electronic device for screening defective battery cells. The method includes: collecting multiple battery indicators of a number of battery cells during the process from production start to production end, and creating a screening matrix based on the battery indicators of each battery cell; performing outlier analysis on the screening matrix to obtain outlier indicators in the battery indicators of each battery cell; and eliminating the defective battery cells corresponding to the outlier indicators from the number of battery cells according to the outlier indicators. The technical solution provided by the present invention collects corresponding battery indicators for each process in battery cell production and comprehensively analyzes them, further improving the accuracy of screening defective battery cells.

Description

Technical Field

[0001] The present invention relates to the field of new energy batteries, and in particular to a method, device and electronic device for screening defective battery cells. Background Art

[0002] In new energy battery manufacturing enterprises, screening defective battery cells is an essential step before the battery cells leave the factory. Defective battery cells usually exhibit abnormal phenomena such as capacity attenuation, increased internal resistance, decreased discharge characteristics, reduced rate performance, gas generation, liquid leakage, short circuit, deformation, thermal runaway, lithium plating, etc., which bring great troubles and losses to subsequent vehicle safety, after-sales operation and maintenance, etc.

[0003] Currently, the commonly used method for screening defective battery cells is screening based on the value range of the K value (in the lithium battery industry, the K value refers to the voltage drop of the battery per unit time, usually with the unit of mV / d, and is an index used to measure the self-discharge rate of lithium batteries), or outlier screening based on 3 times the standard deviation of the average value of the K value. However, the existing methods for screening defective battery cells are not accurate enough, and it is inevitable that some defective battery cells are not screened out. Therefore, how to further improve the accuracy of screening defective battery cells is an urgent problem to be solved. Summary of the Invention

[0004] In view of this, embodiments of the present invention provide a method, device and electronic device for screening defective battery cells, thereby further improving the accuracy of screening defective battery cells.

[0005] According to a first aspect, an embodiment of the present invention provides a method for screening defective battery cells, the method comprising: collecting a plurality of battery indexes of a plurality of battery cells during the process from production start to production end, and creating a screening matrix based on the battery indexes of each battery cell; performing outlier analysis on the screening matrix to obtain outlier indexes in the battery indexes of each battery cell; and removing the defective battery cells corresponding to the outlier indexes from the plurality of battery cells according to the outlier indexes.

[0006] Optionally, the collecting a plurality of battery indexes of a plurality of battery cells during the process from production start to production end includes: obtaining a plurality of battery cells of the same production batch and the same model; collecting at least one of the battery cell temperature, discharge DC internal resistance, actual insulation resistance value between the housing and the negative electrode, actual insulation resistance value between the positive electrode and the negative electrode, leakage rate, pre-welding resistance value of the internal electrode group of the battery cell, AC internal resistance for a preset number of times, K value under a preset environmental condition, open circuit voltage under a preset environmental condition, pressure of the battery cell pole assembly inserted into the housing, and battery cell leakage current during the production process of each battery cell as the battery index.

[0007] Optionally, creating a screening matrix based on the battery metrics of each battery cell includes: using the cell numbers of each battery cell as the row vector indices and each battery metric as the column vector indices to create the screening matrix.

[0008] Optionally, performing outlier analysis on the screening matrix to obtain outlier metrics in the battery metrics of each battery cell includes: based on the average values of the column vectors of the screening matrix, extracting an offset matrix from the screening matrix that offsets the average values of the column vectors; generating an outlier vector representing the degree of outlier in each metric based on the matrix product of the offset matrix and the matrix composed of the reciprocals of the elements of the average vector, where the average vector is the vector composed of the average values of the column vectors; determining the outlier metrics based on the battery metrics greater than a preset threshold in the outlier vector.

[0009] Optionally, based on the average values of the column vectors of the screening matrix, extracting an offset matrix from the screening matrix that offsets the average values of the column vectors includes: calculating the product of the average vector and the unit vector to obtain an average matrix; calculating the difference matrix between the screening matrix and the average matrix, and taking the absolute values of the elements in the difference matrix to generate the offset matrix.

[0010] Optionally, performing outlier analysis on the screening matrix to obtain outlier metrics in the battery metrics of each battery cell includes: determining whether each battery metric in the screening matrix falls within a corresponding preset metric range; determining the outlier metrics based on the battery metrics that do not fall within the corresponding metric range.

[0011] Optionally, performing outlier analysis on the screening matrix to obtain outlier metrics in the battery metrics of each battery cell includes: determining whether each battery metric in the screening matrix falls within the preset error range of the standard normal distribution of the column vector where it is located; determining the outlier metrics based on the battery metrics that do not fall within the preset error range of the standard normal distribution of the column vector where they are located.

[0012] According to a second aspect, an embodiment of the present invention provides a screening device for defective battery cells, where the device includes: a data preparation module for collecting multiple battery metrics of several battery cells from the start to the end of production, and creating a screening matrix based on the battery metrics of each battery cell; an outlier analysis module for performing outlier analysis on the screening matrix to obtain outlier metrics in the battery metrics of each battery cell; and a defect screening module for removing the defective battery cells corresponding to the outlier metrics from the several battery cells according to the outlier metrics.

[0013] According to a third aspect, an embodiment of the present invention provides an electronic device, including: a memory and a processor, the memory and the processor are communicatively connected to each other, the memory stores computer instructions, and the processor executes the computer instructions to execute the method described in the first aspect or any optional implementation manner of the first aspect.

[0014] According to a fourth aspect, an embodiment of the present invention provides a computer-readable storage medium, the computer-readable storage medium stores computer instructions, and the computer instructions are used to cause the computer to execute the method described in the first aspect or any optional implementation manner of the first aspect.

[0015] The technical solution provided by this application has the following advantages:

[0016] Compared with the prior art method of screening defective battery cells using a single index, in the embodiment of the present invention, for each process in the production of battery cells, corresponding battery indexes are collected, and then the collected battery indexes are formed into a matrix as a screening matrix for applying an outlier analysis algorithm to eliminate outlier indexes from the screening matrix. As long as one or several indexes of a battery cell are eliminated, it is determined that the battery cell is a defective battery cell, thereby improving the screening accuracy of defective battery cells.

[0017] In addition, in one embodiment, during the above-mentioned battery cell production process, the collected battery indexes at least include one of the battery cell temperature, discharge DC internal resistance, actual insulation resistance value between the housing and the negative electrode, actual insulation resistance value between the positive electrode and the negative electrode, leakage rate, pre-welding resistance value of the internal electrode group of the battery cell, AC internal resistance for a preset number of times, K value under a preset environmental condition, open circuit voltage under a preset environmental condition, pressure of the battery cell electrode assembly inserted into the housing, and battery cell leakage current. By measuring the above indexes, it is basically possible to cover the index anomalies caused by improper operations in each process, thereby comprehensively including the defective conditions of the battery cells. Based on the above indexes, a screening matrix is created and then outlier analysis is performed, further improving the screening accuracy of defective battery cells. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] The features and advantages of the present invention will be more clearly understood by referring to the accompanying drawings. The drawings are schematic and should not be construed as imposing any limitation on the present invention. In the drawings:

[0019] Figure 1 A schematic diagram of the steps of a method for screening defective battery cells in an embodiment of the present invention is shown;

[0020] Figure 2 A schematic diagram of the structure of a device for screening defective battery cells in an embodiment of the present invention is shown;

[0021] Figure 3The structural schematic diagram of an electronic device in an embodiment of the present invention is shown. Detailed implementation manners

[0022] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Apparently, the described embodiments are only a part rather than all of the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0023] Please refer to Figure 1 , in one embodiment, a method for screening defective battery cells specifically includes the following steps:

[0024] Step S101: Collect a plurality of battery cell indicators during the process from the start to the end of production of a plurality of battery cells, and create a screening matrix based on the battery cell indicators of each battery cell.

[0025] Step S102: Perform outlier analysis on the screening matrix to obtain the outlier indicators in the battery cell indicators of each battery cell.

[0026] Step S103: Eliminate the defective battery cells corresponding to the outlier indicators from the plurality of battery cells according to the outlier indicators.

[0027] Specifically, in order to further improve the screening accuracy of battery cell defects, considering that during the overall production process of battery cells, different abnormalities may occur due to improper production operations, and different abnormalities are often reflected by different indicators. Therefore, in this embodiment, a plurality of battery cell indicators are collected during the process from the start to the end of production of a batch of battery cells, and a screening matrix is formed based on the collected battery cell indicators of each battery cell. Then, an outlier algorithm is applied to analyze the outlier indicators that are far from most of the indicators in the screening matrix, and the battery cells corresponding to the outlier indicators are used as defective battery cells, and the battery cells corresponding to the non-outlier indicators are used as qualified battery cells. In the screening matrix, as long as a battery cell has one or several indicators to be eliminated, it is determined that the battery cell is a defective battery cell, thereby improving the screening accuracy of defective battery cells.

[0028] Specifically, in this embodiment, a number of battery cells of the same production batch and the same model are obtained to ensure that the collected data belongs to the data of the same production time, guarantee data comparability, and improve the accuracy of the outlier algorithm. Then, at least one of the battery cell temperature, discharge DC internal resistance, actual insulation resistance value between the housing and the negative electrode, actual insulation resistance value between the positive electrode and the negative electrode, leakage rate, pre-welding resistance value of the internal electrode group of the battery cell, AC internal resistance for a preset number of times, K value under preset environmental conditions, open circuit voltage under preset environmental conditions, pressure of the battery cell electrode group inserted into the housing, and battery cell leakage current during the production process of each battery cell is collected as a battery index. This is used to show, through the above indicators, the possible abnormal situations in each production process during the overall production process of the battery cell.

[0029] Specifically, in this embodiment, the battery cell temperature and discharge DC internal resistance of each battery cell are obtained through DC internal resistance testing; the actual insulation resistance value between the housing and the negative electrode, the actual insulation resistance value between the positive and negative electrode posts, and the leakage rate after the battery cell is packed are obtained through a single helium leak detection or Hi-Pot test (high potential test - electrical safety stress test); the AC internal resistance is tested 4 times; the open circuit voltage is tested 5 times under different environmental temperatures and SOC conditions; two groups of K values are tested again for different environmental temperatures and open circuit voltages; during the process of packing the battery cell electrode group, the pressure of the battery cell electrode group pushed into the housing is recorded; the leakage current after the battery cell is packed is measured. If an abnormality occurs inside the battery cell, the above internal resistance, temperature, K value, and voltage will show being too large or too small to varying degrees during the production process. If the external structure of the battery cell does not meet the standard, it can be shown through the pressure during insertion into the housing and the battery cell leakage current.

[0030] Based on this, in this embodiment, using the battery cell label of each battery cell as the index of the row vector, each row vector representing all the indicators of each battery cell, and each battery indicator as the index of the column vector, that is, the indicators in one column vector are all of the same type, a screening matrix is created, and the expression is as follows:

[0031]

[0032] In the formula, A nm represents the screening matrix, n represents the number of battery cells, and m represents the number of indicators.

[0033] Specifically, in one embodiment, the above step S102 specifically includes the following steps:

[0034] Step 1: Based on the average value of each column vector of the screening matrix, an offset matrix that offsets the average value of each column vector is extracted from the screening matrix.

[0035] Step 2: Generate an outlier vector representing the degree of outlier in each indicator based on the product of the offset matrix and the matrix composed of the reciprocals of the elements of the average vector. The average vector is the vector composed of the averages of each column vector.

[0036] Step 3: Determine the outlier indicators based on the battery indicators in the outlier vector that are greater than the preset threshold.

[0037] Specifically, in this embodiment, to determine the outlier indicators in the screening matrix, first calculate the average value of each column vector in the screening matrix, that is, calculate the average value of the same indicator of each battery cell to obtain multiple average values. Then, based on the obtained multiple average values, extract the offset matrix that offsets each average value from the screening matrix. The specific extraction steps are as follows:

[0038] 1. Calculate the product of the average vector and the unit vector to obtain the average matrix;

[0039] 2. Calculate the difference matrix between the screening matrix and the average matrix, and take the absolute value of each element in the difference matrix to generate the offset matrix.

[0040] Specifically, find the average value of each column vector in the screening matrix A nm and then form a row vector B 1m with 1 row and m columns to obtain the average vector. The calculation formula is as follows:

[0041]

[0042] After that, multiply the unit vector e n1 by B 1m to expand the dimension of the average vector and obtain an average matrix with the same dimension as A nm . Finally, calculate the difference between A nm and the average matrix to obtain the difference matrix D nm . Then, take the absolute value of each element d in D nm to obtain the offset matrix C nm . Thus, C nm can be used to represent the offset degree of each indicator in the screening matrix relative to the average value of each indicator.

[0043]

[0044] Finally, in this embodiment, calculate the product of C nm and the reciprocals of each element in B 1m to obtain a column vector r n1 with n rows. The column vector r n1That is, the outlier vector. Each battery cell in the outlier vector corresponds to only one comprehensive index, realizing the use of one comprehensive index to measure the deviation of each index of the battery cell relative to the corresponding average value. Thus, each battery cell is comprehensively analyzed using one comprehensive index, and for each battery cell compared with most battery cells, a conclusion is obtained as to whether the external performance of a single battery cell is significantly different.

[0045]

[0046] Finally, the outlier index is determined using the battery cell indicators greater than the preset threshold in the outlier vector. In this embodiment, the preset threshold is 1. For example, r n1 = {r1, r2, … rn}. If r1 is greater than 1, it is considered that the No. 1 battery cell in the current batch of battery cells is significantly different from other battery cells, and the No. 1 battery cell is a defective battery cell.

[0047] Specifically, in one embodiment, step S102 described above includes the following steps:

[0048] Step Four: Determine whether each battery cell indicator in the screening matrix falls within the corresponding preset index range.

[0049] Step Five: Determine the outlier index based on the battery cell indicators that do not fall within the corresponding index range.

[0050] Specifically, in this embodiment, a preset index range is set for each battery cell indicator in the screening matrix, such as [a, b], [c, d], etc. Determine whether each battery cell indicator in the screening matrix falls within the corresponding preset index range. If it does not fall within the corresponding index range, the corresponding battery cell indicator is considered an outlier index, and the battery cell data corresponding to this indicator is removed from the screening matrix, that is, all row vectors of this battery cell are removed, and the remaining data is considered the data of normal battery cells, thereby obtaining the battery cell labels of normal battery cells and further improving the accuracy of battery cell screening.

[0051] Specifically, in one embodiment, step S102 described above includes the following steps:

[0052] Step Six: Determine whether each battery cell indicator in the screening matrix falls within the preset error range of the standard normal distribution of the column vector where it is located.

[0053] Step Seven: Determine the outlier index based on the battery cell indicators that do not fall within the preset error range of the standard normal distribution of the column vector where they are located.

[0054] Specifically, in order to further improve the accuracy of defective battery cell screening, outlier battery cell indicators are also found from the screening matrix based on the principle of standard normal distribution, so as to mark defective battery cells. Assuming that the battery cell indicators conform to the standard normal distribution, the values of the same type of battery cell indicators should be concentrated on both sides of the mean value of this indicator. Therefore, the embodiments of the present invention obtain a preset error range based on the standard normal distribution. In this embodiment, the standard deviation and mean value of each column vector of the screening matrix are calculated, and then the range obtained by adding and subtracting three times the standard deviation from the mean value of each column vector is used as the standard, which can cover 99.7% of the data. It is judged in turn whether the battery cell indicators in each column vector fall within this range. If the current indicator does not fall within this range, it can be judged that the current indicator is an outlier indicator, and the battery cell corresponding to the row vector where the current indicator is located is a defective battery cell. Record the battery cell label of this row vector to complete the screening.

[0055] Specifically, in one embodiment, the methods of the above steps 1 to 7 are also applied simultaneously to determine the outlier indicators in the screening matrix, and then the defective battery cells corresponding to the outlier indicators are removed, thereby further improving the screening accuracy of defective battery cells. The following is an explanatory description with a specific application scenario embodiment.

[0056] For example: 100 battery cells of the same production batch and the same model are obtained, and then the battery cell indicators of each battery cell are collected respectively, including 11 indicators such as battery cell temperature, discharge DC internal resistance, actual insulation resistance value between the housing and the negative electrode, actual insulation resistance value between the positive electrode and the negative electrode, leakage rate, pre-welding resistance value of the internal electrode group of the battery cell, AC internal resistance for a preset number of times, K value under preset environmental conditions, open circuit voltage under preset environmental conditions, pressure of the battery cell electrode assembly installed in the housing, and battery cell leakage current. Then a screening matrix with a dimension of 100 * 11 is created:

[0057]

[0058] Among them, each row vector represents 11 indicators of the same battery cell, and each column vector represents the data shown by 100 battery cells in the same indicator.

[0059] First, based on the methods of steps 4 to 5, it is judged whether each indicator in the matrix falls within the corresponding preset indicator range. For example, x 1,1 is within the preset indicator range [a, b], but x 100,1 is greater than the upper limit b of the preset indicator range [a, b], then it is considered that there is an outlier indicator x in the battery cell indicators of the 100th battery cell 100,1 . Therefore, as long as there is one outlier indicator in the 100th battery cell, regardless of the performance of the remaining 10 indicators, the 100th battery cell is determined to be a defective battery cell and removed from the 100 battery cells in this batch.

[0060] Suppose that through the method of steps four to five, the 100th, 50th, and 10th battery cells are removed from the screening matrix, and there are still 97 row vectors left in the screening matrix, that is, the battery indicators corresponding to 97 battery cells. Then, using the method of steps six to seven above, calculate the mean and standard deviation of each column vector. Assuming that each indicator conforms to the standard normal distribution, the preset error range of the preset standard normal distribution is the mean plus or minus three times the standard deviation. Determine whether each indicator of the remaining 97 battery cells falls within the preset error range of the standard normal distribution corresponding to the column vector where it is located. For example, for the column vector of the first column, there is an indicator x 42,1 , which does not fall within the range of the mean plus or minus three times the standard deviation corresponding to the first column, then it is considered that x 42,1 belongs to an outlier indicator, and it is determined that the corresponding 42nd battery cell is a defective battery cell.

[0061] Suppose that after removing the 42nd battery cell, the data of other battery cells is okay, then enter the process of steps one to three. At this time, there are 96 row vectors left in the screening matrix, that is, the battery indicators corresponding to 96 battery cells, and the dimension of the screening matrix becomes 96*11. Based on the matrix transformation operations of steps one to three above, convert the screening matrix into an outlier vector r 96，1 = {r1, r2, … r99} (it should be noted that r1 to r99 represent 96 indicators in total. The battery cell numbers removed in the above steps are not shown in the current writing of the outlier vector except for the 100th, but they have been removed), and compare each indicator in the outlier vector r 96，1 with the preset threshold 1. It is found that the indicators greater than the preset threshold 1 are r2 and r99. Therefore, the 2nd and 99th battery cells are determined to be defective battery cells, and the remaining 94 battery cells are normal battery cells. Up to this point, the screening of defective battery cells is completed. By combining the above screening methods of battery cell indicators, the accuracy of battery cell screening is further improved.

[0062] Through the above steps, for the technical solution provided by this application, compared with the prior art method of screening defective battery cells using a single indicator, in the embodiments of the present invention, corresponding battery indicators are collected for each process in the production of battery cells, and then the collected battery indicators are formed into a matrix as a screening matrix to apply the outlier analysis algorithm, so as to remove outlier indicators from the screening matrix. As long as one or several indicators of a certain battery cell are removed, it is determined that the battery cell is a defective battery cell, thereby improving the screening accuracy of defective battery cells.

[0063] In addition, in one embodiment, the battery metrics collected during the production process of the above-mentioned battery cells at least include one of the battery cell temperature, discharge DC internal resistance, actual insulation resistance value between the housing and the negative electrode, actual insulation resistance value between the positive electrode and the negative electrode, leakage rate, pre-welding resistance value of the internal electrode group in the battery cell, AC internal resistance for a preset number of times, K value under preset environmental conditions, open circuit voltage under preset environmental conditions, pressure of the battery cell electrode group inserted into the housing, and battery cell leakage current. Basically, by measuring the above-mentioned metrics, it can cover the abnormal metrics caused by improper operation of each process, thereby comprehensively including the defect conditions of the battery cells. A screening matrix is created based on the above-mentioned metrics, and then outlier analysis is performed to further improve the accuracy of screening defective battery cells.

[0064] As Figure 2 shown, an embodiment of the present invention further provides a screening device for defective battery cells, and the device includes:

[0065] A data preparation module 101, configured to collect a plurality of battery metrics of several battery cells from the start to the end of production, and create a screening matrix based on the battery metrics of each battery cell. For detailed content, refer to the relevant description of step S101 in the above method embodiment, and details will not be repeated here.

[0066] An outlier analysis module 102, configured to perform outlier analysis on the screening matrix to obtain outlier metrics in the battery metrics of each battery cell. For detailed content, refer to the relevant description of step S102 in the above method embodiment, and details will not be repeated here.

[0067] A defect screening module 103, configured to remove the defective battery cells corresponding to the outlier metrics from several battery cells according to the outlier metrics. For detailed content, refer to the relevant description of step S103 in the above method embodiment, and details will not be repeated here.

[0068] A screening device for defective battery cells provided by an embodiment of the present invention is used to execute a screening method for defective battery cells provided by the above embodiment, and its implementation manner and principle are the same. For detailed content, refer to the relevant description of the above method embodiment, and details will not be repeated.

[0069] Through the collaborative cooperation of the above-mentioned various components, the technical solution provided by the present application, compared with the prior art method of screening defective battery cells using a single metric, in the embodiments of the present invention, corresponding battery metrics are collected for each process in the production of battery cells, and then the collected battery metrics are formed into a matrix as a screening matrix for applying the outlier analysis algorithm, so as to remove the outlier metrics from the screening matrix. As long as a certain battery cell has one or several removed metrics, it is determined that the battery cell is a defective battery cell, thereby improving the accuracy of screening defective battery cells.

[0070] In addition, in one embodiment, during the production process of the above-mentioned battery cells, the collected battery indicators at least include one of the cell temperature, discharge DC internal resistance, actual insulation resistance value between the housing and the negative electrode, actual insulation resistance value between the positive electrode and the negative electrode, leakage rate, pre-welding resistance value of the internal electrode group in the cell, AC internal resistance for a preset number of times, K value under preset environmental conditions, open-circuit voltage under preset environmental conditions, pressure of the cell electrode assembly inserted into the housing, and cell leakage current. Basically, by measuring the above indicators, it can cover the abnormal indicators caused by improper operation of each process, thereby comprehensively including the defect conditions of the battery cells. Based on the above indicators, a screening matrix is created, and then outlier analysis is performed to further improve the accuracy of defect cell screening.

[0071] Figure 3 FIG. shows an electronic device according to an embodiment of the present invention. The device includes a processor 901 and a memory 902, which can be connected through a bus or other means. Figure 3 Taking the connection through the bus as an example.

[0072] The processor 901 can be a central processing unit (CPU). The processor 901 can also be other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. chips, or a combination of the above types of chips.

[0073] The memory 902, as a non-transitory computer-readable storage medium, can be used to store non-transitory software programs, non-transitory computer-executable programs, and modules, such as the program instructions / modules corresponding to the methods in the above method embodiments. The processor 901 executes various functional applications and data processing of the processor by running the non-transitory software programs, instructions, and modules stored in the memory 902, that is, implementing the methods in the above method embodiments.

[0074] The memory 902 may include a program storage area and a data storage area. Among them, the program storage area may store an operating system and application programs required for at least one function; the data storage area may store data created by the processor 901 and the like. In addition, the memory 902 may include a high-speed random access memory, and may also include a non-transitory memory, such as at least one magnetic disk storage device, a flash memory device, or other non-transitory solid-state storage devices. In some embodiments, the memory 902 may optionally include a memory remotely provided relative to the processor 901, and these remote memories may be connected to the processor 901 through a network. Examples of the above-mentioned network include but are not limited to the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0075] One or more modules are stored in the memory 902 and, when executed by the processor 901, execute the methods in the above method embodiments.

[0076] For the specific details of the above electronic device, reference may be made to the corresponding relevant descriptions and effects in the above method embodiments for understanding, and details will not be repeated here.

[0077] Those skilled in the art can understand that to implement all or part of the processes in the above method embodiments, it can be completed by instructing relevant hardware through a computer program. The implemented program can be stored in a computer-readable storage medium, and when the program is executed, it may include the processes of the above method embodiments. Among them, the storage medium may be a magnetic disk, an optical disk, a read-only memory (ROM), a random access memory (RAM), a flash memory, a hard disk drive (abbreviation: HDD), or a solid-state drive (SSD), etc.; the storage medium may also include a combination of the above types of memories.

[0078] Although the embodiments of the present invention have been described in conjunction with the accompanying drawings, those skilled in the art can make various modifications and variations without departing from the spirit and scope of the present invention, and such modifications and variations fall within the scope defined by the appended claims.

Claims

1. A screening method for defective battery cells, characterized in that, The method includes: Collecting a plurality of battery indicators of several battery cells from the start to the end of production, and creating a screening matrix based on the battery indicators of each battery cell; creating the screening matrix based on the battery indicators of each battery cell includes: using the cell label of each battery cell as the row vector index and each battery indicator as the column vector index to create the screening matrix; Performing outlier analysis on the screening matrix to obtain outlier indicators in the battery indicators of each battery cell; performing outlier analysis on the screening matrix to obtain outlier indicators in the battery indicators of each battery cell includes: based on the average value of each column vector of the screening matrix, extracting an offset matrix that offsets the average value of each column vector from the screening matrix; generating an outlier vector representing the outlier degree in each indicator based on the matrix product of the offset matrix and the matrix composed of the reciprocals of the elements of the average vector, where the average vector is the vector composed of the average values of each column vector; determining the outlier indicators based on the battery indicators greater than a preset threshold in the outlier vector; extracting an offset matrix that offsets the average value of each column vector from the screening matrix based on the average value of each column vector of the screening matrix includes: calculating the product of the average vector and the unit vector to obtain an average matrix; calculating the difference matrix between the screening matrix and the average matrix, and taking the absolute value of each element in the difference matrix to generate the offset matrix; removing the defective battery cells corresponding to the outlier indicators from the several battery cells according to the outlier indicators.

2. The method according to claim 1, characterized in that, The collecting a plurality of battery indicators of several battery cells from the start to the end of production includes: Obtaining several battery cells of the same production batch and the same model; Collecting at least one of the cell temperature, discharge DC internal resistance, actual insulation resistance value between the housing and the negative electrode, actual insulation resistance value between the positive electrode and the negative electrode, leakage rate, pre-welding resistance value of the internal electrode group in the cell, AC internal resistance for a preset number of times, K value under a preset environmental condition, open circuit voltage under a preset environmental condition, pressure of the cell electrode assembly inserted into the housing, and cell leakage current during the production process of each battery cell as the battery indicator.

3. The method according to claim 1, characterized in that The performing outlier analysis on the screening matrix to obtain outlier indicators in the battery indicators of each battery cell includes: Judging whether each battery indicator in the screening matrix falls within the corresponding preset indicator range; Determining the outlier indicators based on the battery indicators that do not fall within the corresponding indicator range.

4. The method according to claim 1, wherein The performing outlier analysis on the screening matrix to obtain outlier indicators in the battery indicators of each battery cell includes: Judging whether each battery indicator in the screening matrix falls within the preset error range of the standard normal distribution of the column vector where it is located; Determining the outlier indicators based on the battery indicators that do not fall within the preset error range of the standard normal distribution of the column vector where they are located.

5. A screening device for defective battery cells, characterized in that, The device includes: A data preparation module, configured to collect a plurality of battery metrics during the process of several battery cells from production start to production end, and create a screening matrix based on the battery metrics of each battery cell; creating the screening matrix based on the battery metrics of each battery cell includes: using the cell numbers of each battery cell as row vector indices and each battery metric as column vector indices to create the screening matrix; An outlier analysis module, configured to perform outlier analysis on the screening matrix to obtain outlier metrics in the battery metrics of each battery cell; performing outlier analysis on the screening matrix to obtain outlier metrics in the battery metrics of each battery cell includes: based on the average values of each column vector of the screening matrix, extracting an offset matrix that offsets the average values of each column vector from the screening matrix; generating an outlier vector representing the outlier degree in each metric based on the matrix product of the offset matrix and the matrix composed of the reciprocals of each element of the average vector, where the average vector is the vector composed of the average values of each column vector; determining the outlier metrics based on the battery metrics greater than a preset threshold in the outlier vector; based on the average values of each column vector of the screening matrix, extracting an offset matrix that offsets the average values of each column vector from the screening matrix includes: calculating the product of the average vector and the unit vector to obtain an average matrix; calculating the difference matrix between the screening matrix and the average matrix, and taking the absolute value of each element in the difference matrix to generate the offset matrix; A defect screening module, configured to remove the defective battery cells corresponding to the outlier metrics from the several battery cells according to the outlier metrics.

6. An electronic device, characterized in that, Includes: A memory and a processor, which are communicatively connected to each other. The memory stores computer instructions, and the processor executes the computer instructions to execute the method according to any one of claims 1-4.

7. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions, and the computer instructions are used to cause the computer to execute the method according to any one of claims 1-4.

Images