Battery cell sorting method, device, electronic equipment and storage medium

Abstract

The present invention provides a cell sorting method, device, electronic device, and storage medium. The method comprises: obtaining parameters of each cell to be sorted, the parameters including the K value of the cell to be sorted; determining a K value pass threshold for the cell to be sorted based on a preset pass rate; performing a primary screening of the cells to be sorted based on the K value of the cell to be sorted and the K value pass threshold to obtain a first-pass cell among the cells to be sorted; clustering the first-pass cells based on the parameters of the cell to be sorted and a preset number of cell positions, and determining the cluster center of each cell position based on the clustering results; performing a secondary screening of the clustered first-pass cells based on the cluster center of each cell position and the screening threshold corresponding to each parameter in each cell position to obtain cells sorted corresponding to the cell position. The present invention can improve the consistency of cells in the same group after cell sorting.

Description

Technical Field

[0001] The present invention relates to the field of battery technology, and in particular to a battery cell sorting method, device, electronic equipment and storage medium. Background Art

[0002] A battery's performance is determined by its weakest cell. Good consistency maximizes the performance of all cells, slows cell aging, and reduces the burden on balancing circuits. Existing cell sorting schemes categorize cells into six groups based on capacity and internal resistance. Cells that fall outside these six groups are deemed unqualified, resulting in a low pass rate for the sorted cells. Alternatively, some methods use multiple indicators to sort cells sequentially. The quality of these sorting results is closely tied to the order in which the indicators are used, resulting in low consistency among cells grouped together. Therefore, it is crucial to determine how to sort cells to ensure both a high pass rate and good consistency. Summary of the Invention

[0003] Embodiments of the present invention provide a cell sorting method, device, electronic device, and storage medium to solve the problem of low consistency of cells in the same group obtained by existing cell sorting methods.

[0004] In a first aspect, an embodiment of the present invention provides a cell sorting method, comprising:

[0005] Obtain parameters of each cell to be sorted, including the K value of the cell to be sorted;

[0006] According to the preset qualified rate, determine the K value qualified threshold of the battery cells to be sorted;

[0007] According to the K value of the target battery cell and the K value qualified threshold, the battery cells to be sorted are initially screened to obtain the qualified battery cells among the battery cells to be sorted;

[0008] Based on the parameters of the cells to be sorted and the number of preset cell levels, the qualified cells are clustered and the cluster center of each cell level is determined according to the clustering results.

[0009] According to the clustering center of each battery cell gear and the screening threshold corresponding to each parameter in each battery cell gear, the qualified battery cells after clustering are screened twice to obtain the battery cells sorted corresponding to the battery cell gear.

[0010] In a possible implementation, after the cells to be sorted are initially screened, the method further includes:

[0011] Determine the power characteristics of the battery cells to be sorted;

[0012] If the battery cell to be sorted is a power type battery cell, the battery cell with an internal resistance not greater than a preset internal resistance threshold is determined as a primary qualified battery cell;

[0013] If the battery cells to be sorted are energy-type battery cells, the battery cells with a capacity not less than a preset capacity threshold are determined as primary qualified battery cells.

[0014] In a possible implementation, before clustering the qualified primary cells, the following steps are further included:

[0015] according to Normalize the various parameters of a qualified battery cell;

[0016] And according to the preset importance of each parameter, update the normalized value of each parameter;

[0017] Among them, z is the normalized value of each sample value of a parameter of a qualified battery cell, x is the sample value of the parameter of a qualified battery cell, μ is the mean of the sample values ​​of the parameter of a qualified battery cell, and σ is the standard deviation of the sample values ​​of the parameter of a qualified battery cell.

[0018] In one possible implementation, clustering the qualified cells based on the parameters of the cells to be sorted and the number of preset cell levels includes:

[0019] Step 1: Set the relevant parameters for clustering the qualified cells, including the number of iterations and the maximum number of iterations;

[0020] Step 2: According to the preset number of battery cell gears, a corresponding number of battery cells are randomly determined from the qualified battery cells, and the parameters of the determined battery cells are used as the cluster centers of each gear;

[0021] Step 3: Calculate the Euclidean distance between each qualified cell and each cluster center based on the parameters of the cells to be sorted, and cluster each cell to the nearest cell position;

[0022] Step 4: Calculate the average parameter value of each battery cell in each battery cell gear, and use the average parameter value as the new cluster center;

[0023] Step 5: Determine whether the number of iterations has reached the maximum number of iterations. If not, increase the number of iterations by one and jump to step 3.

[0024] Step 6: If the number of iterations reaches the maximum number of iterations, the iteration is stopped to obtain the clustering result of the qualified battery cells.

[0025] In a possible implementation, the parameters of the target battery cell also include battery cell capacity, battery cell AC internal resistance, battery cell DC internal resistance, battery cell voltage, and coulombic efficiency.

[0026] In a possible implementation, the calculation process of the screening threshold corresponding to each parameter in each battery cell gear is as follows:

[0027] Based on the clustering results and the cluster centers of each cell gear, the standard deviation of the difference between each parameter of all cells in each cell gear and the corresponding parameters of the corresponding cluster centers is calculated;

[0028] A value three times the standard deviation of each parameter in each battery cell gear is determined as a screening threshold value of the corresponding parameter in the corresponding battery cell gear.

[0029] In one possible implementation, secondary screening is performed on the clustered primary qualified cells according to the cluster centers of the respective cell levels and the screening thresholds corresponding to the respective parameters in the respective cell levels, including:

[0030] Calculate the distance between the parameters of the battery cells in each battery cell gear and the parameters of the corresponding battery cell gear cluster center;

[0031] The cells whose distance is not greater than the screening threshold among the qualified cells are retained to obtain the cells sorted corresponding to the cell levels.

[0032] In a second aspect, an embodiment of the present invention provides a cell sorting device, comprising:

[0033] An acquisition module is used to obtain the parameters of each battery cell to be sorted, including the K value of the battery cell to be sorted;

[0034] A determination module is used to determine the K value qualification threshold of the battery cells to be sorted according to a preset qualification rate;

[0035] The primary screening module is used to perform a primary screening on the cells to be sorted according to the K value and K value qualified threshold of the cells to be sorted, and obtain the qualified cells among the cells to be sorted;

[0036] A clustering module is used to cluster qualified cells based on the parameters of the cells to be sorted and the number of preset cell levels, and to determine the cluster center of each cell level according to the clustering results;

[0037] The secondary screening module is used to perform secondary screening on the clustered qualified primary cells according to the clustering center of each cell gear and the screening threshold corresponding to each parameter in each cell gear, and obtain the cells sorted corresponding to the cell gear.

[0038] In a third aspect, an embodiment of the present invention provides an electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the steps of the method according to the first aspect or any possible implementation of the first aspect are implemented.

[0039] In a fourth aspect, an embodiment of the present invention provides a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, it implements the steps of the method of the first aspect or any possible implementation method of the first aspect.

[0040] An embodiment of the present invention provides a battery cell sorting method, which obtains the parameters of the battery cells to be sorted and obtains qualified battery cells through screening according to a K value qualified threshold determined by a preset qualified rate, thereby improving the qualified rate of the qualified products and increasing the number of battery cells entering the secondary screening; by performing cluster analysis, a suitable gear can be determined for the battery cells to be sorted, and the battery cells to be sorted can be clustered, so that each battery cell to be sorted can be divided into a corresponding gear, ensuring that the consistency of the battery cells in the gear is good, and then a secondary screening is performed through the screening threshold, so that the battery cells with unqualified battery cell parameters and the battery cells with low consistency with most of the battery cells in the gear can be removed, and the battery cells that are finally qualified in the sorting can be obtained, that is, the consistency of the battery cells in the same gear can be improved, thereby achieving the improvement of the qualified rate of battery cell sorting and the consistency of the battery cells in the same gear. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0042] Figure 1 This is a flow chart of the implementation of the cell sorting method provided by an embodiment of the present invention;

[0043] Figure 2 is a probability cumulative graph of a normal distribution provided by an embodiment of the present invention;

[0044] Figure 3 is a probability cumulative graph of the Weibull distribution provided by an embodiment of the present invention;

[0045] Figure 4 is a probability cumulative distribution diagram of AC internal resistance provided by an embodiment of the present invention;

[0046] Figure 5 This is a cell level diagram for cluster analysis provided by an embodiment of the present invention;

[0047] Figure 6 This is a diagram of the battery cell positions in a traditional grading method provided by an embodiment of the present invention;

[0048] Figure 7 1 is a schematic structural diagram of a cell sorting device provided by an embodiment of the present invention;

[0049] Figure 8 is a schematic diagram of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0050] In the following description, specific details such as particular system structures and techniques are provided for purposes of illustration, not limitation, to facilitate a thorough understanding of the embodiments of the present invention. However, it will be apparent to those skilled in the art that the present invention may be practiced in other embodiments without these specific details. In other cases, detailed descriptions of well-known systems, devices, circuits, and methods are omitted so as not to obscure the description of the present invention with unnecessary detail.

[0051] In order to make the purpose, technical solutions and advantages of the present invention more clear, specific embodiments will be described below with reference to the accompanying drawings.

[0052] Figure 1 The implementation flow chart of the cell sorting method provided in the embodiment of the present invention is detailed as follows:

[0053] Step 101: Acquire parameters of each battery cell to be sorted, including the K value of the battery cell to be sorted.

[0054] The K value is the average daily drop in cell voltage over the first 14 days after the cell leaves the production line. If a cell's K value is too high, it indicates a high self-discharge rate and a possible short circuit, making it potentially substandard. Therefore, it's important to obtain the K value of the cells to be sorted to facilitate screening.

[0055] In addition, the parameters of the battery cells to be sorted may also include battery cell capacity, battery cell AC internal resistance, battery cell DC internal resistance, battery cell voltage and coulombic efficiency.

[0056] In this embodiment, the required cell parameters are determined according to the actual sorting requirements of the cells to be sorted.

[0057] Step 102: Determine the K value qualification threshold of the battery cells to be sorted according to a preset qualification rate.

[0058] Specifically, the impact of the K value on the short circuit and life of the battery cell is a qualitative analysis, not a quantitative analysis. Therefore, determining the qualified threshold of the K value based on a reasonable qualified rate can increase the number of battery cells entering the secondary screening and improve the qualified rate of the final sorting.

[0059] Step 103 : performing a primary screening on the cells to be sorted according to the K values ​​and K value qualified thresholds of the cells to be sorted, and obtaining primary qualified cells among the cells to be sorted.

[0060] Specifically, an initial screening is performed based on the K value to screen out the battery cells with too large a K value, that is, unqualified filter elements are first screened out to avoid the parameters of unqualified filter elements from affecting subsequent cluster analysis, so that cluster analysis can better perform cluster analysis on qualified filter elements.

[0061] Step 104 : clustering the qualified cells based on the parameters of the cells to be sorted and the number of preset cell levels, and determining the cluster center of each cell level according to the clustering results.

[0062] Specifically, cluster analysis is performed on the qualified battery cells to obtain clustering results, and the qualified battery cells are classified. The cluster centers of the obtained battery cell levels are also the centers of the parameters of each group after classification. At this time, only the classified battery cells are obtained, but there are still battery cells in each group with unqualified parameters, or battery cells that are far away from each level. Therefore, further screening is required to ensure that the battery cell parameters are qualified and consistent.

[0063] Step 105 : performing secondary screening on the clustered primary qualified cells according to the cluster centers of the respective cell levels and the screening thresholds corresponding to the respective parameters in the respective cell levels, to obtain the cells sorted corresponding to the cell levels.

[0064] Specifically, according to the screening thresholds corresponding to the parameters in each battery cell gear and the previously determined cluster center, the battery cells in each gear are screened, and the battery cells exceeding the screening threshold, that is, unqualified battery cells or battery cells far away from the cluster center are screened out to obtain qualified battery cells corresponding to the battery cell gear, thereby obtaining battery cells with better consistency with the gear.

[0065] The embodiment of the present invention obtains the parameters of the battery cells to be sorted, and obtains the qualified battery cells by screening according to the K value qualified threshold determined by the preset qualified rate, thereby improving the qualified rate of the qualified products and increasing the number of battery cells entering the secondary screening; by performing cluster analysis, the appropriate gear can be determined for the battery cells to be sorted, and the battery cells to be sorted can be clustered, and each battery cell to be sorted can be divided into the corresponding gear to ensure that the consistency of the battery cells in the gear is good. Then, a secondary screening is performed through the screening threshold, and the battery cells with unqualified battery cell parameters and the battery cells with low consistency with most of the battery cells in the gear can be removed to obtain the finally qualified battery cells, that is, the consistency of the battery cells in the same gear can be improved, thereby achieving the improvement of the qualified rate of battery cell sorting and the consistency of the battery cells in the same gear.

[0066] In one possible implementation, after the initial screening of the battery cells to be sorted, it also includes: judging the power characteristics of the battery cells to be sorted; if the battery cells to be sorted are power-type battery cells, determining the battery cells with internal resistance not greater than a preset internal resistance threshold as primary qualified battery cells; if the battery cells to be sorted are energy-type battery cells, determining the battery cells with capacity not less than a preset capacity threshold as primary qualified battery cells.

[0067] In this embodiment, the power characteristics of the battery cells are different, and the important parameters for the battery cells are also different. Specifically, for power-type battery cells, the size of the internal resistance is an important parameter. A larger internal resistance is not conducive to the operation of the power-type battery cell. Therefore, the battery cells with an internal resistance greater than a preset internal resistance threshold are screened out, and the battery cells with a smaller internal resistance are retained. For energy-type battery cells, the size of the capacity is an important parameter. A smaller capacity cannot meet the requirements of the energy-type battery cell operation. Therefore, the battery cells with a capacity less than a preset capacity threshold are screened out, and the battery cells with a larger capacity are retained. This ensures that the retained battery cells can meet the corresponding work requirements. At the same time, it can also screen out battery cells with more extreme parameters, improving the consistency of the battery cells.

[0068] In a possible implementation, before clustering the qualified cells, the method further includes: Normalize the various parameters of the qualified battery cells; and update the normalized values ​​of the various parameters according to the preset importance of the various parameters; wherein z is the normalized value of each sample value of a parameter of the qualified battery cells, x is the sample value of the parameter of the qualified battery cells, μ is the mean of the sample values ​​of the parameter of the qualified battery cells, and σ is the standard deviation of the sample values ​​of the parameter of the qualified battery cells.

[0069] In this embodiment, the units of the battery cell parameters are different, and the corresponding parameter values ​​are not in the same order of magnitude. Based on this, clustering is performed, and the parameters with larger values ​​also account for a larger proportion. It may be the case that only one parameter is actually considered. For example, the parameters include capacity and internal resistance, etc. The value of capacity may be 1000, while the value of voltage is smaller. Accordingly, capacity will occupy an absolute dominant position, and internal resistance actually does not play a role. Therefore, it is necessary to normalize the parameters so that the parameters are in the same order of magnitude to ensure that each parameter is valid, thereby improving the consistency of the finally sorted battery cells.

[0070] In addition, for different types or different uses of battery cells, the importance of the parameters for screening battery cells is also different. Therefore, higher weights can be assigned to the corresponding parameters according to their importance, so that the parameters play a greater role in subsequent sorting. For example, when the screening parameters include capacity, voltage, and internal resistance, and the internal resistance and voltage are important parameters, higher weights can be assigned to the internal resistance and voltage; specifically, the capacity can be 0.9 times the normalized capacity, the internal resistance can be 2 times the normalized internal resistance, and the voltage can be 1.5 times the normalized voltage; the capacity can also be 1.1 times the normalized capacity, the internal resistance can be 1.6 times the normalized internal resistance, and the voltage can be 1.8 times the normalized voltage; accordingly, when the importance of each parameter is the same, no weight can be assigned, or the weights assigned to each parameter can be consistent, and the weights can be determined based on the importance of the parameters of the actual battery cell.

[0071] In one possible implementation, clustering the qualified cells based on the parameters of the cells to be sorted and the number of preset cell levels includes:

[0072] Step 1: Set the relevant parameters for clustering the qualified cells, including the number of iterations and the maximum number of iterations;

[0073] Step 2: According to the preset number of battery cell gears, a corresponding number of battery cells are randomly determined from the qualified battery cells, and the parameters of the determined battery cells are used as the cluster centers of each gear;

[0074] Step 3: Calculate the Euclidean distance between each qualified cell and each cluster center based on the parameters of the cells to be sorted, and cluster each cell to the nearest cell position;

[0075] Step 4: Calculate the average parameter value of each battery cell in each battery cell gear, and use the average parameter value as the new cluster center;

[0076] Step 5: Determine whether the number of iterations has reached the maximum number of iterations. If not, increase the number of iterations by one and jump to step 3.

[0077] Step 6: If the number of iterations reaches the maximum number of iterations, the iteration is stopped to obtain the clustering result of the qualified battery cells.

[0078] In this embodiment, qualified battery cells are clustered by a clustering algorithm, and corresponding cluster centers are determined according to a preset number of battery cell gears, thereby dividing the battery cells to be sorted into a corresponding number of groups; the distance from each battery cell to the cluster center is calculated, and the Euclidean distance can be calculated according to the parameters, so that the battery cell gear to which each battery cell belongs can be determined according to the distance, and then a new cluster center is determined according to the battery cell gear, ensuring that the cluster center is located at the center of the gear, so that the sum of the distances from each battery cell to the cluster center is minimized; the aforementioned process of determining the battery cell gear and the new cluster center is repeated, so that the sum of the distances of all battery cells to the cluster centers of their corresponding gears is minimized, thereby completing the classification of qualified battery cells, so that each battery cell in the qualified battery cell is divided into a suitable gear, and the Euclidean distance obtained according to the parameters is determined, which can ensure that the battery cells in each battery cell gear have good consistency.

[0079] In one possible implementation, the Euclidean distance is calculated as:

[0080]

[0081] Where Dist_N is the Euclidean distance from each cell to the corresponding cluster center, i represents the i-th parameter of each cell or cluster center, n represents the total number of n parameters of each cell or cluster center, and x i is the i-th parameter of each cell, y i is the i-th parameter of the corresponding cluster center.

[0082] The Euclidean distance is calculated based on the parameters of each battery cell and the parameters of each cluster center, that is, the distance between each battery cell and the center of each gear is calculated. The smaller the distance, the closer the battery cell is to the center of the battery gear, and the more consistent the battery cell is with the battery gear. The battery gear to which the battery cell belongs is determined based on the shortest distance, thereby realizing the classification of the battery cells.

[0083] In one possible implementation, the calculation process of the screening threshold corresponding to each parameter in each battery cell gear is as follows: based on the clustering results and the cluster center of each battery cell gear, the standard deviation of the difference between each parameter of all battery cells in each battery cell gear and the corresponding parameter of the corresponding cluster center is calculated; and three times the value of the standard deviation of each parameter in each battery cell gear is determined as the screening threshold of the corresponding parameter in the corresponding battery cell gear.

[0084] In this embodiment, the standard deviation of the parameters of each battery cell in each battery cell position is calculated according to the clustering results, and the screening threshold of the secondary screening is determined based on the calculated standard deviation; in each battery cell position, if the difference between the parameter of the battery cell and the corresponding parameter of the cluster center exceeds three times the standard deviation of the parameter in the battery cell position, it means that the battery cell is far away from the cluster center, which will seriously affect the consistency of the finally sorted battery cells. Therefore, three times the standard deviation of the parameter in the battery cell position is determined as the screening threshold.

[0085] Specifically, σ1 is the standard deviation of a parameter in a certain battery cell gear, and three times the value of the standard deviation is the screening threshold of the corresponding parameter in the corresponding battery cell gear. That is to say, the determined screening threshold is 3σ1. For example, the standard deviation of the capacity parameters of all battery cells in a certain battery cell gear is 100mAh, then the screening threshold of the capacity parameter in the battery cell gear during the secondary screening is determined to be 3σ1, that is, 300mAh.

[0086] Furthermore, because normalization processing is performed on each parameter during cluster analysis, during secondary screening, the screening threshold of the secondary screening can also be determined based on the normalized parameters.

[0087] In one possible implementation, the clustered qualified cells are subjected to a secondary screening based on the clustering centers of each cell position and the screening thresholds corresponding to the parameters in each cell position, including: calculating the distance between the parameters of the cells in each cell position and the parameters of the clustering center of the corresponding cell position; retaining the qualified cells whose distance is not greater than the screening threshold among the qualified cells, and obtaining the cells sorted corresponding to the cell position.

[0088] In this embodiment, the distance between the parameters of the battery cell and the corresponding parameters of the corresponding cluster center is calculated, that is, the difference between the parameters of the battery cell and the corresponding parameters of the center of the corresponding battery cell gear is determined. If the obtained distance is large, it means that the distance between the parameter of the battery cell and the corresponding parameter of the corresponding cluster center is far, and the difference between the parameter of the battery cell and the corresponding parameter of the center of the corresponding gear is large, which may affect the consistency of the final sorted battery cells; and if the obtained distance is small, it means that the parameter of the battery cell is close to the parameter corresponding to the cluster center, and the difference between the parameter of the battery cell and the parameter corresponding to the center of the corresponding gear is small.

[0089] Specifically, σ1 is the standard deviation of a parameter in a certain battery cell position, and the battery cells that are three times the standard deviation away from the parameter at the cluster center are retained, that is, the distance between the parameter of the battery cells in the battery cell position and the parameter at the cluster center is 3σ1. For example, the standard deviation of the capacity parameters of all battery cells in a certain battery cell position is 100mAh, and the screening threshold of the capacity parameter of the battery cell position in the secondary screening is determined to be 3σ1, that is, 300mAh. Then, the difference between the capacity parameter of the battery cells in the battery cell position and the capacity parameter of the cluster center is calculated, and the battery cells with the difference not greater than 300mAh are retained, and the battery cells with the difference greater than 300mAh are screened out, and each parameter of all battery cells in the battery cell position is screened; further, because each parameter is normalized during cluster analysis, secondary screening can also be performed on the normalized parameters during secondary screening.

[0090] Based on the screening thresholds corresponding to the parameters in each cell gear, it is determined whether the distance between the parameters of the cells in each gear and the corresponding parameters of the cluster center meets the requirements, that is, whether the parameters of the cells are qualified, so as to screen the cells in each gear, thereby filtering out cells with large parameter differences from the cluster center, that is, unqualified cells, and ensuring good consistency of the cells in each gear. At the same time, secondary screening can remove outliers of each sample parameter, limit the maximum and minimum values ​​of the cell parameters in each cell gear, and facilitate subsequent battery management systems to analyze according to the worst-case scenario.

[0091] For example, the cell sorting method proposed in this application is verified using 7377 power cells shipped from the factory.

[0092] Determine the CDF probability cumulative distribution diagram based on the K value and cumulative distribution probability of the battery cell, see Figure 2 The probability plot of the normal distribution is shown, where the Y axis is deformed so that the normal distribution becomes Figure 2 The straight line in Figure 3 This is a probability plot of the Weibull distribution, where the X-axis and Y-axis are deformed so that the Weibull distribution becomes Figure 3 It can be seen that the K value of the sample battery cell does not completely obey the normal distribution or Weibull distribution. Figure 2 and Figure 3 It can be seen that when the K value of the sample battery cell is greater than 0.6, the slope of the sample curve is smaller than the normal distribution curve, indicating that the battery cells distributed in this area are less than those under the normal distribution or Weibull distribution conditions. This may be due to serious outliers caused by systematic deviations. Therefore, the screening threshold is selected in this area.

[0093] Based on experience and the probability cumulative distribution diagram, it can be determined that the qualified rate is 95%, that is, the cumulative probability is 0.95. At this time, the corresponding K value is 1.1195, and the K value qualified threshold is determined to be 1.1195, thereby retaining 95% of the battery cells for further screening.

[0094] In this embodiment, power type battery cells are selected, so screening is performed based on the internal resistance of each battery cell. Specifically, the preset internal resistance threshold is determined to be an AC internal resistance (ACR) of 0.7, that is, battery cells with an AC internal resistance greater than 0.7 are screened out, and battery cells with an AC internal resistance not greater than 0.7 are determined as qualified battery cells.

[0095] For details, see Figure 4 From the probability cumulative distribution diagram of the AC internal resistance shown, it can be seen that when the preset internal resistance threshold is 0.7, the cumulative probability in the battery cells after K value screening is 0.98949, corresponding to 6842 battery cells determined as qualified battery cells at one time, and the qualified rate for the initial battery cells is 92.75%.

[0096] Furthermore, to perform cluster analysis on qualified cells, it is necessary to first normalize the selected parameters and then perform cluster analysis. Specifically, for the convenience of display and understanding, capacity and internal resistance are selected as screening parameters, and the data are not normalized. The final clustering results are shown in Figure 5 The cluster analysis gear diagram provided by the embodiment of the present invention is shown, and Figure 6 The gear position diagram of the traditional gear division method is shown.

[0097] Depend on Figure 5 and Figure 6 It can be seen that in Figure 5 There is also a part of battery cells at the top, which are divided into gears using cluster analysis. The upper and lower limits of capacity and internal resistance are not directly set, which enables more battery cells to be divided into gears and enter the next step of screening, which can improve the final battery cell qualification rate. In addition, using the cluster analysis method, the distance from each category to the cluster center is smaller, and the dispersion of battery cell parameters is smaller, so that the consistency of batteries in each battery cell gear can be better.

[0098] Furthermore, after the grouping is completed, the battery is generally managed according to the parameters of the cluster center of each cell gear. The closer the cell parameters of each gear are to the center point, the better the corresponding parameters are. In other words, the closer the cell parameters are to the cluster center, the better the corresponding parameters are. Therefore, a secondary screening can be performed on cells far from the cluster center. The comparison of the qualified cells at different secondary screening thresholds is shown in Table 1:

[0099] Table 1 Comparison of two-dimensional parameter secondary screening

[0100]

[0101] As shown in Table 1, the qualified rates obtained by clustering sorting with different secondary screening thresholds are significantly better than those of the traditional marking method. The normalized average parameter distance is the average of the normalized Euclidean distances from the cells in each gear to the corresponding cluster center. The normalized average parameter distances obtained by cluster analysis and secondary screening are also significantly better than the traditional marking method, which also shows that the consistency of the cells in the cell gear is good. Among them, σ1 is the standard deviation of each parameter of the cells in each cell gear before secondary screening, and σ2 is the standard deviation of each qualified cell to its respective cluster center after the cell sorting is completed. The standard deviation of the normalized Euclidean distance from the center; specifically, in this embodiment, the screening threshold for secondary screening is determined to be three times the standard deviation of the various parameters of all battery cells in each battery cell gear and the corresponding parameters of the distance cluster center, that is, the screening threshold is 3σ1. For example, the standard deviation σ1 corresponding to the capacity parameters of the battery cells in a certain battery cell gear is 100mAh, then the secondary screening threshold is determined to be 300mAh. Further, the screening threshold of each parameter in each battery cell gear can be determined, and the battery cells are secondary screened according to the screening threshold to obtain the battery cells finally sorted corresponding to the battery cell gear.

[0102] Furthermore, relying solely on capacity and internal resistance cannot guarantee that the consistency of the battery cell can be maintained for a long time. It is also necessary to consider the battery cell K value, battery cell DC internal resistance, battery cell voltage and coulombic efficiency. For example, the battery cell parameters selected in this embodiment include K value, battery cell line voltage, battery cell capacity, battery cell DC internal resistance, and battery cell AC internal resistance, and the data of the secondary screening of the five-dimensional parameters are further determined, as shown in Table 2:

[0103] Table 2 Secondary screening table of five-dimensional parameters

[0104]

[0105] It can be seen from Table 2 that the screening threshold of the secondary screening determined in this embodiment is 3σ1, that is, the difference between the various parameters of the battery cells and the parameters corresponding to the cluster center is not greater than three times the standard deviation of the battery cells. The qualified rate obtained by cluster analysis and screening through five-dimensional parameters is 90.76%, and the normalized average parameter distance of the corresponding battery cell gear is 1.151σ2. Because there are five parameters considered at this time, the calculated Euclidean distance is slightly increased, and the corresponding normalized average distance will also be slightly increased compared to the normalized average parameter distance corresponding to the two parameters. The normalized average parameter distance of 1.151σ2 also shows that the consistency of the battery cells finally screened is good, where σ1 represents the standard deviation of the distance between the various parameters of the battery cells in each battery cell gear and the parameters corresponding to the cluster center, and σ2 represents the standard deviation of the normalized Euclidean distance of each qualified battery cell to its respective cluster center after the battery cell sorting is completed; specifically, the denormalized cluster centers of the six battery cell gears obtained by cluster analysis, that is, the values ​​of the center points of the battery cell gears are shown in Table 3:

[0106] Table 3 Battery gear center point data table

[0107]

[0108]

[0109] The screening threshold of each parameter of each battery cell gear in the secondary screening is determined to be 3σ1, and the battery cells are screened to complete the sorting of the battery cells to be sorted, and finally the parameters of the battery cells sorted corresponding to the battery cell gear and the center point of each battery cell gear are obtained.

[0110] The embodiment of the present invention obtains the parameters of the battery cells to be sorted, and obtains qualified products in the first stage according to the K value qualified threshold value determined by the preset qualified rate, thereby improving the qualified rate of the first stage qualified products and increasing the number of battery cells entering the secondary screening; by judging the power characteristics of the battery cells, the battery cells that pass the K value qualified threshold value are further screened, and some unqualified battery cells are screened out in advance, thereby reducing the complexity of subsequent calculations and avoiding large errors in the subsequent cluster analysis process; before performing cluster analysis, the required parameters are normalized to avoid the absolute dominance of some parameters due to different orders of magnitude of the parameters, so that each required parameter can be fully utilized, and by determining each parameter The weight of the number distinguishes the importance of each parameter, so that the relatively important parameters can occupy a larger proportion in the subsequent clustering, so that the parameter plays a greater role; by performing cluster analysis, the appropriate gear can be determined for the battery cells to be sorted, and the battery cells to be sorted can be clustered, and each battery cell to be sorted can be divided into the corresponding gear, ensuring that the consistency of the battery cells in the gear is good, and then a secondary screening is performed through the screening threshold to remove the battery cells with unqualified battery parameters and the battery cells with low consistency with most of the battery cells in the gear, so as to obtain the battery cells that are finally qualified for sorting, which can improve the consistency of the battery cells in the same gear, thereby achieving the improvement of the qualified rate of battery cell sorting and the consistency of the battery cells in the same gear.

[0111] It should be understood that the size of the serial numbers of the steps in the above embodiments does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.

[0112] The following are device embodiments of the present invention. For details not fully described therein, reference may be made to the corresponding method embodiments described above.

[0113] Figure 7 The following is a schematic diagram of the structure of a cell sorting device provided by an embodiment of the present invention. For ease of explanation, only the parts related to the embodiment of the present invention are shown, which are detailed as follows:

[0114] like Figure 7 As shown, the cell sorting device 7 includes:

[0115] An acquisition module 71 is used to acquire parameters of each battery cell to be sorted, including the K value of the target battery cell;

[0116] A determination module 72 is used to determine a K value qualification threshold of the battery cells to be sorted according to a preset qualification rate;

[0117] The primary screening module 73 performs a primary screening on the cells to be sorted according to the K value and the K value qualified threshold of the cells to be sorted, and obtains the qualified cells among the cells to be sorted;

[0118] Clustering module 74, used to cluster the qualified cells based on the parameters of the cells to be sorted and the number of preset cell levels, and determine the cluster center of each cell level according to the clustering results;

[0119] The secondary screening module 75 performs secondary screening on the clustered primary qualified cells according to the cluster centers of the respective cell levels and the screening thresholds corresponding to the respective parameters in the respective cell levels, and obtains the cells sorted corresponding to the cell levels.

[0120] In one possible implementation, the primary screening module 73 is also used to determine the power characteristics of the battery cells to be sorted after the primary screening of the battery cells to be sorted; if the battery cells to be sorted are power-type battery cells, the battery cells with an internal resistance not greater than a preset internal resistance threshold are determined as qualified primary battery cells; if the battery cells to be sorted are energy-type battery cells, the battery cells with a capacity not less than a preset capacity threshold are determined as qualified primary battery cells.

[0121] In a possible implementation, the clustering module 74 is further configured to, before clustering the qualified cells, Normalize the various parameters of the qualified battery cells; and update the normalized values ​​of the various parameters according to the preset importance of the various parameters; wherein z is the normalized value of each sample value of a parameter of the qualified battery cells, x is the sample value of the parameter of the qualified battery cells, μ is the mean of the sample values ​​of the parameter of the qualified battery cells, and σ is the standard deviation of the sample values ​​of the parameter of the qualified battery cells.

[0122] In a possible implementation, the clustering module 74 is specifically configured to:

[0123] Step 1: Set the relevant parameters for clustering the qualified cells, including the number of iterations and the maximum number of iterations;

[0124] Step 2: According to the preset number of battery cell gears, a corresponding number of battery cells are randomly determined from the qualified battery cells, and the parameters of the determined battery cells are used as the cluster centers of each gear;

[0125] Step 3: Calculate the Euclidean distance between each qualified cell and each cluster center based on the parameters of the cells to be sorted, and cluster each cell to the nearest cell position;

[0126] Step 4: Calculate the average parameter value of each battery cell in each battery cell gear, and use the average parameter value as the new cluster center;

[0127] Step 5: Determine whether the number of iterations has reached the maximum number of iterations. If not, increase the number of iterations by one and jump to step 3.

[0128] Step 6: If the number of iterations reaches the maximum number of iterations, the iteration is stopped to obtain the clustering result of the qualified battery cells.

[0129] In a possible implementation, the parameters of the battery cells to be sorted further include battery cell capacity, battery cell AC internal resistance, battery cell DC internal resistance, battery cell voltage, and coulombic efficiency.

[0130] In one possible implementation, the Euclidean distance is calculated as:

[0131]

[0132] Where Dist_N is the Euclidean distance from each cell to the corresponding cluster center, i represents the i-th parameter of each cell or cluster center, n represents the total number of n parameters of each cell or cluster center, and x i is the i-th parameter of each cell, y i is the i-th parameter of the corresponding cluster center.

[0133] In one possible implementation, the calculation process of the screening threshold corresponding to each parameter in each battery cell gear is as follows: based on the clustering results and the cluster center of each battery cell gear, the standard deviation of the difference between each parameter of all battery cells in each battery cell gear and the corresponding parameter of the corresponding cluster center is calculated; and three times the value of the standard deviation of each parameter in each battery cell gear is determined as the screening threshold of the corresponding parameter in the corresponding battery cell gear.

[0134] In one possible implementation, the secondary screening module 75 is specifically used to calculate the distance between the parameters of the battery cells in each battery cell position and the parameters of the corresponding battery cell position cluster center; retain the battery cells whose distance is not greater than the screening threshold among the qualified battery cells, and obtain the battery cells sorted corresponding to the battery cell position.

[0135] Figure 5 Schematic diagram of an electronic device provided by an embodiment of the present invention. Figure 8 As shown, the electronic device 8 of this embodiment includes: a processor 80, a memory 81, and a computer program 82 stored in the memory 81 and executable on the processor 80. When the processor 80 executes the computer program 82, the steps in the above-mentioned various cell sorting method embodiments are implemented, such as Figure 1 Alternatively, when the processor 80 executes the computer program 82, the functions of the modules in the above-mentioned device embodiments are realized, for example, Figure 7 The functions of the modules 71 to 75 are shown.

[0136] For example, the computer program 82 may be divided into one or more modules / units, one or more modules / units being stored in the memory 81 and executed by the processor 80 to implement the present invention. One or more modules / units may be a series of computer program instruction segments capable of implementing specific functions, and the instruction segments are used to describe the execution process of the computer program 82 in the electronic device 8. For example, the computer program 82 may be divided into Figure 7 Modules 71 to 75 are shown.

[0137] The electronic device 8 may include, but is not limited to, a processor 80 and a memory 81. Those skilled in the art will appreciate that Figure 8 It is only an example of the electronic device 8 and does not constitute a limitation of the electronic device 8. It may include more or fewer components than shown in the figure, or a combination of certain components, or different components. For example, the electronic device may also include input and output devices, network access devices, buses, etc.

[0138] The processor 80 may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or any conventional processor.

[0139] The memory 81 can be an internal storage unit of the electronic device 8, such as the hard disk or memory of the electronic device 8. The memory 81 can also be an external storage device of the electronic device 8, such as a plug-in hard disk, a smart media card (SMC), a secure digital (SD) card, a flash memory card, etc. equipped on the electronic device 8. Furthermore, the memory 81 can also include both the internal storage unit of the electronic device 8 and an external storage device. The memory 81 is used to store computer programs and other programs and data required by the electronic device. The memory 81 can also be used to temporarily store data that has been output or is about to be output.

[0140] Those skilled in the art can clearly understand that, for the convenience and brevity of description, only the division of the above-mentioned functional units and modules is used as an example for illustration. In actual applications, the above-mentioned functions can be distributed and completed by different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiment can be integrated into one processing unit, or each unit can exist physically alone, or two or more units can be integrated into one unit. The above-mentioned integrated unit can be implemented in the form of hardware or in the form of software functional units. In addition, the specific names of the functional units and modules are only for the convenience of distinguishing each other, and are not used to limit the scope of protection of this application. The specific working process of the units and modules in the above-mentioned system can refer to the corresponding process in the aforementioned method embodiment, and will not be repeated here.

[0141] In the above embodiments, the description of each embodiment has its own focus. For parts that are not described or recorded in detail in a certain embodiment, reference can be made to the relevant description of other embodiments.

[0142] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professionals and technicians can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of the present invention.

[0143] In the embodiments provided by the present invention, it should be understood that the disclosed devices / electronic devices and methods can be implemented in other ways. For example, the device / electronic device embodiments described above are merely schematic. For example, the division of modules or units is merely a logical function division. In actual implementation, there may be other division methods, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0144] Units described as separate components may or may not be physically separate, and components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of these units may be selected to achieve the purpose of this embodiment according to actual needs.

[0145] In addition, the functional units in the various embodiments of the present invention may be integrated into a single processing unit, each unit may exist physically separately, or two or more units may be integrated into a single unit. The aforementioned integrated units may be implemented in the form of hardware or software functional units.

[0146] If the integrated module / unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the present invention implements all or part of the processes in the above-mentioned embodiment method, and can also be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium, and when the computer program is executed by the processor, it can implement the steps of the above-mentioned various method embodiments. Among them, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form, etc. Computer-readable media may include: any entity or device that can carry computer program code, recording medium, USB flash drive, mobile hard disk, magnetic disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signal, telecommunication signal and software distribution medium, etc.

[0147] The above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. These modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the various embodiments of the present invention, and should all be included in the scope of protection of the present invention.

Claims

1. A cell sorting method, characterized in that: include: Obtaining parameters of each battery cell to be sorted, wherein the parameters include a K value of the battery cell to be sorted; Determine a K value qualification threshold of the battery cells to be sorted according to a preset qualification rate; wherein the preset qualification rate is determined according to a cumulative distribution probability of the K values ​​of the battery cells to be sorted; Performing a primary screening of the battery cells to be sorted according to the K value of the battery cells to be sorted and the K value qualified threshold value to obtain a primary qualified battery cell among the battery cells to be sorted; Based on the parameters of the battery cells to be sorted and the preset number of battery cell gears, the qualified battery cells are clustered, and the cluster centers of the respective battery cell gears are determined according to the clustering results; wherein the parameters of the battery cells to be sorted further include battery cell capacity, battery cell AC internal resistance, battery cell DC internal resistance, battery cell voltage and coulombic efficiency; According to the clustering center of each battery cell gear and the screening threshold corresponding to each parameter in each battery cell gear, the qualified battery cells after clustering are screened twice to obtain the battery cells sorted corresponding to the battery cell gear.

2. The battery cell sorting method according to claim 1, characterized in that: After the cells to be sorted are initially screened, the method further includes: Determine the power characteristics of the battery cells to be sorted; If the battery cell to be sorted is a power type battery cell, the battery cell having an internal resistance not greater than a preset internal resistance threshold is determined as the primary qualified battery cell; If the battery cells to be sorted are energy-type battery cells, the battery cells having a capacity not less than a preset capacity threshold are determined as the primary qualified battery cells.

3. The battery cell sorting method according to claim 1, characterized in that: Before clustering the qualified primary cells, the method further includes: according to Normalizing various parameters of the primary qualified battery cells; And according to the preset importance of each parameter, update the normalized value of each parameter; Among them, z is the normalized value of each sample value of a parameter of the once qualified battery cell, x is the sample value of the parameter of the once qualified battery cell, μ is the mean of the sample values ​​of the parameter of the once qualified battery cell, and σ is the standard deviation of the sample values ​​of the parameter of the once qualified battery cell.

4. The battery cell sorting method according to claim 3, characterized in that: Clustering the primary qualified cells based on the parameters of the cells to be sorted and the number of preset cell gears includes: Step 1: setting relevant parameters for clustering the qualified cells, wherein the relevant parameters include the number of iterations and the maximum number of iterations; Step 2: randomly determine a corresponding number of cells from the qualified cells according to the preset number of cell gears, and use the parameters of the determined cells as the cluster centers of each gear; Step 3: Calculate the Euclidean distance between each qualified cell and each cluster center based on the parameters of the cells to be sorted, and cluster each cell to the nearest cell position; Step 4: Calculate the average value of the parameters of each battery cell in each battery cell gear, and use the average value of the parameters as the new cluster center; Step 5: Determine whether the number of iterations has reached the maximum number of iterations. If not, increase the number of iterations by one and jump to step 3. Step 6: If the number of iterations reaches the maximum number of iterations, the iteration is stopped to obtain the clustering result of the qualified battery cells.

5. The battery cell sorting method according to claim 1, characterized in that: The calculation process of the screening threshold corresponding to each parameter in each battery cell gear is as follows: Calculate the standard deviation of the difference between each parameter of all battery cells in each battery cell gear and the corresponding parameter of the corresponding cluster center according to the clustering result and the cluster center of each battery cell gear; A value three times the standard deviation of each parameter in each battery cell gear is determined as a screening threshold value of the corresponding parameter in the corresponding battery cell gear.

6. The battery cell sorting method according to claim 5, characterized in that: According to the cluster center of each cell level and the screening thresholds corresponding to each parameter in each cell level, the qualified cells after clustering are screened for the second time, including: Calculate the distance between the parameters of the battery cells in each battery cell gear and the parameters of the corresponding battery cell gear cluster center; The cells whose distance is not greater than the screening threshold value among the qualified cells are retained to obtain the cells sorted corresponding to the cell levels.

7. A battery cell sorting device, characterized in that: include: An acquisition module is used to obtain parameters of each battery cell to be sorted, wherein the parameters include the K value of the battery cell to be sorted; A determination module, configured to determine a qualified threshold value of the K value of the battery cells to be sorted according to a preset qualified rate; wherein the preset qualified rate is determined according to a cumulative distribution probability of the K value of the battery cells to be sorted; A primary screening module, configured to perform a primary screening on the battery cells to be sorted according to the K value of the battery cells to be sorted and the K value qualified threshold value, to obtain a primary qualified battery cell among the battery cells to be sorted; A clustering module is used to cluster the qualified cells based on the parameters of the cells to be sorted and the number of preset cell levels, and determine the cluster center of each cell level according to the clustering results; wherein the parameters of the cells to be sorted also include cell capacity, cell AC internal resistance, cell DC internal resistance, cell voltage and coulombic efficiency; The secondary screening module is used to perform secondary screening on the clustered qualified primary cells according to the clustering center of each cell gear and the screening threshold corresponding to each parameter in each cell gear, and obtain the cells sorted corresponding to the cell gear.

8. An electronic device comprising a memory and a processor, wherein the memory is used to store a computer program, and the processor is used to call and run the computer program stored in the memory, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 6 are implemented.

9. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

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