Abnormality analysis method and device of battery pack, computer equipment and storage medium

Abstract

The invention relates to a battery pack anomaly analysis method and device, computer equipment, a storage medium and a computer program product. The method comprises the following steps: for each battery cell in a battery pack, obtaining respective battery cell detection values of the battery cell in different detection dimensions, connecting a plurality of battery cells in the battery pack through a connecting line, and for each detection dimension, determining the deviation degree of the battery cell detection values corresponding to the detection dimension based on the deviation degree corresponding to the plurality of battery cell detection values corresponding to the detection dimension; the method comprises the steps of determining the consistency score of a battery pack in detection dimensions, determining the consistency score of the battery pack based on the respective consistency score of each detection dimension and the respective detection weight of each detection dimension, and determining whether the battery pack contains a connecting line in a fault state based on the consistency score of the battery pack. The consistency score of the battery pack reflects the consistency score of the battery pack in multiple detection dimensions, so that whether the connecting line is in a fault state or not is accurately determined, and the accuracy of abnormal analysis of the battery pack is improved.

Description

Technical Field

[0001] The present application relates to the field of battery management technology, and in particular to a method, device, computer equipment, storage medium and computer program product for analyzing abnormalities of a battery pack. Background Art

[0002] With the continuous improvement of science and technology, the focus of the energy industry has shifted from traditional energy to new energy. As an indispensable energy supply and storage tool in contemporary society, batteries play an important role in various electrical appliances. In order to ensure the normal charging and discharging of multiple cells in the battery, it is necessary to perform abnormal analysis on the battery pack.

[0003] In traditional technology, voltage consistency is judged for multiple cells in a battery pack. When the numerical relationship between the consistency score of the battery pack and the preset score is determined, it is determined whether the battery pack has an abnormality. This analysis method still has the problem of inaccurate analysis results. Summary of the invention

[0004] Based on this, it is necessary to provide a battery pack abnormality analysis method, device, computer equipment, computer-readable storage medium and computer program product that can improve the accuracy of abnormality analysis in response to the above technical problems.

[0005] In a first aspect, the present application provides a method for analyzing abnormalities of a battery pack. The method comprises:

[0006] For each battery cell in the battery pack, obtain the battery cell detection value of each battery cell in different detection dimensions; the multiple battery cells in the battery pack are connected by connecting lines; for each detection dimension, based on the deviations corresponding to the multiple battery cell detection values ​​corresponding to the detection dimension, determine the consistency score of the battery pack in the detection dimension; based on the consistency score of each detection dimension and the detection weight of each detection dimension, determine the consistency score of the battery pack; based on the consistency score of the battery pack, determine whether the battery pack contains a connecting line in a faulty state.

[0007] In one of the embodiments, the consistency score of the battery pack in the detection dimension is determined based on the deviations corresponding to each of the multiple battery cell detection values ​​corresponding to the detection dimension, including: determining a median cell detection value corresponding to each of the multiple battery cell detection values ​​corresponding to the detection dimension; for each of the battery cell detection values, determining the deviation of the battery cell detection value corresponding to each of the battery cell detection values ​​based on the numerical difference between the battery cell detection value and the median cell detection value; and determining the consistency score of the battery pack in the detection dimension based on the sum of the deviations and the deviations corresponding to each of the battery cell detection values.

[0008] In one embodiment, the consistency score of the battery pack is determined based on the consistency score of each detection dimension and the detection weight of each detection dimension, including: among the detection weights, screening the target detection weight that meets the preset screening conditions; determining the associated weights of the detection dimensions corresponding to the target detection weights for each detection dimension; determining the comprehensive weights of each detection dimension based on the target detection weights and the associated weights; determining the consistency score of the battery pack based on the consistency score of each detection dimension and the comprehensive weight of each detection dimension.

[0009] In one of the embodiments, determining whether there is a connecting line in a faulty state in the battery pack based on the consistency score of the battery pack includes: when the consistency score of the battery pack is less than or equal to a preset score, determining that there is no connecting line in a faulty state in the battery pack; when the consistency score of the battery pack is greater than a preset score, for each battery cell in the battery pack, obtaining multiple battery cell sampling values ​​of each battery cell in a target detection dimension; and determining whether there is a connecting line in a faulty state in the battery pack based on statistical results of the battery cell sampling values.

[0010] In one of the embodiments, the method of determining whether there is a connecting line in a faulty state in the battery pack based on the statistical results of the sampling values ​​of each of the battery cells includes: for each of the battery cells, determining the sampling value fluctuation range of the battery cell based on multiple battery cell sampling values ​​of the battery cell; among each of the battery cells, screening candidate battery cells whose sampling value fluctuation range is greater than a preset range; and determining whether there is a connecting line in a faulty state in the battery pack based on the statistical results of the multiple battery cell sampling values ​​corresponding to the candidate battery cells.

[0011] In one embodiment, the determining whether there is a connection line in a faulty state in the battery pack based on the statistical results of the multiple battery cell sampling values ​​corresponding to the candidate battery cell includes: for each of the candidate battery cells, determining the sampling maximum value, the sampling minimum value, and the sampling median value of the candidate battery cell among the multiple battery cell sampling values ​​corresponding to the candidate battery cell; determining that there is no connection line in the battery pack in a faulty state when the numerical difference between the sampling maximum value and the sampling median value is less than or equal to a first preset threshold, and / or the numerical difference between the sampling median value and the sampling minimum value is less than or equal to a second preset threshold; determining that there is a connection line in the battery pack in a faulty state when the numerical difference between the sampling maximum value and the sampling median value is greater than the first preset threshold, and the numerical difference between the sampling median value and the sampling minimum value is greater than the second preset threshold.

[0012] In a second aspect, the present application also provides a battery pack abnormality analysis device. The device comprises:

[0013] An acquisition module is used to obtain, for each battery cell in a battery pack, respective battery cell detection values ​​of the battery cell in different detection dimensions; the multiple battery cells in the battery pack are connected by connecting lines; a first determination module is used to determine, for each of the detection dimensions, a consistency score of the battery pack in the detection dimension based on the deviations corresponding to the multiple battery cell detection values ​​corresponding to the detection dimension; a second determination module is used to determine the consistency score of the battery pack based on the respective consistency scores of each of the detection dimensions and the respective detection weights of each of the detection dimensions; an analysis module is used to determine, based on the consistency score of the battery pack, whether the battery pack contains a connecting line in a faulty state.

[0014] In a third aspect, the present application further provides a computer device, wherein the computer device comprises a memory and a processor, wherein the memory stores a computer program, and the processor implements the above method steps when executing the computer program.

[0015] In a fourth aspect, the present application further provides a computer-readable storage medium having a computer program stored thereon, and the computer program implements the above method steps when executed by a processor.

[0016] In a fifth aspect, the present application further provides a computer program product, wherein the computer program product comprises a computer program, and the computer program implements the above method steps when executed by a processor.

[0017] The above-mentioned abnormal analysis method, device, computer equipment, storage medium and computer program product of the battery pack obtains the respective battery cell detection values ​​of the battery cell in different detection dimensions for each battery cell in the battery pack. The multiple battery cells in the battery pack are connected by connecting wires to obtain multi-dimensional battery cell detection values, which provide a basis for subsequent multi-dimensional consistency analysis and improve the accuracy of consistency analysis. For each detection dimension, based on the deviation corresponding to each of the multiple battery cell detection values ​​corresponding to the detection dimension, the consistency score of the battery pack in the detection dimension is determined, and based on the respective consistency score of each detection dimension and the respective detection weight of each detection dimension, the consistency score of the battery pack is determined. With the above-mentioned method, the consistency score of the battery pack reflects the consistency score of the battery pack in multiple detection dimensions, which improves the accuracy of the consistency score of the battery pack. Furthermore, based on the consistency score of the battery pack, it is determined whether the battery pack contains a connecting wire in a faulty state, and whether the connecting wire is in a faulty state is accurately determined, which improves the accuracy of abnormal analysis of the battery pack. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 A diagram showing an application environment of a battery pack abnormality analysis method in one embodiment;

[0019] Figure 2 is a schematic flow chart of a method for analyzing abnormality of a battery pack in one embodiment;

[0020] Figure 3 A schematic diagram of a flow chart of a lithium battery pack connection line fault diagnosis method based on inconsistency assessment in one embodiment;

[0021] Figure 4 It is a flowchart of a method for diagnosing a battery pack connection line fault in a data missing scenario in one embodiment;

[0022] Figure 5 is a structural block diagram of a battery pack abnormality analysis device in one embodiment;

[0023] Figure 6 FIG. 4 is a diagram showing the internal structure of a computer device in one embodiment. DETAILED DESCRIPTION

[0024] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0025] The abnormality analysis method of the battery pack provided in the embodiment of the present application can be applied to Figure 1 In the application environment shown, the detection terminal 102 communicates with the battery management platform 104. The battery management platform 104 includes multiple battery packs, and each battery pack includes multiple battery cells.

[0026] The detection terminal 102 can obtain the respective cell detection values ​​of the cells in different detection dimensions for each cell in the battery pack of the battery management platform 104; the multiple cells in the battery pack are connected by connecting wires. The detection terminal 102 can determine the consistency score of the battery pack in the detection dimension based on the deviations corresponding to the multiple cell detection values ​​corresponding to the detection dimension for each detection dimension. The detection terminal 102 can determine the consistency score of the battery pack based on the respective consistency score of each detection dimension and the respective detection weight of each detection dimension. The detection terminal 102 can determine whether the battery pack contains a connecting wire in a faulty state based on the consistency score of the battery pack.

[0027] The detection terminal 102 may be, but is not limited to, various personal computers, laptops, smart phones, tablet computers, IoT devices, and portable wearable devices. IoT devices may be smart speakers, smart TVs, smart air conditioners, smart vehicle-mounted devices, etc. Portable wearable devices may be smart watches, smart bracelets, head-mounted devices, etc. The battery management platform 104 may be a platform for monitoring, managing, and optimizing battery performance and safety.

[0028] In one embodiment, Figure 2 As shown, a battery pack abnormality analysis method is provided, and the method is applied to Figure 1 The detection terminal 102 in FIG. 1 is used as an example to illustrate, including:

[0029] S202, for each battery cell in the battery pack, obtaining the battery cell detection value of each battery cell in different detection dimensions. The multiple battery cells in the battery pack are connected by connecting wires.

[0030] Among them, the battery cell is the basic unit of the battery, which is composed of a positive electrode, a negative electrode, an electrolyte and a separator. It is usually not used directly and needs to be combined with other components to form a module or battery pack.

[0031] A battery pack can be an energy storage unit composed of multiple battery cells connected in series, parallel or hybrid.

[0032] The detection dimension may indicate the detection type for the battery cell, for example, the detection dimension may include voltage, current, full charge capacity, sampling time, etc.

[0033] The cell detection value may represent the detection value of the cell under the detection dimension. For example, the cell detection value may be the voltage value, current value, sampling duration, etc. of a cell.

[0034] The connection line may represent the connection line between the cells in the battery pack, and the connection line may also represent the sampling line of each cell in the battery pack.

[0035] Specifically, for each battery cell in the battery pack, the detection terminal can obtain the battery cell detection values ​​of each battery cell in different detection dimensions through the sampling line corresponding to the battery cell.

[0036] Specifically, for each battery cell in the battery pack, the detection terminal can also obtain the battery cell detection values ​​of each battery cell in different detection dimensions through the connecting line.

[0037] S204, for each detection dimension, based on the deviations corresponding to the plurality of battery cell detection values ​​corresponding to the detection dimension, determine the consistency score of the battery pack in the detection dimension.

[0038] The deviation can represent the proportion of the absolute value of the difference between the actual data and the target data to the target data. Specifically, the deviation can represent the degree of deviation of each battery cell detection value among all battery cell detection values. For example, the greater the deviation, the farther the battery cell detection value is from the median corresponding to each battery cell detection value.

[0039] The consistency score can reflect the score corresponding to the numerical consistency of the test values ​​of each battery cell. The higher the consistency score, the more concentrated and consistent the test values ​​of each battery cell are. For example, a consistency score of 1 can indicate that the test values ​​of each battery cell are equal.

[0040] The consistency score of the battery pack in the detection dimension can indicate the concentration of the cell detection values ​​of each cell in the battery pack for a certain detection dimension. If the detection dimension is the voltage dimension, the higher the consistency score of the voltage dimension, the closer the voltage values ​​of each cell in the battery pack are to being equal. If the consistency score of the voltage dimension is 1, it means that the voltage values ​​of each cell are the same value.

[0041] It should be noted that if the consistency score of the battery pack in the test dimension is lower, the following problems are likely to occur: (1) Capacity loss: Due to the inconsistency of the capacity of the single cells in the battery pack, the worst cell will determine the capacity of the entire battery pack. This phenomenon conforms to the "barrel principle", that is, the overall performance is determined by the weakest part. (2) Shortened life: In a battery pack with poor consistency, some cells may fail prematurely due to overcharging or over-discharging, resulting in a greatly shortened service life of the entire battery pack. (3) Increased internal resistance: The inconsistency of the internal resistance of different cells will cause the high internal resistance cell to generate more heat at the same current, thereby accelerating battery degradation. The internal resistance and temperature rise form a negative feedback, further aggravating the degradation of the high internal resistance cell. (4) Safety issues: Inconsistent battery packs may experience overvoltage or undervoltage during charging or discharging, causing safety risks. For example, overcharging of some cells may cause thermal runaway or even fire. (5) Performance degradation: Due to the performance differences between single cells, the overall charging and discharging efficiency of the battery pack will be reduced. For example, in parallel, batteries with low discharge capacity will become a bottleneck, limiting the discharge capacity of the entire battery pack; in series, batteries with deep aging will become a charging bottleneck, affecting charging efficiency. (6) Uneven temperature: Uneven temperature distribution of each cell in the battery pack may cause local overheating or overcooling, further exacerbating the consistency problem. Therefore, it is necessary to determine the consistency score of the battery pack and perform abnormal analysis of the battery pack based on the consistency score.

[0042] Specifically, the detection terminal can determine the cell detection median value corresponding to each cell detection value among the multiple cell detection values ​​corresponding to the detection dimension. The detection terminal can determine the deviation of the cell detection value corresponding to each cell detection value based on the numerical difference between the cell detection value and the cell detection median value for each cell detection value. The detection terminal can determine the consistency score of the battery pack in the detection dimension based on the sum of the deviations and the deviations corresponding to each cell detection value.

[0043] In one embodiment, the detection terminal may also determine the cell detection median value corresponding to each cell detection value among the multiple cell detection values ​​corresponding to the detection dimension. The detection terminal may determine the deviation of the cell detection value corresponding to each cell detection value based on the numerical difference between the cell detection value and the cell detection median value for each cell detection value.

[0044] S206 , determining a consistency score of the battery pack based on the consistency score of each detection dimension and the detection weight of each detection dimension.

[0045] The detection weight may represent a weight configured for a detection dimension. A higher detection weight may represent a more important detection dimension corresponding to the detection weight. For example, the importance of the voltage dimension may be greater than that of the current dimension.

[0046] The consistency score of a battery pack reflects the consistency score corresponding to each detection dimension in the battery pack.

[0047] Specifically, for each detection dimension, the detection terminal may multiply the detection weight corresponding to the detection dimension and the consistency score of the detection dimension to obtain a calculation result corresponding to the detection dimension. The detection terminal may add up the calculation results to obtain a consistency score of the battery pack.

[0048] In one of the embodiments, the detection terminal may obtain detection weights pre-configured for each detection dimension.

[0049] The detection terminal may also obtain the amount of abnormal data for each detection dimension. The detection terminal may determine a detection weight that matches the amount of abnormal data. The amount of abnormal data is positively correlated with the detection weight.

[0050] In one embodiment, the detection terminal may screen the target detection weights that meet the preset screening conditions among the detection weights. The detection terminal may determine the associated weights of the detection dimensions corresponding to the target detection weights for each detection dimension respectively. The detection terminal may determine the respective comprehensive weights of each detection dimension based on the target detection weights and the respective associated weights. The detection terminal may determine the consistency score of the battery pack based on the respective consistency scores of each detection dimension and the respective comprehensive weights of each detection dimension.

[0051] S208: Determine whether the battery pack contains a connection line in a faulty state based on the consistency score of the battery pack.

[0052] The connection line being in a fault state may indicate that the connection line is loose, broken, or short-circuited.

[0053] Specifically, the detection terminal can determine that there is no connection line in a faulty state in the battery pack when the consistency score of the battery pack is less than or equal to a preset score. The detection terminal can obtain multiple cell sampling values ​​of each cell in the target detection dimension for each cell in the battery pack when the consistency score of the battery pack is greater than a preset score. The detection terminal can determine whether there is a connection line in a faulty state in the battery pack based on the statistical results of the sampling values ​​of each cell.

[0054] Specifically, the detection terminal may obtain an operation analysis report for each cell in the battery pack. If the operation analysis report is abnormal, the detection terminal may determine whether the battery pack contains a connection line in a faulty state based on the consistency score of the battery pack.

[0055] In one embodiment, the detection terminal may obtain a preset threshold value set for the consistency score of the battery pack. When the consistency score of the battery pack is greater than the preset threshold value, the detection terminal may determine that the battery pack contains a connection line in a faulty state. When the consistency score of the battery pack is less than or equal to the preset threshold value, the detection terminal may determine that the battery pack contains no connection line in a faulty state.

[0056] In the above-mentioned abnormal analysis method for the battery pack, for each battery cell in the battery pack, the battery cell detection value of each battery cell in different detection dimensions is obtained, and multiple battery cells in the battery pack are connected by connecting wires to obtain multi-dimensional battery cell detection values, which provide a basis for subsequent multi-dimensional consistency analysis and improve the accuracy of consistency analysis. For each detection dimension, based on the deviation corresponding to each of the multiple battery cell detection values ​​corresponding to the detection dimension, the consistency score of the battery pack in the detection dimension is determined, and based on the consistency score of each detection dimension and the detection weight of each detection dimension, the consistency score of the battery pack is determined. With the above-mentioned method, the consistency score of the battery pack reflects the consistency score of the battery pack in multiple detection dimensions, which improves the accuracy of the consistency score of the battery pack. Furthermore, based on the consistency score of the battery pack, it is determined whether the battery pack contains a connecting wire in a faulty state, and whether the connecting wire is in a faulty state is accurately determined, which improves the accuracy of abnormal analysis of the battery pack.

[0057] In one embodiment, a consistency score of a battery pack in a detection dimension is determined based on the deviations corresponding to each of a plurality of battery cell detection values ​​corresponding to the detection dimension, including: determining a median cell detection value corresponding to each battery cell detection value among a plurality of battery cell detection values ​​corresponding to the detection dimension, for each battery cell detection value, determining the deviation of the battery cell detection value corresponding to each battery cell detection value based on the numerical difference between the battery cell detection value and the median cell detection value, and determining the consistency score of the battery pack in the detection dimension based on the sum of the deviations and the deviations corresponding to each battery cell detection value.

[0058] The cell detection median value may represent the median of multiple cell detection values.

[0059] The numerical difference between the cell test value and the cell test median value can represent the absolute value of the difference between the cell test value and the cell test median value. The calculation formula for the deviation of the cell test value corresponding to each cell test value is as follows:

[0060]

[0061] in, It can indicate the deviation of the cell detection value corresponding to each cell detection value. Can indicate the battery cell detection value, It can represent the median value of the battery cell detection, and j can represent the number of the battery cell.

[0062] Specifically, the detection terminal can be based on the sum of the deviations ( ), and the deviation of each cell test value ( ), determine the consistency score of the battery pack in the detection dimension .

[0063] In one embodiment, the calculation formula of the consistency score can be:

[0064]

[0065] Wherein, a represents the magnitude parameter. The magnitude parameter a is different for different detection dimensions.

[0066] In the present embodiment, among the multiple battery cell detection values ​​corresponding to the detection dimension, the median value of the battery cell detection corresponding to each battery cell detection value is determined, and for each battery cell detection value, the deviation of the battery cell detection value corresponding to each battery cell detection value is determined based on the numerical difference between the battery cell detection value and the median value of the battery cell detection, and the consistency score of the battery pack in the detection dimension is determined based on the sum of the deviations and the deviation corresponding to each battery cell detection value, and the accurate consistency score of the battery pack in the detection dimension is determined based on the numerical difference between the battery cell detection value and the median value of the battery cell detection, thereby improving the accuracy of abnormality analysis of the battery pack.

[0067] In one embodiment, the consistency score of the battery pack is determined based on the consistency score of each detection dimension and the detection weight of each detection dimension, including: among each detection weight, screening the target detection weight that meets the preset screening conditions, determining the associated weights of each detection dimension for the detection dimension corresponding to the target detection weight, determining the respective comprehensive weights of each detection dimension based on the target detection weight and the respective associated weights, and determining the consistency score of the battery pack based on the consistency score of each detection dimension and the respective comprehensive weight of each detection dimension.

[0068] The preset screening condition may be a screening condition set for each detection weight. For example, the preset screening condition may be a random screening, or the preset screening condition may be a maximum value or a minimum value among the screening detection weights.

[0069] The target detection weight may be a detection weight that meets a preset screening condition. The target detection weight may be the smallest weight among the detection weights, the largest weight, or a randomly selected weight.

[0070] The association weight may represent the relative importance of each detection dimension. For example, the relative importance of the voltage dimension is higher than that of the current dimension, that is, the voltage dimension is more important than the current dimension. For example, as shown in the following table:

[0071] Table 1 Schematic table of association weights

[0072]

[0073] in, The detection dimensions corresponding to the target detection weights can be represented by their associated weights for the detection dimensions.

[0074] The comprehensive weight can be calculated for the detection dimension.

[0075] Specifically, the detection terminal may determine the comprehensive weight of each detection dimension based on the target detection weight and the associated weights corresponding to the target detection weight. The comprehensive weight of the detection dimension may be equal to the product of the target detection weight and the associated weight.

[0076] Specifically, the detection terminal can detect the consistency score of the detection dimension and the comprehensive weight of the detection dimension to obtain the calculation result of the detection dimension. The detection terminal can sum the calculation results of each detection dimension to determine the consistency score of the battery pack.

[0077] In this embodiment, among each detection weight, the target detection weight that meets the preset screening conditions is screened, and the associated weights of each detection dimension corresponding to the target detection weight are determined. Based on the target detection weight and the associated weights, the respective comprehensive weights of each detection dimension are determined. Based on the respective consistency score of each detection dimension and the respective comprehensive weight of each detection dimension, the consistency score of the battery pack is determined, which fully considers the relative importance (correlation degree) between the detection weights of each detection dimension, thereby improving the accuracy of determining the consistency score of the battery pack.

[0078] In one embodiment, based on the consistency score of the battery pack, determining whether there is a connecting line in a faulty state in the battery pack includes: when the consistency score of the battery pack is less than or equal to a preset score, determining that there is no connecting line in a faulty state in the battery pack; when the consistency score of the battery pack is greater than the preset score, obtaining, for each battery cell in the battery pack, multiple battery cell sampling values ​​of each battery cell in a target detection dimension, and determining whether there is a connecting line in a faulty state in the battery pack based on statistical results of the sampling values ​​of each battery cell.

[0079] The preset score may be a score set for battery pack consistency.

[0080] When the consistency score of the battery pack is less than or equal to the preset score, it can be considered that the consistency of the cells in the battery pack is good and there are no connecting wires in a faulty state, that is, the connecting wires are not loose, broken, short-circuited, etc.

[0081] When the consistency score of the battery pack is less than or equal to the preset score, it can be considered that the consistency of the cells in the battery pack is poor, there may be connecting wires in a faulty state, and the connecting wires may be loose, broken, short-circuited, etc.

[0082] The target detection dimension may be a pre-set detection dimension. For example, the target detection dimension may be any one of a voltage dimension, a current dimension, and a full charge capacity dimension.

[0083] The cell sampling value may represent the sampled value of the cell in the target detection dimension, for example, multiple voltage values ​​of a cell in the voltage dimension.

[0084] The statistical result may represent the result obtained by performing statistical calculation on the sampling values ​​of each battery cell.

[0085] There are many ways to perform statistical calculations. Specifically, the detection terminal can determine whether there is a connection line in a faulty state in the battery pack based on the difference between the average value and the median value of the sampling values ​​of each battery cell.

[0086] Specifically, the detection terminal can also determine the sampling value fluctuation range of each battery cell based on multiple battery cell sampling values ​​of the battery cell. The detection terminal can screen candidate batteries whose sampling value fluctuation range is greater than a preset range in each battery cell. The detection terminal can determine whether there is a connection line in a faulty state in the battery pack based on the statistical results of multiple battery cell sampling values ​​corresponding to the candidate battery cells.

[0087] In one of the embodiments, for each candidate battery cell, a sampling maximum value, a sampling minimum value, and a sampling median value of the candidate battery cell are determined from a plurality of battery cell sampling values ​​corresponding to the candidate battery cell. When the numerical difference between the sampling maximum value and the sampling median value is less than or equal to a first preset threshold, and / or the numerical difference between the sampling median value and the sampling minimum value is less than or equal to a second preset threshold, it is determined that there is no connection line in the battery pack that is in a faulty state. When the numerical difference between the sampling maximum value and the sampling median value is greater than the first preset threshold, and the numerical difference between the sampling median value and the sampling minimum value is greater than the second preset threshold, it is determined that there is a connection line in the battery pack that is in a faulty state.

[0088] In the present embodiment, when the consistency score of the battery pack is greater than the preset score, for each battery cell in the battery pack, multiple battery cell sampling values ​​of each battery cell in the target detection dimension are obtained, and further based on the statistical results of the sampling values ​​of each battery cell, it is determined whether the battery pack contains a connecting line in a faulty state, and whether the connecting line is in a faulty state is accurately determined, thereby improving the accuracy of abnormal analysis of the battery pack.

[0089] In one embodiment, based on the statistical results of the sampling values ​​of each battery cell, determining whether there is a connecting line in a faulty state in the battery pack includes: for each battery cell, determining the sampling value fluctuation range of the battery cell based on multiple battery cell sampling values ​​of the battery cell, screening candidate battery cells whose sampling value fluctuation range is greater than a preset range in each battery cell, and determining whether there is a connecting line in a faulty state in the battery pack based on the statistical results of the multiple battery cell sampling values ​​corresponding to the candidate battery cells.

[0090] The fluctuation range of the sampling values ​​of the battery cells may represent the difference between the minimum value and the maximum value corresponding to the sampling values ​​of each battery cell.

[0091] The preset range may be a range set for the fluctuation range of the sampling value. For example, the preset range may be a voltage difference of 0.5 V or the like.

[0092] The candidate battery cell may be a battery cell whose sampling value fluctuation range is greater than a preset range.

[0093] The statistical result of the multiple cell sampling values ​​corresponding to the candidate cell may be the mean of the multiple sampling values ​​or the variance of the multiple cell sampling values.

[0094] Specifically, the detection terminal may determine whether there is a connection line in a faulty state in the battery pack based on the mean or variance of a plurality of battery cell sampling values ​​corresponding to the candidate battery cells.

[0095] In one of the embodiments, after obtaining the variance of the multiple battery cell sampling values, the detection terminal can determine whether there is a connection line in a faulty state in the battery pack based on the variance and a preset variance size.

[0096] In the present embodiment, for each battery cell, the sampling value fluctuation range of the battery cell is determined based on multiple battery cell sampling values ​​of the battery cell, and among each battery cell, candidate battery cells whose sampling value fluctuation range is greater than a preset range are screened, and based on the statistical results of multiple battery cell sampling values ​​corresponding to the candidate battery cells, it is determined whether there are connecting lines in a faulty state in the battery pack, and candidate battery cells whose sampling value fluctuation range is greater than the preset range are selected from each battery cell, thereby reducing the amount of subsequent calculations and improving the efficiency of abnormal analysis of the battery pack.

[0097] In one embodiment, based on the statistical results of multiple battery cell sampling values ​​corresponding to the candidate battery cells, determining whether there is a connecting line in a faulty state in the battery pack includes: for each candidate battery cell, determining the sampling maximum value, the sampling minimum value, and the sampling median value of the candidate battery cell among the multiple battery cell sampling values ​​corresponding to the candidate battery cell; when the numerical difference between the sampling maximum value and the sampling median value is less than or equal to a first preset threshold, and / or the numerical difference between the sampling median value and the sampling minimum value is less than or equal to a second preset threshold, determining that there is no connecting line in the battery pack in a faulty state; when the numerical difference between the sampling maximum value and the sampling median value is greater than the first preset threshold, and the numerical difference between the sampling median value and the sampling minimum value is greater than the second preset threshold, determining that there is a connecting line in the battery pack in a faulty state.

[0098] The first preset threshold may be a threshold set for the numerical difference between the sampling maximum value and the sampling median value. The second preset threshold may be a threshold set for the numerical difference between the sampling median value and the sampling maximum value.

[0099] The first preset threshold may be equal to the second preset threshold.

[0100] Exemplarily, taking the case where the cell sampling value is a voltage value and the first preset threshold is equal to the second preset threshold, the detection terminal can determine the sampling maximum value Vmax, the sampling minimum value Vmin, and the sampling median value Vmid of the candidate cell for each candidate cell among the multiple cell sampling values ​​corresponding to the candidate cell. The detection terminal can determine that there is no connection line in the battery pack that is in a faulty state when the numerical difference (Vmax-Vmid) between the sampling maximum value and the sampling median value is less than or equal to the first preset threshold a1, and / or the numerical difference (Vmid-Vmin) between the sampling median value and the sampling minimum value is less than or equal to the second preset threshold a2. The detection terminal can determine that there is a connection line in the battery pack that is in a faulty state when the numerical difference (Vmax-Vmid) between the sampling maximum value and the sampling median value is greater than the first preset threshold a1, and the numerical difference (Vmid-Vmin) between the sampling median value and the sampling minimum value is greater than the second preset threshold a2.

[0101] It should be noted that the fault characteristics of the sampling connection line are volatility and symmetry: the sampling voltage may suddenly produce symmetrical very high / very low sampling voltages, and appear at the same time in different battery cells, so it is possible to determine whether the highest and lowest voltages simultaneously exceed the threshold to determine whether a connection line fault has occurred (the threshold here is generally relatively large, exceeding the voltage range that can be reached during normal charging and discharging).

[0102] In the present embodiment, for each candidate battery cell, the sampling maximum value, sampling minimum value, and sampling median value of the candidate battery cell are determined among the multiple battery cell sampling values ​​corresponding to the candidate battery cell. When the numerical difference between the sampling maximum value and the sampling median value is less than or equal to the first preset threshold value, and / or the numerical difference between the sampling median value and the sampling minimum value is less than or equal to the second preset threshold value, it is determined that there is no connecting line in the battery pack in a faulty state. When the numerical difference between the sampling maximum value and the sampling median value is greater than the first preset threshold value, and the numerical difference between the sampling median value and the sampling minimum value is greater than the second preset threshold value, it is determined that there is a connecting line in the battery pack in a faulty state. The volatility and symmetry of the connecting line when a fault occurs are used to distinguish between battery cell failure and connecting line failure, and to accurately determine whether the connecting line has a fault.

[0103] In one embodiment, Figure 3 As shown, a lithium battery pack connecting line fault diagnosis method based on inconsistency assessment is provided, comprising:

[0104] S301, for each battery cell in the battery pack, obtaining the battery cell detection value of each battery cell in different detection dimensions.

[0105] Among them, multiple cells in the battery pack are connected by connecting wires.

[0106] S302: For each detection dimension, among a plurality of battery cell detection values ​​corresponding to the detection dimension, determine a battery cell detection median value corresponding to each battery cell detection value.

[0107] S303 , for each cell detection value, based on the numerical difference between the cell detection value and the cell detection median value, determine the deviation of the cell detection value corresponding to each cell detection value.

[0108] S304: Determine the consistency score of the battery pack in the detection dimension based on the sum of the deviations and the deviations corresponding to the detection values ​​of the battery cells.

[0109] S305, screening target detection weights that meet preset screening conditions among the detection weights.

[0110] S306, determining the associated weights of the detection dimensions corresponding to the target detection weights for the respective detection dimensions.

[0111] S307, based on the target detection weight and each associated weight, determine the comprehensive weight of each detection dimension.

[0112] S308: Determine the consistency score of the battery pack based on the consistency score of each detection dimension and the comprehensive weight of each detection dimension.

[0113] S309: When the consistency score of the battery pack is less than or equal to a preset score, determine that there is no connection line in a faulty state in the battery pack.

[0114] S310, when the consistency score of the battery pack is greater than a preset score, for each battery cell in the battery pack, a plurality of battery cell sampling values ​​of each battery cell in a target detection dimension are obtained.

[0115] S311 , for each battery cell, determining a sampling value fluctuation range of the battery cell based on a plurality of battery cell sampling values ​​of the battery cell.

[0116] S312, among the battery cells, selecting candidate battery cells whose sampling value fluctuation range is greater than a preset range.

[0117] S313 , for each candidate battery cell, determine a sampling maximum value, a sampling minimum value, and a sampling median value of the candidate battery cell among a plurality of battery cell sampling values ​​corresponding to the candidate battery cell.

[0118] S314, when the numerical difference between the sampling maximum value and the sampling median value is less than or equal to the first preset threshold, and / or the numerical difference between the sampling median value and the sampling minimum value is less than or equal to the second preset threshold, determine that there is no connection line in the battery pack that is in a faulty state.

[0119] S315, when the numerical difference between the sampling maximum value and the sampling median value is greater than the first preset threshold, and the numerical difference between the sampling median value and the sampling minimum value is greater than the second preset threshold, determine that there is a connection line in the battery pack that is in a faulty state.

[0120] For example, Figure 4 The battery pack connection line fault diagnosis method in the data missing scenario shown includes:

[0121] Step 1: Obtain battery pack related data based on the cloud platform or other hardware and software platforms, including each cell voltage, cell current, cell full charge capacity, sampling time, etc.;

[0122] Step 2: Calculate the deviation of each cell at each time for a certain feature :

[0123]

[0124] Step 3: Calculate the total deviation at each moment :

[0125]

[0126] Step 4: Calculate the single feature consistency score :

[0127]

[0128] Among them: the parameter a is adjusted according to the different orders of magnitude of different features. Since the data orders of different features are not necessarily the same (taking voltage and internal resistance as an example, since voltage data is generally in thousands of millivolts, while ohm internal resistance is generally in tens of milliohms, a difference of two orders of magnitude), this will result in the parameters of features with larger orders of magnitude being too small, so an adjustable parameter a is added after the formula.

[0129] Repeat steps 2 to 4 to calculate the consistency scores of all selected features.

[0130] Step 5: Sort the importance of all selected features: Assume there are n features, which are , according to subjective needs, these characteristics are ranked as .

[0131] Step 6: Give features of adjacent importance j and j-1 The importance between :

[0132]

[0133] in, The bigger the representative j-1 Relative to j More important.

[0134] Step 7: Calculate the weights of each feature :

[0135]

[0136] in: ,and .

[0137] Step 8: Get the battery pack consistency score:

[0138]

[0139] Step 9: Determine if there is a connection failure in the battery pack:

[0140] 1. If the consistency score is less than or equal to the threshold , it is considered that there is no connection line fault in the battery pack and the device diagnosis ends;

[0141] 2. If the consistency score is greater than the threshold , it is considered that the battery pack may have a connection cable failure, and the subsequent steps are performed to determine the faulty battery cell.

[0142] Step 10: Calculate the voltage range of each battery cell in the battery pack, and find P batteries with the largest voltage range as candidate batteries.

[0143] Step 11: Traverse the candidate cells and perform the following operations for each candidate cell:

[0144] 1. Find the numerical value and index value of the highest voltage and the lowest voltage;

[0145] 2. Find the maximum voltage, minimum voltage and median voltage.

[0146] Step 12: Check whether the difference Δ1 between the highest voltage and the median of each candidate cell and the difference Δ2 between the median and the lowest voltage exceed the set threshold upper_vol:

[0147] 1. If the above conditions are not met, it is considered that there is no connection line fault in the battery pack;

[0148] 2. If the above conditions are met, the cell number high_cell whose difference between the highest voltage and the median voltage exceeds upper_vol and the cell number low_cell whose difference between the median voltage and the lowest voltage exceeds upper_vol are returned. high_cell and low_cell are the faulty cell numbers.

[0149] In this embodiment, for each battery cell in the battery pack, the battery cell detection value of each battery cell in different detection dimensions is obtained. Multiple battery cells in the battery pack are connected by connecting wires to obtain multi-dimensional battery cell detection values, which provide a basis for subsequent multi-dimensional consistency analysis and improve the accuracy of consistency analysis. For each detection dimension, based on the deviation corresponding to each of the multiple battery cell detection values ​​corresponding to the detection dimension, the consistency score of the battery pack in the detection dimension is determined. Based on the consistency score of each detection dimension and the detection weight of each detection dimension, the consistency score of the battery pack is determined. With the above method, the consistency score of the battery pack reflects the consistency score of the battery pack in multiple detection dimensions, which improves the accuracy of the consistency score of the battery pack. Furthermore, based on the consistency score of the battery pack, it is determined whether the battery pack contains a connecting wire in a faulty state, and whether the connecting wire is in a faulty state is accurately determined, which improves the accuracy of abnormal analysis of the battery pack.

[0150] The above method can also bring the following beneficial effects:

[0151] Based on the sparse data obtained from the cloud platform, software and hardware platforms, the consistency of the battery pack can be accurately evaluated and the battery cells with connection cable faults can be diagnosed. While retaining the coefficient of variation to measure the discreteness of the data set, the outliers of individual data points are optimized, which is more in line with the situation of connection cable failure. At the same time, the G1 method of subjective weight allocation method is adopted, which takes into account the influence of different features on the consistency of the battery pack and allocates weights according to the importance of the features. The final consistency score can quickly and accurately reflect the consistency of the battery pack, which can greatly improve the diagnostic efficiency of battery connection cable failure.

[0152] It should be understood that, although the various steps in the flowcharts involved in the above-mentioned embodiments are displayed in sequence according to the indication of the arrows, these steps are not necessarily executed in sequence according to the order indicated by the arrows. Unless there is a clear explanation in this article, the execution of these steps does not have a strict order restriction, and these steps can be executed in other orders. Moreover, at least a part of the steps in the flowcharts involved in the above-mentioned embodiments can include multiple steps or multiple stages, and these steps or stages are not necessarily executed at the same time, but can be executed at different times, and the execution order of these steps or stages is not necessarily to be carried out in sequence, but can be executed in turn or alternately with other steps or at least a part of the steps or stages in other steps.

[0153] Based on the same inventive concept, the embodiment of the present application also provides a battery pack abnormality analysis device for implementing the battery pack abnormality analysis method involved above. The implementation scheme for solving the problem provided by the device is similar to the implementation scheme recorded in the above method, so the specific limitations in the embodiments of the abnormality analysis device for one or more battery packs provided below can refer to the limitations of the abnormality analysis method for battery packs above, and will not be repeated here.

[0154] In one embodiment, Figure 5 As shown, a battery pack abnormality analysis device is provided, comprising: an acquisition module 501, a first determination module 502, a second determination module 503 and an analysis module 504, wherein:

[0155] An acquisition module 501 is used to acquire, for each battery cell in a battery pack, a battery cell detection value of each battery cell in different detection dimensions; multiple battery cells in the battery pack are connected by connecting wires;

[0156] A first determination module 502 is used to determine, for each detection dimension, a consistency score of the battery pack in the detection dimension based on the deviations corresponding to the multiple battery cell detection values ​​corresponding to the detection dimension;

[0157] A second determination module 503, configured to determine a consistency score of the battery pack based on the consistency score of each detection dimension and the detection weight of each detection dimension;

[0158] The analysis module 504 is used to determine whether the battery pack contains a connection line in a faulty state based on the consistency score of the battery pack.

[0159] In one embodiment, the first determination module 502 is also used to determine a median cell detection value corresponding to each cell detection value among multiple cell detection values ​​corresponding to the detection dimension; for each cell detection value, based on the numerical difference between the cell detection value and the median cell detection value, determine the deviation of the cell detection value corresponding to each cell detection value; based on the sum of the deviations and the deviation corresponding to each cell detection value, determine the consistency score of the battery pack in the detection dimension.

[0160] In one embodiment, the second determination module 503 is also used to screen the target detection weights that meet the preset screening conditions among the detection weights; determine the associated weights of the detection dimensions corresponding to the target detection weights for each detection dimension; determine the respective comprehensive weights of each detection dimension based on the target detection weights and the respective associated weights; determine the consistency score of the battery pack based on the respective consistency scores of each detection dimension and the respective comprehensive weights of each detection dimension.

[0161] In one embodiment, the analysis module 504 is further used to determine that there is no connection line in a faulty state in the battery pack when the consistency score of the battery pack is less than or equal to a preset score; when the consistency score of the battery pack is greater than the preset score, for each battery cell in the battery pack, obtain multiple battery cell sampling values ​​of each battery cell in the target detection dimension; based on the statistical results of the battery cell sampling values, determine whether there is a connection line in a faulty state in the battery pack.

[0162] In one embodiment, the analysis module 504 is also used to determine, for each battery cell, a sampling value fluctuation range of the battery cell based on multiple battery cell sampling values ​​of the battery cell; screen candidate battery cells whose sampling value fluctuation range is greater than a preset range in each battery cell; and determine whether there are any connecting wires in a faulty state in the battery pack based on the statistical results of multiple battery cell sampling values ​​corresponding to the candidate battery cells.

[0163] In one embodiment, the analysis module 504 is also used to determine, for each candidate battery cell, a sampling maximum value, a sampling minimum value, and a sampling median value of the candidate battery cell among the multiple battery cell sampling values ​​corresponding to the candidate battery cell; when the numerical difference between the sampling maximum value and the sampling median value is less than or equal to a first preset threshold, and / or the numerical difference between the sampling median value and the sampling minimum value is less than or equal to a second preset threshold, determine that there is no connection line in the battery pack that is in a faulty state; when the numerical difference between the sampling maximum value and the sampling median value is greater than the first preset threshold, and the numerical difference between the sampling median value and the sampling minimum value is greater than the second preset threshold, determine that there is a connection line in the battery pack that is in a faulty state.

[0164] Each module in the above-mentioned abnormal analysis device for a battery pack can be implemented in whole or in part by software, hardware, or a combination thereof. Each of the above-mentioned modules can be embedded in or independent of a processor in a computer device in the form of hardware, or can be stored in a memory in a computer device in the form of software, so that the processor can call and execute the operations corresponding to each of the above modules.

[0165] In one embodiment, a computer device is provided. The computer device may be a server, and its internal structure diagram may be as follows: Figure 6As shown. The computer device includes a processor, a memory, an input / output interface (Input / Output, referred to as I / O) and a communication interface. Among them, the processor, the memory and the input / output interface are connected through a system bus, and the communication interface is connected to the system bus through the input / output interface. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store battery cell detection value data. The input / output interface of the computer device is used to exchange information between the processor and an external device. The communication interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, a battery pack abnormality analysis method is implemented.

[0166] Those skilled in the art will understand that Figure 6 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0167] In one embodiment, a computer device is provided, including a memory and a processor, wherein a computer program is stored in the memory, and the above method steps are implemented when the processor executes the computer program.

[0168] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored, and when the computer program is executed by a processor, the above method steps are implemented.

[0169] In one embodiment, a computer program product is provided, comprising a computer program, which implements the above method steps when executed by a processor.

[0170] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to the memory, database or other medium used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetoresistive random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. As an illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM). The database involved in each embodiment provided in this application may include at least one of a relational database and a non-relational database. Non-relational databases may include distributed databases based on blockchains, etc., but are not limited to this. The processor involved in each embodiment provided in this application may be a general-purpose processor, a central processing unit, a graphics processor, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, etc., but are not limited to this.

[0171] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0172] The above-described embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the present application. It should be pointed out that, for a person of ordinary skill in the art, several variations and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the attached claims.

Claims

1. A battery pack abnormality analysis method, characterized in that: The method comprises: For each battery cell in the battery pack, obtaining the battery cell detection value of each battery cell in different detection dimensions; the multiple battery cells in the battery pack are connected by connecting wires; For each of the detection dimensions, based on the deviations corresponding to the plurality of battery cell detection values ​​corresponding to the detection dimension, determine a consistency score of the battery pack in the detection dimension; Determining a consistency score of the battery pack based on the consistency score of each of the detection dimensions and the detection weight of each of the detection dimensions; Based on the consistency score of the battery pack, it is determined whether the battery pack contains a connection line in a faulty state.

2. The method according to claim 1, characterized in that The determining the consistency score of the battery pack in the detection dimension based on the deviations corresponding to the plurality of battery cell detection values ​​corresponding to the detection dimension comprises: Determine, among the multiple battery cell detection values ​​corresponding to the detection dimension, a battery cell detection median value corresponding to each of the battery cell detection values; For each of the battery cell detection values, based on the numerical difference between the battery cell detection value and the battery cell detection median value, determine the deviation of the battery cell detection value corresponding to each of the battery cell detection values; Based on the sum of the deviations and the deviations corresponding to the respective battery cell detection values, a consistency score of the battery pack in the detection dimension is determined.

3. The method according to claim 1, characterized in that The determining the consistency score of the battery pack based on the consistency score of each of the detection dimensions and the detection weight of each of the detection dimensions comprises: Among the detection weights, select the target detection weights that meet the preset screening conditions; Determine the detection dimensions corresponding to the target detection weights and their associated weights for each of the detection dimensions; Determining the comprehensive weight of each of the detection dimensions based on the target detection weight and each of the associated weights; A consistency score of the battery pack is determined based on the consistency score of each of the detection dimensions and the comprehensive weight of each of the detection dimensions.

4. The method according to claim 1, characterized in that: The determining, based on the consistency score of the battery pack, whether there is a connection line in a faulty state in the battery pack comprises: When the consistency score of the battery pack is less than or equal to a preset score, determining that no connection line in a faulty state exists in the battery pack; When the consistency score of the battery pack is greater than a preset score, for each battery cell in the battery pack, a plurality of battery cell sampling values ​​of each battery cell in the target detection dimension are obtained; Based on the statistical results of the sampling values ​​of each battery cell, it is determined whether there is a connecting line in a fault state in the battery pack.

5. The method according to claim 4, characterized in that The determining whether there is a connection line in a faulty state in the battery pack based on the statistical results of the sampling values ​​of each battery cell includes: For each of the battery cells, based on a plurality of battery cell sampling values ​​of the battery cell, determining a sampling value fluctuation range of the battery cell; Among the battery cells, selecting candidate battery cells whose sampling value fluctuation range is greater than a preset range; Based on the statistical results of the multiple battery cell sampling values ​​corresponding to the candidate battery cells, it is determined whether there is a connection line in a fault state in the battery pack.

6. The method according to claim 5, characterized in that The determining whether there is a connection line in a faulty state in the battery pack based on the statistical results of the plurality of battery cell sampling values ​​corresponding to the candidate battery cells comprises: For each of the candidate battery cells, determine a sampling maximum value, a sampling minimum value, and a sampling median value of the candidate battery cell among a plurality of battery cell sampling values ​​corresponding to the candidate battery cell; When the numerical difference between the sampling maximum value and the sampling median value is less than or equal to a first preset threshold value, and / or the numerical difference between the sampling median value and the sampling minimum value is less than or equal to a second preset threshold value, it is determined that there is no connection line in the battery pack that is in a faulty state; When the numerical difference between the sampling maximum value and the sampling median value is greater than the first preset threshold, and the numerical difference between the sampling median value and the sampling minimum value is greater than the second preset threshold, it is determined that there is a connection line in the battery pack that is in a faulty state.

7. A battery pack abnormality analysis device, characterized in that: The device comprises: An acquisition module is used to acquire, for each battery cell in a battery pack, respective battery cell detection values ​​of the battery cell in different detection dimensions; the multiple battery cells in the battery pack are connected by connecting wires; A first determination module is used to determine, for each of the detection dimensions, a consistency score of the battery pack in the detection dimension based on the deviations corresponding to the plurality of battery cell detection values ​​corresponding to the detection dimension; A second determination module, configured to determine a consistency score of the battery pack based on the consistency score of each of the detection dimensions and the detection weight of each of the detection dimensions; The analysis module is used to determine whether the battery pack contains a connection line in a faulty state based on the consistency score of the battery pack.

8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 6 are implemented.

9. A computer-readable storage medium having a computer program stored thereon, 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.

10. A computer program product, comprising 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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