Detection method of battery cell to be processed, electronic equipment and storage medium

Abstract

The invention discloses a detection method of a to-be-processed battery cell, electronic equipment and a computer readable storage medium. The method comprises the steps that a first residual capacity set of a target level in the battery is acquired, the target level comprises a plurality of second battery cells, and the first residual capacity set comprises the residual capacity of each second battery cell; then, according to the first residual capacity set, a residual capacity deviation value set is determined, and the residual capacity deviation value set comprises a deviation value between the residual capacity of each second battery cell and the median number of the first residual capacity set; then, according to the deviation threshold value, a target deviation value in the residual capacity deviation value set is determined; and finally, determining the second battery cell corresponding to the target deviation value as the battery cell to be processed. Therefore, by taking the parameter of the residual capacity as an analysis object, the problem can be found in time when the capacity of the battery cell is slightly attenuated. Moreover, the median number is used as the capacity reference, so that the interference of the extreme abnormal battery cell on the judgment standard can be avoided.

Description

Technical Field

[0001] This invention relates to the field of battery testing technology, and in particular to a method for testing battery cells to be processed, an electronic device, and a computer-readable storage medium. Background Technology

[0002] The omission or misjudgment of unprocessed battery cells directly affects the operational safety of power storage systems, exacerbates equipment wear, and reduces the energy utilization rate of power storage systems. Therefore, how to detect unprocessed battery cells has become an urgent problem to be solved. Summary of the Invention

[0003] This application provides a method for detecting battery cells to be processed, an electronic device, and a computer-readable storage medium.

[0004] This application provides a method for detecting battery cells to be processed, the method comprising: From the battery remaining capacity dataset, obtain the first remaining capacity set of the target level in the battery, wherein the battery remaining capacity dataset includes the remaining capacity of each first cell in the battery, the target level includes multiple second cells, and the first remaining capacity set includes the remaining capacity of each second cell. Based on the first set of remaining capacity, a set of remaining capacity deviation values ​​is determined, wherein the set of remaining capacity deviation values ​​includes the deviation value of each remaining capacity from the median number of the first set of remaining capacity; Based on the deviation threshold, determine the target deviation value in the set of remaining capacity deviation values; The second cell corresponding to the target deviation value is identified as the cell to be processed.

[0005] Thus, from the battery remaining capacity dataset, a first remaining capacity set for the target level in the battery is obtained. This dataset includes the remaining capacity of each first cell in the battery, and the target level includes multiple second cells; the first remaining capacity set includes the remaining capacity of each second cell. Next, based on the first remaining capacity set, a remaining capacity deviation value set is determined. This set includes the deviation of each second cell's remaining capacity from the median of the first remaining capacity set. Then, based on a deviation threshold, a target deviation value is determined within the remaining capacity deviation value set. Finally, the second cell corresponding to the target deviation value is identified as the cell to be processed. In this way, by using remaining capacity as the analysis object, problems can be detected promptly when cell capacity shows only slight degradation. Furthermore, by using the median as the capacity benchmark, interference from extremely abnormal cells on the judgment standard can be avoided, making the benchmark more closely reflect the actual capacity level of most healthy cells, reducing the risk of misjudgment due to benchmark distortion. In addition, it can directly and accurately locate a single faulty cell, narrowing the maintenance scope from the entire battery pack or battery cluster to the cell, thereby reducing maintenance costs.

[0006] In some embodiments, the method further includes: Collect raw data for each first cell in the battery, including the current, voltage and temperature information of the first cell; Based on a preset algorithm, the current information, voltage information, and temperature information are processed to determine the remaining capacity of each first cell; The remaining battery capacity dataset is determined based on the remaining capacity of each of the first cells.

[0007] In this way, raw data is collected from each first cell in the battery, including current, voltage, and temperature information. Next, based on a preset algorithm, the current, voltage, and temperature information are processed to determine the remaining capacity of each first cell. Finally, based on the remaining capacity of each first cell, a dataset of the battery's remaining capacity is determined. This process, by processing the collected current, voltage, and temperature data and incorporating a preset algorithm to correct for the influence of environmental and operational conditions, reduces the error in remaining capacity calculation, providing a data foundation for the subsequent calculation and determination of the remaining capacity deviation value set.

[0008] In some implementations, the deviation value includes a first deviation value, and determining the set of remaining capacity deviation values ​​based on the first set of remaining capacity includes: Sort the remaining capacities in the first remaining capacity set in descending order to determine the median of the first remaining capacity set; For the remaining capacity of each second cell, the first deviation value is determined by calculating based on the first preset relationship and the median of the first remaining capacity set. Based on the first deviation value, determine the set of remaining capacity deviation values.

[0009] Thus, the remaining capacities in the first remaining capacity set are sorted in descending order to determine the median of the first remaining capacity set. Next, for each second cell, the remaining capacity is calculated based on the first preset formula and the median of the first remaining capacity set to determine the first deviation value. Finally, based on the first deviation value, a set of remaining capacity deviation values ​​is determined. In this way, by using the first preset formula and combining it with the median to calculate the deviation value, the degree of deviation of the cell at different remaining capacity levels can be accurately reflected.

[0010] In some implementations, the deviation value includes a second deviation value, and determining the set of remaining capacity deviation values ​​based on the first set of remaining capacity includes: Sort the remaining capacities in the first remaining capacity set in descending order to determine the median of the first remaining capacity set; For each of the second battery cells, the remaining capacity is calculated based on the first preset relationship and the median of the first remaining capacity set to determine the initial deviation value corresponding to the second battery cell. The position weight of each second cell is multiplied by the corresponding initial deviation value to determine the second deviation value, wherein the position weight is determined based on the position of the second cell within the battery. The set of remaining capacity deviation values ​​is determined based on the second deviation value.

[0011] Thus, the remaining capacities in the first remaining capacity set are sorted in descending order to determine the median of the first remaining capacity set. Next, for each second cell, the remaining capacity is calculated based on the first preset formula and the median of the first remaining capacity set to determine the initial deviation value corresponding to the second cell. Then, the position weight of each second cell is multiplied by the corresponding initial deviation value to determine the second deviation value; the position weight is determined based on the second cell's position within the battery. Finally, based on the second deviation value, a set of remaining capacity deviation values ​​is determined. In this way, the inherent aging risk at different locations is quantified through position weights, avoiding the underestimation of slight deviations in cells at high-risk locations and the misjudgment of normal deviations in cells at low-risk locations, ensuring that the calculated deviation values ​​closely reflect actual operating scenarios.

[0012] In some embodiments, the method further includes: Determine the current standard deviation based on the first set of remaining capacity; Based on the second preset relationship, the deviation threshold is determined according to the pre-configured benchmark standard deviation, discharge coefficient, benchmark deviation threshold and current standard deviation.

[0013] Thus, based on the first set of remaining capacity, the current standard deviation is determined. Next, based on the second preset relationship, and according to the pre-configured baseline standard deviation, discharge coefficient, baseline deviation threshold, and current standard deviation, the deviation threshold is determined. In this way, the temperature rise rate threshold for ambient temperatures exceeding the first ambient temperature threshold or falling below the second ambient temperature threshold is corrected by a first preset relaxation factor, ensuring that the temperature rise rate threshold accurately matches the normal temperature rise patterns under different environments, covering all environmental application scenarios of the battery management system.

[0014] In some implementations, determining the target deviation value in the set of remaining capacity deviation values ​​based on a deviation threshold includes: The deviation values ​​whose absolute value is greater than the deviation threshold in the set of remaining capacity deviation values ​​are determined as the target deviation values.

[0015] Thus, the deviation values ​​whose absolute value in the set of remaining capacity deviation values ​​is greater than the deviation threshold are determined as the target deviation values. By defining the target deviation values ​​as those whose absolute value in the set of remaining capacity deviation values ​​is greater than the deviation threshold, the determination of subsequent cells to be processed can be made without additional judgment.

[0016] In some embodiments, the method further includes: Obtain the DC internal resistance set of the target level, the DC internal resistance set including the DC internal resistance of each of the second cells; Based on the DC internal resistance set, a DC internal resistance deviation value set corresponding to the target level is determined. The DC internal resistance deviation value set includes the deviation value between the DC internal resistance of each second cell and the median number of the DC internal resistance deviation value set. Obtain a second set of remaining capacity for the battery cell to be processed, wherein the second set of remaining capacity includes the remaining capacity of the battery cell to be processed within a preset period; Based on the second remaining capacity set, calculate the capacity decay rate of the cell to be processed; The tag information of the cell to be processed is determined based on the DC internal resistance deviation value set, the capacity decay rate, and the preset DC internal resistance threshold.

[0017] Thus, the DC internal resistance set for the target level is obtained, including the DC internal resistance of each second cell. Next, based on the DC internal resistance set, a DC internal resistance deviation value set corresponding to the target level is determined, including the deviation between the DC internal resistance of each second cell and the median of the DC internal resistance deviation value set. Then, a second remaining capacity set for the cells to be processed is obtained, including the remaining capacity of the cells to be processed within a preset period. Subsequently, based on the second remaining capacity set, the capacity decay rate of the cells to be processed is calculated. Finally, based on the DC internal resistance deviation value set, the capacity decay rate, and a preset DC internal resistance threshold, the tag information of the cells to be processed is determined. In this way, by comprehensively analyzing the cells to be processed using the DC internal resistance deviation value set, the capacity decay rate, and the preset DC internal resistance threshold, the tag information of the cells to be processed can be determined, improving the comprehensiveness of the judgment of the status of the cells to be processed and improving the accuracy of tag information generation.

[0018] In some embodiments, determining the tag information of the cell to be processed based on the DC internal resistance deviation value set, the capacity decay rate, and a preset DC internal resistance threshold includes: Based on the set of DC internal resistance deviation values, determine the target DC internal resistance deviation value corresponding to the cell to be processed; If the target DC internal resistance deviation value is greater than or equal to the preset DC internal resistance threshold, the label of the cell to be processed is determined to be the first label; If the target DC internal resistance deviation value is less than the preset DC internal resistance threshold, or the capacity decay rate is greater than the preset decay threshold, the tag of the cell to be processed is determined to be the second tag.

[0019] Thus, based on the set of DC internal resistance deviation values, the target DC internal resistance deviation value corresponding to the cell to be processed is determined. Next, if the target DC internal resistance deviation value is greater than or equal to a preset DC internal resistance threshold, the cell to be processed is designated as the first tag. Finally, if the target DC internal resistance deviation value is less than the preset DC internal resistance threshold, or the capacity decay rate is greater than a preset decay threshold, the cell to be processed is designated as the second tag. In this way, the tagged alarm information can intuitively reflect the cell's risk level, and with corresponding handling suggestions, it can provide clear action guidance for maintenance personnel.

[0020] This application provides an electronic device, which includes a processor and a memory. The memory stores a computer program, and the processor executes the computer program to implement the steps of the method described above.

[0021] This application provides a computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements the steps of the method described above.

[0022] Additional aspects and advantages of embodiments of this application will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of embodiments of this application. Attached Figure Description

[0023] The above and / or additional aspects and advantages of this application will become apparent and readily understood from the description of the embodiments taken in conjunction with the following drawings, wherein: Figure 1 This is one of the flowcharts illustrating a method for detecting battery cells to be processed according to certain embodiments of this application; Figure 2 This is a second schematic flowchart of a method for detecting battery cells to be processed according to certain embodiments of this application; Figure 3 This is a third schematic flowchart of a method for detecting battery cells to be processed according to certain embodiments of this application; Figure 4 This is a fourth schematic flowchart of a method for detecting battery cells to be processed according to certain embodiments of this application; Figure 5 This is the fifth flowchart illustrating the detection method for battery cells to be processed according to certain embodiments of this application; Figure 6 This is a schematic flowchart of a method for detecting battery cells to be processed according to certain embodiments of this application; Figure 7 This is the seventh flowchart illustrating the detection method for battery cells to be processed according to certain embodiments of this application; Figure 8 This is the eighth flowchart of a method for detecting battery cells to be processed according to certain embodiments of this application; Figure 9 This is the ninth flowchart of a method for detecting battery cells to be processed according to certain embodiments of this application. Detailed Implementation

[0024] The embodiments of this application are described in detail below. Examples of the embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the embodiments of this application, and should not be construed as limiting the embodiments of this application.

[0025] Electric energy storage systems have become a key infrastructure in fields such as new energy storage and electric vehicles. As the smallest functional unit for energy storage and release in a system, the consistency of the health status of the battery cell can affect the overall performance of the electric energy storage system.

[0026] Specifically, energy storage systems often consist of multiple battery cells connected in series or parallel to form battery clusters or stacks. If the performance degradation of a single cell is not identified in time, it can propagate to higher levels through the bottleneck effect. The omission or misjudgment of these problematic cells directly impacts the operational safety of the energy storage system, exacerbates equipment wear, and reduces the system's energy utilization rate. For example, a neglected degraded cell may reach full charge prematurely due to insufficient actual capacity during the charging phase, leading to overcharging, electrolyte decomposition, and a sudden temperature rise. Furthermore, blindly replacing healthy cells as degraded can disrupt the consistency and matching of the battery pack; differences in capacity and internal resistance between new and old cells can create new safety hazards.

[0027] Therefore, how to test the treated battery cells has become an urgent problem to be solved.

[0028] Based on the above issues, please refer to Figure 1 This application provides a method for detecting battery cells to be processed, the method comprising: 011: Obtain the first set of remaining capacity for the target level in the battery from the remaining battery capacity dataset; 012: Determine the set of remaining capacity deviation values ​​based on the first set of remaining capacity; 013: Determine the target deviation value in the set of remaining capacity deviation values ​​based on the deviation threshold; 014: The second cell corresponding to the target deviation value is identified as the cell to be processed.

[0029] This application also provides an electronic device, including a memory and a processor. The method for detecting a battery cell to be processed according to this application can be implemented by the electronic device described in this application. Specifically, the memory stores a computer program, and the processor is used to obtain a first set of remaining capacity at a target level in the battery from a set of remaining battery capacity data. And, based on the first set of remaining capacity, determine a set of remaining capacity deviation values. The processor is further used to determine a target deviation value in the set of remaining capacity deviation values ​​based on a deviation threshold. And determine a second battery cell corresponding to the target deviation value as a battery cell to be processed.

[0030] This application also provides a battery testing apparatus. The method for detecting the cell to be processed according to this application can be implemented by the battery testing apparatus of this application. Specifically, the battery testing apparatus includes an acquisition module and a determination module. The acquisition module is used to acquire a first set of remaining capacity at a target level in the battery from a set of remaining capacity data. The determination module is used to determine a set of remaining capacity deviation values ​​based on the first set of remaining capacity, and to determine a target deviation value in the set of remaining capacity deviation values ​​based on a deviation threshold. The second cell corresponding to the target deviation value is then determined as the cell to be processed.

[0031] Specifically, a battery refers to the battery system in an electric energy storage system, which includes five levels: battery compartment, battery stack, battery cluster, battery pack, and battery cell.

[0032] Battery remaining capacity dataset refers to a pre-stored set of remaining capacity data covering all battery cells.

[0033] The target level refers to the selected analytical unit in cell testing. In some implementations, the target level is typically a selected battery stack or cluster. A battery compartment may include multiple battery stacks or clusters, and a battery stack or cluster typically includes dozens of battery packs and thousands of cells. Thus, by obtaining the first set of remaining capacity at the target level, a sufficient number of cells can be covered to form a healthy population benchmark, while avoiding insufficient sample size due to a too-low level.

[0034] The first remaining capacity set refers to the data set consisting of the remaining capacity of all second-level cells within the target level. The remaining capacity is the effective amount of electricity that a cell can release in its current state, calculated by the battery management system using a preset algorithm; it is a parameter reflecting the health status of the cell.

[0035] The first cell refers to all the individual energy storage cells in a battery that require health status testing; it is the smallest functional unit for energy storage.

[0036] The second cell refers to a single cell within the target level that is to be tested. It should be noted that the second cell is essentially the same as the aforementioned first cell, and the only difference in name is due to the different contexts. The first cell encompasses all cells in the battery, from the battery compartment level to the cell level.

[0037] The median value refers to the middle value after sorting all remaining capacity values ​​in the first remaining capacity set in ascending (or descending) order. If the number of cells in the first remaining capacity set is even, the arithmetic mean of the two middle values ​​is taken.

[0038] The remaining capacity deviation value set refers to the set of differences calculated based on the remaining capacity and median number of each cell in the first remaining capacity set. Each difference, i.e., the remaining capacity deviation value, reflects the degree of capacity deviation of the corresponding cell relative to the group of healthy cells.

[0039] The deviation threshold refers to the critical value used to determine whether the remaining capacity is abnormal. It is not a fixed value and can be dynamically adjusted according to the real-time operating conditions of the battery system to ensure the accuracy of fault state detection in different scenarios.

[0040] The target deviation value refers to the deviation value where the absolute value of the remaining capacity deviation value set is greater than the deviation threshold. Among them, the cells corresponding to the target deviation value have deviated from the capacity benchmark of the healthy cell group and are considered abnormal objects with the risk of performance degradation, requiring intervention.

[0041] The cells to be processed refer to the second cells that correspond one-to-one with the target deviation value. These are individual cells whose capacity deviation exceeds the allowable range, poses a safety hazard, or affects system energy efficiency, and need to be processed through methods such as supplementary charging, equalization maintenance, or online replacement.

[0042] Retrieve the remaining capacity of each second cell in the target level from a database containing the remaining capacity of all battery cells, forming the first set of remaining capacity.

[0043] Next, for the remaining capacity of each second cell, the deviation value between each cell and the median value of the first remaining capacity set is calculated based on the median value, and all deviation values ​​are integrated to form a set of remaining capacity deviation values.

[0044] Subsequently, each deviation value in the remaining capacity deviation value set is compared with the preset deviation threshold one by one, and the deviation values ​​with an absolute value greater than the deviation threshold are selected as the target deviation values.

[0045] Finally, based on the target deviation value, the identification information of the corresponding second cell is retrieved, and these cells are officially identified as cells to be processed, providing precise guidance for subsequent maintenance. In this way, by clearly marking the cells to be processed, maintenance personnel are provided with accurate maintenance targets, improving maintenance efficiency and ensuring the energy utilization rate of the battery.

[0046] In summary, for the detection method and electronic device for the battery cell to be processed provided in this application, a first remaining capacity set of the target level in the battery is obtained from the battery remaining capacity dataset. The battery remaining capacity dataset includes the remaining capacity of each first battery cell in the battery, the target level includes multiple second battery cells, and the first remaining capacity set includes the remaining capacity of each second battery cell. Next, a remaining capacity deviation value set is determined based on the first remaining capacity set. This set includes the deviation value between the remaining capacity of each second battery cell and the median of the first remaining capacity set. Then, a target deviation value is determined based on a deviation threshold. Finally, the second battery cell corresponding to the target deviation value is identified as the battery cell to be processed. In this way, by using the remaining capacity parameter as the analysis object, problems can be detected promptly when the battery cell capacity shows only a slight decrease. Furthermore, by using the median as the capacity benchmark, interference from extremely abnormal battery cells on the judgment standard can be avoided, making the benchmark more closely match the actual capacity level of most healthy battery cells, reducing the risk of misjudgment due to benchmark distortion. In addition, it can directly and accurately locate a single faulty cell, narrowing the maintenance scope from the entire battery pack or battery cluster to the cell, thereby reducing maintenance costs.

[0047] Please see Figure 2 In some implementations, the method further includes: 015: Collect raw data for each first cell in the battery; 016: Based on a preset algorithm, the current, voltage, and temperature information are processed to determine the remaining capacity of each first cell; 017: Determine the battery remaining capacity dataset based on the remaining capacity of each first cell.

[0048] In some implementations, the processor is also used to acquire raw data for each first cell in the battery, and to process current, voltage, and temperature information based on a preset algorithm to determine the remaining capacity of each first cell. Furthermore, based on the remaining capacity of each first cell, a dataset of the remaining battery capacity is determined.

[0049] In some embodiments, the battery testing apparatus further includes a data acquisition module, which is used to acquire raw data from each first cell in the battery, and to process the current, voltage, and temperature information based on a preset algorithm to determine the remaining capacity of each first cell. Based on the remaining capacity of each first cell, a dataset of the remaining battery capacity is then determined.

[0050] Specifically, raw data refers to basic operating parameters collected directly from the operation of the first battery cell without any processing. These parameters reflect the real-time working status and health level of the first battery cell and include three key parameters: current information, voltage information, and temperature information.

[0051] Current information refers to the current-related data formed by the directional movement of charge during the charging and discharging process of the first cell, including current magnitude, current direction and current change rate, etc. The unit is usually ampere (A), which directly reflects the rate of energy input and output of the cell.

[0052] Voltage information refers to the potential difference data between the positive and negative terminals of the first cell, including real-time operating voltage, resting voltage, and charge / discharge cutoff voltage, and the unit is usually volt (V).

[0053] Temperature information refers to the temperature data of the first battery cell during operation, including the cell surface temperature, internal core temperature, temperature change trend, etc., and the unit is usually degrees Celsius (°C).

[0054] Preset algorithms refer to standardized algorithms used to analyze raw cell data and calculate remaining capacity. They typically include ampere-hour integration, model estimation (such as Kalman filter model and equivalent circuit model), and charge-discharge curve analysis.

[0055] The remaining capacity of the first cell refers to the maximum amount of electricity that the first cell can release after being fully charged in its current healthy state. It is a parameter that reflects the degree of cell aging and energy storage capacity, and the unit is usually ampere-hour (Ah).

[0056] The battery management system (BMS) utilizes a cell-level data acquisition module to capture comprehensive data for each individual cell, employing either real-time or periodic acquisition methods. The acquisition frequency can be dynamically adjusted based on the system's operating status. For example, high-frequency real-time acquisition (e.g., once per second) is used during peak charging and discharging periods, while low-frequency periodic acquisition (e.g., once per minute) is used during periods of inactivity, ensuring data timeliness while reducing system energy consumption. During acquisition, high-precision sensors (current, voltage, and temperature sensors) directly acquire raw data such as the cell's current magnitude and direction, instantaneous voltage values, and real-time temperature values, ensuring data errors are controlled within industry-acceptable limits.

[0057] The pre-processed multi-dimensional data is comprehensively analyzed by calling a preset algorithm: If the ampere-hour integration method is used, the charge and discharge capacity needs to be calculated based on the time integration of the current information, and the remaining capacity is obtained by combining the initial capacity of the cell and the attenuation coefficient; if the model estimation method is used, the current, voltage and temperature data need to be input into the preset electrochemical model or equivalent circuit model, and the remaining capacity is obtained by iterative algorithm solution; if the charge and discharge curve analysis method is used, the feature points in the charge and discharge curve need to be extracted, compared with the standard curve, and the remaining capacity is inferred.

[0058] Finally, the remaining capacity of each first cell, calculated by the algorithm, is associated with the cell's unique identifier and stored in the database to determine the battery remaining capacity dataset. It should be noted that the recorded content may include the remaining capacity value, the recording timestamp, the type of algorithm used, and the original data source.

[0059] In some implementations, before calling a preset algorithm to perform comprehensive analysis on the preprocessed multidimensional data, it may be necessary to preprocess the collected raw data, including outlier removal, data filtering, and data alignment.

[0060] In this way, raw data is collected from each first cell in the battery, including current, voltage, and temperature information. Next, based on a preset algorithm, the current, voltage, and temperature information are processed to determine the remaining capacity of each first cell. Finally, based on the remaining capacity of each first cell, a dataset of the battery's remaining capacity is determined. This process, by processing the collected current, voltage, and temperature data and incorporating a preset algorithm to correct for the influence of environmental and operational conditions, reduces the error in remaining capacity calculation, providing a data foundation for the subsequent calculation and determination of the remaining capacity deviation value set.

[0061] Please see Figure 3 In some implementations, the deviation value includes a first deviation value, and step 012 (determining the set of remaining capacity deviation values ​​based on the first set of remaining capacity) includes: 0121: Sort the remaining capacities in the first remaining capacity set in descending order and determine the median of the first remaining capacity set; 0122: For the remaining capacity of each second cell, calculate the first deviation value based on the first preset relationship and the median of the first remaining capacity set; 0123: Determine the set of remaining capacity deviation values ​​based on the first deviation value.

[0062] In some embodiments, the processor is further configured to sort the remaining capacities in the first remaining capacity set in descending order to determine the median of the first remaining capacity set. And for each second cell, to calculate the deviation value based on a first preset formula and the median of the first remaining capacity set. And to determine a set of remaining capacity deviation values ​​based on the deviation values.

[0063] In some implementations, the determining module is further configured to sort the remaining capacities in the first remaining capacity set in descending order and determine the median of the first remaining capacity set. And for each second cell, the module calculates the deviation value based on a first preset formula and the median of the first remaining capacity set. And based on the deviation values, it determines a set of remaining capacity deviation values.

[0064] Specifically, descending order refers to arranging all remaining capacity data in the first remaining capacity set in descending order. In some implementations, ascending order can also be used, that is, arranging all remaining capacity data in the first remaining capacity set in ascending order.

[0065] The first preset relationship refers to the formula used to calculate the deviation between the remaining capacity and the median value, which can accurately quantify the relative deviation of the battery cell from the healthy population. It is usually the formula for calculating the relative deviation rate, namely (median value - remaining capacity) / median value.

[0066] The first deviation value refers to the quantitative value that reflects the degree of deviation between a single first remaining capacity and the median number, calculated through the first preset relational formula.

[0067] First, extract all remaining capacity data from the first remaining capacity set and sort them in descending order to form an ordered data sequence. Then, determine the median number based on the length of the data sequence, which is the number of second-level cells within the target tier.

[0068] Subsequently, for each second cell's remaining capacity, the first preset formula is called one by one to calculate the deviation value, that is, the median number of the first remaining capacity set and the remaining capacity are substituted into the first preset formula to calculate the corresponding first deviation value.

[0069] Then, the first deviation values ​​of all the second cells are associated and stored in the order of cell identification, and a unique mapping relationship is established between each first deviation value and the corresponding second cell.

[0070] Thus, the remaining capacities in the first remaining capacity set are sorted in descending order to determine the median of the first remaining capacity set. Next, for each second cell, the remaining capacity is calculated based on the first preset formula and the median of the first remaining capacity set to determine the first deviation value. Finally, based on the first deviation value, a set of remaining capacity deviation values ​​is determined. In this way, by using the first preset formula and combining it with the median to calculate the deviation value, the degree of deviation of the cell at different remaining capacity levels can be accurately reflected.

[0071] Please see Figure 4 In some implementations, the deviation value includes a second deviation value. Step 012 (determining the set of remaining capacity deviation values ​​based on the first set of remaining capacity) includes: 0124: Sort the remaining capacities in the first remaining capacity set in descending order and determine the median of the first remaining capacity set; 0125: For the remaining capacity of each second cell, calculate based on the first preset relationship and the median of the first remaining capacity set to determine the initial deviation value corresponding to the second cell; 0126: Multiply the position weight of each second cell with the corresponding initial deviation value to determine the second deviation value. The position weight is determined based on the position of the second cell within the battery. 0127: Determine the set of remaining capacity deviation values ​​based on the second deviation value.

[0072] In some embodiments, the processor is further configured to sort the remaining capacities in the first remaining capacity set in descending order to determine the median of the first remaining capacity set. And for each second cell, the processor calculates an initial deviation value corresponding to the second cell based on a first preset formula and the median of the first remaining capacity set. The processor is further configured to multiply the position weight of each second cell by its corresponding initial deviation value to determine a second deviation value. And based on the second deviation value, the processor determines a set of remaining capacity deviation values.

[0073] In some embodiments, the determining module is further configured to sort the remaining capacities in the first remaining capacity set in descending order and determine the median of the first remaining capacity set. And for each second cell, the module calculates the initial deviation value corresponding to the second cell based on a first preset formula and the median of the first remaining capacity set. The determining module is further configured to multiply the position weight of each second cell by its corresponding initial deviation value to determine a second deviation value. And based on the second deviation value, the module determines a set of remaining capacity deviation values.

[0074] Specifically, the initial deviation value refers to the quantitative value calculated by the first preset relationship, which only reflects the inherent deviation of the cell capacity from the median number, without considering additional risk factors such as physical location. That is, the cells in different locations within the battery pack are affected by voltage distribution, temperature conduction, mechanical stress, etc., and have different aging rates.

[0075] Location weight refers to the risk coefficient assigned to the second cell based on its physical location within the battery pack, and is set according to the difference in aging rate of cells in different locations. For example, high-risk locations are assigned a weight higher than 1.0, such as 1.2. Low-risk locations are assigned a standard weight of 1.0.

[0076] The second deviation value refers to the product of the initial deviation value and the corresponding position weight. It reflects both the degree of capacity deviation and the risk of physical location, and is a core quantitative indicator that can truly reflect the health risk of the battery cell.

[0077] First, extract all remaining capacity data from the first remaining capacity set and sort them in descending order to form an ordered data sequence. Then, determine the median number based on the length of the data sequence, which is the number of second-level cells within the target tier.

[0078] Subsequently, for each second cell's remaining capacity, the first preset formula is called one by one to calculate the deviation value, that is, the median number of the first remaining capacity set and the remaining capacity are substituted into the first preset formula to calculate the corresponding initial deviation value.

[0079] Then, the initial deviation value of each cell is multiplied by the corresponding position weight to obtain the second deviation value.

[0080] Finally, the second deviation values ​​of all the second cells are associated and stored in the order of cell identification, and a unique mapping relationship is established between each second deviation value and the corresponding second cell.

[0081] Thus, the remaining capacities in the first remaining capacity set are sorted in descending order to determine the median of the first remaining capacity set. Next, for each second cell, the remaining capacity is calculated based on the first preset formula and the median of the first remaining capacity set to determine the initial deviation value corresponding to the second cell. Then, the position weight of each second cell is multiplied by the corresponding initial deviation value to determine the second deviation value; the position weight is determined based on the second cell's position within the battery. Finally, based on the second deviation value, a set of remaining capacity deviation values ​​is determined. In this way, the inherent aging risk at different locations is quantified through position weights, avoiding the underestimation of slight deviations in cells at high-risk locations and the misjudgment of normal deviations in cells at low-risk locations, ensuring that the calculated deviation values ​​closely reflect actual operating scenarios.

[0082] Please see Figure 5 In some implementations, the method further includes: 018: Determine the current standard deviation based on the first set of remaining capacity; 019: Based on the second preset relationship, the deviation threshold is determined according to the pre-configured benchmark standard deviation, discharge coefficient, benchmark deviation threshold and current standard deviation.

[0083] In some implementations, the determining module is further configured to determine the current standard deviation based on a first set of remaining capacity, and to determine a deviation threshold based on a second preset relationship, according to a pre-configured reference standard deviation, discharge coefficient, reference deviation threshold, and current standard deviation.

[0084] In some implementations, the processor is further configured to determine the current standard deviation based on a first set of remaining capacity, and to determine a deviation threshold based on a second preset relation, according to a pre-configured reference standard deviation, discharge coefficient, reference deviation threshold, and current standard deviation.

[0085] Specifically, the pre-configured baseline deviation threshold refers to a basic critical value set in advance before system commissioning or during maintenance, based on battery type, design capacity, industry technical specifications, and operation and maintenance strategies. It serves as a reference benchmark for the dynamic generation of deviation thresholds. In some implementations, the baseline deviation threshold is typically a fixed value or a fixed range, such as 3Ah-5Ah, which can be flexibly adjusted on the monitoring interface according to actual needs.

[0086] Deviation threshold refers to a dynamic critical value used to filter abnormal capacity deviations. It can determine whether the remaining capacity deviation exceeds the normal range. The value of the deviation threshold will be adaptively adjusted according to changes in the system capacity status.

[0087] The current standard deviation (SSD) is a quantified value representing the dispersion of all remaining capacity data in the first set of remaining capacity relative to the set's mean. It reflects the capacity consistency of the first set of remaining capacity. A smaller SSD indicates a more concentrated capacity distribution and a higher level of system health. A larger SSD indicates greater capacity dispersion and poorer system consistency.

[0088] The baseline standard deviation refers to the standard deviation of an energy storage system in the early stages of operation, such as the first 100 charge-discharge cycles, calculated based on the first remaining capacity set. It is a benchmark value that characterizes the inherent level of dispersion in the healthy state of the system and is used to compare and analyze the degree of consistency deterioration of the current batteries.

[0089] The discharge coefficient refers to a pre-set characteristic adaptation coefficient based on factors such as battery type, charge / discharge rate, and operating environment. It is used to correct the influence of charge / discharge state on capacity deviation determination. It should be noted that the discharge coefficient is usually a fixed value verified by experiments, such as 0.8-1.2, and can be flexibly adjusted according to the actual application scenario.

[0090] The second preset relation refers to the standardized mathematical formula used to fuse multi-dimensional parameters to generate the final deviation threshold. In some implementations, the second preset relation may be: ΔQ = ΔQ0 + k*max(0, (σ x -σ0)), where ΔQ0 is the benchmark deviation threshold, σ x σ0 is the current standard deviation, σ0 is the baseline standard deviation, k is the discharge coefficient, and ΔQ is the deviation threshold.

[0091] The deviation threshold refers to the dynamic critical value calculated by the second preset formula, which can determine whether the remaining capacity deviation exceeds the normal range. The value of the deviation threshold is dynamically adjusted according to the current standard deviation, discharge coefficient and other parameters.

[0092] After obtaining the first remaining capacity set of the target level, calculate the mean of the dataset, and then calculate the current standard deviation using the statistical standard deviation formula.

[0093] Next, the pre-configured baseline standard deviation σ0, discharge coefficient k, and baseline deviation threshold ΔQ0 are extracted. In some implementations, it is also necessary to verify the validity of all parameters to ensure that they are within a reasonable range of values, so as to avoid invalid parameters affecting the threshold calculation results.

[0094] Then, the second preset relation is called to perform multi-parameter fusion of the benchmark standard deviation, discharge coefficient, benchmark deviation threshold and current standard deviation to determine the deviation threshold.

[0095] Thus, based on the first set of remaining capacity, the current standard deviation is determined. Next, based on the second preset relationship, and according to the pre-configured baseline standard deviation, discharge coefficient, baseline deviation threshold, and current standard deviation, the deviation threshold is determined. In this way, the temperature rise rate threshold for ambient temperatures exceeding the first ambient temperature threshold or falling below the second ambient temperature threshold is corrected by a first preset relaxation factor, ensuring that the temperature rise rate threshold accurately matches the normal temperature rise patterns under different environments, covering all environmental application scenarios of the battery management system.

[0096] Please see Figure 6 In some implementations, step 013 (determining the target deviation value in the set of remaining capacity deviation values ​​based on the deviation threshold) includes: 0131: The deviation values ​​whose absolute value is greater than the deviation threshold in the set of remaining capacity deviation values ​​are determined as the target deviation values.

[0097] In some implementations, the determining module is further configured to determine the deviation values ​​whose absolute value is greater than the deviation threshold from the set of remaining capacity deviation values ​​as the target deviation values.

[0098] In some implementations, the processor is further configured to determine the target deviation value as the deviation value that has an absolute value greater than the deviation threshold from the set of remaining capacity deviation values.

[0099] Specifically, the absolute value of each deviation value in the set of remaining capacity deviation values ​​is compared with the deviation threshold. The deviation values ​​whose absolute values ​​in the set of remaining capacity deviation values ​​are greater than the deviation threshold are determined as the target deviation values.

[0100] Thus, the deviation values ​​whose absolute value in the set of remaining capacity deviation values ​​is greater than the deviation threshold are determined as the target deviation values. By defining the target deviation values ​​as those whose absolute value in the set of remaining capacity deviation values ​​is greater than the deviation threshold, the determination of subsequent cells to be processed can be made without additional judgment.

[0101] Please see Figure 7 In some implementations, the method further includes: 019: Obtain the DC internal resistance set of the target level; 020: Based on the DC internal resistance set, determine the DC internal resistance deviation value set corresponding to the target level; 021: Obtain the second set of remaining capacity for the cells to be processed; 022: Calculate the capacity decay rate of the cell to be processed based on the second remaining capacity set; 023: Determine the tag information of the cell to be processed based on the DC internal resistance deviation value set, capacity decay rate and preset DC internal resistance threshold.

[0102] In some embodiments, the determining module is further configured to acquire a set of DC internal resistance values ​​for a target level, and to determine a set of DC internal resistance deviation values ​​corresponding to the target level based on the set of DC internal resistance values. It also acquires a second set of remaining capacity values ​​for the cells to be processed. The determining module is further configured to calculate the capacity decay rate of the cells to be processed based on the second set of remaining capacity values, and to determine the tag information of the cells to be processed based on the set of DC internal resistance deviation values, the capacity decay rate, and a preset DC internal resistance threshold.

[0103] In some embodiments, the processor is further configured to acquire a set of DC internal resistance values ​​for a target level, and to determine a set of DC internal resistance deviation values ​​corresponding to the target level based on the set of DC internal resistance values. It also acquires a second set of remaining capacity values ​​for the cell to be processed. The processor is further configured to calculate the capacity decay rate of the cell to be processed based on the second set of remaining capacity values, and to determine the tag information of the cell to be processed based on the set of DC internal resistance deviation values, the capacity decay rate, and a preset DC internal resistance threshold.

[0104] Specifically, the DC internal resistance set refers to the dataset composed of the DC internal resistances of all second-level cells within the target layer. DC internal resistance is the resistance value inside the cell that impedes the flow of current. It reflects the degree of cell aging and the deterioration of its internal electrochemical characteristics. A higher DC internal resistance usually indicates problems such as loss of active materials and aging of the separator.

[0105] The DC internal resistance deviation value set refers to the dataset consisting of the difference between the DC internal resistance of each second cell in the target level and the number of DC internal resistance set values. It is used to quantify the degree of deviation of the internal resistance of a single cell from that of a healthy cell.

[0106] The second remaining capacity set refers to the remaining capacity-time series dataset of the cells to be processed within a preset period, recording the trajectory of remaining capacity changes over time or the number of cycles. It should be noted that the preset period can be set according to system operation and maintenance requirements.

[0107] Capacity decay rate refers to the rate at which the cell capacity decreases over time (or number of cycles) based on the second remaining capacity set, using algorithms such as linear fitting and slope calculation. It reflects how fast the cell capacity deteriorates; a higher rate indicates that the cell performance deteriorates more rapidly and the risk of future failure is higher.

[0108] The preset DC internal resistance threshold refers to the critical value of internal resistance deviation that is set in advance based on battery type, design standards, and operation and maintenance experience. It can distinguish between normal internal resistance fluctuations and abnormal internal resistance mutations, thereby determining the risk of cell internal resistance.

[0109] Tag information refers to standardized information generated based on multi-dimensional verification results, including risk level, handling suggestions, risk causes, etc., providing clear action guidelines for operation and maintenance personnel.

[0110] First, the internal resistance detection module of the battery management system collects DC internal resistance data of all second cells within the target level in real time or periodically, and integrates them to form a DC internal resistance set. The DC internal resistance data collection frequency is synchronized with the remaining capacity data collection to ensure data timeliness.

[0111] Subsequently, the set of DC internal resistance deviation values ​​corresponding to the target level is determined: First, the DC internal resistance set is sorted and its median number is calculated. Next, for each second cell, the difference between its DC internal resistance and the median number is calculated, and all differences are integrated to form the set of DC internal resistance deviation values.

[0112] Then, the historical data of the remaining capacity of the cells to be processed within a preset period is retrieved from the system database and arranged in chronological order to form a second set of remaining capacity.

[0113] Next, the capacity decay rate of the cells to be processed is calculated: a linear fitting algorithm is used to process the second remaining capacity set, and a fitting straight line is established with the number of cycles as the x-axis and the remaining capacity as the y-axis. The absolute value of the slope of the straight line is the capacity decay rate.

[0114] Finally, the DC internal resistance deviation value of the cell to be processed is compared with the preset DC internal resistance threshold, and the capacity decay rate is referenced to generate the tag information of the cell to be processed.

[0115] Thus, the DC internal resistance set for the target level is obtained, including the DC internal resistance of each second cell. Next, based on the DC internal resistance set, a DC internal resistance deviation value set corresponding to the target level is determined. This set includes the deviation between the DC internal resistance of each second cell and the median of the deviation value set. Then, a second remaining capacity set for the cells to be processed is obtained, including the remaining capacity of the cells within a preset period. Subsequently, based on the second remaining capacity set, the capacity decay rate of the cells to be processed is calculated. Finally, based on the DC internal resistance deviation value set, the capacity decay rate, and a preset DC internal resistance threshold, the tag information of the cells to be processed is determined. In this way, by analyzing the cells to be processed using the DC internal resistance deviation value set, the capacity decay rate, and the preset DC internal resistance threshold to determine their tag information, the comprehensiveness of the status assessment of the cells to be processed can be improved, and the accuracy of tag information generation can be enhanced.

[0116] Please see Figure 8 In some implementations, step 022 (determining the tag information of the cell to be processed based on the DC internal resistance deviation value set, the capacity decay rate, and a preset DC internal resistance threshold) includes: 0221: Determine the target DC internal resistance deviation value corresponding to the cell to be processed based on the DC internal resistance deviation value set; 0222: If the target DC internal resistance deviation value is greater than or equal to the preset DC internal resistance threshold, the label of the cell to be processed is determined as the first label; 0223: If the target DC internal resistance deviation value is less than the preset DC internal resistance threshold, or the capacity decay rate is greater than the preset decay threshold, the label of the cell to be processed is determined to be the second label.

[0117] In some embodiments, the determining module is further configured to determine a target DC internal resistance deviation value corresponding to the cell to be processed. If the target DC internal resistance deviation value is greater than or equal to a preset DC internal resistance threshold, the cell to be processed is determined to be labeled as a first label. If the target DC internal resistance deviation value is less than the preset DC internal resistance threshold, or the capacity decay rate is greater than a preset decay threshold, the cell to be processed is determined to be labeled as a second label.

[0118] In some embodiments, the processor is further configured to determine a target DC internal resistance deviation value corresponding to the cell to be processed. If the target DC internal resistance deviation value is greater than or equal to a preset DC internal resistance threshold, the processor determines the cell to be processed to be labeled as a first label. If the target DC internal resistance deviation value is less than the preset DC internal resistance threshold, or the capacity decay rate is greater than a preset decay threshold, the processor determines the cell to be processed to be labeled as a second label.

[0119] Specifically, the target DC internal resistance deviation value refers to the specific deviation value corresponding to the cell to be processed, which is selected from the set of DC internal resistance deviation values. It is a quantitative indicator that reflects the degree of deviation of the internal resistance of the cell to be processed from the healthy population, and can directly reflect the abnormality of the current internal resistance of the cell.

[0120] The first label can be understood as a high-urgency risk label, indicating that the battery cell has experienced severe internal resistance degradation, which may lead to system safety hazards or a sudden drop in performance if not dealt with in time.

[0121] The preset degradation threshold refers to a critical value for the capacity degradation rate set based on the battery's design life and industry degradation standards. It can be used to distinguish between normal degradation and accelerated degradation. If the capacity degradation rate of the battery cell exceeds this preset degradation threshold, it indicates that the cell's performance degradation rate is abnormal.

[0122] The second label can be understood as a potential risk label, indicating that the cell's current internal resistance is not seriously abnormal, but there are potential risks such as accelerated degradation, which need to be checked first in subsequent maintenance.

[0123] First, determine the target DC internal resistance deviation value corresponding to the cell to be processed.

[0124] Subsequently, the labels for the cells to be processed are determined: when the target DC internal resistance deviation value of the cell to be processed is greater than or equal to a preset DC internal resistance threshold, the cell to be processed is marked with the first label. When the target DC internal resistance deviation value of the cell to be processed is less than the preset DC internal resistance threshold, or the capacity decay rate is greater than a preset decay threshold, the cell to be processed is marked with the second label.

[0125] Please see Figure 9 , Figure 9 This is a flowchart illustrating the detection method for unprocessed battery cells provided in this application. The process officially begins when the battery management system triggers a cell health detection task. First, the battery management system's cell-level data acquisition unit collects remaining capacity data from different battery levels in real-time or periodically, providing basic data support for subsequent detection. Next, a target level to be detected is selected from the collected levels, and the median remaining capacity of all cells within that target level is calculated. Then, a deviation threshold is calculated, based on a second preset formula, according to a pre-configured baseline standard deviation, discharge coefficient, baseline deviation threshold, and current standard deviation. Then, the deviation value of the remaining capacity of each cell is compared to see if it exceeds the deviation threshold. If so, the cell is marked as an unprocessed cell. Next, it is checked whether there are any uncompared cells within the target level. If so, the next cell in the target level is selected for comparison. If not, all marked unprocessed cells are output. Finally, the tag information of the unprocessed cells is further determined based on the DC internal resistance deviation value and the DC internal resistance deviation value.

[0126] In this way, the target DC internal resistance deviation value corresponding to the cell to be processed is determined. Next, if the target DC internal resistance deviation value is greater than or equal to a preset DC internal resistance threshold, the cell to be processed is designated as the first tag. Finally, if the target DC internal resistance deviation value is less than the preset DC internal resistance threshold, or the capacity decay rate is greater than a preset decay threshold, the cell to be processed is designated as the second tag. In this way, the tagged alarm information can intuitively reflect the cell's risk level, and with corresponding handling suggestions, it can provide clear action guidance for maintenance personnel.

[0127] This application also provides a computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, it implements the steps of the detection method for the battery cell to be processed as described above.

[0128] It is understood that a computer program includes computer program code. Computer program code can be in the form of source code, object code, executable files, or some intermediate form. Computer-readable storage media can include: any entity or device capable of carrying computer program code, recording media, USB flash drives, portable hard drives, magnetic disks, optical disks, computer memory, read-only memory (ROM), random access memory (RAM), and software distribution media, etc.

[0129] This application also provides a computer program product, including a computer program / instructions that, when executed by a processor, implement the above-described method.

[0130] In this specification, the terms "specifically," "furthermore," "particularly," "understandably," etc., refer to specific features, structures, materials, or characteristics described in connection with embodiments or examples that are included in at least one embodiment or example of this application. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Moreover, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of different embodiments or examples.

[0131] Any process or method description in the flowchart or otherwise herein can be understood as representing a module, segment, or portion of executable request code comprising one or more steps for implementing a particular logical function or process, and the scope of the preferred embodiments of this application includes additional implementations in which functions may be performed not in the order shown or discussed, including substantially simultaneously or in reverse order according to the functions involved, as should be understood by those skilled in the art to which embodiments of this application pertain.

[0132] Although embodiments of this application have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting this application. Those skilled in the art can make changes, modifications, substitutions and variations to the above embodiments within the scope of this application.

Claims

1. A method for detecting battery cells to be processed, characterized in that, The method includes: From the battery remaining capacity dataset, obtain the first remaining capacity set of the target level in the battery, wherein the battery remaining capacity dataset includes the remaining capacity of each first cell in the battery, the target level includes multiple second cells, and the first remaining capacity set includes the remaining capacity of each second cell. Based on the first set of remaining capacity, a set of remaining capacity deviation values ​​is determined, wherein the set of remaining capacity deviation values ​​includes the deviation value between the remaining capacity of each second cell and the median number of the first set of remaining capacity; Based on the deviation threshold, determine the target deviation value in the set of remaining capacity deviation values; The second cell corresponding to the target deviation value is identified as the cell to be processed.

2. The method according to claim 1, characterized in that, The method further includes: Collect raw data for each of the first cells in the battery, including the current, voltage and temperature information of the first cell; Based on a preset algorithm, the current information, voltage information, and temperature information are processed to determine the remaining capacity of each first cell; The remaining battery capacity dataset is determined based on the remaining capacity of each of the first cells.

3. The method according to claim 1, characterized in that, The deviation value includes a first deviation value, and the step of determining the set of remaining capacity deviation values ​​based on the first set of remaining capacity includes: Sort the remaining capacities in the first remaining capacity set in descending order to determine the median of the first remaining capacity set; For the remaining capacity of each second cell, the first deviation value is determined by calculating based on the first preset relationship and the median of the first remaining capacity set. Based on the first deviation value, determine the set of remaining capacity deviation values.

4. The method according to any one of claims 1-3, characterized in that, The deviation value includes a second deviation value, and the step of determining the set of remaining capacity deviation values ​​based on the first set of remaining capacity includes: Sort the remaining capacities in the first remaining capacity set in descending order to determine the median of the first remaining capacity set; For each of the second battery cells, the remaining capacity is calculated based on the first preset relationship and the median of the first remaining capacity set to determine the initial deviation value corresponding to the second battery cell. The position weight of each second cell is multiplied by the corresponding initial deviation value to determine the second deviation value, wherein the position weight is determined based on the position of the second cell within the battery. The set of remaining capacity deviation values ​​is determined based on the second deviation value.

5. The method according to any one of claims 1-3, characterized in that, The method further includes: Determine the current standard deviation based on the first set of remaining capacity; Based on the second preset relationship, the deviation threshold is determined according to the pre-configured benchmark standard deviation, discharge coefficient, benchmark deviation threshold and current standard deviation.

6. The method according to any one of claims 1-3, characterized in that, Determining the target deviation value in the set of remaining capacity deviation values ​​based on the deviation threshold includes: The deviation values ​​whose absolute value is greater than the deviation threshold in the set of remaining capacity deviation values ​​are determined as the target deviation values.

7. The method according to any one of claims 1-3, characterized in that, The method further includes: Obtain the DC internal resistance set of the target level, the DC internal resistance set including the DC internal resistance of each of the second cells; Based on the DC internal resistance set, a DC internal resistance deviation value set corresponding to the target level is determined. The DC internal resistance deviation value set includes the deviation value between the DC internal resistance of each second cell and the median number of the DC internal resistance deviation value set. Obtain a second set of remaining capacity for the battery cell to be processed, wherein the second set of remaining capacity includes the remaining capacity of the battery cell to be processed within a preset period; Based on the second remaining capacity set, calculate the capacity decay rate of the cell to be processed; The tag information of the cell to be processed is determined based on the DC internal resistance deviation value set, the capacity decay rate, and the preset DC internal resistance threshold.

8. The method according to claim 7, characterized in that, The step of determining the tag information of the cell to be processed based on the DC internal resistance deviation value set, the capacity decay rate, and the preset DC internal resistance threshold includes: Based on the set of DC internal resistance deviation values, determine the target DC internal resistance deviation value corresponding to the cell to be processed; If the target DC internal resistance deviation value is greater than or equal to the preset DC internal resistance threshold, the label of the cell to be processed is determined to be the first label; If the target DC internal resistance deviation value is less than the preset DC internal resistance threshold, or the capacity decay rate is greater than the preset decay threshold, the tag of the cell to be processed is determined to be the second tag.

9. An electronic device, characterized in that, It includes a processor and a memory, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps of any one of claims 1-8.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when executed by a processor, implements the steps of the method according to any one of claims 1-8.

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