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 discharge end voltage data set of a target level in a battery is acquired, the target level comprises a plurality of first battery cells, and the first discharge end voltage data set comprises a first discharge end voltage of each first battery cell; then, according to the first discharge end voltage data set, a deviation value set is determined, and the deviation value set comprises deviation values of each first discharge end voltage and the median number of the first discharge end voltage data set; then, according to the first voltage deviation threshold, a target deviation value in the deviation value set is determined. And then, determining the first battery cell corresponding to the target deviation value as a candidate abnormal battery cell. And finally, determining a to-be-processed battery cell according to the candidate abnormal battery cell. In this way, the battery cell with the problem of the discharge end voltage in the target level can be found in time, and the battery cell can be processed in time. And moreover, the interference of the extremely 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: Obtain the first end-of-discharge voltage dataset of the target level in the battery, wherein the target level includes multiple first cells, and the first end-of-discharge voltage dataset includes the first end-of-discharge voltage of each first cell; Based on the first discharge end voltage dataset, a deviation value set is determined, the deviation value set including the deviation value of each first discharge end voltage from the median number of the first discharge end voltage dataset; Based on the first voltage deviation threshold, determine the target deviation value in the set of deviation values; The first battery cell corresponding to the target deviation value is identified as a candidate abnormal battery cell; Based on the candidate abnormal cells, the cell to be processed is determined.

[0005] Thus, a first discharge end-of-discharge voltage dataset for the target level in the battery is obtained. The target level includes multiple first cells, and the first discharge end-of-discharge voltage dataset includes the first discharge end-of-discharge voltage of each first cell. Next, based on the first discharge end-of-discharge voltage dataset, a deviation value set is determined. This deviation value set includes the deviation value between each first discharge end-of-discharge voltage and the median of the first discharge end-of-discharge voltage dataset. Then, based on a first voltage deviation threshold, a target deviation value is determined in the deviation value set. Subsequently, the first cells corresponding to the target deviation values ​​are identified as candidate abnormal cells. Finally, based on the candidate abnormal cells, cells to be processed are determined. In this way, by using the discharge end-of-discharge voltage as the analysis object, cells with problematic discharge end-of-discharge voltages in the target level can be identified in a timely manner and processed promptly. Furthermore, by using the median as the capacity benchmark, interference from extreme abnormal cells on the judgment criteria 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 individual faulty cells, narrowing the maintenance scope from the entire battery pack or battery cluster to the cell level, thereby reducing maintenance costs.

[0006] In some implementations, determining the deviation value set based on the first discharge end voltage dataset includes: The first discharge end voltages in the first discharge end voltage dataset are sorted in descending order to determine the median of the first discharge end voltage dataset. For each of the first cells, the first discharge end voltage is calculated against the median of the first discharge end voltage dataset to determine the deviation value; The deviation value set is determined based on the deviation value.

[0007] Therefore, the first discharge final voltages in the first discharge final voltage dataset are sorted in descending order to determine the median of the dataset. Next, for each first cell, the first discharge final voltage is compared with the median of the first discharge final voltage dataset to determine the deviation value. Subsequently, based on the deviation values, a deviation value set is determined. In this way, by calculating the deviation value using the first discharge final voltage and median of each first cell, the degree of deviation of the cell at different remaining capacity levels can be accurately reflected.

[0008] In some embodiments, the operating data includes the historical voltage, internal resistance growth rate, ambient temperature, and number of cycles of the target level; the method further includes: Determine the standard deviation of the historical voltage based on the historical voltage. Based on a preset relationship, the first voltage deviation threshold is determined according to the historical voltage standard deviation, the internal resistance growth rate, the ambient temperature, and the number of cycles.

[0009] Thus, the historical voltage standard deviation is determined based on the historical voltage. Next, based on a preset formula, a first voltage deviation threshold is determined according to the historical voltage standard deviation, internal resistance growth rate, ambient temperature, and cycle count. In this way, by adjusting and optimizing the first voltage deviation threshold based on the historical voltage standard deviation, internal resistance growth rate, ambient temperature, and cycle count, the first voltage deviation threshold can be adapted to the lifecycle of the target level.

[0010] In some implementations, determining the target deviation value in the set of deviation values ​​based on a first voltage deviation threshold includes: The deviation values ​​whose absolute value is greater than or equal to the first voltage deviation threshold are determined as the target deviation values.

[0011] Thus, the deviation values ​​whose absolute values ​​are greater than or equal to the first voltage deviation threshold are determined as the target deviation values. In this way, by comparing the absolute value of the deviation value with the first voltage deviation threshold, normal fluctuations and abnormal deviations can be accurately distinguished.

[0012] In some implementations, determining the cell to be processed based on the candidate abnormal cells includes: Obtain the second discharge end voltage dataset of the candidate abnormal battery cell. The second discharge end voltage dataset includes multiple second discharge end voltages of the candidate abnormal battery cell within a target sampling period. The target sampling period includes multiple cyclic sampling periods. The second discharge end voltage corresponds one-to-one with the cyclic sampling period. Based on the second discharge end voltage dataset, the deviation duration of the candidate abnormal cell is determined; Candidate abnormal cells with a deviation duration greater than or equal to a preset deviation duration threshold are identified as cells to be processed.

[0013] Thus, a second discharge end voltage dataset of candidate abnormal cells is obtained. This dataset includes multiple second discharge end voltages of the candidate abnormal cells within a target sampling period. The target sampling period includes multiple cyclic sampling periods, with each cyclic sampling period corresponding to a specific second discharge end voltage. Next, based on the second discharge end voltage dataset, the deviation duration of the candidate abnormal cells is determined. Finally, candidate abnormal cells with a deviation duration greater than or equal to a preset deviation duration threshold are identified as cells to be processed. In this way, by comparing the deviation duration with the preset deviation duration threshold, normal fluctuations and persistent abnormalities in the discharge end voltage of candidate abnormal cells can be distinguished, thereby improving the accuracy of cell selection.

[0014] In some implementations, determining the deviation duration of the candidate abnormal cell based on the second discharge end voltage dataset includes: The number of deviations in which the second discharge end voltage is greater than or equal to the second voltage deviation threshold is determined, wherein the second voltage deviation threshold is the voltage deviation threshold of the cyclic sampling period in which the second discharge end voltage is located; The duration of the deviation is determined by dividing the number of deviations by the total number of cyclic sampling periods.

[0015] Thus, the number of deviations where the second discharge-end voltage is greater than or equal to the second voltage deviation threshold is determined. The second voltage deviation threshold is the voltage deviation threshold of the cyclic sampling period in which the second discharge-end voltage is located. Next, the number of deviations is divided by the total number of cyclic sampling periods to determine the deviation duration. In this way, by comparing each second discharge-end voltage with the second voltage deviation threshold of the same cyclic sampling period and counting the number of deviations, the deviation duration is determined, providing a data basis for subsequent processing.

[0016] In some implementations, determining the cell to be processed based on the candidate abnormal cells includes: Determine the set of deviation values, and determine the interquartile range of the deviation values ​​for the target level; Based on the interquartile range of the deviation value, the boundary of the abnormal distribution is determined, and the boundary of the abnormal distribution includes a first abnormal distribution boundary and a second abnormal distribution boundary. If the target deviation value is less than the first abnormal distribution boundary, or if the target deviation value is greater than the second abnormal distribution boundary, the candidate abnormal cell is identified as the cell to be processed.

[0017] Thus, the set of deviation values ​​is determined, and the interquartile range of the deviation values ​​at the target level is determined. Then, based on the interquartile range of the deviation values, the abnormal distribution boundaries are determined, including a first abnormal distribution boundary and a second abnormal distribution boundary. Next, if the target deviation value is less than the first abnormal distribution boundary, or the target deviation value is greater than the second abnormal distribution boundary, the candidate abnormal cell is identified as a cell to be processed. In this way, by comparing the target deviation value and the abnormal distribution boundaries, abnormal fluctuations in the discharge end voltage of the candidate abnormal cell can be distinguished, thereby improving the accuracy of the cell to be processed identification.

[0018] In some embodiments, the method further includes: Based on the obtained auxiliary verification data, the identification information of the battery cell to be processed is determined.

[0019] Thus, based on the acquired auxiliary verification data, the identification information of the battery cell to be processed is determined. By generating this identification information, relevant technicians can be provided with information on the degree of fault and appropriate handling methods for the battery cell, thereby improving operational efficiency.

[0020] In some implementations, the auxiliary verification data includes the internal resistance value of the cell to be processed and the reference internal resistance value of the target level. Determining the identification information of the cell to be processed based on the acquired auxiliary verification data includes: If the first discharge end voltage of the battery cell to be processed is less than the median of the first discharge end voltage data set, and if the internal resistance value of the battery cell to be processed is greater than the reference internal resistance value, and the internal resistance deviation value between the internal resistance value of the battery cell to be processed and the reference internal resistance value is greater than a preset internal resistance deviation value threshold, the identification information is determined to be a first identification, wherein the first identification is used to indicate that the battery cell to be processed has a first type of fault and needs to be replaced. If the internal resistance value of the cell to be processed is less than or equal to the reference internal resistance value, or the internal resistance deviation value is less than or equal to the preset internal resistance deviation value threshold, the identification information is determined to be a second identifier, wherein the second identifier is used to indicate that the cell to be processed needs to be recharged.

[0021] Thus, when the first discharge-end voltage of the cell to be processed is less than the median of the first discharge-end voltage dataset, if the internal resistance of the cell to be processed is greater than the reference internal resistance, and the internal resistance deviation between the cell to be processed and the reference internal resistance is greater than a preset internal resistance deviation threshold, the identification information is determined as a first identifier. This first identifier indicates that the cell to be processed has a first-type fault and needs to be replaced. Next, if the internal resistance of the cell to be processed is less than or equal to the reference internal resistance, or the internal resistance deviation is less than or equal to the preset internal resistance deviation threshold, the identification information is determined as a second identifier. This second identifier indicates that the cell to be processed needs to be recharged. In this way, when the first discharge-end voltage of the cell to be processed is less than the median of the first discharge-end voltage dataset, by comparing the internal resistance of the cell to be processed, the reference internal resistance, and the preset internal resistance deviation threshold, cells that need to be replaced and cells that need recharge can be accurately distinguished, avoiding ineffective replacements and thus improving maintenance efficiency.

[0022] In some implementations, the auxiliary verification data includes the temperature value or temperature rise rate of the cell to be processed, and determining the identification information of the cell to be processed based on the acquired auxiliary verification data includes: If the temperature value is greater than a preset temperature threshold, or the temperature rise rate is greater than a preset temperature rise rate threshold, the identification information is determined to be a third identifier, wherein the third identifier is used to indicate that the battery cell to be processed has a second type of fault and needs to be replaced.

[0023] Thus, if the temperature value exceeds a preset temperature threshold, or the temperature rise rate exceeds a preset temperature rise rate threshold, the identification information is determined to be a third identifier. This third identifier indicates that the battery cell to be processed has a second-type fault and needs to be replaced. In this way, by comparing the temperature value with a preset temperature threshold, or comparing the temperature rise rate with a preset temperature rise rate threshold, the battery cell that needs to be replaced can be accurately determined, avoiding ineffective replacements and thus improving maintenance efficiency.

[0024] In some implementations, the auxiliary verification data includes the remaining capacity degradation rate of the cell to be processed and the baseline capacity degradation rate of the target level. Determining the identification information of the cell to be processed based on the acquired auxiliary verification data includes: If the difference between the remaining capacity decay rate of the cell to be processed and the reference capacity decay rate is greater than a preset decay rate difference threshold, the identification information is determined to be a fourth identifier, wherein the fourth identifier is used to indicate that the cell to be processed has a third type of fault and needs to be replaced.

[0025] Thus, if the difference between the remaining capacity degradation rate of the cell to be processed and the baseline capacity degradation rate exceeds a preset degradation rate difference threshold, the identification information is determined to be the fourth identifier. This fourth identifier indicates that the cell to be processed has a third-type fault and needs to be replaced. In this way, by comparing the remaining capacity degradation rate, the baseline capacity degradation rate, and the preset degradation rate difference threshold, the cells that need to be replaced can be accurately identified, avoiding ineffective replacements and thus improving maintenance efficiency.

[0026] 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.

[0027] 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.

[0028] 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

[0029] 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 2This 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; Figure 10 This is the tenth flowchart illustrating the detection method for battery cells to be processed according to certain embodiments of this application; Figure 11 This is eleventh of the flowcharts illustrating the detection method for battery cells to be processed according to certain embodiments of this application; Figure 12 This is the twelfth schematic flowchart of a method for detecting battery cells to be processed according to certain embodiments of this application. Detailed Implementation

[0030] 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.

[0031] 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.

[0032] 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.

[0033] Therefore, how to test the treated battery cells has become an urgent problem to be solved.

[0034] 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 discharge end voltage dataset of the target level in the battery; 012: Determine the deviation value set based on the first discharge end voltage dataset; 013: Determine the target deviation value in the deviation value set based on the first voltage deviation threshold; 014: The first cell corresponding to the target deviation value is identified as a candidate abnormal cell; 015: Based on the candidate abnormal cells, determine the cells to be processed.

[0035] 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 acquire a first end-of-discharge voltage dataset of a target level in the battery, and to determine a set of deviation values ​​based on the first end-of-discharge voltage dataset. The processor is also used to determine a target deviation value in the deviation value set based on a first voltage deviation threshold, and to identify a first battery cell corresponding to the target deviation value as a candidate abnormal battery cell. Finally, based on the candidate abnormal battery cells, a battery cell to be processed is determined.

[0036] 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 determine the cell to be processed based on candidate abnormal cells. The determination module is used to determine a set of deviation values ​​based on a first discharge end voltage dataset, and to determine a target deviation value in the set of deviation values ​​based on a first voltage deviation threshold. The determination module is further used to determine the first cell corresponding to the target deviation value as a candidate abnormal cell, and to determine the cell to be processed based on the candidate abnormal cells.

[0037] 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.

[0038] 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. This ensures sufficient coverage of cells to form a healthy population benchmark while avoiding insufficient sample size due to a low target level.

[0039] The first discharge end voltage dataset refers to the collection of the first discharge end voltages of all first cells within the target level. Specifically, the first discharge end voltage refers to the real-time terminal voltage of a single first cell at the end of its discharge phase. The end of the discharge phase refers to the critical stage where the battery system is nearing the end of its discharge, typically when the remaining charge is 5% or 1-10 minutes before the discharge ends. At this time, cell voltage differences are more pronounced due to the capacity depletion trend, making anomalies easier to identify.

[0040] The first cell refers to a single cell within the target level that is to be tested.

[0041] The median value refers to the value in the middle position after sorting all the first discharge final voltages in the first discharge final voltage dataset in ascending (or descending) order. If the number of cells in the first discharge final voltage dataset is even, the arithmetic mean of the two middle first discharge final voltages is taken.

[0042] The deviation value set refers to the set of differences calculated based on the first discharge end voltage and median number of each cell in the first discharge end voltage dataset. Each difference, or deviation value, reflects the degree of deviation of the corresponding cell's discharge end voltage relative to the healthy cell group.

[0043] The first voltage deviation threshold refers to the critical value used to determine whether the first discharge end voltage 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.

[0044] The target deviation value refers to the deviation value in the set of deviation values ​​that exceeds the deviation threshold. Among them, the battery cells corresponding to the target deviation value have deviated from the capacity benchmark of the healthy battery cell group and are considered abnormal objects with the risk of performance degradation, requiring intervention.

[0045] Candidate abnormal cells refer to the first cells that correspond one-to-one with the target deviation value. That is, the deviation value exceeds the normal range, there may be safety hazards or the system energy efficiency may be affected, and the cells are initially suspected of being abnormal and need to be further judged to see if there is an abnormality.

[0046] Cells awaiting processing refer to those that, after further verification, are confirmed to have health issues. These cells are the precise targets for maintenance operations and require processing through methods such as supplemental charging, equalization maintenance, or online replacement.

[0047] The first discharge end voltage of each first cell in the target level is obtained from the database containing the discharge end voltage of all battery cells, forming the first discharge end voltage dataset.

[0048] Next, for the first discharge end voltage of each first cell, the deviation value between each cell and the median value of the first discharge end voltage dataset is calculated one by one based on the median value, and all deviation values ​​are integrated to form a deviation value set.

[0049] Subsequently, each deviation value in the deviation value set is compared with a preset first voltage deviation threshold, and the deviation values ​​with an absolute value greater than the first voltage deviation threshold are selected as the target deviation values.

[0050] Then, based on the target deviation value, the identification information of the corresponding first cell is queried in reverse, and these cells are officially identified as candidate abnormal cells, providing accurate guidance for subsequent operation and maintenance.

[0051] Finally, the candidate abnormal cells are further verified to identify those requiring further processing. By clearly marking these cells, maintenance personnel are provided with precise targets for maintenance, improving operational efficiency and ensuring battery energy utilization.

[0052] In summary, the detection method and electronic device for the battery cell to be processed provided in this application obtain a first discharge end voltage dataset of the target level in the battery, wherein the target level includes multiple first battery cells, and the first discharge end voltage dataset includes the first discharge end voltage of each first battery cell. Next, based on the first discharge end voltage dataset, a deviation value set is determined, which includes the deviation value between each first discharge end voltage and the median of the first discharge end voltage dataset. Then, based on a first voltage deviation threshold, a target deviation value is determined in the deviation value set. Subsequently, the first battery cell corresponding to the target deviation value is identified as a candidate abnormal battery cell. Finally, based on the candidate abnormal battery cells, the battery cell to be processed is determined. In this way, by using the discharge end voltage as the analysis object, battery cells with problematic discharge end voltages in the target level can be identified in a timely manner and processed promptly. Furthermore, by using the median as the capacity benchmark, the interference of extreme 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.

[0053] Please see Figure 2 In some implementations, step 012 (determining the deviation value set based on the first discharge end voltage dataset) includes: 0121: Sort the first discharge end voltages in the first discharge end voltage dataset in descending order to determine the median of the first discharge end voltage dataset; 0122: For the first discharge end voltage of each first cell, calculate the deviation value by comparing it with the median of the first discharge end voltage dataset; 0123: Determine the set of deviation values ​​based on the deviation values.

[0054] In some implementations, the processor is further configured to sort the first end-of-discharge voltages in the first end-of-discharge voltage dataset in descending order to determine the median of the first end-of-discharge voltage dataset; and to calculate, for each first cell, a deviation value by comparing the first end-of-discharge voltage with the median of the first end-of-discharge voltage dataset; and to determine a set of deviation values ​​based on the deviation values.

[0055] In some implementations, the determining module is further configured to sort the first end-of-discharge voltages in the first end-of-discharge voltage dataset in descending order, and determine the median of the first end-of-discharge voltage dataset. It also calculates the deviation value for each first cell's first end-of-discharge voltage against the median of the first end-of-discharge voltage dataset. Finally, it determines a set of deviation values ​​based on the deviation values.

[0056] Specifically, descending order refers to arranging all first discharge final voltages in the first discharge final voltage dataset in descending order. In some implementations, ascending order can also be used, that is, arranging all first discharge final voltages in the first discharge final voltage dataset in ascending order.

[0057] The deviation value refers to the difference between the first discharge end voltage of a single first cell and the median number of the target layer. The calculation logic is first discharge end voltage - median number, and it can be positive or negative. A positive value indicates that the voltage of the cell is higher than the voltage level of most cells in the layer, while a negative value indicates that it is lower than the voltage level of most cells. The absolute value of the deviation value represents the degree of deviation.

[0058] First, all first discharge final voltages in the first discharge final voltage dataset are extracted and sorted in descending order to form an ordered data sequence. Then, the median is determined based on the length of the data sequence, i.e., the number of first cells within the target level.

[0059] Subsequently, for each first cell, the deviation value is calculated one by one for the first discharge end voltage. That is, the median number of the first discharge end voltage data set is subtracted from the first discharge end voltage to calculate the corresponding first deviation value.

[0060] Then, the deviation values ​​of all the first cells are associated and stored in the order of cell identification, and a unique mapping relationship is established between each deviation value and the corresponding first cell.

[0061] Therefore, the first discharge final voltages in the first discharge final voltage dataset are sorted in descending order to determine the median of the dataset. Next, for each first cell, the first discharge final voltage is compared with the median of the first discharge final voltage dataset to determine the deviation value. Subsequently, based on the deviation values, a deviation value set is determined. In this way, by calculating the deviation value using the first discharge final voltage and median of each first cell, the degree of deviation of the cell at different remaining capacity levels can be accurately reflected.

[0062] Please see Figure 3 In some implementations, the method further includes: 016: Determine the standard deviation of historical voltage based on historical voltage; 017: Based on the preset relationship, determine the first voltage deviation threshold according to the historical voltage standard deviation, internal resistance growth rate, ambient temperature and number of cycles.

[0063] In some implementations, the processor is further configured to determine the historical voltage standard deviation based on the historical voltage, and to determine a first voltage deviation threshold based on a preset formula, the historical voltage standard deviation, the internal resistance growth rate, the ambient temperature, and the number of cycles.

[0064] In some implementations, the determining module is further configured to determine the historical voltage standard deviation based on the historical voltage, and to determine a first voltage deviation threshold based on a preset formula, the historical voltage standard deviation, the internal resistance growth rate, the ambient temperature, and the number of cycles.

[0065] Specifically, operating condition data refers to a set of key parameters that reflect the operating status, aging degree, and environmental impact of the target cell. In some implementations, operating condition data includes the target cell's historical voltage, internal resistance growth rate, ambient temperature, and cycle count. Historical voltage data refers to the dataset of the final discharge voltage at the end of each discharge cycle within a preset time period for the target cell; the long-term voltage consistency of the target cell is quantified by calculating the standard deviation of historical voltage. The internal resistance growth rate is the percentage obtained by dividing the current internal resistance of the target cell by its initial internal resistance (e.g., the value measured at the factory), reflecting the overall aging degree of the target cell. Ambient temperature refers to the real-time temperature of the environment in which the target cell is located, directly affecting the lithium-ion migration rate and voltage stability within the cell. Total cycle count refers to the total number of charge-discharge cycles completed by the target cell since its commissioning, reflecting the cumulative fatigue degree of the cell; the more cycles, the more significant the degradation of the cell's active materials and the worse the voltage consistency. One cycle refers to the complete process of fully charging and discharging.

[0066] Historical voltage standard deviation refers to a statistical measure calculated based on historical voltages, which quantifies the long-term voltage dispersion at a target level. A smaller historical voltage standard deviation indicates better consistency, while a larger standard deviation indicates worse consistency.

[0067] The preset formula refers to a standardized mathematical model that transforms multi-dimensional operating parameters into a first voltage deviation threshold. This model quantifies the synergistic effects of historical voltage standard deviation, internal resistance growth rate, ambient temperature, and cycle number on the first voltage deviation threshold. In some implementations, the preset formula may be: ΔV = K * (σ_historical + α * R_growth + β * C_cycle) * γ(T), where ΔV is the first voltage deviation threshold, σ_historical is the historical voltage standard deviation, R_growth is the internal resistance growth rate, T is the ambient temperature, C_cycle is the cycle number, K, α, and β are correction coefficients obtained experimentally beforehand, and γ(T) is the formula for quantifying ambient temperature.

[0068] The battery management system stores historical voltages through a built-in storage module, monitors internal resistance through a pulse detection module, detects ambient temperature through a temperature sensor, and counts the number of cycles through a charge / discharge counter. Then, these automatically collected parameters are substituted into a preset formula to determine the first voltage deviation threshold.

[0069] Thus, the historical voltage standard deviation is determined based on the historical voltage. Next, based on a preset formula, a first voltage deviation threshold is determined according to the historical voltage standard deviation, internal resistance growth rate, ambient temperature, and cycle count. In this way, by adjusting and optimizing the first voltage deviation threshold based on the historical voltage standard deviation, internal resistance growth rate, ambient temperature, and cycle count, the first voltage deviation threshold can be adapted to the lifecycle of the target level.

[0070] Please see Figure 4 In some embodiments, step 013 (determining the target deviation value in the deviation value set based on the first voltage deviation threshold) includes: 0131: The deviation values ​​whose absolute values ​​are greater than or equal to the first voltage deviation threshold are determined as the target deviation values.

[0071] In some implementations, the determining module is further configured to determine the deviation values ​​in the deviation value set whose absolute value is greater than or equal to a first voltage deviation threshold as target deviation values.

[0072] In some implementations, the processor further uses the deviation values ​​whose absolute values ​​are greater than or equal to a first voltage deviation threshold as the target deviation values.

[0073] Specifically, the absolute value of each deviation value in the deviation value set is compared with the deviation threshold in turn. The deviation values ​​whose absolute values ​​are greater than the deviation threshold are determined as the target deviation values.

[0074] Thus, the deviation values ​​whose absolute values ​​are greater than or equal to the first voltage deviation threshold are determined as the target deviation values. In this way, by comparing the absolute value of the deviation value with the first voltage deviation threshold, normal fluctuations and abnormal deviations can be accurately distinguished.

[0075] Please see Figure 5 In some implementations, step 015 (determining the cell to be processed based on the candidate abnormal cells) includes: 0151: Obtain the second discharge end voltage dataset of candidate abnormal cells; 0152: Determine the duration of deviation for candidate abnormal cells based on the second discharge end voltage dataset; 0153: Candidate abnormal cells with a deviation duration greater than or equal to the preset deviation duration threshold are identified as cells to be processed.

[0076] In some implementations, the determining module is further configured to acquire a second end-of-discharge voltage dataset of candidate abnormal cells, and determine the deviation duration of the candidate abnormal cells based on the second end-of-discharge voltage dataset. Candidate abnormal cells with a deviation duration greater than or equal to a preset deviation duration threshold are identified as cells to be processed.

[0077] In some implementations, the processor is further configured to acquire a second end-of-discharge voltage dataset of candidate abnormal cells, and determine the deviation duration of the candidate abnormal cells based on the second end-of-discharge voltage dataset. Candidate abnormal cells with a deviation duration greater than or equal to a preset deviation duration threshold are identified as cells to be processed.

[0078] Specifically, the second discharge end voltage dataset refers to the set of discharge end voltages corresponding to each cyclic sampling cycle within the target sampling period for candidate abnormal cells. Unlike the first discharge end voltage dataset, the second discharge end voltage dataset reflects the long-term trend of the discharge end voltage of candidate abnormal cells by collecting the discharge end voltage of individual candidate abnormal cells multiple times. The data format is usually: {Cycle 1: 3.21V, Cycle 2: 3.19V, Cycle 3: 3.17V, ..., Cycle N: 3.18V}, and each data is associated with a specific cycle number to ensure traceability.

[0079] The target sampling period refers to the total number of consecutive charge-discharge cycles selected for analyzing the persistence of abnormalities in candidate cells, which is the time range for collecting the second discharge end voltage.

[0080] A cyclic sampling period refers to one complete charge-discharge cycle of an energy storage system, which is the process of a candidate abnormal cell going from a fully charged state through discharge to the end of discharge, and then being recharged back to a fully charged state. Each cyclic sampling period corresponds to one second discharge end voltage.

[0081] Deviation persistence refers to the ratio of the number of times, within the target sampling period, the deviation of the second discharge end voltage from the median discharge end voltage of the target level of the candidate abnormal cell is greater than or equal to the first voltage deviation threshold (i.e., the number of abnormalities in the second discharge end voltage), to the total number of cycles in the target sampling period, usually expressed as a percentage. For example, if there are 8 abnormalities in 10 cycles, the deviation persistence is 80%.

[0082] The preset deviation persistence threshold refers to the quantitative standard for determining whether an abnormality is persistent. It is a percentage value set based on engineering experience, usually 60%, and can be changed according to actual needs.

[0083] The following example illustrates this. If the first voltage deviation threshold for a certain target level is calculated to be 50mV based on operating data, two candidate abnormal cells are obtained through a single deviation screening: cell A and cell B. The target sampling period is set to 10 cyclic sampling cycles.

[0084] The battery management system will automatically collect the second discharge end voltage of cell A and cell B at the end of each cycle within the target sampling period, and simultaneously calculate the median discharge end voltage corresponding to each cycle.

[0085] Subsequently, the number of times the deviation of cell A and cell B exceeded the first voltage deviation threshold of 50mV was counted, and the deviation duration of cell A and cell B was calculated respectively.

[0086] Thus, a second discharge end voltage dataset of candidate abnormal cells is obtained. This dataset includes multiple second discharge end voltages of the candidate abnormal cells within a target sampling period. The target sampling period includes multiple cyclic sampling periods, with each cyclic sampling period corresponding to a specific second discharge end voltage. Next, based on the second discharge end voltage dataset, the deviation duration of the candidate abnormal cells is determined. Finally, candidate abnormal cells with a deviation duration greater than or equal to a preset deviation duration threshold are identified as cells to be processed. In this way, by comparing the deviation duration with the preset deviation duration threshold, normal fluctuations and persistent abnormalities in the discharge end voltage of candidate abnormal cells can be distinguished, thereby improving the accuracy of cell selection.

[0087] Please see Figure 6 In some implementations, step 0152 (determining the deviation duration of candidate abnormal cells based on the second discharge end voltage dataset) includes: 01521: Determine the number of deviations where the second discharge end voltage is greater than or equal to the second voltage deviation threshold; 01522: Divide the number of deviations by the total number of cyclic sampling periods to determine the duration of the deviation.

[0088] In some implementations, the determining module is further configured to determine the number of deviations where the second discharge end voltage is greater than or equal to the second voltage deviation threshold, and to determine the deviation duration by dividing the number of deviations by the total number of cyclic sampling periods.

[0089] In some implementations, the processor is further configured to determine the number of deviations where the second discharge end voltage is greater than or equal to the second voltage deviation threshold, and to determine the deviation duration by dividing the number of deviations by the total number of cyclic sampling periods.

[0090] Specifically, the second voltage deviation threshold refers to the dynamic threshold calculated separately for the second discharge end voltage of each cyclic sampling cycle. It is a threshold calculated based on the real-time operating data of the target level within the cyclic sampling cycle, using the same preset formula as the first voltage deviation threshold.

[0091] The number of deviations refers to the number of cyclic sampling periods in which the absolute value of the deviation between the second discharge end voltage of the candidate abnormal cell and the corresponding second voltage deviation threshold is greater than or equal to the second voltage deviation threshold within the target sampling period.

[0092] The number of cyclic sampling periods refers to the total number of complete charge-discharge cycles included in the target sampling period.

[0093] Thus, the number of deviations where the second discharge-end voltage is greater than or equal to the second voltage deviation threshold is determined. The second voltage deviation threshold is the voltage deviation threshold of the cyclic sampling period in which the second discharge-end voltage is located. Next, the number of deviations is divided by the total number of cyclic sampling periods to determine the deviation duration. In this way, by comparing each second discharge-end voltage with the second voltage deviation threshold of the same cyclic sampling period and counting the number of deviations, the deviation duration is determined, providing a data basis for subsequent processing.

[0094] Please see Figure 7 In some implementations, step 015 (determining the cell to be processed based on the candidate abnormal cells) includes: 0154: Determine the set of deviation values ​​and the interquartile range of the deviation values ​​at the target level; 0155: Determine the boundary of the abnormal distribution based on the interquartile range of the deviation value; 0156: If the target deviation value is less than the first abnormal distribution boundary or the target deviation value is greater than the second abnormal distribution boundary, the candidate abnormal cell is identified as a cell to be processed.

[0095] In some implementations, the determining module is further configured to determine a set of deviation values, determine the interquartile range of deviation values ​​at the target level, and determine the boundary of anomaly distribution based on the interquartile range of deviation values. If the target deviation value is less than the first boundary of anomaly distribution, or the target deviation value is greater than the second boundary of anomaly distribution, the candidate abnormal cell is determined as a cell to be processed.

[0096] In some implementations, the processor is further configured to determine a set of deviation values, determine the interquartile range of deviation values ​​at the target level, and determine the boundary of anomaly distribution based on the interquartile range of deviation values. If the target deviation value is less than a first anomaly distribution boundary, or the target deviation value is greater than a second anomaly distribution boundary, the candidate abnormal cell is identified as a cell to be processed.

[0097] Specifically, the interquartile range (IQR) is a core statistical indicator used to measure the dispersion of data distribution. The formula is: IQR = 3rd quartile Q3 - 1st quartile Q1. Here, the 1st quartile Q1 refers to the value at the 25th percentile after sorting the deviation values ​​in ascending order; that is, 25% of the deviation values ​​are less than or equal to Q1, and 75% are greater than or equal to Q1, reflecting the level near the lower limit of the distribution. The 3rd quartile Q3 refers to the value at the 75th percentile after sorting; that is, 75% of the deviation values ​​are less than or equal to Q3, and 25% are greater than or equal to Q3, reflecting the level near the upper limit of the distribution.

[0098] The abnormal distribution boundary refers to the boundary of the normal deviation range defined based on the interquartile range, including the first abnormal distribution boundary and the second abnormal distribution boundary. The first abnormal distribution boundary is the lower limit, which can be expressed as: Q1 - 1.5 × IQR. It represents the normal lower limit of the deviation distribution, i.e., deviation values ​​below this first abnormal distribution boundary are considered extremely low values ​​exceeding the distribution of most normal cells. The second abnormal distribution boundary is the upper limit, which can be expressed as: Q3 + 1.5 × IQR. It represents the normal upper limit of the deviation distribution, i.e., deviation values ​​above this second abnormal distribution boundary are considered extremely high values ​​exceeding the distribution of most normal cells.

[0099] Suppose a target level contains 20 first-stage cells. The calculated first-discharge final voltage dataset yields the following deviation value set: [-62, -58, -55, -51, -48, -45, -42, -39, -36, -33, -30, -27, -24, -21, -18, -15, -12, -9, -6, -3]. The first voltage deviation threshold for the target level, calculated from operating data, is 50mV. Through absolute value comparison, the target deviation values ​​are selected for cell C: -62mV, cell D: -58mV, and cell F: -55mV.

[0100] Subsequently, the interquartile range (IQR) of the deviation values ​​was calculated and determined: Q1 position was the weighted average of the 5th and 6th data points, -47.25mV. Q3 position was the weighted average of the 15th and 16th data points, -15.75mV. The IQR of the deviation values ​​was Q3 - Q1 = (-15.75) - (-47.25) = 31.5mV.

[0101] Next, based on the interquartile range of the deviation values, the boundaries of the abnormal distribution are determined: First abnormal distribution boundary = Q1 - 1.5 × IQR = -47.25 - 1.5 × 31.5 = -47.25 - 47.25 = -94.5mV; Second abnormal distribution boundary = Q3 + 1.5 × IQR = -15.75 + 1.5 × 31.5 = -15.75 + 47.25 = 31.5mV.

[0102] Finally, by comparing the target deviation values ​​and abnormal distribution boundaries of cells C, D, and F, cells C, D, and F are determined to be cells not to be processed.

[0103] Thus, the set of deviation values ​​is determined, and the interquartile range of the deviation values ​​at the target level is determined. Then, based on the interquartile range of the deviation values, the abnormal distribution boundaries are determined, including a first abnormal distribution boundary and a second abnormal distribution boundary. Next, if the target deviation value is less than the first abnormal distribution boundary, or the target deviation value is greater than the second abnormal distribution boundary, the candidate abnormal cell is identified as a cell to be processed. In this way, by comparing the target deviation value and the abnormal distribution boundaries, abnormal fluctuations in the discharge end voltage of the candidate abnormal cell can be distinguished, thereby improving the accuracy of the cell to be processed identification.

[0104] Please see Figure 8 In some implementations, the method further includes: 018: Based on the obtained auxiliary verification data, determine the identification information of the battery cell to be processed.

[0105] In some implementations, the determining module is also used to determine the identification information of the battery cell to be processed based on the acquired auxiliary verification data.

[0106] In some implementations, the processor is also used to determine the identification information of the battery cell to be processed based on the acquired auxiliary verification data.

[0107] Specifically, auxiliary verification data refers to supplementary health data used to further determine the type and risk level of the cell to be treated. In some implementations, auxiliary verification data may be internal resistance values, temperature data, or capacity degradation rates.

[0108] Internal resistance is the resistance within the battery cell that impedes current flow, reflecting the degradation of the cell's active materials and the integrity of its internal structure. Healthy cells have stable internal resistance, while aging cells or those with internal short circuits will show a significant increase in internal resistance. This can be detected by applying AC or DC pulses to the battery management system when the system is idle, such as before charging and during periods of rest.

[0109] Temperature data refers to the real-time temperature and temperature rise rate of the battery cell to be processed, which is collected by a temperature sensor attached to the surface of the battery cell.

[0110] Capacity decay rate refers to the percentage difference between the current actual capacity and the initial capacity of the battery cell to be processed, which is estimated by statistically analyzing the amount of electricity flowing through the charging and discharging process.

[0111] The identification information refers to the classification label assigned to the battery cell to be processed based on auxiliary verification data, which has two attributes: fault type and operation suggestion.

[0112] Thus, based on the acquired auxiliary verification data, the identification information of the battery cell to be processed is determined. By generating this identification information, relevant technicians can be provided with information on the degree of fault and appropriate handling methods for the battery cell, thereby improving operational efficiency.

[0113] Please see Figure 9 In some implementations, the auxiliary verification data includes the internal resistance value of the cell to be processed and the reference internal resistance value of the target level. Step 018 (determining the identification information of the cell to be processed based on the acquired auxiliary verification data) includes: 0181: If the first discharge end voltage of the cell to be processed is less than the median of the first discharge end voltage data set, and if the internal resistance value of the cell to be processed is greater than the reference internal resistance value, and the internal resistance deviation value between the internal resistance value of the cell to be processed and the reference internal resistance value is greater than the preset internal resistance deviation value threshold, the identification information is determined to be the first identification. 0182: If the internal resistance of the cell to be processed is less than or equal to the reference internal resistance, or the internal resistance deviation is less than or equal to the preset internal resistance deviation threshold, the identification information is determined to be the second identification.

[0114] In some embodiments, the determining module is further configured to, when the first discharge-end voltage of the cell to be processed is less than the median of the first discharge-end voltage dataset, determine the identification information as a first identifier if the internal resistance value of the cell to be processed is greater than a reference internal resistance value and the internal resistance deviation between the internal resistance value of the cell to be processed and the reference internal resistance value is greater than a preset internal resistance deviation threshold. And if the internal resistance value of the cell to be processed is less than or equal to the reference internal resistance value, or the internal resistance deviation value is less than or equal to the preset internal resistance deviation threshold, determine the identification information as a second identifier.

[0115] In some embodiments, the processor is further configured to, when the first discharge-end voltage of the cell to be processed is less than the median of the first discharge-end voltage dataset, determine the identification information as a first identifier if the internal resistance value of the cell to be processed is greater than a reference internal resistance value and the internal resistance deviation value between the internal resistance value of the cell to be processed and the reference internal resistance value is greater than a preset internal resistance deviation value threshold; and determine the identification information as a second identifier if the internal resistance value of the cell to be processed is less than or equal to the reference internal resistance value, or the internal resistance deviation value is less than or equal to the preset internal resistance deviation value threshold.

[0116] Specifically, the internal resistance of a battery cell to be processed refers to the resistance value inside the cell that hinders the flow of current, and is a core indicator reflecting the health status of the cell.

[0117] The reference internal resistance value of the target level refers to the average internal resistance value of all healthy cells within the target level to which the cell to be processed belongs, or the initial average internal resistance value of the target level when it leaves the factory. It is a reference standard for measuring whether the internal resistance is normal. For example, if a target level contains 20 cells and the average internal resistance of the healthy cells is 65mΩ, then the reference internal resistance value of the target level is 65mΩ.

[0118] If the voltage at the end of the first discharge is less than the median, it means that the voltage level of the cell to be processed at the end of the discharge is lower than that of half of the cells in the target level. This is a prerequisite for screening and avoids invalid judgments on cells with high voltage.

[0119] The internal resistance deviation value refers to the difference between the internal resistance value of the cell to be processed and the reference internal resistance value. The calculation formula is: Internal resistance deviation value = Internal resistance value of the cell to be processed - Reference internal resistance value, with the unit being mΩ. It reflects the degree to which the internal resistance of the cell deviates from the normal level. That is, the larger the positive internal resistance deviation value, the more abnormal the internal resistance. The negative internal resistance deviation value or close to zero indicates that the internal resistance is normal.

[0120] The preset internal resistance deviation threshold refers to a quantitative limit set based on the operation and maintenance experience of the energy storage industry and the safety standards of battery cells, used to distinguish between normal fluctuations in internal resistance and abnormal increases in internal resistance.

[0121] The first identifier refers to a standardized maintenance label, which indicates the first type of fault, namely, the aging of the battery cell or the internal failure of the battery cell itself, and the handling method for replacing the battery cell.

[0122] The second identifier refers to a standardized operation and maintenance label, which includes the processing method of needing to recharge the battery cells to be processed.

[0123] Thus, based on the acquired auxiliary verification data, the identification information of the battery cell to be processed is determined. By generating this identification information, relevant technicians can be provided with information on the degree of fault and appropriate handling methods for the battery cell, thereby improving operational efficiency.

[0124] Please see Figure 10 In some implementations, the auxiliary verification data includes the temperature value or temperature rise rate of the cell to be processed. Step 018 (determining the identification information of the cell to be processed based on the acquired auxiliary verification data) includes: 0183: If the temperature value is greater than the preset temperature threshold or the temperature rise rate is greater than the preset temperature rise rate threshold, the identification information is determined to be the third identification.

[0125] In some implementations, the determining module is further configured to determine the identification information as a third identifier when the temperature value is greater than a preset temperature threshold or the temperature rise rate is greater than a preset temperature rise rate threshold.

[0126] In some implementations, the processor is further configured to determine the identification information as a third identifier when the temperature value is greater than a preset temperature threshold or the temperature rise rate is greater than a preset temperature rise rate threshold.

[0127] Specifically, the temperature value of the cell to be processed refers to the real-time temperature of the surface or interior of the cell, which is a direct indicator reflecting the heating state of the cell.

[0128] The temperature rise rate of the battery cell to be processed refers to the amount of temperature change of the battery cell to be processed per unit time.

[0129] The preset temperature threshold refers to the dangerous temperature boundary set based on the chemical characteristics and safety standards of the battery cell, and can be adjusted according to the battery cell type and application scenario.

[0130] The preset temperature rise rate threshold refers to the dangerous temperature rise boundary set based on the fault heating law, which can be adjusted according to the cell type and application scenario.

[0131] The third identifier refers to a standardized label used to indicate Type II faults such as micro-short circuits, and the handling method for requiring replacement of the battery cell. Specifically, Type II faults include internal micro-short circuits, excessive tab contact resistance, and electrolyte decomposition.

[0132] Thus, if the temperature value exceeds a preset temperature threshold, or the temperature rise rate exceeds a preset temperature rise rate threshold, the identification information is determined to be a third identifier. This third identifier indicates that the battery cell to be processed has a second-type fault and needs to be replaced. In this way, by comparing the temperature value with a preset temperature threshold, or comparing the temperature rise rate with a preset temperature rise rate threshold, the battery cell that needs to be replaced can be accurately determined, avoiding ineffective replacements and thus improving maintenance efficiency.

[0133] Please see Figure 11 In some implementations, the auxiliary verification data includes the remaining capacity degradation rate of the cell to be processed and the baseline capacity degradation rate of the target level. Step 018 (determining the identification information of the cell to be processed based on the acquired auxiliary verification data) includes: 0184: If the difference between the remaining capacity decay rate of the cell to be processed and the baseline capacity decay rate is greater than the preset decay rate difference threshold, the identification information is determined to be the fourth identification.

[0134] In some implementations, the determining module is further configured to determine the identification information as a fourth identifier when the difference between the remaining capacity decay rate of the cell to be processed and the reference capacity decay rate is greater than a preset decay rate difference threshold.

[0135] In some implementations, the processor is further configured to determine the identification information as a fourth identifier if the difference between the remaining capacity decay rate of the cell to be processed and the reference capacity decay rate is greater than a preset decay rate difference threshold.

[0136] Specifically, the remaining capacity decay rate of the battery cell to be processed refers to the percentage of the difference between the current actual discharge capacity of the battery cell and the initial rated capacity to the initial rated capacity. It is a core indicator reflecting the long-term performance degradation of the battery cell. The calculation formula is: Remaining capacity decay rate = (initial rated capacity - current actual capacity) / initial rated capacity × 100%.

[0137] The current actual capacity is obtained through the coulomb counting method or capacity calibration test of the battery management system.

[0138] The baseline capacity decay rate of the target level refers to the average remaining capacity decay rate of all healthy cells within the target level to which the cell to be treated belongs. It is a reference standard for measuring whether the capacity decay is normal.

[0139] The fourth label refers to a standardized tagging system that includes Type III faults such as active material loss, and the handling method requiring replacement of the battery cell. Specifically, Type III faults are those that cause irreversible degradation of the battery cell's capacity. These faults do not pose an immediate safety risk, but they continuously reduce the charge and discharge capabilities of the target level and cannot be repaired by recharging or equalization; the battery cell must be replaced to restore the target level's capacity.

[0140] Please see Figure 12 , Figure 12 This is a flowchart illustrating the detection method for battery cells to be processed provided in this application. The trigger point is the end of the discharge phase. Since the end of the discharge phase is the stage where the energy storage system is nearing the end of its discharge, the cell voltage is more significantly affected by the near depletion of capacity, making it a commonly used detection window for identifying anomalies.

[0141] First, the real-time end-discharge voltage of all cells is obtained synchronously through the battery management system to avoid voltage data distortion caused by time difference and ensure the consistency of basic data for subsequent analysis.

[0142] Next, the voltage data is grouped according to the physical hierarchy, that is, the hierarchical structure of the energy storage system, namely system-cabin-stack-cluster-battery pack-cell, and the voltage data is grouped by battery pack.

[0143] Subsequently, the median voltage of each cell in the target tier is calculated, and the median voltage of all cells in the target tier is used as the benchmark.

[0144] Next, the voltage deviation threshold ΔV is determined based on the operating condition data of the target level. This threshold is dynamically calculated by combining historical voltage, internal resistance growth rate, ambient temperature, cycle count, and other operating condition data of the target level. For example, the threshold for aged batteries will be appropriately relaxed, while the threshold for new batteries will be tightened, so that the voltage deviation threshold is adapted to the actual state of the target level, reducing invalid alarms.

[0145] Then, the deviation between the discharge end voltage of each cell and the median discharge end voltage of the target level is calculated, and the absolute value of the deviation is determined.

[0146] Next, the absolute value of the deviation is compared with the voltage deviation threshold ΔV to perform preliminary screening and identify candidate abnormal cells. That is, cells with an absolute deviation value greater than the voltage deviation threshold ΔV are identified as cells to be processed.

[0147] Next, based on the candidate abnormal cells, the cells to be processed are determined. This includes statistically analyzing the persistence of deviation over multiple cycles for the cell or analyzing whether its voltage deviation exceeds the normal distribution of the target level, eliminating misjudgments caused by single voltage fluctuations, and identifying the cells that truly require maintenance.

[0148] Finally, the identification information of the cells to be processed is determined based on the auxiliary verification data. That is, by combining data such as the internal resistance, temperature or capacity decay rate of the cells to be processed, the cells are classified into fault categories and assigned corresponding identification information.

[0149] Thus, if the difference between the remaining capacity degradation rate of the cell to be processed and the baseline capacity degradation rate exceeds a preset degradation rate difference threshold, the identification information is determined to be the fourth identifier. This fourth identifier indicates that the cell to be processed has a third-type fault and needs to be replaced. In this way, by comparing the remaining capacity degradation rate, the baseline capacity degradation rate, and the preset degradation rate difference threshold, the cells that need to be replaced can be accurately identified, avoiding ineffective replacements and thus improving maintenance efficiency.

[0150] 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.

[0151] 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.

[0152] This application also provides a computer program product, including a computer program / instructions that, when executed by a processor, implement the above-described method.

[0153] 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.

[0154] 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.

[0155] 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: Obtain the first end-of-discharge voltage dataset of the target level in the battery, wherein the target level includes multiple first cells, and the first end-of-discharge voltage dataset includes the first end-of-discharge voltage of each first cell; Based on the first discharge end voltage dataset, a deviation value set is determined, the deviation value set including the deviation value of each first discharge end voltage from the median number of the first discharge end voltage dataset; Based on the first voltage deviation threshold, determine the target deviation value in the set of deviation values; The first battery cell corresponding to the target deviation value is identified as a candidate abnormal battery cell; Based on the candidate abnormal cells, the cell to be processed is determined.

2. The method according to claim 1, characterized in that, The step of determining the deviation value set based on the first discharge end voltage dataset includes: The first discharge end voltages in the first discharge end voltage dataset are sorted in descending order to determine the median of the first discharge end voltage dataset. For each of the first cells, the first discharge end voltage is calculated against the median of the first discharge end voltage dataset to determine the deviation value; The deviation value set is determined based on the deviation value.

3. The method according to claim 1, characterized in that, The operating condition data includes the historical voltage, internal resistance growth rate, ambient temperature, and number of cycles of the target level. The method further includes: Determine the standard deviation of the historical voltage based on the historical voltage. Based on a preset relationship, the first voltage deviation threshold is determined according to the historical voltage standard deviation, the internal resistance growth rate, the ambient temperature, and the number of cycles.

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

5. The method according to any one of claims 1-3, characterized in that, The step of determining the cell to be processed based on the candidate abnormal cells includes: Obtain the second discharge end voltage dataset of the candidate abnormal battery cell. The second discharge end voltage dataset includes multiple second discharge end voltages of the candidate abnormal battery cell within a target sampling period. The target sampling period includes multiple cyclic sampling periods. The second discharge end voltage corresponds one-to-one with the cyclic sampling period. Based on the second discharge end voltage dataset, the deviation duration of the candidate abnormal cell is determined; Candidate abnormal cells with a deviation duration greater than or equal to a preset deviation duration threshold are identified as cells to be processed.

6. The method according to claim 5, characterized in that, The step of determining the deviation duration of the candidate abnormal cells based on the second discharge end voltage dataset includes: The number of deviations in which the second discharge end voltage is greater than or equal to the second voltage deviation threshold is determined, wherein the second voltage deviation threshold is the voltage deviation threshold of the cyclic sampling period in which the second discharge end voltage is located; The duration of the deviation is determined by dividing the number of deviations by the total number of cyclic sampling periods.

7. The method according to any one of claims 1-3, characterized in that, The step of determining the cell to be processed based on the candidate abnormal cells includes: Determine the set of deviation values, and determine the interquartile range of the deviation values ​​for the target level; Based on the interquartile range of the deviation value, the boundary of the abnormal distribution is determined, and the boundary of the abnormal distribution includes a first abnormal distribution boundary and a second abnormal distribution boundary. If the target deviation value is less than the first abnormal distribution boundary, or if the target deviation value is greater than the second abnormal distribution boundary, the candidate abnormal cell is identified as the cell to be processed.

8. The method according to any one of claims 1-3, characterized in that, The method further includes: Based on the obtained auxiliary verification data, the identification information of the battery cell to be processed is determined.

9. The method according to claim 8, characterized in that, The auxiliary verification data includes the internal resistance value of the cell to be processed and the reference internal resistance value of the target level. Determining the identification information of the cell to be processed based on the acquired auxiliary verification data includes: If the first discharge end voltage of the battery cell to be processed is less than the median of the first discharge end voltage data set, and if the internal resistance value of the battery cell to be processed is greater than the reference internal resistance value, and the internal resistance deviation value between the internal resistance value of the battery cell to be processed and the reference internal resistance value is greater than a preset internal resistance deviation value threshold, the identification information is determined to be a first identification, wherein the first identification is used to indicate that the battery cell to be processed has a first type of fault and needs to be replaced. If the internal resistance value of the cell to be processed is less than or equal to the reference internal resistance value, or the internal resistance deviation value is less than or equal to the preset internal resistance deviation value threshold, the identification information is determined to be a second identifier, wherein the second identifier is used to indicate that the cell to be processed needs to be recharged.

10. The method according to claim 8, characterized in that, The auxiliary verification data includes the temperature value or temperature rise rate of the cell to be processed. Determining the identification information of the cell to be processed based on the acquired auxiliary verification data includes: If the temperature value is greater than a preset temperature threshold, or the temperature rise rate is greater than a preset temperature rise rate threshold, the identification information is determined to be a third identifier, wherein the third identifier is used to indicate that the battery cell to be processed has a second type of fault and needs to be replaced.

11. The method according to claim 8, characterized in that, The auxiliary verification data includes the remaining capacity degradation rate of the cell to be processed and the baseline capacity degradation rate of the target level. Determining the identification information of the cell to be processed based on the acquired auxiliary verification data includes: If the difference between the remaining capacity decay rate of the cell to be processed and the reference capacity decay rate is greater than a preset decay rate difference threshold, the identification information is determined to be a fourth identifier, wherein the fourth identifier is used to indicate that the cell to be processed has a third type of fault and needs to be replaced.

12. 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 1-11.

13. 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 1-11.

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