Battery cell aging consistency evaluation method and device, electronic equipment, storage medium and program product

Abstract

The application provides a battery cell aging consistency evaluation method and device, electronic equipment, storage medium and program product, and relates to the technical field of batteries. By obtaining the leakage current data of a plurality of to-be-tested battery cells under constant voltage conditions for different test durations, an aging prediction model for each to-be-tested battery cell under different test durations can be obtained according to the leakage current data, and an aging prediction model corresponding to a target test duration that meets a set requirement is selected as a final prediction model according to the consistency between the aging prediction models. In this way, a reliable aging prediction model can be selected for consistency evaluation, and the consistency evaluation result can be ensured to be more reliable. Moreover, the leakage current data is highly sensitive to battery cell aging, and changes more sensitively and rapidly. Therefore, by detecting the leakage current data of the battery cell under the constant voltage condition, change data can be quickly obtained in a short period of time, and the aging consistency is evaluated based on the leakage current data, so that the test time is shorter, the test efficiency is higher, and the test cost can be effectively reduced.

Description

Technical Field

[0001] This application relates to the field of battery technology, and more specifically, to a method, apparatus, electronic device, storage medium, and program product for evaluating the consistency of battery cell aging. Background Technology

[0002] In practical applications, to meet power and energy requirements, battery cells are typically connected in series and parallel to form high-voltage battery packs. Due to differences in manufacturing processes and cell positions within the battery pack, the aging conditions of the internal cells vary, leading to differences in open-circuit voltage, internal resistance, temperature, capacity, and state of charge (SOC). This deteriorates the consistency between the internal cells, adversely affecting the energy utilization, safety, and overall health of the battery pack. Therefore, it is necessary to assess the consistency of the cells to ensure the safety and reliability of the battery pack.

[0003] Currently, the aging consistency of battery cells is tested by measuring their voltage, internal resistance, and other parameters. However, in practical applications, these parameters change relatively slowly, making it difficult to detect changes in a short period of time, thus requiring a long testing time. Summary of the Invention

[0004] The purpose of this application is to provide a method, apparatus, electronic device, storage medium, and program product for evaluating the consistency of battery cell aging, so as to improve the problem of long testing time in existing solutions.

[0005] In a first aspect, embodiments of this application provide a method for evaluating the consistency of battery cell aging, the method comprising:

[0006] Acquire leakage current data of multiple cells under test for different test durations under constant voltage conditions;

[0007] Based on the leakage current data, an aging prediction model for each cell under test is obtained under different test durations, wherein the aging prediction model characterizes the relationship between leakage current data and time.

[0008] For different test durations, the consistency of the aging prediction models among the multiple cells under test is evaluated under the test duration, and the evaluation results are obtained.

[0009] Based on the evaluation results, a target test duration is determined to meet the set requirements for consistency, and the aging prediction model under the target test duration is used as the final prediction model.

[0010] Using the final prediction model, the evaluation result of the aging consistency of the target cell is determined, wherein the relevant parameters of the target cell are consistent or similar to those of the plurality of cells to be tested, and the relevant parameters include at least one of production batch, production time, type, material, and production process.

[0011] In the above implementation process, leakage current data of multiple cells under test are acquired at different test durations under constant voltage conditions. Then, an aging prediction model for each cell under test at different test durations can be obtained based on the leakage current data. The aging prediction model corresponding to the target test duration that meets the set requirements is selected as the final prediction model based on the consistency among the aging prediction models. This allows for the selection of a highly reliable aging prediction model for consistency evaluation, ensuring more reliable evaluation results. Furthermore, since leakage current data is highly sensitive to cell aging and its changes are more sensitive and rapid, detecting leakage current data under constant voltage conditions allows for rapid acquisition of change data in a short period. This enables aging consistency evaluation based on leakage current data, resulting in shorter testing time, higher testing efficiency, and effectively reduced testing costs.

[0012] Optionally, obtaining an aging prediction model for each cell under test at different test durations based on the leakage current data includes:

[0013] For each cell under test, leakage current data of the same test duration are fitted to obtain aging prediction models corresponding to different test durations.

[0014] In the above implementation process, for each cell under test, an aging prediction model corresponding to different test durations can be fitted. This allows for the evaluation of the consistency of the aging prediction model under different test durations, so as to select a more reliable aging prediction model for consistency evaluation and improve the reliability of the consistency evaluation results.

[0015] Optionally, the step of fitting leakage current data for the same test duration to obtain aging prediction models corresponding to different test durations for each cell under test includes:

[0016] For each cell under test, obtain the integral value of the leakage current corresponding to the leakage current data for the same test duration;

[0017] The integrated value of the leakage current is fitted to obtain the aging prediction model corresponding to the test duration.

[0018] In the above implementation process, when using the integral value of leakage current for fitting, the data is smoother and more stable, making it easier to find a fitting model that matches the trend of data change, thereby improving the accuracy and stability of the fitting.

[0019] Optionally, the consistency refers to the target test duration determined based on the evaluation results to meet the set consistency requirements, including:

[0020] Based on the evaluation results, a target test duration with consistency exceeding a set threshold is determined. Consistency exceeding the set threshold indicates more stable and reliable prediction results; therefore, using the aging prediction model corresponding to the target test duration for consistency evaluation will be more accurate and reliable.

[0021] Optionally, determining the aging consistency assessment result of the target cell using the final prediction model includes:

[0022] Based on the final prediction model, multiple target leakage current data corresponding to the target aging time are obtained;

[0023] Based on the distribution of the multiple target leakage current data, the evaluation result of the aging consistency of the target cell under the target aging time is determined.

[0024] In the above implementation process, multiple final prediction models can be used to accurately predict the aging consistency of target cells under the same aging time.

[0025] Optionally, determining the aging consistency assessment result of the target cell using the final prediction model includes:

[0026] Based on the final prediction model, multiple aging times corresponding to the target leakage current data are obtained;

[0027] Based on the distribution of the multiple aging times, the evaluation result of the aging consistency of the target cell under the target leakage current data is determined.

[0028] In the above implementation process, the aging consistency of the cells can be accurately estimated based on multiple final prediction models, under the same leakage current data.

[0029] Optionally, acquiring leakage current data of multiple cells under test for different test durations under constant voltage conditions includes:

[0030] Determine the initial voltage of multiple cells under test after they have been placed in a vacuum environment for a set period of time;

[0031] The multiple cells under test are replenished to the initial voltage at a set current multiplier to detect and obtain leakage current data of each cell under test at different test durations under the initial voltage.

[0032] In the above process, placing the battery cell under test in a vacuum environment can accelerate the electrical reaction inside the battery cell, thereby causing its leakage current data to change rapidly. This allows for rapid and accurate detection of the battery cell's leakage current data in a short time, effectively shortening the test time and improving test efficiency.

[0033] Optionally, before determining the initial voltage of the multiple test cells after being placed in a vacuum environment for a set period of time, the method further includes:

[0034] Adjust the voltage of the plurality of battery cells under test to a specified voltage;

[0035] The specified voltage is determined in the following manner:

[0036] Obtain the OCV curve of one of the multiple cells under test;

[0037] The voltage at the point where the rate of change of the positive and negative voltages in the OCV curve is the maximum is determined as the specified voltage.

[0038] In the above implementation process, the voltage at the point where the positive and negative voltage change rate is the largest is determined from the OCV curve as the specified voltage, indicating that the voltage change of the cell is relatively large under this condition, so as to achieve more sensitive leakage current data detection.

[0039] Secondly, embodiments of this application provide a cell aging consistency evaluation device, the device comprising:

[0040] The data acquisition module is used to acquire leakage current data of multiple cells under test for different test durations under constant voltage conditions;

[0041] The model determination module is used to obtain an aging prediction model for each cell under test under different test durations based on the leakage current data, wherein the aging prediction model characterizes the relationship between leakage current data and time.

[0042] The model evaluation module is used to evaluate the consistency of the aging prediction models among the multiple cells under test for different test durations, and obtain the evaluation results.

[0043] The model selection module is used to determine the target test duration that meets the set requirements based on the evaluation results, and to use the aging prediction model under the target test duration as the final prediction model.

[0044] The consistency assessment module is used to determine the assessment result of the aging consistency of the target cell using the final prediction model, wherein the relevant parameters of the target cell are consistent or similar with those of the plurality of cells to be tested, and the relevant parameters include at least one of production batch, production time, type, material, and production process.

[0045] Thirdly, embodiments of this application provide an electronic device, including a processor and a memory, wherein the memory stores computer-readable instructions, and when the computer-readable instructions are executed by the processor, the steps of the method provided in the first aspect above are performed.

[0046] Fourthly, embodiments of this application provide a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, performs the steps of the method provided in the first aspect above.

[0047] Fifthly, embodiments of this application provide a computer program product, including computer program instructions, which, when read and executed by a processor, perform the steps of the method provided in the first aspect above.

[0048] Other features and advantages of this application will be set forth in the following description and will be apparent in part from the description or may be learned by practicing embodiments of this application. The objectives and other advantages of this application may be realized and obtained by means of the structures particularly pointed out in the written description, claims, and drawings. Attached Figure Description

[0049] To more clearly illustrate the technical solutions of the embodiments of this application, the accompanying drawings used in the embodiments of this application will be briefly introduced below. It should be understood that the following drawings only show some embodiments of this application and should not be regarded as a limitation of the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0050] Figure 1 A flowchart of a cell aging consistency assessment method provided in this application embodiment;

[0051] Figure 2 A schematic diagram of the fitting relationship curve between the integral value of leakage current and time provided for an embodiment of this application;

[0052] Figure 3 A schematic diagram illustrating the distribution of days predicted by various aging prediction models under the same leakage current integral value, provided for an embodiment of this application;

[0053] Figure 4 A schematic diagram of the OCV curve of a battery cell provided in an embodiment of this application;

[0054] Figure 5 A structural block diagram of a cell aging consistency evaluation device provided in this application embodiment;

[0055] Figure 6 This is a schematic diagram of the structure of an electronic device for performing a cell aging consistency assessment method, provided as an embodiment of this application. Detailed Implementation

[0056] The technical solutions in the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings.

[0057] It should be noted that the terms "system" and "network" in the embodiments of this invention can be used interchangeably. "Multiple" refers to two or more; therefore, in the embodiments of this invention, "multiple" can also be understood as "at least two". "And / or" describes the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, and B existing alone. Additionally, the character " / ", unless otherwise specified, generally indicates that the preceding and following related objects have an "or" relationship.

[0058] It should also be noted that all actions involving the acquisition of signals, information, or data in this application are carried out in compliance with the relevant data protection laws and policies of the country where the application is located, and with the authorization granted by the owner of the relevant device.

[0059] In practical applications, to meet power and energy requirements, battery cells are typically connected in series and parallel to form high-voltage battery packs. Due to differences in manufacturing processes and cell positions within the battery pack, the aging conditions of the internal cells vary, leading to differences in open-circuit voltage, internal resistance, temperature, capacity, and state of charge (SOC). This deteriorates the consistency between the internal cells, adversely affecting the energy utilization, safety, and overall health of the battery pack. Therefore, it is necessary to assess the consistency of the cells to ensure the safety and reliability of the battery pack.

[0060] Currently, the aging consistency of battery cells is tested by measuring their voltage, internal resistance, and other parameters. However, in practical applications, these parameters change relatively slowly, making it difficult to detect changes in a short period of time, thus requiring a long testing time.

[0061] The defects in the above-mentioned prior art solutions are all results obtained by the inventors after practice and careful research. Therefore, the discovery process of the above problems and the solutions proposed by the embodiments of the present invention in the following text should be considered as contributions made by the inventors to the present invention.

[0062] The inventors discovered that self-discharge is a significant characteristic of battery cell aging, typically manifested as a decrease in positive electrode potential, an increase in negative electrode potential, and capacity loss. Self-discharge follows electrochemical reaction laws, and its reaction rate is equivalent to a measurable side reaction current. This side reaction current reflects the charge flow rate generated within the battery cell due to self-discharge, and its magnitude reflects the rate of self-discharge. Leakage current refers to the current flowing through the external circuitry of the battery cell due to various reasons (including self-discharge). Leakage current data can serve as a means of quantitatively analyzing this rate. Therefore, leakage current data can be used to quantitatively analyze the impact of self-discharge on battery cell behavior, thereby analyzing the consistency of the battery cell aging process.

[0063] Therefore, to solve the above problems, this application provides a method for evaluating the consistency of battery cell aging. This method acquires leakage current data from multiple battery cells under test at different test durations under constant voltage conditions. Then, based on the leakage current data, an aging prediction model for each battery cell under test at different test durations can be obtained. The aging prediction model corresponding to the target test duration that meets the set requirements is selected as the final prediction model based on the consistency among the aging prediction models. This allows for the selection of a highly reliable aging prediction model for consistency evaluation, ensuring more reliable evaluation results. Furthermore, since leakage current data is highly sensitive to battery cell aging and its changes are more sensitive and rapid, detecting leakage current data under constant voltage conditions allows for rapid acquisition of change data in a short period. This enables aging consistency evaluation based on leakage current data, resulting in shorter testing time, higher testing efficiency, and effectively reduced testing costs.

[0064] Please refer to Figure 1 , Figure 1 A flowchart of a cell aging consistency assessment method provided in this application embodiment is included, the method comprising the following steps:

[0065] Step S110: Obtain leakage current data of multiple cells under test for different test durations under constant voltage conditions.

[0066] The multiple cells under test can be selected from a group of cells, and the number of multiple cells under test can be at least two. The aging consistency of a large number of cells can be evaluated by detecting the leakage current data of these multiple cells under test. That is, the aging consistency evaluation result of these multiple cells under test is used as the aging consistency evaluation result of the large number of cells, or it can be used only to evaluate the aging consistency of these multiple cells under test. Of course, leakage current data can also be detected for a large number of cells to obtain a more accurate aging consistency evaluation result.

[0067] Leakage current is an important indicator of cell self-discharge, which refers to the process by which a battery cell gradually loses its charge due to internal chemical reactions in the absence of an external load. During cell aging, the active materials in the positive and negative electrodes may accelerate some side effects, such as gas generation and electrolyte decomposition, which can lead to internal micro-short circuits and increase leakage current. Therefore, leakage current data is highly sensitive to cell aging, and its aging status can be assessed by detecting leakage current data. Moreover, leakage current data acquisition is relatively stable and less affected by external factors, resulting in more accurate assessment results.

[0068] To quickly and conveniently detect the leakage current data of the battery cells, this solution tests multiple battery cells under constant voltage conditions. Constant voltage conditions can be understood as maintaining each battery cell under test at a set voltage for testing. For example, the voltage of a battery cell will drop due to self-discharge. In order to maintain the battery cell at the set voltage, the battery cell under test can be charged to maintain it at the set voltage. The charging current capacity can be used as leakage current data, thus indirectly detecting the leakage current data of the battery cell under test.

[0069] The different test durations here can be understood as the test duration of the battery cell under constant voltage conditions. For example, if the battery cell under test is continuously tested for 5 days and 10 days under constant voltage conditions, leakage current data can be obtained for test durations of 5 days and 10 days.

[0070] For each cell under test, leakage current data with different test durations can be obtained.

[0071] Step S120: Based on the leakage current data, obtain the aging prediction model for each cell under test under different test durations.

[0072] The aging prediction model characterizes the relationship between leakage current data and time. For example, for each cell under test, leakage current data can be obtained at different test durations, and then the aging prediction model can be determined based on this leakage current data.

[0073] For example, for cell 1 under test, leakage current data can be obtained from tests conducted over 3, 5, 8, and 10 days. Then, for each test duration, such as the leakage current data over 3 days, an aging prediction model can be derived. For each test duration, leakage current data can be acquired according to a set test interval, such as one leakage current data point every minute, thus obtaining a large number of leakage current data points. Based on this leakage current data, an aging prediction model can be obtained. Therefore, for each cell under test, if there are M test durations, M aging prediction models can be obtained, each corresponding to one test duration. If there are N cells under test, N*M aging prediction models will be obtained.

[0074] Step S130: For different test durations, evaluate the consistency among the aging prediction models of multiple cells under test at the test duration and obtain the evaluation results.

[0075] In other words, the consistency between aging prediction models for each test duration can be evaluated to obtain evaluation results. For example, if there are N cells to be tested, there are N aging prediction models for a test duration of 3 days, with one aging prediction model for each cell. For a test duration of 5 days, there are N aging prediction models. Therefore, the consistency between the N aging prediction models for each test duration can be evaluated.

[0076] The consistency among aging prediction models can be considered as the distribution among them, such as... Figure 2 As shown, the aging prediction model can be understood as a fitted curve. Figure 2 The figure shows the curves of each cell under test for a 5-day test duration and the curves of each cell under test for a 10-day test duration.

[0077] When determining the consistency between various aging prediction models, the vertical axis can be used as a benchmark. The vertical axis represents the leakage current integral (obtained from leakage current data, which can also be leakage current data), and the horizontal axis represents the number of days. The horizontal axis (i.e., the number of days) corresponding to each curve obtained under the same leakage current integral can be calculated, and then the distribution between these numbers of days can be calculated to evaluate their consistency.

[0078] For example, given N cells under test, there are N curves for a 5-day test duration and N curves for a 10-day test duration. Taking a 30mAh ordinate as an example, we can obtain the x-axis of the curve corresponding to a 30mAh leakage current integral over a 5-day test duration, resulting in N days. The distribution of these N days can then be obtained, such as variance or standard deviation, which can be used to assess consistency. Similarly, for a 30mAh leakage current integral over a 10-day test duration, the x-axis also yields N days. The distribution of these N days can then be obtained, such as variance, standard deviation, or coefficient of variation (the coefficient of variation can be the standard deviation or the average of the variance and the N days). The consistency assessment of aging prediction models for other test durations is similar and will not be elaborated upon here.

[0079] like Figure 3 As shown, Figure 3 The figure shows the distribution of days predicted by the aging prediction model for each cell under test when the leakage current integral value is 30mAh. When the leakage current integral value is 30mAh, it is found that the difference in the distribution of N days under a 10-day test duration decreases or flattens out, that is, the coefficient of variation increases, indicating that the prediction results are relatively stable. It can be considered that the aging prediction model corresponding to a 10-day test duration is more reliable for evaluating the aging consistency of the cell. This indicates that it is more reliable to test the cell under test for at least 10 days to obtain leakage current data for subsequent aging consistency evaluation.

[0080] In some other implementations, consistency can be assessed by obtaining the distribution of the ordinate under the same x-axis (days). For example, with an x-axis of 30, the ordinate under the curve corresponding to a 5-day test duration can be obtained, resulting in N leakage current integrals. The distribution of these N leakage current integrals can then be obtained, such as variance, standard deviation, or coefficient of variation (the coefficient of variation can be the standard deviation or the average of the N leakage current integrals). Consistency can be assessed using variance, standard deviation, or coefficient of variation. The consistency assessment of aging prediction models under other test durations is similar and will not be elaborated further here.

[0081] Following the above method, the consistency between aging prediction models for each test duration can be obtained. The evaluation results can include the consistency between aging prediction models for each test duration, such as variance or standard deviation, or coefficient of variation.

[0082] Step S140: Determine the target test duration that meets the set requirements based on the evaluation results, and use the aging prediction model under the target test duration as the final prediction model.

[0083] In some implementations, a target test duration with consistency greater than a set threshold can be determined based on the evaluation results. Consistency greater than the set threshold indicates that the prediction results are more stable and reliable. Therefore, using the aging prediction model corresponding to the target test duration for consistency evaluation will be more accurate and reliable.

[0084] For example, if consistency is characterized by variance, standard deviation, or coefficient of variation as mentioned above, different thresholds can be set accordingly. For instance, a target test duration can be determined if the variance is greater than a set variance threshold, or a target test duration can be determined if the standard deviation is greater than a set standard deviation threshold, or a target test duration can be determined if the coefficient of variation is greater than a set coefficient of variation threshold.

[0085] Taking the coefficient of variation as an example, the coefficient of variation between aging prediction models under each test duration can be obtained. If there are M test durations, M coefficients of variation can be obtained. Then, the coefficient of variation with a coefficient greater than the set coefficient of variation threshold can be selected from the M coefficients of variation, and the test duration corresponding to the coefficient of variation is taken as the target test duration. For example, if the coefficients of variation corresponding to the 10-day test duration and the 8-day test duration are both greater than the set coefficient of variation threshold, then the 10-day test duration and the 8-day test duration can be determined as the target test duration, and the aging prediction models under the 10-day test duration and the 8-day test duration can be taken as the final prediction models.

[0086] In some other implementations, the target test duration with the greatest consistency can be determined based on the evaluation results. For example, if the coefficient of variation is the largest for the 10-day test duration mentioned above, then the 10-day test duration can be determined as the target test duration, and the aging prediction model under the 10-day test duration can be used as the final prediction model.

[0087] Step S150: Determine the aging consistency assessment result of the target cell based on the final prediction model.

[0088] The target battery cell has the same or similar parameters as multiple battery cells under test. These parameters may include at least one of the following: production batch, production time, type, material, and production process. Understandably, "same" means that these parameters are identical, and "similar" means that the differences between them are within a set range. Of course, different parameters correspond to different set ranges. For example, production batches can be two or three adjacent batches, which can be identified by the batch number. For instance, if multiple cells under test belong to batch 1, the target cell could be from batch 1, 2, or 3. Similarly, production times can be adjacent, such as the time difference between the production times of multiple cells under test and the target cell being within a set range (e.g., within one month). Type similarity is also important; for example, if multiple cells under test are cylindrical, the target cell could be prismatic or pouch cell. If multiple cells under test are liquid lithium-ion, the target cell could be polymer lithium-ion. Understandably, if cell type is classified by material, similarity can be understood as similar material functions and uses. Similar materials, as mentioned above, can refer to similar material functions and / or uses. Similar production processes mean that the cell production processes are basically the same or have minor differences that do not affect the final product.

[0089] Understandably, in some implementations, the relevant parameters of each cell under test can be consistent or similar.

[0090] Once the final prediction model is obtained, it can be used to assess the aging consistency of the target cell.

[0091] For example, if there are K final prediction models, the consistency of these K models can be used to assess the aging consistency of the target cell. For instance, the same leakage current data can be input into each of the K final prediction models to obtain K time points. The distribution of these K time points, such as variance, standard deviation, or coefficient of variation, can then be calculated to assess the aging consistency of the target cell. Alternatively, the same time point can be input into each of the K final prediction models to obtain K leakage current data points. The distribution of these K leakage current data points, such as variance, standard deviation, or coefficient of variation, can then be calculated to assess the aging consistency of the target cell.

[0092] Understandably, the evaluation results can be output to the testers, who can then determine the aging consistency of the cells based on the evaluation results. This provides data for subsequent cell selection and grouping, such as selecting cells with consistent aging to form battery modules. This ensures that the performance of the cells in the battery module is as consistent as possible, thereby improving the overall performance and reliability of the battery system and extending the battery's lifespan.

[0093] In the above implementation process, leakage current data of multiple cells under test are acquired at different test durations under constant voltage conditions. Then, an aging prediction model for each cell under test at different test durations can be obtained based on the leakage current data. The aging prediction model corresponding to the target test duration that meets the set requirements is selected as the final prediction model based on the consistency among the aging prediction models. This allows for the selection of a highly reliable aging prediction model for consistency evaluation, ensuring more reliable evaluation results. Furthermore, since leakage current data is highly sensitive to cell aging and its changes are more sensitive and rapid, detecting leakage current data under constant voltage conditions allows for rapid acquisition of change data in a short period. This enables aging consistency evaluation based on leakage current data, resulting in shorter testing time, higher testing efficiency, and effectively reduced testing costs.

[0094] Based on the above embodiments, in the method of obtaining the aging prediction model, leakage current data of the same test duration can be fitted for each cell under test to obtain the aging prediction model corresponding to different test durations.

[0095] For example, if each cell under test has a test duration of M, then for each test duration, the leakage current data of the cell under test within that test duration is used for fitting to obtain the aging prediction model corresponding to that test duration. In this way, corresponding aging prediction models are constructed for different test durations of each cell under test. This accurately reflects the aging characteristics of the cell under different test durations. By fitting multiple aging prediction models, the aging consistency under the same test duration can be accurately evaluated.

[0096] Since the aging prediction model concerns the relationship between leakage current data and time, an appropriate model can be selected to fit the relationship between leakage current data and time. These models may include linear regression models, nonlinear regression models, exponential models, polynomial models, etc.

[0097] Through manual observation or statistical analysis, it was found that the leakage current data exhibits nonlinear characteristics. Therefore, a nonlinear model can be selected for fitting, such as an exponential model or a polynomial model.

[0098] Then, using the leakage current data, parameters are estimated for the selected fitted model, such as the exponential model. Statistical software, such as Python, can be used for parameter estimation, or the least squares method can be employed. Alternatively, multiple fitted models can be selected for parameter estimation, resulting in multiple aging prediction models.

[0099] For example, exponential and multinomial models can be selected for fitting. The accuracy of the fitted model can be verified. If the verification is successful, for example, actual leakage current data can be substituted into the model for verification. The difference between the time corresponding to the leakage current data and the time calculated by the model can be verified to see if it is within the set range. If it is, it can be used as the final aging prediction model. Otherwise, it means that the selected model is inaccurate, and other models can be selected for fitting. Finally, the model with the highest accuracy is selected as the final aging prediction model.

[0100] For each test cell, a test duration can be used to obtain an aging prediction model. Thus, multiple aging prediction models can be obtained for multiple test durations.

[0101] For example, using the exponential model f(x) = ax b Taking +c as an example. Multiple leakage current data points of the cell under test N1 within test duration T1 are obtained and fitted to obtain an aging prediction model f(x1). Multiple leakage current data points of the cell under test N1 within test duration T2 are obtained and fitted to obtain an aging prediction model f(x2). Multiple leakage current data points of the cell under test N1 within test duration T3 are obtained and fitted to obtain an aging prediction model f(x3). The parameters a, b, and c obtained from the fitting in each aging prediction model can be different, so three different aging prediction models may be obtained. The fitting method for the leakage current data of other cells under test at various test durations is similar to that of the cell under test N1, and will not be elaborated further here for the sake of simplicity.

[0102] At this point, each cell under test can have multiple aging prediction models, and under the same test duration, there are also multiple aging prediction models.

[0103] In the above implementation process, for each cell under test, an aging prediction model corresponding to different test durations can be fitted. This allows for the evaluation of the consistency of the aging prediction model under different test durations, so as to select a more reliable aging prediction model for consistency evaluation and improve the reliability of the consistency evaluation results.

[0104] Based on the above embodiments, in order to achieve a more accurate evaluation, in the method of fitting the aging prediction model, for each cell under test, first obtain the leakage current integral value corresponding to the leakage current data for the same test duration, and then fit the leakage current integral value to obtain the aging prediction model corresponding to the test duration.

[0105] Since the leakage current integral value is obtained by integrating the original leakage current data, this process can smooth out random fluctuations and noise in the data, thus more accurately reflecting the overall trend and characteristics of leakage current. Therefore, using the leakage current integral value for fitting can reduce errors and improve the accuracy of fitting.

[0106] Understandably, the method of fitting using leakage current integral values ​​is similar to the method of fitting using leakage current data described above. For example, some fitting models can be pre-selected, and then relevant statistical software can be used to determine the parameters in the fitting models. Then, the final fitting model can be verified using actual leakage current integral values. If the verification is successful, the final fitting model can be used as an aging prediction model.

[0107] In the above implementation process, when using the integral value of leakage current for fitting, the data is smoother and more stable, making it easier to find a fitting model that matches the trend of data change, thereby improving the accuracy and stability of the fitting.

[0108] Based on the above embodiments, after determining the final prediction model, the aging consistency of the target cell under different aging times can be evaluated according to the final prediction model. Specifically, multiple target leakage current data corresponding to the target aging time can be obtained according to the final prediction model, and then the evaluation result of the aging consistency of the target cell under the target aging time can be determined according to the distribution of the multiple target leakage current data.

[0109] The target aging time can be the desired aging time, such as 50 days, 100 days, etc.

[0110] For example, if the final prediction models are f1(x), f2(x), and f3(x), and the target aging time is 50 days, then x = 50 can be substituted into these three final prediction models to obtain three values ​​of f(x). These three values ​​of f(x) represent multiple target leakage current data. The variance, standard deviation, or coefficient of variation of these three target leakage current data can then be calculated to obtain the evaluation result of the aging consistency of the target cell at an aging time of 50 days. Of course, to evaluate the aging consistency at other aging times, the corresponding aging time can be substituted into the final prediction models in the same way to calculate the leakage current data, and then the corresponding evaluation result can be obtained based on the distribution of the leakage current data.

[0111] In the above implementation process, multiple final prediction models can be used to accurately predict the aging consistency of target cells under the same aging time.

[0112] Based on the above embodiments, after determining the final prediction model, it can also be used to evaluate the aging consistency of the battery cell under different health conditions. The health condition of the battery cell can be reflected by leakage current data. Therefore, according to the final prediction model, multiple aging times corresponding to the target leakage current data can be obtained. Then, based on the distribution of multiple aging times, the evaluation result of the aging consistency of the target battery cell under the target leakage current data can be determined.

[0113] The target leakage current data refers to the leakage current data that you want to evaluate, such as 100mA, 200mA, etc.

[0114] For example, if the final prediction model is f1(x), f2(x), and f3(x), and the target leakage current data is 100mA, then f1(x) = f2(x) = f3(x) = 100mA can be substituted into the aging prediction model to obtain three x values. These three x values ​​represent multiple aging times. The variance, standard deviation, or coefficient of variation of these three aging times can then be calculated to obtain the assessment result of the aging consistency of the target cell when the leakage current data is 100mA. Of course, to assess the aging consistency under other health conditions, the corresponding leakage current data can be substituted into the aging prediction model in the same way to calculate the aging time, and then the corresponding assessment result can be obtained based on the distribution of the aging times.

[0115] In the above implementation process, the aging consistency of the target cell under the same leakage current data can be accurately estimated based on multiple final prediction models.

[0116] It should be noted that if the aging prediction model represents the relationship between the leakage current integral value and time, then when evaluating aging consistency, the leakage current integral value can be substituted into the model to calculate the aging time for evaluation, or the aging time can be substituted into the model to calculate the leakage current integral value for evaluation. It is just that the leakage current data in the above example is converted into the leakage current integral value. The implementation method is similar, and for the sake of simplicity, it will not be elaborated on here.

[0117] Based on the above embodiments, in order to obtain more accurate leakage current data, multiple cells under test can be first regulated to a specified voltage, then placed in a vacuum environment for a set time, and the initial voltage of the multiple cells under test after being placed in the vacuum environment for a set time is determined. Then, during the test, the multiple cells under test are replenished to the initial voltage according to a set current multiplier, so as to detect and obtain leakage current data of each cell under test at different test times under the initial voltage.

[0118] Specifically, in the method of adjusting the voltage of multiple test cells to a specified voltage, the multiple test cells can first be left to stand at room temperature for a certain period of time, such as 10 minutes. Then, they are charged at a constant current of 0.33C to 3.65V, with a cutoff current of 0.05C. After that, they are left to stand for another 10 minutes. Then, they are discharged at a constant current of 0.33C to 3.24V or 3.32V, with a cutoff current of 0.04C, and then left to stand for another 10 minutes. Understandably, the specific values ​​of the charging current and discharging current can be set according to the actual situation, the charging voltage and discharging voltage can also be flexibly set according to the actual situation, and the standing time can also be flexibly set according to the actual situation.

[0119] In the example above, the specified voltage is 3.24V or 3.32V. Adjusting the voltage of each cell under test to the specified voltage before testing ensures that all cells under test are in the same initial state before testing, thereby eliminating test errors caused by different initial voltages and ensuring the accuracy and comparability of subsequent test data.

[0120] Since high temperatures accelerate the chemical reactions of the materials inside the battery cell, thus simulating the aging process of the battery cell during long-term use, after adjusting the voltage of multiple battery cells to the specified voltage, multiple battery cells to be tested can be placed in a vacuum oven. The temperature of the oven can be 60℃. The high-temperature environment can cause the battery cells to generate leakage current quickly, so it is easy to quickly detect the changes in the leakage current data of the battery cells to be tested.

[0121] A charge-discharge tester can be used to recharge each battery cell under test. The English term for this tester is Ultra High Precision Charger (UHPC), a high-precision charge-discharge testing system used for efficient coulombic efficiency measurement. The UHPC achieves high stability (typical values ​​below 10 ppm) and accuracy (below 50 ppm), thus accurately detecting the leakage current data of the battery cells.

[0122] After placing the battery cell to be tested into the vacuum oven, the temperature of the oven can be adjusted to 60℃ and kept at that temperature for a certain period of time, such as 3 hours, so that the voltage of the battery cell to be tested tends to a stable value. Then, the initial voltage of the battery cell to be tested after standing for 3 hours is collected. This initial voltage can be used as the initial voltage under constant voltage conditions.

[0123] During testing, the battery cell under test is charged with a constant current of 0.5mA using a UHPC to maintain it at its initial voltage for a period of time, such as 10 days. Then, leakage current data can be obtained for 1 day, 3 days, 5 days, 8 days and 10 days, thus enabling the acquisition of short-cycle leakage current data.

[0124] When determining the aging prediction model, for each cell under test, the leakage current integral value corresponding to the leakage current data within 1 day, 3 days, 5 days, 8 days and 10 days can be used for fitting to obtain the aging prediction model.

[0125] In the above process, the cell under test is placed in a vacuum environment. The high temperature environment accelerates the electrical reaction inside the cell, which causes the leakage current data to change rapidly. This allows for rapid and accurate detection of the leakage current data of the cell in a short time, effectively shortening the test time and improving the test efficiency.

[0126] Based on the above embodiments, in the method of adjusting the voltage of multiple cells under test to a specified voltage, the specified voltage can be determined by the following method: obtaining the OCV (Open Circuit Voltage) curve of one of the multiple cells under test, and then determining the voltage at the point where the positive and negative voltage change rate is the largest in the OCV curve as the specified voltage.

[0127] The OCV curve refers to the graph of how the voltage of a battery cell changes as the state of charge (SOC) changes in an open-circuit state. Therefore, you can arbitrarily select one battery cell from multiple test cells to obtain its OCV curve, or you can select other cells with similar or identical parameters to the multiple test cells to obtain their OCV curves.

[0128] like Figure 4 As shown, Figure 4 This is a schematic diagram of the OCV curve of a battery cell, where the cell type is a fuel cell (Full cell). Anode represents the voltage corresponding to the positive electrode, and Cathode represents the voltage corresponding to the negative electrode. The dashed box represents the point where the voltage slope is the largest, that is, the point where the voltage change rate is the largest.

[0129] Understandably, the greater the voltage slope, the greater the voltage change under the same SOC variation. In this case, even a small change in SOC will lead to a significant change in voltage, which makes the detection of leakage current data more sensitive.

[0130] Therefore, when determining a specific voltage, the voltage at the point of maximum voltage change rate can be selected, such as... Figure 4 The voltage at the point with the largest voltage slope within the three dashed boxes can be used as the specified voltage; any one of them can be selected.

[0131] In the above implementation process, the voltage at the point where the positive and negative voltage change rate is the largest is determined from the OCV curve as the specified voltage, indicating that the voltage change of the cell is relatively large under this condition, so as to achieve more sensitive leakage current data detection.

[0132] Based on the above embodiments, besides using data fitting to determine the aging prediction model, other methods can also be used to construct the aging prediction model. For example, machine learning algorithms (such as random forests and neural networks) can be used to construct the aging prediction model. The principle of machine learning algorithms is to analyze data to discover patterns and relationships in the data, and use these patterns and relationships to perform tasks such as prediction and classification. Therefore, leakage current data can be used to construct an aging prediction model using machine learning algorithms. This aging prediction model can be understood as a machine learning model. It is equivalent to using leakage current data to train the machine learning model. During the training process, the machine learning model can learn the correlation between leakage current data of different durations and time, thereby predicting the leakage current data corresponding to different times, and then performing aging consistency assessment.

[0133] In the specific implementation process, for each cell under test, leakage current data within multiple test durations can be input into a machine learning model for training to obtain the corresponding machine learning model. At this time, each cell under test can be trained to obtain a machine learning model, and N cells under test will have N machine learning models. These N machine learning models can be used together as the final prediction model. Combined with the final prediction model determined in the above embodiment, the evaluation result of the aging consistency of the target cell under the target aging time can be determined.

[0134] For example, for cell N1 under test, leakage current data under M test durations are input into a machine learning model for training. After training, machine learning model 1 is obtained. For cell N2 under test, leakage current data under M test durations are input into another machine learning model for training. After training, machine learning model 2 is obtained. Machine learning model N can be trained in the same way, that is, N machine learning models. These N machine learning models can also be used as the final prediction model.

[0135] When conducting aging consistency assessment, if K final prediction models are determined in the above example, these N+K final prediction models can be used to conduct aging consistency assessment. For example, the target aging time T can be input into the N+K final prediction models to predict N+K leakage current data at the target aging time. Then, the distribution of these N+K leakage current data can be calculated to obtain the aging consistency assessment result of the target cell at the target aging time.

[0136] Alternatively, since machine learning models learn the correlation between leakage current data and time, leakage current data can be input to predict time. For example, multiple machine learning models can predict the target aging time under the target leakage current data, and then the distribution of multiple target aging times can be used to determine the aging consistency assessment result of the target cell under the target leakage current data. That is, when performing aging consistency assessment, the target leakage current data can be input into N+K final prediction models to predict N+K target aging times corresponding to the target leakage current data, and then the distribution of these N+K target aging times can be calculated to obtain the aging consistency assessment result of the target cell under the target leakage current data.

[0137] In some other implementations, for each cell under test, the leakage current data within each test duration can be input into a machine learning model for training, resulting in a machine learning model corresponding to each test duration. The resulting machine learning model can then undergo consistency evaluation, similar to the evaluation method for the aging prediction model described above. For example, the same leakage current data can be input into the machine learning model corresponding to test duration T1 to predict multiple times, and then the distribution of these multiple times can be obtained for evaluation. The specific implementation process is not described in detail here, but can be referred to the relevant descriptions in the above embodiments. Finally, the machine learning model that meets the set consistency requirements can be used as the final prediction model.

[0138] In the above implementation process, a machine learning model is trained by using leakage current data with different test durations. Since the machine learning model can learn the correlation between leakage current data and time, it can make accurate predictions and thus accurately assess the aging consistency of the target cell.

[0139] Please refer to the above method embodiments. Figure 5 , Figure 5 This is a structural block diagram of a battery cell aging consistency assessment device 200 provided in an embodiment of this application. The battery cell aging consistency assessment device 200 can be a module, program segment, or code on an electronic device. It should be understood that this battery cell aging consistency assessment device 200 is similar to the one described above. Figure 1 The method implementation corresponds to this and can be executed. Figure 1 The specific functions of the device involved in the method embodiments can be found in the description above. To avoid repetition, detailed descriptions are omitted here.

[0140] Optionally, the cell aging consistency assessment device 200 includes:

[0141] Data acquisition module 210 is used to acquire leakage current data of multiple cells under test for different test durations under constant voltage conditions;

[0142] The model determination module 220 is used to obtain an aging prediction model for each cell under test under different test durations based on the leakage current data, wherein the aging prediction model characterizes the relationship between leakage current data and time.

[0143] The model evaluation module 230 is used to evaluate the consistency of the aging prediction models among the multiple cells under test for different test durations, and obtain the evaluation results.

[0144] The model selection module 240 is used to determine the target test duration that meets the set requirements based on the evaluation results, and to use the aging prediction model under the target test duration as the final prediction model.

[0145] The consistency assessment module 250 is used to determine the assessment result of the aging consistency of the target cell using the final prediction model, wherein the relevant parameters of the target cell are consistent or similar with those of the plurality of cells to be tested, and the relevant parameters include at least one of production batch, production time, type, material, and production process.

[0146] Optionally, the model determination module 220 is used to fit the leakage current data of the same test duration for each cell under test to obtain an aging prediction model corresponding to different test durations.

[0147] Optionally, the model determination module 220 is used to obtain the leakage current integral value corresponding to the leakage current data for the same test duration for each cell under test; and to fit the leakage current integral value to obtain the aging prediction model corresponding to the test duration.

[0148] Optionally, the model selection module 240 is used to determine a target test duration with consistency greater than a set threshold based on the evaluation results.

[0149] Optionally, the consistency evaluation module 250 is used to obtain multiple target leakage current data corresponding to the target aging time according to the final prediction model; and to determine the evaluation result of the aging consistency of the target cell at the target aging time according to the distribution of the multiple target leakage current data.

[0150] Optionally, the consistency evaluation module 250 is used to obtain multiple aging times corresponding to the target leakage current data according to the final prediction model; and to determine the evaluation result of the aging consistency of the target cell under the target leakage current data according to the distribution of the multiple aging times.

[0151] Optionally, the data acquisition module 210 is used to determine the initial voltage of multiple test cells after being placed in a vacuum environment for a set time; and to replenish the multiple test cells to the initial voltage with a set current multiplier, so as to detect and obtain leakage current data of each test cell at different test times under the initial voltage.

[0152] Optionally, the data acquisition module 210 is further configured to adjust the voltage of the plurality of cells under test to a specified voltage;

[0153] The specified voltage is determined in the following manner:

[0154] Obtain the OCV curve of one of the multiple cells under test;

[0155] The voltage at the point where the rate of change of the positive and negative voltages in the OCV curve is the maximum is determined as the specified voltage.

[0156] It should be noted that those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working process of the device described above can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0157] Please refer to Figure 6 , Figure 6 This is a schematic diagram of an electronic device for performing a cell aging consistency assessment method, provided in an embodiment of this application. The electronic device may include: at least one processor 310, such as a CPU; at least one communication interface 320; at least one memory 330; and at least one communication bus 340. The communication bus 340 is used to establish communication between these components. In this embodiment, the communication interface 320 is used for signaling or data communication with other node devices. The memory 330 may be a high-speed RAM or a non-volatile memory, such as at least one disk storage device. Optionally, the memory 330 may also be at least one storage device located remotely from the aforementioned processor. The memory 330 stores computer-readable instructions. When these computer-readable instructions are executed by the processor 310, the electronic device performs the aforementioned... Figure 1 The method and process are shown.

[0158] Understandable. Figure 6 The structure shown is for illustrative purposes only; the electronic device may also include components that are more advanced than those shown. Figure 6 The more or fewer components shown, or having the same Figure 6 The different configurations shown. Figure 6 The components shown can be implemented using hardware, software, or a combination thereof.

[0159] This application provides a computer-readable storage medium storing a computer program thereon, which, when executed by a processor, performs the following... Figure 1 The method process executed by the electronic device in the illustrated method embodiment.

[0160] This embodiment discloses a computer program product, which includes a computer program stored on a non-transitory computer-readable storage medium. The computer program includes program instructions, and when the program instructions are executed by a computer, the computer can perform the methods provided in the above-described method embodiments, such as including:

[0161] Acquire leakage current data of multiple cells under test for different test durations under constant voltage conditions;

[0162] Based on the leakage current data, an aging prediction model for each cell under test is obtained under different test durations, wherein the aging prediction model characterizes the relationship between leakage current data and time.

[0163] For different test durations, the consistency of the aging prediction models among the multiple cells under test is evaluated under the test duration, and the evaluation results are obtained.

[0164] Based on the evaluation results, a target test duration is determined to meet the set requirements for consistency, and the aging prediction model under the target test duration is used as the final prediction model.

[0165] Using the final prediction model, the evaluation result of the aging consistency of the target cell is determined, wherein the relevant parameters of the target cell are consistent or similar to those of the plurality of cells to be tested, and the relevant parameters include at least one of production batch, production time, type, material, and production process.

[0166] In summary, the embodiments of this application provide a method, apparatus, electronic device, storage medium, and program product for assessing the aging consistency of battery cells. By acquiring leakage current data from multiple battery cells under test at different test durations under constant voltage conditions, an aging prediction model for each battery cell under test at different test durations can be obtained based on the leakage current data. The aging prediction model corresponding to the target test duration that meets the set requirements is selected as the final prediction model based on the consistency among the aging prediction models. This allows for the selection of a highly reliable aging prediction model for consistency assessment, ensuring more reliable results. Furthermore, since leakage current data is highly sensitive to battery cell aging and its changes are more sensitive and rapid, detecting leakage current data under constant voltage conditions allows for rapid acquisition of change data in a short period. This enables aging consistency assessment based on leakage current data, resulting in shorter testing time, higher testing efficiency, and effectively reduced testing costs.

[0167] In the embodiments provided in this application, it should be understood that the disclosed apparatus and methods can be implemented in other ways. The apparatus embodiments described above are merely illustrative. For example, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. Furthermore, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Additionally, the displayed or discussed mutual couplings, direct couplings, or communication connections may be through some communication interfaces; indirect couplings or communication connections between devices or units may be electrical, mechanical, or other forms.

[0168] Furthermore, the units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0169] Furthermore, the functional modules in the various embodiments of this application can be integrated together to form an independent part, or each module can exist independently, or two or more modules can be integrated to form an independent part.

[0170] In this document, relational terms such as first and second are used only to distinguish one entity or operation from another entity or operation, without necessarily requiring or implying any such actual relationship or order between these entities or operations.

[0171] The above description is merely an embodiment of this application and is not intended to limit the scope of protection of this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of protection of this application.

Claims

1. A method for evaluating the consistency of battery cell aging, characterized in that, The method includes: Acquire leakage current data of multiple cells under test for different test durations under constant voltage conditions; Based on the leakage current data, an aging prediction model for each cell under test is obtained under different test durations, wherein the aging prediction model characterizes the relationship between leakage current data and time. For different test durations, the consistency of the aging prediction models among the multiple cells under test is evaluated under the test duration, and the evaluation results are obtained. Based on the evaluation results, a target test duration is determined to meet the set requirements for consistency, and the aging prediction model under the target test duration is used as the final prediction model. Using the final prediction model, the evaluation result of the aging consistency of the target cell is determined, wherein the relevant parameters of the target cell are consistent or similar to those of the plurality of cells to be tested, and the relevant parameters include at least one of production batch, production time, type, material, and production process.

2. The method of claim 1, wherein, The step of obtaining an aging prediction model for each cell under test at different test durations based on the leakage current data includes: For each cell under test, leakage current data of the same test duration are fitted to obtain aging prediction models corresponding to different test durations.

3. The method of claim 2, wherein, For each cell under test, the leakage current data for the same test duration is fitted to obtain aging prediction models corresponding to different test durations, including: For each cell under test, obtain the integral value of the leakage current corresponding to the leakage current data for the same test duration; The integrated value of the leakage current is fitted to obtain the aging prediction model corresponding to the test duration.

4. The method of claim 1, wherein, The consistency refers to the target test duration determined based on the evaluation results to meet the set consistency requirements, including: Based on the evaluation results, determine the target test duration where consistency exceeds the set threshold.

5. The method of claim 1, wherein, The evaluation result of determining the aging consistency of the target cell using the final prediction model includes: Based on the final prediction model, multiple target leakage current data corresponding to the target aging time are obtained; Based on the distribution of the multiple target leakage current data, the evaluation result of the aging consistency of the target cell under the target aging time is determined.

6. The method of claim 1, wherein, The evaluation result of determining the aging consistency of the target cell using the final prediction model includes: Based on the final prediction model, multiple aging times corresponding to the target leakage current data are obtained; Based on the distribution of the multiple aging times, the evaluation result of the aging consistency of the target cell under the target leakage current data is determined.

7. The method of claim 1, wherein, The acquisition of leakage current data of multiple cells under test for different test durations under constant voltage conditions includes: Determine the initial voltage of multiple cells under test after they have been placed in a vacuum environment for a set period of time; The multiple cells under test are replenished to the initial voltage at a set current multiplier to detect and obtain leakage current data of each cell under test at different test durations under the initial voltage.

8. The method of claim 7, wherein, Before determining the initial voltage of the multiple test cells after being placed in a vacuum environment for a set period of time, the method further includes: Adjust the voltage of the plurality of battery cells under test to a specified voltage; The specified voltage is determined in the following manner: Obtain the OCV curve of one of the multiple cells under test; The voltage at the point where the rate of change of the positive and negative voltages in the OCV curve is the maximum is determined as the specified voltage.

9. A cell aging consistency evaluation device, characterized in that, The device includes: The data acquisition module is used to acquire leakage current data of multiple cells under test for different test durations under constant voltage conditions; The model determination module is used to obtain an aging prediction model for each cell under test under different test durations based on the leakage current data, wherein the aging prediction model characterizes the relationship between leakage current data and time. The model evaluation module is used to evaluate the consistency of the aging prediction models among the multiple cells under test for different test durations, and obtain the evaluation results. The model selection module is used to determine the target test duration that meets the set requirements based on the evaluation results, and to use the aging prediction model under the target test duration as the final prediction model. The consistency assessment module is used to determine the assessment result of the aging consistency of the target cell using the final prediction model, wherein the relevant parameters of the target cell are consistent or similar with those of the plurality of cells to be tested, and the relevant parameters include at least one of production batch, production time, type, material, and production process.

10. An electronic device, comprising: It includes a processor and a memory, the memory storing computer-readable instructions that, when executed by the processor, perform the method as described in any one of claims 1-8.

11. A computer readable storage medium having stored thereon a computer program, characterized in that, When the computer program is executed by a processor, it performs the method as described in any one of claims 1-8.

12. A computer program product, characterised in that, It includes computer program instructions, which, when read and executed by a processor, perform the method as described in any one of claims 1-8.

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