A method and system for determining differences in aging degrees of battery cells

Abstract

The present invention relates to the technical field of battery cell aging judgment, and in particular to a method and system for judging the difference in the degree of aging of a battery cell. The method comprises the following steps: performing a battery cell performance release evaluation and a battery cell internal resistance instability evaluation according to the factory specification data of the battery cell, and obtaining the battery cell internal resistance instability data; performing an internal resistance value limit variation interval calculation on the battery cell internal resistance instability data, and obtaining the internal resistance value limit variation interval; performing a power tilt characteristic analysis according to the internal resistance value limit variation interval, and obtaining the power tilt characteristic data; performing a battery cell degradation environmental impact simulation according to the power tilt characteristic data, and obtaining the battery cell environmental impact simulation data; performing a battery cell aging degree judgment model according to the battery cell environmental impact simulation data and performing a battery cell aging degree judgment process, and obtaining the battery cell aging degree data. The present invention makes the battery cell aging judgment technology more perfect by optimizing the battery cell aging judgment technology.

Description

Technical Field

[0001] The present invention relates to the technical field of battery cell aging judgment, and in particular to a method and system for judging differences in aging degrees of battery cells. Background Art

[0002] The degree of battery cell aging directly impacts its performance and lifespan, particularly in applications such as portable electronic devices, electric vehicles, and energy storage systems. As batteries use, internal chemical reactions gradually reduce their capacity and output power, increase internal resistance, and reduce charging and discharging efficiency. Different battery cells age at different rates and patterns due to manufacturing variations, usage conditions, and environmental factors. To effectively manage battery packs and extend their overall lifespan, identifying and replacing cells with advanced aging is crucial. Traditional methods rely primarily on periodic capacity testing and internal resistance measurements, but these are often time-consuming and require the battery to be removed from the device. In recent years, online monitoring technologies based on battery voltage, current, and temperature data combined with machine learning algorithms have emerged, enabling real-time assessment of battery cell health. By comparing and analyzing the performance indicators and aging characteristics of different battery cells, these advanced methods can more accurately determine differences in cell aging, thereby optimizing battery management systems and improving device reliability and user experience. Furthermore, in-depth research on battery aging mechanisms and the development of corresponding predictive models will facilitate the development of more effective battery maintenance strategies and extend battery pack lifespan. The application of these technologies and methods is particularly important in electric vehicles and energy storage systems, which require efficient battery management to ensure system stability and safety. In short, methods for accurately determining the degree of battery cell aging are of great significance to the development of modern battery management systems, helping to improve energy efficiency and reduce battery replacement costs. However, traditional methods for determining the degree of battery cell aging are unable to accurately determine the degree of battery cell aging and cannot accurately analyze the impact of the environment on the degree of aging. Summary of the Invention

[0003] Based on this, it is necessary to provide a method and system for determining the difference in aging degree of battery cells to solve at least one of the above technical problems.

[0004] To achieve the above object, a method for determining differences in aging degrees of battery cells is provided, the method comprising the following steps:

[0005] Step S1: Obtaining factory specification data of the battery cell; performing a battery cell performance release evaluation based on the factory specification data of the battery cell to obtain battery cell performance release evaluation data;

[0006] Step S2: performing a battery cell temperature change analysis based on the battery cell performance release evaluation data to obtain battery cell temperature change data; performing a temperature non-uniformity distribution difference analysis on the battery cell temperature change data to obtain temperature non-uniformity distribution difference data; performing a battery cell internal resistance instability evaluation on the temperature non-uniformity distribution difference data based on the battery cell performance release evaluation data to obtain battery cell internal resistance instability data;

[0007] Step S3: Calculating the internal resistance value limit variation interval for the battery cell internal resistance instability data to obtain the internal resistance value limit variation interval; performing a battery cell cycle life test based on the internal resistance value limit variation interval to obtain battery cell cycle life test data; and performing a power tilt characteristic analysis on the battery cell cycle life test data to obtain power tilt characteristic data;

[0008] Step S4: performing a battery cell degradation environmental impact simulation based on the battery cell cycle life test data and the power tilt characteristic data to obtain battery cell environmental impact simulation data; performing a battery cell aging environmental impact analysis based on the battery cell environmental impact simulation data to obtain battery cell aging environmental impact data; constructing a battery cell aging degree judgment model based on the battery cell aging environmental impact data to obtain a battery cell aging degree judgment model; performing a battery cell aging degree judgment process based on the battery cell aging degree judgment model to obtain battery cell aging degree data.

[0009] The present invention collects factory specification data for battery cells, providing essential information for subsequent evaluation. This data includes the rated capacity, voltage range, and charge / discharge rate of the battery cells. Based on the factory specification data, this step evaluates the performance of the battery cells. This helps determine the actual performance of the battery cells in applications, including their voltage response and capacity decay at different charge / discharge rates. By analyzing the temperature changes of the battery cells, the thermal characteristics of the battery during use can be understood. This helps identify potential thermal issues, such as performance degradation or safety risks caused by overheating. This step further analyzes the temperature distribution differences within the battery cells. Uneven temperature distribution within the battery cells can lead to unstable performance or shortened lifespan. Analyzing this difference can help design more optimized heat dissipation solutions or temperature management strategies. Based on the battery cell performance release evaluation data and the temperature nonuniformity distribution difference data, the internal resistance instability of the battery cells is assessed. Changes in internal resistance can affect battery performance and safety, so evaluating its instability can help predict battery life and reliability. By calculating the extreme range of internal resistance variation, the range of internal resistance variation during use can be determined. This helps predict battery performance under different operating conditions and assess its lifespan. By conducting battery cell cycle life testing, the charge and discharge cycles experienced by actual batteries can be simulated and relevant lifespan data obtained. This data can be used to assess battery durability and performance stability. By simulating the degradation process of batteries under various environmental conditions, we can better understand the impact of environmental factors on battery lifespan. This includes factors such as temperature, humidity, and vibration. Analyzing data on environmental impacts on battery cell aging can identify battery aging characteristics and trends under specific conditions. This helps formulate appropriate protection measures and maintenance strategies to extend battery lifespan. An aging assessment model can be constructed to comprehensively assess battery aging based on multiple data indicators, such as internal resistance changes and cycle life test results. This allows users to more accurately understand the battery's health and take appropriate measures, such as battery replacement or maintenance. Therefore, the present invention improves upon traditional methods for determining differences in battery cell aging, resolving issues such as the inability to accurately determine the aging degree of battery cells and the inability to accurately analyze the environmental impact of aging. This improves the accuracy of determining the aging degree of battery cells and enhances the accuracy of analyzing the environmental impact of aging.

[0010] Preferably, step S1 includes the following steps:

[0011] Step S11: Obtaining factory specification data of the battery cell;

[0012] Step S12: simulating the battery cell operating state according to the factory specification data of the battery cell to obtain battery cell operating state simulation data;

[0013] Step S13: performing battery cell performance release evaluation according to the battery cell operating state simulation data to obtain battery cell performance release evaluation data.

[0014] This step of the present invention forms the foundation for this evaluation. By collecting factory-specification data for the battery cells, including rated capacity, voltage range, and charge / discharge rates, this provides basic battery information. Using simulation technology, the battery's operating state under different operating conditions is simulated based on this factory-specification data. This simulation can predict actual battery performance, such as charge / discharge characteristics and temperature response, providing a reference for subsequent evaluation. Based on the simulated operating data, a performance evaluation of the battery cells is performed. This evaluation helps determine the actual performance of the battery cells, including their voltage response and capacity decay at different charge / discharge rates. This evaluation provides a better understanding of the performance characteristics of the battery cells, providing a basis for battery system design and optimization.

[0015] Preferably, step S2 includes the following steps:

[0016] Step S21: analyzing the battery cell temperature change according to the battery cell performance release evaluation data to obtain battery cell temperature change data;

[0017] Step S22: Calculating the temperature rise rate of the battery cell temperature change data to obtain the battery cell temperature rise rate data;

[0018] Step S23: performing temperature non-uniformity distribution difference analysis on the battery cell temperature change data according to the battery cell temperature rise rate data to obtain temperature non-uniformity distribution difference data;

[0019] Step S24: performing battery cell internal resistance instability evaluation on the temperature non-uniformity distribution difference data according to the battery cell performance release evaluation data to obtain battery cell internal resistance instability data.

[0020] By analyzing battery cell performance release evaluation data, the present invention can understand the temperature variations of battery cells under different operating conditions. This helps determine the temperature range encountered by the battery cells during actual operation and provides basic data for subsequent temperature-related analysis. The rate of rise of the battery cell temperature is an important indicator that reflects the thermal characteristics of the battery cell during charging or discharging. By calculating the rate of temperature rise, the thermal management performance of the battery cell under different operating conditions can be evaluated, providing an important reference for preventing overheating or temperature runaway. Internal temperature non-uniformity within the battery cell can lead to instability and safety hazards in the battery system. By analyzing the temperature rise rate data, the distribution of internal temperature differences within the battery cell can be assessed to determine whether there are localized excessively high or low temperatures, providing a basis for formulating temperature management strategies. Temperature non-uniformity can affect the stability of the battery cell internal resistance, thereby affecting the performance and safety of the battery system. By evaluating the temperature non-uniformity distribution based on the battery cell performance release evaluation data, it is possible to determine the instability of the battery cell internal resistance, identify potential problems in advance, and implement appropriate measures to ensure stable operation of the battery system.

[0021] Preferably, step S24 includes the following steps:

[0022] Step S241: performing performance release rate increment calculation on the battery cell performance release evaluation data to obtain performance release rate increment data;

[0023] Step S242: performing battery cell thermal effect coupling analysis based on the performance release rate increment data and the temperature non-uniformity distribution difference data to obtain battery cell thermal effect coupling data;

[0024] Step S243: performing nonlinear current response simulation on the battery cell thermal effect coupling data to obtain nonlinear current response simulation data;

[0025] Step S244: performing heat loss cycle degradation coupling processing according to the battery cell thermal effect coupling data and the nonlinear current response simulation data to obtain heat loss cycle degradation coupling data;

[0026] Step S245: performing battery cell internal resistance instability assessment based on the heat loss cycle degradation coupling data to obtain battery cell internal resistance instability data.

[0027] The performance release rate increase of the present invention refers to the ability of the battery to release energy at different charge and discharge rates. By calculating the performance release rate increase data, the performance of the battery under different working conditions can be understood, providing a basis for subsequent analysis. The thermal effect coupling analysis takes into account the relationship between the temperature distribution inside the battery core and the performance release rate. This helps to understand the thermal characteristics of the battery core during high-rate discharge or charging, predict the temperature changes that occur, and provide a basis for the formulation of thermal management strategies. In the battery system, the current response will be affected by multiple factors and exhibit nonlinear characteristics. By performing nonlinear current response simulation on the battery core thermal effect coupling data, the current response can be predicted more accurately, and the control strategy of the battery system can be further optimized. The thermal loss cycle degradation coupling takes into account the complex interaction between thermal effects, current response and battery core internal resistance instability. By processing this data, the factors that lead to battery performance degradation and shortened life can be identified, providing guidance for extending battery life and improving performance. Internal resistance is an important parameter in the battery system, which directly affects the performance and safety of the battery. By evaluating the coupled data of heat loss cycle degradation, the stability of the battery cell internal resistance can be determined, the trend of internal resistance change can be discovered in a timely manner, and corresponding measures can be taken to maintain the stable operation of the battery system.

[0028] Preferably, step S3 includes the following steps:

[0029] Step S31: calculating the internal resistance instability fluctuation amplitude of the battery cell internal resistance instability data to obtain internal resistance instability fluctuation amplitude data;

[0030] Step S32: calculating the internal resistance value limit variation range of the battery cell internal resistance instability data according to the internal resistance instability fluctuation amplitude data to obtain the internal resistance value limit variation range;

[0031] Step S33: performing battery cell capacity attenuation analysis based on the internal resistance value limit variation range to obtain battery cell capacity attenuation data;

[0032] Step S34: performing a battery cell cycle life test according to the internal resistance value limit variation range and the battery cell capacity attenuation data to obtain battery cell cycle life test data;

[0033] Step S35: performing power tilt characteristic analysis on the battery cell cycle life test data to obtain power tilt characteristic data.

[0034] The internal resistance of the present invention is one of the important indicators of battery performance, and its stability directly affects the battery's performance and lifespan. By calculating the internal resistance instability fluctuation amplitude data, the degree of internal resistance variation can be quantified, providing a basis for subsequent analysis. Based on the internal resistance instability fluctuation amplitude data, the extreme variation range of the internal resistance value can be further calculated. This range reflects the range of internal resistance variation and can be used to determine the stability of the internal resistance and provide a basis for subsequent capacity decay analysis. Capacity decay refers to the phenomenon of gradual decrease in battery capacity during use, which directly affects the battery's available energy and service life. By performing capacity decay analysis based on the extreme variation range of the internal resistance value, the battery's service life and performance decay trend can be understood. The battery cell cycle life test evaluates the ability of a battery to maintain a specified capacity after a certain number of charge and discharge cycles under specific operating conditions. By performing cycle life testing based on the extreme variation range of the internal resistance value and capacity decay data, the battery's durability and lifespan can be directly evaluated. The power tilt characteristic refers to the battery's performance at different charge and discharge rates. By performing power tilt characteristic analysis on the cycle life test data, the battery's performance under different operating conditions can be understood, providing reference and guidance for practical battery applications.

[0035] Preferably, step S35 includes the following steps:

[0036] Step S351: performing internal resistance stage-by-stage dynamic trend analysis on the battery cell cycle life test data to obtain internal resistance stage-by-stage dynamic trend data;

[0037] Step S352: performing a stage-by-stage temperature effect analysis based on the stage-by-stage dynamic trend data of the internal resistance to obtain stage-by-stage temperature effect data;

[0038] Step S353: performing cumulative power loss evaluation based on the internal resistance periodic dynamic trend data and the periodic temperature effect data to obtain cumulative power loss data;

[0039] Step S354: performing power tilt characteristic analysis based on the accumulated power loss data, the staged temperature effect data, and the staged dynamic trend data of the internal resistance to obtain power tilt characteristic data.

[0040] The present invention analyzes the internal resistance of a battery during different charge and discharge phases by performing a phased dynamic trend analysis of the internal resistance of the battery cell cycle life test data. This helps identify the trend of the internal resistance during the cycle and provides basic data for subsequent temperature effect analysis. During the cycle life test, temperature changes will affect the internal resistance of the battery. By performing a phased temperature effect analysis on the internal resistance phased dynamic trend data, the extent of the impact of temperature on the internal resistance change at different stages can be understood. This helps evaluate the performance of the battery under different temperature conditions. Cumulative power loss refers to the energy loss caused by factors such as increased internal resistance and temperature changes during the cycle life test. By combining the internal resistance phased dynamic trend data and the phased temperature effect data, the cumulative power loss can be evaluated. This helps understand the energy loss of the battery during long-term cycling. The power tilt characteristic of the battery refers to the performance of the battery at different charge and discharge rates. By analyzing the power tilt characteristic based on the cumulative power loss data, the phased temperature effect data, and the internal resistance phased dynamic trend data, a more comprehensive understanding of the battery's performance under different operating conditions can be obtained. This helps optimize battery system design and application scenario selection.

[0041] Preferably, step S4 includes the following steps:

[0042] Step S41: performing a battery cell degradation environmental impact simulation based on the battery cell cycle life test data and the power tilt characteristic data to obtain battery cell environmental impact simulation data;

[0043] Step S42: performing battery cell aging environmental impact analysis on the battery cell cycle life test data according to the battery cell environmental impact simulation data to obtain battery cell aging environmental impact data;

[0044] Step S43: performing cycle number reduction analysis on the battery cell cycle life test data based on the battery cell aging environment impact data to obtain battery cell cycle number reduction data;

[0045] Step S44: using a random forest algorithm to construct a battery cell aging degree judgment model based on the battery cell cycle loss data and the battery cell aging environment impact data, to obtain a battery cell aging degree judgment model;

[0046] Step S45: performing a judgment process on the battery cell aging degree according to the battery cell aging degree judgment model to obtain battery cell aging degree data.

[0047] The present invention simulates battery degradation under different environmental conditions by performing simulations based on battery cell cycle life test data and power tilt characteristic data. This simulation can provide predictions about battery life under specific usage environments, helping to evaluate battery performance under different operating conditions. Analyzing the environmental impact of battery cell aging using simulation data can identify the extent to which different environmental factors affect battery life. This helps understand the mechanisms of battery aging under different environmental conditions and provides a foundation for subsequent aging assessment. By analyzing the cycle loss data of battery cell cycle life test data based on aging environmental impact data, the battery's life loss in actual use can be estimated. This helps predict battery life under different usage conditions and guides proper battery use and maintenance. Using a random forest algorithm, a aging assessment model is constructed by combining cycle loss data and aging environmental impact data to more accurately assess battery aging. This model can dynamically predict battery life based on actual conditions, facilitating timely adjustments to battery usage strategies. Finally, the established aging assessment model is used to assess battery aging to generate battery cell aging data. This data can be used to develop more effective battery management strategies, extending battery life and improving system reliability.

[0048] Preferably, step S42 includes the following steps:

[0049] Step S421: Calculating humidity impact weight based on battery cell aging environmental impact data to obtain humidity impact weight data;

[0050] Step S422: performing humidity impact fluctuation analysis on the battery cell cycle life test data according to the humidity impact weight data to obtain humidity impact fluctuation data;

[0051] Step S423: performing nonlinear statistical process control processing on the humidity impact fluctuation change data to obtain humidity impact stage process fluctuation data;

[0052] Step S424: performing battery cell aging environment impact analysis on the battery cell cycle life test data according to the humidity impact stage process fluctuation data to obtain battery cell aging environment impact data.

[0053] The present invention determines the extent of humidity's impact on battery life by calculating humidity impact weights based on battery cell aging environmental impact data. Humidity can accelerate or slow down the battery aging process, so calculating humidity impact weights helps more accurately account for the impact of humidity on battery life in subsequent analysis. Using humidity impact weight data to analyze humidity impact fluctuations in battery cell cycle life test data can identify fluctuations in battery life under different humidity conditions. This helps understand the impact of humidity changes on battery life stability and provides a foundation for subsequent analysis. Nonlinear statistical process control processing of humidity impact fluctuation data helps identify the nonlinear variation characteristics of battery life under different humidity stages. This processing can improve understanding and prediction of battery life changes and provide a more accurate data foundation for subsequent analysis. Battery cell aging environmental impact analysis is performed on battery cell cycle life test data based on humidity impact stage process fluctuation data to obtain battery cell aging environmental impact data. This data can provide detailed information on battery aging characteristics under different humidity conditions, helping to optimize the battery operating environment and extend battery life.

[0054] Preferably, performing nonlinear statistical process control on humidity impact fluctuation data includes the following steps:

[0055] Perform nonlinear fluctuation clustering on humidity fluctuation change data to obtain humidity fluctuation nonlinear clustering data;

[0056] According to the nonlinear clustering data of humidity fluctuation, cluster binomial distribution processing is performed to obtain the cluster binomial distribution data of humidity fluctuation;

[0057] A nonlinear control chart of humidity fluctuation nonlinear clustering data is constructed based on the humidity fluctuation cluster binomial distribution data to obtain a humidity fluctuation nonlinear control chart;

[0058] Calculate the upper and lower control limits of the humidity fluctuation nonlinear control chart to obtain the fluctuation nonlinear upper and lower control limit data;

[0059] According to the nonlinear upper and lower control limit data of fluctuation, nonlinear statistical process control processing is performed on the humidity influence fluctuation change data to obtain the humidity influence stage process fluctuation data.

[0060] The present invention performs nonlinear fluctuation clustering on humidity-affected fluctuation data, dividing the data into groups with similar fluctuation characteristics. This helps identify the categories of fluctuations under different humidity conditions, providing a foundation for further analysis. In this step, the nonlinear clustered humidity fluctuation data is subjected to a binomial distribution to determine the probability distribution of the fluctuation characteristics of each cluster. This helps understand the statistical characteristics of different fluctuation categories and provides a foundation for subsequent control chart construction. Based on the binomial distribution data, nonlinear humidity fluctuation control charts are constructed. These control charts display the fluctuations under different humidity conditions and allow the identification of abnormal fluctuations or nonlinear characteristics. This provides a visualization tool for further analysis. Upper and lower control limits are calculated for the nonlinear humidity fluctuation control charts to determine the normal range of fluctuations. These control limits help determine whether the fluctuations are within an acceptable range or whether there are abnormalities requiring further investigation. Finally, based on the upper and lower nonlinear fluctuation control limits, nonlinear statistical process control is performed on the humidity-affected fluctuation data. This step allows real-time monitoring and control of fluctuations under different humidity conditions, ensuring the stability and predictability of battery life under different environmental conditions.

[0061] Preferably, the present invention further provides a system capable of determining differences in aging degrees of battery cells, for executing the above-mentioned method for determining differences in aging degrees of battery cells. The system capable of determining differences in aging degrees of battery cells comprises:

[0062] The performance release evaluation module is used to obtain the factory specification data of the battery cell; perform the battery cell performance release evaluation based on the factory specification data of the battery cell to obtain the battery cell performance release evaluation data;

[0063] A battery cell internal resistance instability assessment module is used to analyze battery cell temperature changes based on battery cell performance release assessment data to obtain battery cell temperature change data; perform temperature non-uniformity distribution difference analysis on the battery cell temperature change data to obtain temperature non-uniformity distribution difference data; and perform battery cell internal resistance instability assessment on the temperature non-uniformity distribution difference data based on the battery cell performance release assessment data to obtain battery cell internal resistance instability data.

[0064] The power tilt characteristic analysis module is used to calculate the internal resistance value limit variation range based on the battery cell internal resistance instability data to obtain the internal resistance value limit variation range; perform battery cell cycle life test based on the internal resistance value limit variation range to obtain battery cell cycle life test data; and perform power tilt characteristic analysis on the battery cell cycle life test data to obtain power tilt characteristic data;

[0065] A battery cell aging degree judgment model construction module is used to simulate the environmental impact of battery cell degradation based on battery cell cycle life test data and power tilt characteristic data to obtain battery cell environmental impact simulation data; perform battery cell aging environmental impact analysis based on the battery cell environmental impact simulation data to obtain battery cell aging environmental impact data; construct a battery cell aging degree judgment model based on the battery cell aging environmental impact data to obtain a battery cell aging degree judgment model; and perform battery cell aging degree judgment processing based on the battery cell aging degree judgment model to obtain battery cell aging degree data.

[0066] The present invention has the beneficial effect of collecting factory specification data for battery cells, providing essential information for subsequent evaluation. This data includes the rated capacity, voltage range, and charge / discharge rate of the battery cells. Based on the factory specification data, this step evaluates the performance of the battery cells. This helps determine the actual performance of the battery cells in applications, including their voltage response and capacity fade at different charge / discharge rates. By analyzing the temperature changes of the battery cells, the thermal characteristics of the battery during use can be understood. This helps identify potential thermal issues, such as performance degradation or safety risks caused by overheating. This step further analyzes the temperature distribution differences within the battery cells. Uneven temperature distribution within the battery cells can lead to unstable performance or shortened lifespan. Analyzing this difference can help design more optimized heat dissipation solutions or temperature management strategies. Based on the battery cell performance release evaluation data and the temperature non-uniformity distribution difference data, the internal resistance instability of the battery cells can be assessed. Internal resistance changes can affect battery performance and safety, so evaluating its instability can help predict battery life and reliability. By calculating the extreme variation range of the internal resistance value, the range of variation of the battery's internal resistance during use can be determined. This helps predict battery performance under different operating conditions and assess its lifespan. By conducting battery cell cycle life testing, the charge and discharge cycles experienced by actual batteries can be simulated and relevant lifespan data obtained. This data can be used to assess battery durability and performance stability. By simulating the degradation process of batteries under various environmental conditions, we can better understand the impact of environmental factors on battery lifespan. This includes factors such as temperature, humidity, and vibration. Analyzing data on environmental impacts on battery cell aging can identify battery aging characteristics and trends under specific conditions. This helps formulate appropriate protection measures and maintenance strategies to extend battery lifespan. An aging assessment model can be constructed to comprehensively assess battery aging based on multiple data indicators, such as internal resistance changes and cycle life test results. This allows users to more accurately understand the battery's health and take appropriate measures, such as battery replacement or maintenance. Therefore, the present invention improves upon traditional methods for determining differences in battery cell aging, resolving issues such as the inability to accurately determine the aging degree of battery cells and the inability to accurately analyze the environmental impact of aging. This improves the accuracy of determining the aging degree of battery cells and enhances the accuracy of analyzing the environmental impact of aging. BRIEF DESCRIPTION OF THE DRAWINGS

[0067] Figure 1 A schematic flow chart of the steps of a method for determining differences in the degree of aging of battery cells;

[0068] Figure 2 for Figure 1 Detailed implementation steps of step S2 in FIG.

[0069] Figure 3 for Figure 1 Detailed implementation steps of step S3 in FIG.

[0070] The purpose, features and advantages of the present invention will be further described with reference to the accompanying drawings and in conjunction with the embodiments. DETAILED DESCRIPTION

[0071] The following is a clear and complete description of the technical method of the present invention in conjunction with the accompanying drawings. Obviously, the embodiments described are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without making any creative work are within the scope of protection of the present invention.

[0072] In addition, the accompanying drawings are merely schematic illustrations of the present invention and are not necessarily drawn to scale. Identical reference numerals in the figures denote identical or similar parts, and thus repetitive descriptions thereof will be omitted. Some of the block diagrams shown in the accompanying drawings are functional entities that do not necessarily correspond to physically or logically separate entities. These functional entities may be implemented in software, in one or more hardware modules or integrated circuits, or in different network and / or processor and / or microcontroller approaches.

[0073] It should be understood that although the terms "first," "second," and the like may be used herein to describe various elements, these elements should not be limited by these terms. These terms are used solely to distinguish one element from another. For example, a first element may be referred to as a second element, and similarly, a second element may be referred to as a first element, without departing from the scope of the exemplary embodiments. The term "and / or" as used herein includes any and all combinations of one or more of the listed associated items.

[0074] To achieve this, please refer to Figures 1 to 3 A method for determining differences in aging degrees of battery cells, comprising the following steps:

[0075] Step S1: Obtaining factory specification data of the battery cell; performing a battery cell performance release evaluation based on the factory specification data of the battery cell to obtain battery cell performance release evaluation data;

[0076] Step S2: performing a battery cell temperature change analysis based on the battery cell performance release evaluation data to obtain battery cell temperature change data; performing a temperature non-uniformity distribution difference analysis on the battery cell temperature change data to obtain temperature non-uniformity distribution difference data; performing a battery cell internal resistance instability evaluation on the temperature non-uniformity distribution difference data based on the battery cell performance release evaluation data to obtain battery cell internal resistance instability data;

[0077] Step S3: Calculating the internal resistance value limit variation interval for the battery cell internal resistance instability data to obtain the internal resistance value limit variation interval; performing a battery cell cycle life test based on the internal resistance value limit variation interval to obtain battery cell cycle life test data; and performing a power tilt characteristic analysis on the battery cell cycle life test data to obtain power tilt characteristic data;

[0078] Step S4: performing a battery cell degradation environmental impact simulation based on the battery cell cycle life test data and the power tilt characteristic data to obtain battery cell environmental impact simulation data; performing a battery cell aging environmental impact analysis based on the battery cell environmental impact simulation data to obtain battery cell aging environmental impact data; constructing a battery cell aging degree judgment model based on the battery cell aging environmental impact data to obtain a battery cell aging degree judgment model; performing a battery cell aging degree judgment process based on the battery cell aging degree judgment model to obtain battery cell aging degree data.

[0079] In the embodiment of the present invention, reference Figure 1 The above is a flow chart of the steps of a method for determining the difference in the degree of aging of battery cells according to the present invention. In this example, the method for determining the difference in the degree of aging of battery cells includes the following steps:

[0080] Step S1: Obtaining factory specification data of the battery cell; performing a battery cell performance release evaluation based on the factory specification data of the battery cell to obtain battery cell performance release evaluation data;

[0081] In an embodiment of the present invention, the factory specification data of the battery cell provided by the manufacturer is collected, including information such as size, capacity, rated voltage, and charge and discharge performance. Based on the collected factory specification data, a battery cell performance release evaluation is performed, which generally includes performing charge and discharge cycle tests, capacity tests, internal resistance tests, etc. Professional testing equipment is used to perform various performance tests on the battery cell and record the test data.

[0082] Step S2: performing a battery cell temperature change analysis based on the battery cell performance release evaluation data to obtain battery cell temperature change data; performing a temperature non-uniformity distribution difference analysis on the battery cell temperature change data to obtain temperature non-uniformity distribution difference data; performing a battery cell internal resistance instability evaluation on the temperature non-uniformity distribution difference data based on the battery cell performance release evaluation data to obtain battery cell internal resistance instability data;

[0083] In an embodiment of the present invention, the temperature changes of the battery core during the charging and discharging process are analyzed, and the temperature data is recorded. A temperature sensor can be used to perform real-time temperature monitoring of the battery core under different charging and discharging conditions, and the temperature change data of the battery core is analyzed for temperature non-uniformity distribution differences: the temperature data is statistically analyzed to explore the differences in temperature changes in different parts, and statistical methods or heat conduction theory can be used to quantitatively analyze the temperature distribution differences. The temperature non-uniformity distribution difference data is used to evaluate the instability of the internal resistance of the battery core based on the battery core performance release evaluation data: the battery core performance release evaluation data and the temperature distribution difference data are combined to evaluate the stability of the internal resistance of the battery core under different temperature conditions, and the electrochemical impedance spectroscopy (EIS) and other methods can be used to test and analyze the internal resistance of the battery core at different temperatures.

[0084] Step S3: Calculating the internal resistance value limit variation interval for the battery cell internal resistance instability data to obtain the internal resistance value limit variation interval; performing a battery cell cycle life test based on the internal resistance value limit variation interval to obtain battery cell cycle life test data; and performing a power tilt characteristic analysis on the battery cell cycle life test data to obtain power tilt characteristic data;

[0085] In an embodiment of the present invention, the internal resistance instability data is analyzed to determine the variation range of the internal resistance value, the limit variation range of the internal resistance value is calculated through statistical methods or models, and the battery cell cycle life test is performed based on the limit variation range of the internal resistance value: a cyclic charge and discharge experiment plan is designed, considering different charge and discharge conditions and temperature conditions, and the cycle life test data is recorded in the experiment, including the number of cycles, capacity attenuation, etc., and the power tilt characteristic analysis of the battery cell cycle life test data is performed: the cycle life test data is analyzed to explore the influence of the charge and discharge power on the battery life. Statistical methods or model building can be used to analyze the power tilt characteristics, such as the correlation between power and life.

[0086] Step S4: performing a battery cell degradation environmental impact simulation based on the battery cell cycle life test data and the power tilt characteristic data to obtain battery cell environmental impact simulation data; performing a battery cell aging environmental impact analysis based on the battery cell environmental impact simulation data to obtain battery cell aging environmental impact data; constructing a battery cell aging degree judgment model based on the battery cell aging environmental impact data to obtain a battery cell aging degree judgment model; performing a battery cell aging degree judgment process based on the battery cell aging degree judgment model to obtain battery cell aging degree data.

[0087] In an embodiment of the present invention, a battery cell degradation environmental impact simulation is performed based on the battery cell cycle life test data and the power tilt characteristic data: a battery degradation model is established based on the existing data, and the factors such as the number of cycles and the charge and discharge power are taken into consideration. The battery degradation under different environmental conditions is simulated using simulation software to generate degradation environmental impact simulation data. The battery cell aging environmental impact analysis is performed based on the battery cell environmental impact simulation data: the simulation data is analyzed to understand the degree of influence of different environmental factors on the battery life, and a battery cell aging degree judgment model is constructed based on the battery cell aging environmental impact data: an aging degree judgment model is established based on the analysis results, and parameters such as environmental factors and the number of cycles are taken into consideration. A machine learning algorithm or an empirical model can be used to construct an aging degree judgment model, and the battery cell aging degree judgment processing is performed based on the battery cell aging degree judgment model: the actually collected data is input into the aging degree judgment model to judge the aging degree, and the battery cell aging degree data is output for evaluating the health status of the battery and predicting its life.

[0088] The present invention collects factory specification data for battery cells, providing essential information for subsequent evaluation. This data includes the rated capacity, voltage range, and charge / discharge rate of the battery cells. Based on the factory specification data, this step evaluates the performance of the battery cells. This helps determine the actual performance of the battery cells in applications, including their voltage response and capacity decay at different charge / discharge rates. By analyzing the temperature changes of the battery cells, the thermal characteristics of the battery during use can be understood. This helps identify potential thermal issues, such as performance degradation or safety risks caused by overheating. This step further analyzes the temperature distribution differences within the battery cells. Uneven temperature distribution within the battery cells can lead to unstable performance or shortened lifespan. Analyzing this difference can help design more optimized heat dissipation solutions or temperature management strategies. Based on the battery cell performance release evaluation data and the temperature nonuniformity distribution difference data, the internal resistance instability of the battery cells is assessed. Changes in internal resistance can affect battery performance and safety, so evaluating its instability can help predict battery life and reliability. By calculating the extreme range of internal resistance variation, the range of internal resistance variation during use can be determined. This helps predict battery performance under different operating conditions and assess its lifespan. By conducting battery cell cycle life testing, the charge and discharge cycles experienced by actual batteries can be simulated and relevant lifespan data obtained. This data can be used to assess battery durability and performance stability. By simulating the degradation process of batteries under various environmental conditions, we can better understand the impact of environmental factors on battery lifespan. This includes factors such as temperature, humidity, and vibration. Analyzing data on environmental impacts on battery cell aging can identify battery aging characteristics and trends under specific conditions. This helps formulate appropriate protection measures and maintenance strategies to extend battery lifespan. An aging assessment model can be constructed to comprehensively assess battery aging based on multiple data indicators, such as internal resistance changes and cycle life test results. This allows users to more accurately understand the battery's health and take appropriate measures, such as battery replacement or maintenance. Therefore, the present invention improves upon traditional methods for determining differences in battery cell aging, resolving issues such as the inability to accurately determine the aging degree of battery cells and the inability to accurately analyze the environmental impact of aging. This improves the accuracy of determining the aging degree of battery cells and enhances the accuracy of analyzing the environmental impact of aging.

[0089] Preferably, step S1 includes the following steps:

[0090] Step S11: Obtaining factory specification data of the battery cell;

[0091] Step S12: simulating the battery cell operating state according to the factory specification data of the battery cell to obtain battery cell operating state simulation data;

[0092] Step S13: performing battery cell performance release evaluation according to the battery cell operating state simulation data to obtain battery cell performance release evaluation data.

[0093] In an embodiment of the present invention, the model and specifications of the battery cell to be used are determined, and the factory specification data of the battery cell, including parameters such as capacity, rated voltage, and internal resistance, are obtained from the supplier or manufacturer. A simulation model of the operating status of the battery cell is established, and the battery's charge and discharge characteristics, temperature characteristics, etc. are taken into consideration. The factory specification data of the battery cell is input into the simulation model, and simulation software is used to simulate the operating status of the battery cell to obtain the operating status simulation data of the battery cell under different working conditions, such as voltage, current, and temperature. The battery cell operating status simulation data is analyzed to evaluate the performance release of the battery cell, including capacity, power, cycle life, etc. Data analysis methods, statistical methods, or model building can be used to evaluate the performance release, and the battery cell performance release evaluation data is output for evaluating the actual performance of the battery cell and predicting its performance in actual applications.

[0094] This step of the present invention forms the foundation for this evaluation. By collecting factory-specification data for the battery cells, including rated capacity, voltage range, and charge / discharge rates, this provides basic battery information. Using simulation technology, the battery's operating state under different operating conditions is simulated based on this factory-specification data. This simulation can predict actual battery performance, such as charge / discharge characteristics and temperature response, providing a reference for subsequent evaluation. Based on the simulated operating data, a performance evaluation of the battery cells is performed. This evaluation helps determine the actual performance of the battery cells, including their voltage response and capacity decay at different charge / discharge rates. This evaluation provides a better understanding of the performance characteristics of the battery cells, providing a basis for battery system design and optimization.

[0095] Preferably, step S2 includes the following steps:

[0096] Step S21: analyzing the battery cell temperature change according to the battery cell performance release evaluation data to obtain battery cell temperature change data;

[0097] Step S22: Calculating the temperature rise rate of the battery cell temperature change data to obtain the battery cell temperature rise rate data;

[0098] Step S23: performing temperature non-uniformity distribution difference analysis on the battery cell temperature change data according to the battery cell temperature rise rate data to obtain temperature non-uniformity distribution difference data;

[0099] Step S24: performing battery cell internal resistance instability evaluation on the temperature non-uniformity distribution difference data according to the battery cell performance release evaluation data to obtain battery cell internal resistance instability data.

[0100] As an example of the present invention, refer to Figure 2 As shown, in this example, step S2 includes:

[0101] Step S21: analyzing the battery cell temperature change according to the battery cell performance release evaluation data to obtain battery cell temperature change data;

[0102] In an embodiment of the present invention, battery cell performance release evaluation data, including capacity, power, cycle life and other related parameters, is prepared, and the battery cell performance release evaluation data is analyzed, with special attention paid to temperature-related parameters such as heat release, internal resistance, and number of cycles during charging and discharging. The influence of the external ambient temperature on the battery cell temperature and the non-uniformity of the temperature distribution inside the battery are taken into consideration, and data analysis tools or software are used to model and analyze the battery cell temperature changes to obtain the temperature change data of the battery cell under different working conditions.

[0103] Step S22: Calculating the temperature rise rate of the battery cell temperature change data to obtain the battery cell temperature rise rate data;

[0104] In an embodiment of the present invention, the battery core temperature change data obtained through analysis is prepared, including temperature change curves or data points under different operating conditions, the battery core temperature change data is processed, and the temperature change rate or rise rate is calculated. Generally, the temperature rise rate can be calculated by selecting the temperature change between two time points and dividing it by the time interval. A mathematical formula or computer program can be used to implement the calculation of the temperature rise rate, ensuring that the nonlinearity and dynamics of the temperature change are taken into account, and interpolation or smoothing processing is required to obtain more accurate results.

[0105] Step S23: performing temperature non-uniformity distribution difference analysis on the battery cell temperature change data according to the battery cell temperature rise rate data to obtain temperature non-uniformity distribution difference data;

[0106] In an embodiment of the present invention, the battery core temperature rise rate data obtained in the previous step is prepared to ensure the accuracy and completeness of the data. The battery core temperature change data and the temperature rise rate data are used to further analyze the battery core temperature change. The temperature rise rate data is applied to the temperature change data to simulate the non-uniform distribution of the battery core temperature. The temperature changes in different parts are compared to check the distribution differences of the temperature non-uniformity. Statistical methods, charts or visualization tools can be used to identify the differences in temperature distribution, such as histograms and density maps, and the analysis results are converted into temperature non-uniformity distribution difference data to further evaluate the internal resistance instability of the battery cell.

[0107] Step S24: performing battery cell internal resistance instability evaluation on the temperature non-uniformity distribution difference data according to the battery cell performance release evaluation data to obtain battery cell internal resistance instability data.

[0108] In an embodiment of the present invention, battery cell performance release evaluation data, including capacity, power, cycle life and other related parameters, is prepared. The temperature non-uniformity distribution difference data obtained in the previous step is used in combination with the battery cell performance release evaluation data and the temperature non-uniformity distribution difference data to evaluate the instability of the battery cell internal resistance. The impact of temperature non-uniformity on the battery cell internal resistance is analyzed. The internal resistance changes in different temperature zones are considered, and the evaluation results are converted into battery cell internal resistance instability data to describe the stability and change trend of the internal resistance of the battery cell under different temperature conditions.

[0109] By analyzing battery cell performance release evaluation data, the present invention can understand the temperature variations of battery cells under different operating conditions. This helps determine the temperature range encountered by the battery cells during actual operation and provides basic data for subsequent temperature-related analysis. The rate of rise of the battery cell temperature is an important indicator that reflects the thermal characteristics of the battery cell during charging or discharging. By calculating the rate of temperature rise, the thermal management performance of the battery cell under different operating conditions can be evaluated, providing an important reference for preventing overheating or temperature runaway. Internal temperature non-uniformity within the battery cell can lead to instability and safety hazards in the battery system. By analyzing the temperature rise rate data, the distribution of internal temperature differences within the battery cell can be assessed to determine whether there are localized excessively high or low temperatures, providing a basis for formulating temperature management strategies. Temperature non-uniformity can affect the stability of the battery cell internal resistance, thereby affecting the performance and safety of the battery system. By evaluating the temperature non-uniformity distribution based on the battery cell performance release evaluation data, it is possible to determine the instability of the battery cell internal resistance, identify potential problems in advance, and implement appropriate measures to ensure stable operation of the battery system.

[0110] Preferably, step S24 includes the following steps:

[0111] Step S241: performing performance release rate increment calculation on the battery cell performance release evaluation data to obtain performance release rate increment data;

[0112] Step S242: performing battery cell thermal effect coupling analysis based on the performance release rate increment data and the temperature non-uniformity distribution difference data to obtain battery cell thermal effect coupling data;

[0113] Step S243: performing nonlinear current response simulation on the battery cell thermal effect coupling data to obtain nonlinear current response simulation data;

[0114] Step S244: performing heat loss cycle degradation coupling processing according to the battery cell thermal effect coupling data and the nonlinear current response simulation data to obtain heat loss cycle degradation coupling data;

[0115] Step S245: performing battery cell internal resistance instability assessment based on the heat loss cycle degradation coupling data to obtain battery cell internal resistance instability data.

[0116] In an embodiment of the present invention, the accuracy and completeness of the battery cell performance release evaluation data, including relevant parameters such as capacity, power, and voltage, are ensured, and the incremental rate range and step size are set to perform an incremental calculation of the performance release rate. Initially, the performance release rate is set to the minimum value, and the performance release rate is gradually increased. A performance evaluation calculation is performed for each rate value. After each calculation, the corresponding performance indicators, such as the temperature, voltage, capacity, and other data of the battery cell, are recorded. The performance indicator data obtained from each calculation are sorted and recorded to form performance release rate incremental data; the performance release rate incremental data and the temperature non-uniformity distribution difference data are prepared, and the performance release rate incremental data and the temperature non-uniformity distribution difference data are coupled and analyzed, considering the impact of the increase in the performance release rate on the temperature distribution of the battery cell, and then analyzing the thermal effect of the battery cell, and sorting and recording the results of the coupling analysis to obtain the coupling data describing the thermal effect of the battery cell; the battery cell thermal effect coupling data, as well as other necessary simulation parameters and conditions, are prepared based on the thermal effect of the battery cell. The coupled data should be used to simulate the nonlinear current response, considering factors such as the battery core temperature distribution and changes in the performance release rate, to simulate the nonlinear current response of the battery core, and to organize and record the simulated current response data to form nonlinear current response simulation data; prepare battery core thermal effect coupling data and nonlinear current response simulation data, couple the battery core thermal effect coupling data with the nonlinear current response simulation data, consider the impact of temperature changes on current response, simulate the heat loss of the battery during the cycle, including changes in lost energy, heat, etc., organize and record the processed data to form coupling data describing heat loss cycle degradation; prepare heat loss cycle degradation coupling data, as well as other parameters and conditions required for evaluation, and evaluate the instability of the battery core internal resistance based on the heat loss cycle degradation coupling data, analyze the impact of heat loss on the battery internal resistance, consider its changing trend with the number of cycles, organize and record the evaluated internal resistance instability data, and describe the change of the battery core internal resistance during the cycle.

[0117] The performance release rate increase of the present invention refers to the ability of the battery to release energy at different charge and discharge rates. By calculating the performance release rate increase data, the performance of the battery under different working conditions can be understood, providing a basis for subsequent analysis. The thermal effect coupling analysis takes into account the relationship between the temperature distribution inside the battery core and the performance release rate. This helps to understand the thermal characteristics of the battery core during high-rate discharge or charging, predict the temperature changes that occur, and provide a basis for the formulation of thermal management strategies. In the battery system, the current response will be affected by multiple factors and exhibit nonlinear characteristics. By performing nonlinear current response simulation on the battery core thermal effect coupling data, the current response can be predicted more accurately, and the control strategy of the battery system can be further optimized. The thermal loss cycle degradation coupling takes into account the complex interaction between thermal effects, current response and battery core internal resistance instability. By processing this data, the factors that lead to battery performance degradation and shortened life can be identified, providing guidance for extending battery life and improving performance. Internal resistance is an important parameter in the battery system, which directly affects the performance and safety of the battery. By evaluating the coupled data of heat loss cycle degradation, the stability of the battery cell internal resistance can be determined, the trend of internal resistance change can be discovered in a timely manner, and corresponding measures can be taken to maintain the stable operation of the battery system.

[0118] Preferably, step S3 includes the following steps:

[0119] Step S31: calculating the internal resistance instability fluctuation amplitude of the battery cell internal resistance instability data to obtain internal resistance instability fluctuation amplitude data;

[0120] Step S32: calculating the internal resistance value limit variation range of the battery cell internal resistance instability data according to the internal resistance instability fluctuation amplitude data to obtain the internal resistance value limit variation range;

[0121] Step S33: performing battery cell capacity attenuation analysis based on the internal resistance value limit variation range to obtain battery cell capacity attenuation data;

[0122] Step S34: performing a battery cell cycle life test according to the internal resistance value limit variation range and the battery cell capacity attenuation data to obtain battery cell cycle life test data;

[0123] Step S35: performing power tilt characteristic analysis on the battery cell cycle life test data to obtain power tilt characteristic data.

[0124] As an example of the present invention, refer to Figure 3 As shown, in this example, step S3 includes:

[0125] Step S31: calculating the internal resistance instability fluctuation amplitude of the battery cell internal resistance instability data to obtain internal resistance instability fluctuation amplitude data;

[0126] In an embodiment of the present invention, the fluctuation amplitude of the internal resistance instability data can be calculated by using some statistical methods, such as standard deviation or root mean square error, to determine the calculation window size of the fluctuation amplitude to adapt to the required fluctuation analysis accuracy. The calculated fluctuation amplitude data is sorted and recorded to form data describing the internal resistance instability fluctuation amplitude.

[0127] Step S32: calculating the internal resistance value limit variation range of the battery cell internal resistance instability data according to the internal resistance instability fluctuation amplitude data to obtain the internal resistance value limit variation range;

[0128] In an embodiment of the present invention, an appropriate threshold is set based on the internal resistance instability fluctuation amplitude data to determine the boundary of the internal resistance change. Combined with the internal resistance instability data, the extreme change range of the internal resistance value is calculated, and the calculated extreme change range data is organized and recorded to describe the change range of the internal resistance value.

[0129] Step S33: performing battery cell capacity attenuation analysis based on the internal resistance value limit variation range to obtain battery cell capacity attenuation data;

[0130] In an embodiment of the present invention, data on the internal resistance value limit change interval and other parameters and conditions required for battery capacity attenuation are prepared. Based on the internal resistance value limit change interval, the battery capacity attenuation is analyzed, the impact of the internal resistance change on the battery charge and discharge performance is considered, and the change trend of the battery capacity over time is predicted. The battery capacity attenuation data obtained from the analysis is organized and recorded to describe the attenuation of the battery capacity over time.

[0131] Step S34: performing a battery cell cycle life test according to the internal resistance value limit variation range and the battery cell capacity attenuation data to obtain battery cell cycle life test data;

[0132] In an embodiment of the present invention, the test environment and conditions, including environmental factors such as temperature and humidity, are determined, the test equipment is configured to ensure that the equipment can perform cyclic charge and discharge tests stably and reliably, battery samples are prepared to ensure that they represent the characteristics of the battery cells to be tested, and the battery samples are subjected to cyclic charge and discharge tests using the configured test equipment. The parameters and cycles of the cyclic charge and discharge tests are determined based on the internal resistance value limit change range and capacity attenuation data, and the various parameters of the battery during the cyclic charge and discharge process, including voltage, current, and temperature, are continuously recorded. After the test, the battery cyclic charge and discharge test data, including the number of cycles, capacity attenuation, and internal resistance changes, are organized and recorded.

[0133] Step S35: performing power tilt characteristic analysis on the battery cell cycle life test data to obtain power tilt characteristic data.

[0134] In an embodiment of the present invention, battery cell cycle life test data is prepared, and a power tilt characteristic analysis is performed on the battery cell cycle life test data. Various statistical methods or model analysis methods can be used to determine analysis indicators of the power tilt characteristic, such as the cycle life decay rate and the power loss rate. Based on the analysis results, data describing the power tilt characteristics of the battery cell are obtained, such as a cycle life decay curve and a power loss rate curve.

[0135] The internal resistance of the present invention is one of the important indicators of battery performance, and its stability directly affects the battery's performance and lifespan. By calculating the internal resistance instability fluctuation amplitude data, the degree of internal resistance variation can be quantified, providing a basis for subsequent analysis. Based on the internal resistance instability fluctuation amplitude data, the extreme variation range of the internal resistance value can be further calculated. This range reflects the range of internal resistance variation and can be used to determine the stability of the internal resistance and provide a basis for subsequent capacity decay analysis. Capacity decay refers to the phenomenon of gradual decrease in battery capacity during use, which directly affects the battery's available energy and service life. By performing capacity decay analysis based on the extreme variation range of the internal resistance value, the battery's service life and performance decay trend can be understood. The battery cell cycle life test evaluates the ability of a battery to maintain a specified capacity after a certain number of charge and discharge cycles under specific operating conditions. By performing cycle life testing based on the extreme variation range of the internal resistance value and capacity decay data, the battery's durability and lifespan can be directly evaluated. The power tilt characteristic refers to the battery's performance at different charge and discharge rates. By performing power tilt characteristic analysis on the cycle life test data, the battery's performance under different operating conditions can be understood, providing reference and guidance for practical battery applications.

[0136] Preferably, step S35 includes the following steps:

[0137] Step S351: performing internal resistance stage-by-stage dynamic trend analysis on the battery cell cycle life test data to obtain internal resistance stage-by-stage dynamic trend data;

[0138] Step S352: performing a stage-by-stage temperature effect analysis based on the stage-by-stage dynamic trend data of the internal resistance to obtain stage-by-stage temperature effect data;

[0139] Step S353: performing cumulative power loss evaluation based on the internal resistance periodic dynamic trend data and the periodic temperature effect data to obtain cumulative power loss data;

[0140] Step S354: performing power tilt characteristic analysis based on the accumulated power loss data, the staged temperature effect data, and the staged dynamic trend data of the internal resistance to obtain power tilt characteristic data.

[0141] In an embodiment of the present invention, internal resistance data is obtained from a cycle life test of a battery cell. These data include internal resistance measurement values ​​at different cycle times. The internal resistance data is preprocessed, such as removing outliers and smoothing data, and the battery life cycle is divided into multiple stages (for example, initial, intermediate, and final stages). Trend analysis is performed on the internal resistance data of each stage, and regression analysis, moving average, and other methods are used to obtain dynamic trend data of the internal resistance in each stage. During the cycle life test of the battery cell, temperature data is synchronously recorded, consistent with the stage division of step S351, and the temperature data is divided into the same stages. The effect of temperature on internal resistance in each stage is analyzed, and correlation analysis or regression analysis and other methods are used to obtain stage-specific temperature effect data. The power consumption is calculated based on the battery operating current and internal resistance, and the formula is P=I 2 +R, where P is power consumption, I is current, and R is internal resistance. In each stage, the cumulative power loss of that stage is calculated based on the internal resistance stage-by-stage dynamic trend data and temperature effect data. Numerical integration or other accumulation methods are used to obtain the cumulative power loss data for the entire life cycle. The cumulative power loss data, stage-by-stage temperature effect data, and stage-by-stage dynamic trend data of internal resistance are integrated. Based on the integrated data, power tilt characteristic analysis is performed. Multivariate regression analysis, machine learning, and other methods are used to output power tilt characteristic data. This data describes the power loss characteristics and tilt change rules of the battery at different stages.

[0142] The present invention analyzes the internal resistance of a battery during different charge and discharge phases by performing a phased dynamic trend analysis of the internal resistance of the battery cell cycle life test data. This helps identify the trend of the internal resistance during the cycle and provides basic data for subsequent temperature effect analysis. During the cycle life test, temperature changes will affect the internal resistance of the battery. By performing a phased temperature effect analysis on the internal resistance phased dynamic trend data, the extent of the impact of temperature on the internal resistance change at different stages can be understood. This helps evaluate the performance of the battery under different temperature conditions. Cumulative power loss refers to the energy loss caused by factors such as increased internal resistance and temperature changes during the cycle life test. By combining the internal resistance phased dynamic trend data and the phased temperature effect data, the cumulative power loss can be evaluated. This helps understand the energy loss of the battery during long-term cycling. The power tilt characteristic of the battery refers to the performance of the battery at different charge and discharge rates. By analyzing the power tilt characteristic based on the cumulative power loss data, the phased temperature effect data, and the internal resistance phased dynamic trend data, a more comprehensive understanding of the battery's performance under different operating conditions can be obtained. This helps optimize battery system design and application scenario selection.

[0143] Preferably, step S4 includes the following steps:

[0144] Step S41: performing a battery cell degradation environmental impact simulation based on the battery cell cycle life test data and the power tilt characteristic data to obtain battery cell environmental impact simulation data;

[0145] Step S42: performing battery cell aging environmental impact analysis on the battery cell cycle life test data according to the battery cell environmental impact simulation data to obtain battery cell aging environmental impact data;

[0146] Step S43: performing cycle number reduction analysis on the battery cell cycle life test data based on the battery cell aging environment impact data to obtain battery cell cycle number reduction data;

[0147] Step S44: using a random forest algorithm to construct a battery cell aging degree judgment model based on the battery cell cycle loss data and the battery cell aging environment impact data, to obtain a battery cell aging degree judgment model;

[0148] Step S45: performing a judgment process on the battery cell aging degree according to the battery cell aging degree judgment model to obtain battery cell aging degree data.

[0149] In an embodiment of the present invention, battery cell cycle life test data and power tilt characteristic data are collected, simulation software and tools such as MATLAB, Python, etc. are determined, and corresponding models and algorithms are configured. A battery degradation model is established based on the battery cell cycle life test data and power tilt characteristic data. Factors such as the battery's chemical properties, temperature changes, and charge and discharge cycles are considered to establish a mathematical model of battery degradation. The established battery degradation model is used to simulate and calculate battery cells under different environmental conditions, simulate the degradation process of the battery under different environmental conditions such as temperature and humidity, and obtain corresponding simulation data, and collect simulation data on the environmental impact of battery cell degradation. , using the collected simulation data, analyze the impact of different environmental factors on battery cell aging, explore the influence of factors such as temperature, humidity, and frequency of use on battery life, and determine their importance, prepare battery cell cycle life test data, match and integrate the battery cell aging environmental impact data with the cycle life test data, and based on the matched data, analyze the cycle life reduction of the battery cell under different environmental conditions, explore the impact of different environmental factors on battery life, and evaluate their contribution to battery life. Based on the analysis results, derive the expected life of the battery cell under different environmental conditions, and propose optimization suggestions or improvement measures; integrate the number of battery cell cycles Reduce the loss data and the impact data of the battery cell aging environment to ensure data quality and integrity, perform feature selection on the prepared data, determine which features are most important for judging the degree of battery cell aging, standardize or normalize the data to ensure the consistency of scale between features, use the random forest algorithm to build a battery cell aging degree judgment model, divide the data set into a training set and a validation set, train the model with the training set, and evaluate the performance and generalization ability of the model with the validation set, evaluate the model, including indicators such as accuracy, recall rate, F1 score, etc., and according to the evaluation results, tune the model parameters or optimize the feature selection to improve the performance and generalization of the model The model is validated using an independent test data set to ensure its reliability and robustness. The battery cell aging data to be judged is input into the constructed model for preprocessing to ensure that the data format and features are consistent with those during training. The constructed battery cell aging judgment model is used to predict the input battery cell data. The aging degree of the battery cell is judged based on the output of the model. The aging degree of the battery cell is usually represented by a score or grade. The judged battery cell aging degree data is output as a numerical score or a classification grade. The results can be visualized so that users can intuitively understand the aging of the battery cell.

[0150] The present invention simulates battery degradation under different environmental conditions by performing simulations based on battery cell cycle life test data and power tilt characteristic data. This simulation can provide predictions about battery life under specific usage environments, helping to evaluate battery performance under different operating conditions. Analyzing the environmental impact of battery cell aging using simulation data can identify the extent to which different environmental factors affect battery life. This helps understand the mechanisms of battery aging under different environmental conditions and provides a foundation for subsequent aging assessment. By analyzing the cycle loss data of battery cell cycle life test data based on aging environmental impact data, the battery's life loss in actual use can be estimated. This helps predict battery life under different usage conditions and guides proper battery use and maintenance. Using a random forest algorithm, a aging assessment model is constructed by combining cycle loss data and aging environmental impact data to more accurately assess battery aging. This model can dynamically predict battery life based on actual conditions, facilitating timely adjustments to battery usage strategies. Finally, the established aging assessment model is used to assess battery aging to generate battery cell aging data. This data can be used to develop more effective battery management strategies, extending battery life and improving system reliability.

[0151] Preferably, step S42 includes the following steps:

[0152] Step S421: Calculating humidity impact weight based on battery cell aging environmental impact data to obtain humidity impact weight data;

[0153] Step S422: performing humidity impact fluctuation analysis on the battery cell cycle life test data according to the humidity impact weight data to obtain humidity impact fluctuation data;

[0154] Step S423: performing nonlinear statistical process control processing on the humidity impact fluctuation change data to obtain humidity impact stage process fluctuation data;

[0155] Step S424: performing battery cell aging environment impact analysis on the battery cell cycle life test data according to the humidity impact stage process fluctuation data to obtain battery cell aging environment impact data.

[0156] In an embodiment of the present invention, humidity data related to the environmental impact of battery cell aging is collected, and an appropriate algorithm (such as weighted average or a model in a professional field) is used to calculate the weight of the impact of humidity on battery cell aging based on the humidity data. Taking into account the different impacts of humidity on battery cell aging at different stages, it is necessary to perform weight calculations in stages, and output the obtained humidity impact weight data for use in subsequent steps. The battery cell cycle life test data is combined with the humidity impact weight data calculated in the previous step to ensure the accuracy and completeness of the data. The humidity impact weight data is used to analyze the fluctuation of humidity impact on the battery cell cycle life test data. It is necessary to use statistical methods or time series analysis and other techniques to determine the impact of humidity changes on battery life. Influence trends and fluctuations, output the obtained humidity influence fluctuation change data for use in subsequent steps, perform nonlinear statistical process control on the humidity influence fluctuation change data, involving technologies such as anomaly detection, trend analysis or pattern recognition to determine the stage-by-stage fluctuation characteristics, output the obtained humidity influence stage process fluctuation data for use in subsequent steps, combine the humidity influence stage process fluctuation data with the battery cell cycle life test data to ensure data integrity and consistency, and perform aging environment impact analysis on the battery cell cycle life test data based on the humidity influence stage process fluctuation data. Statistical analysis, machine learning or other related technologies are needed to determine the specific impact pattern and degree of humidity influence on battery cell life.

[0157] The present invention determines the extent of humidity's impact on battery life by calculating humidity impact weights based on battery cell aging environmental impact data. Humidity can accelerate or slow down the battery aging process, so calculating humidity impact weights helps more accurately account for the impact of humidity on battery life in subsequent analysis. Using humidity impact weight data to analyze humidity impact fluctuations in battery cell cycle life test data can identify fluctuations in battery life under different humidity conditions. This helps understand the impact of humidity changes on battery life stability and provides a foundation for subsequent analysis. Nonlinear statistical process control processing of humidity impact fluctuation data helps identify the nonlinear variation characteristics of battery life under different humidity stages. This processing can improve understanding and prediction of battery life changes and provide a more accurate data foundation for subsequent analysis. Battery cell aging environmental impact analysis is performed on battery cell cycle life test data based on humidity impact stage process fluctuation data to obtain battery cell aging environmental impact data. This data can provide detailed information on battery aging characteristics under different humidity conditions, helping to optimize the battery operating environment and extend battery life.

[0158] Preferably, performing nonlinear statistical process control on humidity impact fluctuation data includes the following steps:

[0159] Perform nonlinear fluctuation clustering on humidity fluctuation change data to obtain humidity fluctuation nonlinear clustering data;

[0160] According to the nonlinear clustering data of humidity fluctuation, cluster binomial distribution processing is performed to obtain the cluster binomial distribution data of humidity fluctuation;

[0161] A nonlinear control chart of humidity fluctuation nonlinear clustering data is constructed based on the humidity fluctuation cluster binomial distribution data to obtain a humidity fluctuation nonlinear control chart;

[0162] Calculate the upper and lower control limits of the humidity fluctuation nonlinear control chart to obtain the fluctuation nonlinear upper and lower control limit data;

[0163] According to the nonlinear upper and lower control limit data of fluctuation, nonlinear statistical process control processing is performed on the humidity influence fluctuation change data to obtain the humidity influence stage process fluctuation data.

[0164] In an embodiment of the present invention, a nonlinear clustering algorithm, such as density-based clustering (DBSCAN) or spectral clustering, is used to perform cluster analysis on humidity influence fluctuation change data. The obtained nonlinear fluctuation cluster data is processed to adapt to the requirements of the binomial distribution model. The data format needs to be adjusted or the data conversion needs to be performed for subsequent distribution analysis. A nonlinear control chart is constructed based on the processed humidity fluctuation cluster binomial distribution data. Various nonlinear control chart construction methods can be used, such as the exponentially weighted moving average control chart (EWMA) or the chi-square control chart. Based on the constructed nonlinear control chart, the nonlinear upper and lower control limits of the fluctuation are calculated. The calculation of the control limits usually depends on the characteristics of the data and the selected control chart method. The stability and variability of the data need to be taken into account. The calculated fluctuation nonlinear upper and lower control limit data are used to perform nonlinear statistical process control processing on the humidity influence fluctuation change data. By comparing the positional relationship between the actual data and the control limits, abnormal situations are identified and processed to maintain the stability and controllability of the process. After nonlinear statistical process control processing, the humidity influence stage process fluctuation data is obtained.

[0165] The present invention performs nonlinear fluctuation clustering on humidity-affected fluctuation data, dividing the data into groups with similar fluctuation characteristics. This helps identify the categories of fluctuations under different humidity conditions, providing a foundation for further analysis. In this step, the nonlinear clustered humidity fluctuation data is subjected to a binomial distribution to determine the probability distribution of the fluctuation characteristics of each cluster. This helps understand the statistical characteristics of different fluctuation categories and provides a foundation for subsequent control chart construction. Based on the binomial distribution data, nonlinear humidity fluctuation control charts are constructed. These control charts display the fluctuations under different humidity conditions and allow the identification of abnormal fluctuations or nonlinear characteristics. This provides a visualization tool for further analysis. Upper and lower control limits are calculated for the nonlinear humidity fluctuation control charts to determine the normal range of fluctuations. These control limits help determine whether the fluctuations are within an acceptable range or whether there are abnormalities requiring further investigation. Finally, based on the upper and lower nonlinear fluctuation control limits, nonlinear statistical process control is performed on the humidity-affected fluctuation data. This step allows real-time monitoring and control of fluctuations under different humidity conditions, ensuring the stability and predictability of battery life under different environmental conditions.

[0166] Preferably, the present invention further provides a system capable of determining differences in aging degrees of battery cells, for executing the above-mentioned method for determining differences in aging degrees of battery cells. The system capable of determining differences in aging degrees of battery cells comprises:

[0167] The performance release evaluation module is used to obtain the factory specification data of the battery cell; perform the battery cell performance release evaluation based on the factory specification data of the battery cell to obtain the battery cell performance release evaluation data;

[0168] A battery cell internal resistance instability assessment module is used to analyze battery cell temperature changes based on battery cell performance release assessment data to obtain battery cell temperature change data; perform temperature non-uniformity distribution difference analysis on the battery cell temperature change data to obtain temperature non-uniformity distribution difference data; and perform battery cell internal resistance instability assessment on the temperature non-uniformity distribution difference data based on the battery cell performance release assessment data to obtain battery cell internal resistance instability data.

[0169] The power tilt characteristic analysis module is used to calculate the internal resistance value limit variation range based on the battery cell internal resistance instability data to obtain the internal resistance value limit variation range; perform battery cell cycle life test based on the internal resistance value limit variation range to obtain battery cell cycle life test data; and perform power tilt characteristic analysis on the battery cell cycle life test data to obtain power tilt characteristic data;

[0170] A battery cell aging degree judgment model construction module is used to simulate the environmental impact of battery cell degradation based on battery cell cycle life test data and power tilt characteristic data to obtain battery cell environmental impact simulation data; perform battery cell aging environmental impact analysis based on the battery cell environmental impact simulation data to obtain battery cell aging environmental impact data; construct a battery cell aging degree judgment model based on the battery cell aging environmental impact data to obtain a battery cell aging degree judgment model; and perform battery cell aging degree judgment processing based on the battery cell aging degree judgment model to obtain battery cell aging degree data.

[0171] The present invention has the beneficial effect of collecting factory specification data for battery cells, providing essential information for subsequent evaluation. This data includes the rated capacity, voltage range, and charge / discharge rate of the battery cells. Based on the factory specification data, this step evaluates the performance of the battery cells. This helps determine the actual performance of the battery cells in applications, including their voltage response and capacity fade at different charge / discharge rates. By analyzing the temperature changes of the battery cells, the thermal characteristics of the battery during use can be understood. This helps identify potential thermal issues, such as performance degradation or safety risks caused by overheating. This step further analyzes the temperature distribution differences within the battery cells. Uneven temperature distribution within the battery cells can lead to unstable performance or shortened lifespan. Analyzing this difference can help design more optimized heat dissipation solutions or temperature management strategies. Based on the battery cell performance release evaluation data and the temperature non-uniformity distribution difference data, the internal resistance instability of the battery cells can be assessed. Internal resistance changes can affect battery performance and safety, so evaluating its instability can help predict battery life and reliability. By calculating the extreme variation range of the internal resistance value, the range of variation of the battery's internal resistance during use can be determined. This helps predict battery performance under different operating conditions and assess its lifespan. By conducting battery cell cycle life testing, the charge and discharge cycles experienced by actual batteries can be simulated and relevant lifespan data obtained. This data can be used to assess battery durability and performance stability. By simulating the degradation process of batteries under various environmental conditions, we can better understand the impact of environmental factors on battery lifespan. This includes factors such as temperature, humidity, and vibration. Analyzing data on environmental impacts on battery cell aging can identify battery aging characteristics and trends under specific conditions. This helps formulate appropriate protection measures and maintenance strategies to extend battery lifespan. An aging assessment model can be constructed to comprehensively assess battery aging based on multiple data indicators, such as internal resistance changes and cycle life test results. This allows users to more accurately understand the battery's health and take appropriate measures, such as battery replacement or maintenance. Therefore, the present invention improves upon traditional methods for determining differences in battery cell aging, resolving issues such as the inability to accurately determine the aging degree of battery cells and the inability to accurately analyze the environmental impact of aging. This improves the accuracy of determining the aging degree of battery cells and enhances the accuracy of analyzing the environmental impact of aging.

[0172] The present invention is therefore intended to be illustrative and non-restrictive in all respects, with the scope of the invention being defined by the appended claims rather than the foregoing description, and all changes that come within the meaning and range of equivalents of the application documents are intended to be embraced therein.

[0173] The foregoing description is intended only to provide specific embodiments of the present invention, which will enable those skilled in the art to understand and implement the present invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention is not intended to be limited to the embodiments shown herein, but is to be construed in the widest possible manner consistent with the principles and novel features disclosed herein.

Claims

1. A method for determining the difference in aging degree of battery cells, characterized in that: The following steps are involved: Step S1: Obtaining factory specification data of the battery cell; Performing a battery cell performance release evaluation according to the factory specification data of the battery cell to obtain battery cell performance release evaluation data; the battery cell performance release evaluation data is the performance of the specified battery cell in actual operation, including its voltage response or capacity attenuation at different charge and discharge rates; Step S2: performing a battery core temperature change analysis based on the battery core performance release evaluation data to obtain battery core temperature change data; performing a temperature non-uniformity distribution difference analysis on the battery core temperature change data to obtain temperature non-uniformity distribution difference data; performing a battery core internal resistance instability evaluation on the temperature non-uniformity distribution difference data based on the battery core performance release evaluation data to obtain battery core internal resistance instability data; The battery cell internal resistance instability data refers to calculating the internal resistance instability fluctuation amplitude data and quantifying the degree of change of the internal resistance; wherein step S2 includes: Step S21: analyzing the temperature change of the battery cell according to the battery cell performance release evaluation data to obtain the battery cell temperature change data; Step S22: Calculating the temperature rise rate of the battery core temperature change data to obtain the battery core temperature rise rate data; Step S23: performing temperature non-uniformity distribution difference analysis on the battery core temperature change data according to the battery core temperature rise rate data to obtain temperature non-uniformity distribution difference data; Step S24: performing battery cell internal resistance instability evaluation on the temperature non-uniformity distribution difference data according to the battery cell performance release evaluation data to obtain battery cell internal resistance instability data; wherein step S24 includes: Step S241: performing performance release rate increment calculation on the battery cell performance release evaluation data to obtain performance release rate increment data; Step S242: performing battery cell thermal effect coupling analysis according to the performance release rate increment data and the temperature non-uniformity distribution difference data to obtain battery cell thermal effect coupling data; Step S243: performing nonlinear current response simulation on the battery cell thermal effect coupling data to obtain nonlinear current response simulation data; Step S244: performing heat loss cycle degradation coupling processing according to the battery core thermal effect coupling data and the nonlinear current response simulation data to obtain heat loss cycle degradation coupling data; Step S245: evaluating the battery cell internal resistance instability according to the heat loss cycle degradation coupling data to obtain the battery cell internal resistance instability data; Step S3: Calculate the internal resistance value limit variation interval of the battery cell internal resistance instability data to obtain the internal resistance value limit variation interval; perform a battery cell cycle life test according to the internal resistance value limit variation interval to obtain the battery cell cycle life test data; perform a power tilt characteristic analysis on the battery cell cycle life test data to obtain power tilt characteristic data; the power tilt characteristic data refers to the performance of the battery at different charge and discharge rates; wherein step S3 includes: Step S31: calculating the internal resistance instability fluctuation amplitude of the battery cell internal resistance instability data to obtain the internal resistance instability fluctuation amplitude data; Step S32: calculating the internal resistance value limit variation range of the battery cell internal resistance instability data according to the internal resistance instability fluctuation amplitude data to obtain the internal resistance value limit variation range; Step S33: performing battery cell capacity attenuation analysis based on the internal resistance value limit variation range to obtain battery cell capacity attenuation data; Step S34: performing a battery cell cycle life test according to the internal resistance value limit variation range and the battery cell capacity attenuation data to obtain battery cell cycle life test data; Step S35: Performing power tilt characteristic analysis on the battery cell cycle life test data to obtain power tilt characteristic data; wherein step S35 includes: Step S351: performing internal resistance stage-by-stage dynamic trend analysis on the battery cell cycle life test data to obtain internal resistance stage-by-stage dynamic trend data; Step S352: performing a staged temperature effect analysis according to the staged dynamic trend data of the internal resistance to obtain staged temperature effect data; Step S353: evaluating the cumulative power loss according to the internal resistance periodic dynamic trend data and the periodic temperature effect data to obtain the cumulative power loss data; Step S354: performing power tilt characteristic analysis based on the accumulated power loss data, the staged temperature effect data, and the staged dynamic trend data of the internal resistance to obtain power tilt characteristic data; Step S4: Simulate the environmental impact of battery cell degradation based on the battery cell cycle life test data and the power tilt characteristic data to obtain the battery cell environmental impact simulation data; perform battery cell aging environmental impact analysis based on the battery cell environmental impact simulation data to obtain the battery cell aging environmental impact data; construct a battery cell aging degree judgment model based on the battery cell aging environmental impact data to obtain the battery cell aging degree judgment model; perform battery cell aging degree judgment processing based on the battery cell aging degree judgment model to obtain the battery cell aging degree data.

2. The method for determining the difference in aging degree of battery cells according to claim 1, characterized in that: Step S1 includes the following steps: Step S11: Obtaining factory specification data of the battery cell; Step S12: simulating the operating state of the battery cell according to the factory specification data of the battery cell to obtain the operating state simulation data of the battery cell; Step S13: performing battery cell performance release evaluation according to the battery cell operating state simulation data to obtain battery cell performance release evaluation data.

3. The method for determining the difference in aging degree of battery cells according to claim 1, characterized in that: Step S4 includes the following steps: Step S41: performing a battery cell degradation environmental impact simulation according to the battery cell cycle life test data and the power tilt characteristic data to obtain battery cell environmental impact simulation data; Step S42: performing battery cell aging environmental impact analysis on the battery cell cycle life test data according to the battery cell environmental impact simulation data to obtain battery cell aging environmental impact data; Step S43: performing cycle number reduction analysis on the battery cell cycle life test data based on the battery cell aging environment impact data to obtain battery cell cycle number reduction data; Step S44: constructing a battery cell aging degree judgment model based on the battery cell cycle loss data and the battery cell aging environment impact data using a random forest algorithm to obtain a battery cell aging degree judgment model; Step S45: performing a determination process on the battery cell aging degree according to the battery cell aging degree determination model to obtain battery cell aging degree data.

4. The method for determining the difference in aging degree of battery cells according to claim 3, characterized in that: Step S42 includes the following steps: Step S421: Calculate humidity impact weight based on the battery cell environmental impact simulation data to obtain humidity impact weight data; Step S422: performing humidity impact fluctuation analysis on the battery cell cycle life test data according to the humidity impact weight data to obtain humidity impact fluctuation data; Step S423: performing nonlinear statistical process control processing on the humidity influence fluctuation change data to obtain humidity influence stage process fluctuation data; Step S424: performing battery cell aging environment impact analysis on the battery cell cycle life test data according to the humidity impact stage process fluctuation data to obtain battery cell aging environment impact data.

5. The method for determining the difference in aging degree of battery cells according to claim 4, characterized in that: The nonlinear statistical process control processing of humidity fluctuation data includes the following steps: Perform nonlinear fluctuation clustering on the humidity fluctuation change data to obtain humidity fluctuation nonlinear clustering data; According to the nonlinear clustering data of humidity fluctuation, cluster binomial distribution processing is performed to obtain the cluster binomial distribution data of humidity fluctuation; According to the humidity fluctuation cluster binomial distribution data, a nonlinear control chart is constructed for the humidity fluctuation nonlinear cluster data to obtain the humidity fluctuation nonlinear control chart; Calculate the upper and lower control limits of the humidity fluctuation nonlinear control chart to obtain the fluctuation nonlinear upper and lower control limit data; According to the nonlinear upper and lower control limit data of fluctuation, nonlinear statistical process control processing is performed on the humidity influence fluctuation change data to obtain the process fluctuation data of the humidity influence stage.

6. A system capable of determining differences in the degree of aging of battery cells, characterized in that: A method for determining the difference in aging degree of battery cells according to claim 1, wherein the system comprises: The performance release evaluation module is used to obtain the factory specification data of the battery cell; perform the battery cell performance release evaluation according to the factory specification data of the battery cell to obtain the battery cell performance release evaluation data; The battery cell internal resistance instability assessment module is used to analyze the battery cell temperature change according to the battery cell performance release assessment data to obtain the battery cell temperature change data; perform temperature non-uniformity distribution difference analysis on the battery cell temperature change data to obtain temperature non-uniformity distribution difference data; perform battery cell internal resistance instability assessment on the temperature non-uniformity distribution difference data according to the battery cell performance release assessment data to obtain the battery cell internal resistance instability data; The power tilt characteristic analysis module is used to calculate the internal resistance value limit change interval based on the battery cell internal resistance instability data to obtain the internal resistance value limit change interval; perform battery cell cycle life test based on the internal resistance value limit change interval to obtain battery cell cycle life test data; perform power tilt characteristic analysis on the battery cell cycle life test data to obtain power tilt characteristic data; The module for constructing a model for judging the degree of battery cell aging is used to simulate the environmental impact of battery cell degradation based on the battery cell cycle life test data and the power tilt characteristic data to obtain the battery cell environmental impact simulation data; to analyze the environmental impact of battery cell aging based on the battery cell environmental impact simulation data to obtain the battery cell aging environmental impact data; to construct a model for judging the degree of battery cell aging based on the battery cell aging environmental impact data to obtain the battery cell aging degree judgment model; to judge the degree of battery cell aging based on the battery cell aging degree judgment model to obtain the battery cell aging degree data.

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