Lithium battery full life cycle data management method and system

Abstract

The invention belongs to the field of lithium battery data management, and discloses a lithium battery full life cycle data management method and system, and the method comprises the steps: obtaining battery operation data and contextual information in real time, and evaluating the potential value of the data based on the information, thereby dynamically adjusting a data collection strategy, and improving the data collection efficiency. The problems of data redundancy and insufficient key information capture caused by a traditional static acquisition mode are solved, and the efficiency and pertinence of data management are improved; therefore, the acquisition behavior can be dynamically adjusted according to the data value, the data acquisition efficiency is improved, the management cost is reduced, and echelon utilization potential analysis is more effectively supported.

Description

Technical Field

[0001] The present application relates to the field of lithium battery data management, and specifically, to a method and system for managing lithium battery data throughout its life cycle. Background Art

[0002] Lithium batteries, as a vital energy storage medium in modern society, are widely used in electric vehicles, energy storage systems, consumer electronics, and other fields. Throughout their long lifecycle, from manufacturing and use to final retirement and potential recycling, every stage generates massive and diverse data. This data, encompassing key information such as battery performance, health status, and operating conditions, is crucial for assessing the battery's residual value, particularly its potential for recycling.

[0003] During the manufacturing phase, parameters such as the cell's material, process, and initial performance are recorded. During the operational phase, dynamic operating data such as voltage, current, temperature, state of charge, health status, and cycle count, as well as relevant application scenario information, are collected in real time. After retirement, testing and sorting are required to generate new performance evaluation data.

[0004] However, in actual operation, the collection, management and effective utilization of the above-mentioned full life cycle data face many challenges. First, the amount of data is extremely large and growing rapidly, resulting in high data management costs, which limits the long-term refined management and in-depth utilization of data. Secondly, existing data collection strategies are often static and fail to differentiate and optimize based on the actual information value of the data and its potential contribution to the cascade utilization potential analysis, resulting in the collection of a large amount of redundant data and failing to achieve a balance between data collection efficiency and value. Furthermore, the contribution of data to the cascade utilization potential assessment is dynamic, and static collection strategies are difficult to adjust data collection behavior in a targeted manner according to the current status of the battery or assessment needs, and prioritize the acquisition of key information with the most predictive value, affecting the timeliness and accuracy of the assessment. In addition, cascade utilization potential analysis requires the extraction of complex features from full life cycle data, which depends on the availability and sophistication of the original data. Static, coarse-grained data collection may lead to the loss of key information, making it difficult to support refined feature engineering, which in turn affects the accuracy of the assessment model.

[0005] In summary, when faced with massive, multi-source, and heterogeneous full-life cycle data, existing lithium battery data management methods have problems such as high collection costs, data redundancy, insufficient capture of key information, and lack of dynamic adaptability. These problems make it difficult to efficiently, accurately, and economically support the analysis of the cascade utilization potential of lithium batteries.

[0006] In view of the above problems, the existing technology is in urgent need of improvement. Summary of the Invention

[0007] The purpose of this application is to provide a lithium battery full life cycle data management method and system, which can dynamically adjust the collection behavior according to the data value, improve data collection efficiency, reduce management costs, and more effectively support cascade utilization potential analysis.

[0008] In a first aspect, the present application provides a lithium battery full life cycle data management method for managing lithium battery full life cycle data to support cascade utilization potential analysis. The method comprises the following steps:

[0009] A1. Real-time acquisition of lithium battery operating data and corresponding life cycle stage identification and application scenario identification;

[0010] A2. Based on the operational data, the lifecycle stage identifier, and the application scenario identifier, evaluate the potential value of the operational data for the cascade utilization potential analysis;

[0011] A3. Adjust the collection behavior parameters of the operating data according to the potential value, the life cycle stage identifier, and the application scenario identifier;

[0012] A4. The operating data obtained after adjusting the collection behavior parameters is associated with the existing full life cycle data of the lithium battery to obtain updated full life cycle data;

[0013] A5. Based on the updated full life cycle data, extract characteristic data for cascade utilization potential analysis.

[0014] Preferably, the operating data includes at least one of voltage, current, temperature, number of cycles, capacity, internal resistance, state of charge, and health status;

[0015] The life cycle stage identification includes a manufacturing stage identification, a use stage identification, a retirement stage identification and an evaluation stage identification;

[0016] The application scenario identification includes an electric vehicle application scenario identification, an energy storage system application scenario identification, a backup power supply application scenario identification and a consumer electronics application scenario identification.

[0017] Preferably, step A2 includes:

[0018] A201. Based on the operating data, extracting state parameters reflecting the current performance state and operating characteristics of the lithium battery;

[0019] A202. Determine the importance weight of the state parameter for the cascade utilization potential analysis at the current stage and current application scenario based on the life cycle stage identifier and the application scenario identifier;

[0020] A203. Combine the state parameters and the importance weights to calculate the potential value of the operating data for cascade utilization potential analysis.

[0021] Preferably, step A201 includes:

[0022] Preprocessing the operating data;

[0023] Calculating the capacity attenuation rate and internal resistance change rate of the lithium battery according to the preprocessed operating data;

[0024] According to the pre-processed operating data, the operating time or accumulated power of the lithium battery in different temperature ranges and different charge and discharge rate ranges is counted;

[0025] The capacity attenuation rate, the internal resistance change rate, and the operating time or the accumulated power are used as the state parameters.

[0026] Preferably, step A203 includes:

[0027] B1. Obtaining batch operation data of the lithium battery to which the batch belongs; the batch operation data includes historical operation data, historical life cycle stage identification, and historical application scenario identification of other lithium batteries in the same batch as the lithium battery;

[0028] B2. Analyze the performance degradation trends of the lithium battery batches at different historical life cycle stages and different historical application scenarios based on the batch operation data as the overall performance degradation trend;

[0029] B3. Determine the batch correction coefficient for each parameter in the state parameter based on the overall performance degradation trend, the current life cycle stage identifier, and the current application scenario identifier;

[0030] B4. Correcting the importance weight using the batch correction coefficient to obtain a corrected importance weight;

[0031] B5. Based on the state parameters and the modified importance weights, calculate the potential value of the operating data for cascade utilization potential analysis.

[0032] Preferably, after step B3 and before step B4, the method further includes:

[0033] B6. Obtain the individual historical operating data, individual historical life cycle stage identification, and individual historical application scenario identification of the lithium battery;

[0034] B7. Analyze the individual performance attenuation trend of the lithium battery based on the individual historical operating data, the individual historical life cycle stage identifier, and the individual application scenario identifier;

[0035] B8. Calculate an individualized adjustment for the batch correction coefficient based on the difference between the individual performance degradation trend and the overall performance degradation trend, the current lifecycle stage identifier, and the current application scenario identifier;

[0036] B9. Adjust the batch correction coefficient according to the individualized adjustment amount.

[0037] Preferably, in step A3, the adjusted acquisition behavior parameters include acquisition frequency, acquisition accuracy, and at least one item in the list of acquired data items.

[0038] Preferably, step A3 includes:

[0039] A301 obtains the network status information of the transmission channel of each data item in the operation data;

[0040] A302. Determine the importance metric of each data item in the operating data for the cascade utilization potential analysis based on the potential value, the life cycle stage identifier, and the application scenario identifier;

[0041] A303. Adjust the collection frequency, collection accuracy and at least one item in the collection data item list of the operation data according to the importance metric and the network status information.

[0042] Preferably, step A302 includes:

[0043] identifying, based on the operating data, the lifecycle stage identifier, and the application scenario identifier, a combination of associated data items in the operating data to obtain a combination of associated data items;

[0044] Evaluating the combined value of the associated data item combination for cascade utilization potential analysis based on the associated data item combination, the life cycle stage identifier, and the application scenario identifier;

[0045] The importance metric of each data item in the operation data to the cascade utilization potential analysis is determined based on the potential value, the life cycle stage identifier, the application scenario identifier, the combination of associated data items, and the combination value.

[0046] In a second aspect, the present application provides a lithium battery full life cycle data management system for managing lithium battery full life cycle data to support cascade utilization potential analysis. The system includes:

[0047] An acquisition module is used to obtain the operating data of the lithium battery and the corresponding life cycle stage identification and application scenario identification in real time;

[0048] a value assessment module, configured to assess the potential value of the operation data for cascade utilization potential analysis based on the operation data, the life cycle stage identifier, and the application scenario identifier;

[0049] a collection adjustment module, configured to adjust collection behavior parameters of the operation data according to the potential value, the life cycle stage identifier, and the application scenario identifier;

[0050] An associated storage module, configured to associate and store the operating data obtained after adjusting the collection behavior parameters with the existing full life cycle data of the lithium battery to obtain updated full life cycle data;

[0051] The feature extraction module is used to extract feature data for cascade utilization potential analysis based on the updated full life cycle data.

[0052] Beneficial effects: The present application provides a lithium battery full life cycle data management method and system, which obtains battery operation data and its contextual information in real time, and evaluates the potential value of the data based on this information, thereby dynamically adjusting the data collection strategy, solving the problems of data redundancy and insufficient capture of key information brought about by traditional static collection methods, and improving the efficiency and pertinence of data management; thereby, it is possible to dynamically adjust the collection behavior according to the data value, improve data collection efficiency, reduce management costs, and more effectively support cascade utilization potential analysis. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] Figure 1 This is a flowchart of the lithium battery full life cycle data management method provided in an embodiment of the present application.

[0054] Figure 2 This is a schematic diagram of the structure of the lithium battery full life cycle data management system provided in an embodiment of the present application.

[0055] Explanation of the numbers: 1. Acquisition module; 2. Value assessment module; 3. Collection and adjustment module; 4. Association storage module; 5. Feature extraction module. DETAILED DESCRIPTION

[0056] The technical model in this application will be clearly and completely described below in conjunction with the drawings in this application. Obviously, the described embodiments are only a part of the embodiments of this application, rather than all of the embodiments. The components of the present application generally described and shown in the drawings here can be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present application provided in the drawings is not intended to limit the scope of the application for protection, but merely represents selected embodiments of the present application. Based on the embodiments of the present application, all other embodiments obtained by those skilled in the art without making creative work fall within the scope of protection of this application.

[0057] It should be noted that similar reference numerals and letters represent similar items in the following drawings. Therefore, once an item is defined in one drawing, it does not need to be further defined or explained in subsequent drawings. At the same time, in the description of this application, the terms "first", "second", etc. are only used to distinguish the description and should not be understood as indicating or implying relative importance.

[0058] refer to Figure 1 This application proposes a lithium battery full life cycle data management method for managing lithium battery full life cycle data to support cascade utilization potential analysis. The steps of the method include:

[0059] A1. Real-time acquisition of lithium battery operating data and corresponding life cycle stage identification and application scenario identification;

[0060] A2. Based on the operational data, the lifecycle stage identifier, and the application scenario identifier, evaluate the potential value of the operational data for the cascade utilization potential analysis;

[0061] A3. Adjust the collection behavior parameters of the operating data according to the potential value, the life cycle stage identifier, and the application scenario identifier;

[0062] A4. The operating data obtained after adjusting the collection behavior parameters is associated with the existing full life cycle data of the lithium battery to obtain updated full life cycle data;

[0063] A5. Based on the updated full life cycle data, extract characteristic data for cascade utilization potential analysis.

[0064] Operational data refers to data reflecting the operating status and performance of lithium-ion batteries, such as voltage, current, and temperature. Lifecycle stage identification refers to information indicating the current stage of a lithium-ion battery, such as manufacturing, use, or retirement. Application scenario identification refers to information indicating the current environment in which a lithium-ion battery is used, such as electric vehicles and energy storage systems. This information is acquired to perceive the battery's status and environment, providing a foundation for subsequent processing.

[0065] Potential value refers to the importance or contribution of operating data to analyzing the potential of secondary utilization. Evaluating potential value is to identify which data and information are more valuable for predicting the future performance and applicability of batteries, thereby guiding subsequent data processing.

[0066] Collection behavior parameters refer to the settings that control the data collection process, such as collection frequency, collection accuracy, and the types of data collected. Adjusting collection behavior parameters is designed to optimize the data collection process based on the potential value and context of the data, improving efficiency and reducing costs.

[0067] Associative storage involves linking newly acquired data with existing data and saving them, for example, by indexing them using a battery's unique identifier and timestamp. This is done to build a complete and continuous historical battery data set, providing a foundation for subsequent analysis.

[0068] Feature data refers to data extracted from the full lifecycle data and used directly as input into the cascade utilization potential analysis model. This data can include at least one of the following: capacity decay rate, internal resistance change rate, and the cumulative duration of a specific operating condition. Feature data extraction is used to convert raw data into information meaningful for analysis.

[0069] The core innovation of this application lies in dynamically adjusting the data collection strategy by acquiring battery operation data and its contextual information in real time and evaluating the potential value of the data based on this information. This solves the problems of data redundancy and insufficient capture of key information caused by traditional static collection methods, and improves the efficiency and pertinence of data management.

[0070] Specifically, the method first acquires lithium battery operating data in real time, along with the corresponding lifecycle stage and application scenario identifiers. This information provides a comprehensive view of the battery's current state and environment. Next, based on the acquired operating data, lifecycle stage identifier, and application scenario identifier, the system evaluates the potential value of this operating data for cascade utilization potential analysis. This evaluation considers the battery performance and operating conditions reflected in the data itself, as well as the importance of this information within the current lifecycle stage and application scenario. Based on the assessed potential value and the current lifecycle stage and application scenario identifier, the system dynamically adjusts the parameters for subsequent operating data collection. If the data is of high value, the collection frequency or accuracy is increased; if the data is of low value, the collection frequency or accuracy is reduced, or the number of data items collected is reduced. The operating data acquired after adjusting the collection parameters is associated with the existing full lifecycle data for the lithium battery and stored to form an updated full lifecycle dataset. This updated dataset contains optimized, more valuable data. Finally, based on the updated full lifecycle data, key feature data required for cascade utilization potential analysis is extracted. This feature data is directly used in subsequent cascade utilization potential assessment, sorting, and decision-making. The entire process forms a dynamic feedback loop, making data collection more intelligent and efficient.

[0071] Through the above scheme, this application solves the problems of huge data volume, high collection cost, data redundancy and insufficient capture of key information in the existing lithium battery full life cycle data management. By dynamically evaluating the value of data and adjusting the collection strategy, the ineffective collection of low-value data is avoided, and the cost of data collection, transmission and storage is significantly reduced. At the same time, it can prioritize the capture of key information that is of high value for cascade utilization potential analysis in specific stages and scenarios, thereby improving the pertinence and effectiveness of data collection. The optimized collection and storage of full life cycle data provides a high-quality data foundation for subsequent feature extraction and cascade utilization potential analysis, improving the accuracy and reliability of the analysis.

[0072] In some embodiments, the operating data includes at least one of voltage, current, temperature, cycle number, capacity, internal resistance, state of charge, and health status;

[0073] The life cycle stage identification includes a manufacturing stage identification, a use stage identification, a retirement stage identification and an evaluation stage identification;

[0074] The application scenario identification includes an electric vehicle application scenario identification, an energy storage system application scenario identification, a backup power supply application scenario identification and a consumer electronics application scenario identification.

[0075] The operating data reflects the electrical, thermal, physical, or historical usage status of the lithium battery at a specific moment or over a period of time, and can be obtained through sensor acquisition, battery management system (BMS) reading, historical record query, etc. The life cycle stage identifier can be represented by a preset enumeration value, stage code, or stage judgment logic based on battery status or event triggering. The application scenario identifier can be represented by a preset scenario type code, system configuration information, or scenario recognition logic based on the battery operating mode or external environment characteristics.

[0076] This solution lays a solid foundation for subsequent data management processes by specifically defining the types of operational data, lifecycle stage identifiers, and application scenario identifiers. Specifically, operational data such as voltage, current, and temperature enable the system to understand the battery's real-time operating conditions; data such as cycle count, capacity, internal resistance, state of charge, and health status directly reflect the battery's performance degradation and historical usage. These rich and critical operational parameters are used in the subsequent value assessment step, allowing the system to more accurately determine the importance of currently collected data for cascade utilization potential analysis. Furthermore, by clarifying the battery's lifecycle stage (e.g., manufacturing, use, retirement, evaluation) and its application scenario (e.g., electric vehicles, energy storage, backup power, consumer electronics), the system can tailor the evaluation model and data collection strategy during the assessment and collection adjustment steps based on the battery's current state and usage environment. For example, during the retirement phase, the system may prioritize accurate measurements of capacity and internal resistance; in the electric vehicle scenario, it may focus on high-current charge and discharge and temperature data. This dynamic adjustment capability based on clear data types and identifiers ensures that the system can prioritize obtaining the most valuable information for cascade utilization potential analysis in specific stages and scenarios, thereby overcoming the problem of missing key information due to unclear or incomplete data types and improving the effectiveness and accuracy of the entire data management method.

[0077] In some embodiments, step A2 comprises:

[0078] A201. Based on the operating data, extracting state parameters reflecting the current performance state and operating characteristics of the lithium battery;

[0079] A202. Determine the importance weight of the state parameter for the cascade utilization potential analysis at the current stage and current application scenario based on the life cycle stage identifier and the application scenario identifier;

[0080] A203. Combine the state parameters and the importance weights to calculate the potential value of the operating data for cascade utilization potential analysis.

[0081] Among them, based on the operating data, state parameters reflecting the current performance status and operating characteristics of the lithium battery are extracted. The state parameters here refer to key indicators extracted from the original operating data that can quantitatively describe the current health status, performance and actual usage conditions of the lithium battery. These parameters are different from the original instantaneous measurements such as voltage and current. Instead, they are derived data with higher information density obtained after calculation, statistics or analysis, such as capacity decay rate, internal resistance change rate, cumulative operating time at a specific temperature or current, etc. The purpose of extracting these state parameters is to convert massive, high-frequency raw data into more refined and direct information reflecting the key characteristics of the battery, providing a basis for subsequent value assessment. The extraction of state parameters can involve technical means such as data preprocessing and feature engineering.

[0082] Furthermore, based on the life cycle stage identifier and the application scenario identifier, the importance weight of the state parameter for the cascade utilization potential analysis in the current stage and current application scenario is determined. The importance weight is a numerical value used to indicate the importance of a specific state parameter for the evaluation of the cascade utilization potential of the battery in the current life cycle stage (such as the initial use, retirement assessment) and application scenario (such as electric vehicles, energy storage systems). For example, in the retirement assessment stage, the capacity decay rate may be more important than the temperature fluctuation in the initial use, so the capacity decay rate will be given a higher weight. The determination of the weight can be based on a preset rule base, expert experience, historical data analysis or machine learning model. The introduction of life cycle stage and application scenario identifiers is to make the data value assessment have situational awareness, dynamically adjust the attention of different parameters, and ensure that the evaluation results can highlight the information with the most predictive value at the moment.

[0083] Therefore, the potential value of the operating data for the cascade utilization potential analysis is calculated in combination with the state parameters and importance weights. Potential value is a comprehensive score or indicator used to quantify the contribution of the currently acquired operating data (reflected by its extracted state parameters and their corresponding importance weights) to the accurate assessment of the cascade utilization potential of the lithium battery. The process of calculating the potential value is to perform a comprehensive calculation on the extracted state parameters and their importance weights in the current situation. For example, weighted summation, model-based scoring, etc. can be used. The calculated potential value reflects the comprehensive result of the content quality of the data (reflected by the state parameters) and the relevance of the data to the current evaluation target (reflected by the importance weights), providing a quantitative basis for subsequent adjustments to the data collection strategy.

[0084] The present application provides a method for specifically evaluating the potential value of operating data for cascade utilization potential analysis through the above steps. First, based on the real-time acquired operating data, state parameters reflecting the current performance status and operating condition characteristics of the lithium battery are extracted. This step converts the original, massive operating data into key indicators with higher information density and analytical value, allowing subsequent evaluations to focus on information that is truly meaningful to the cascade utilization potential. Then, based on the life cycle stage identifier and application scenario identifier, the importance weights of these state parameters for the cascade utilization potential analysis in the current context are dynamically determined. The context-aware capability is introduced to ensure that the evaluation process can highlight the key information with the most predictive value at the moment, thereby improving the pertinence and accuracy of the evaluation. Finally, the potential value of the operating data for the cascade utilization potential analysis is calculated by combining the extracted state parameters and the determined importance weights. The extracted battery state information and the importance of this information in the current context are comprehensively considered to calculate the potential value of the original operating data for the cascade utilization potential analysis. This combination reflects the dual consideration of data content and data background, so that the calculated potential value can more accurately reflect the contribution of the data to downstream analysis, providing a quantitative basis for subsequent adjustment of the collection strategy based on value. Through the synergistic effect of the above steps, a more refined and differentiated data value assessment is achieved, which solves the problem that the existing assessment methods are not accurate enough and cannot dynamically adapt to different situations, thereby more effectively supporting the analysis of tiered utilization potential.

[0085] As a preferred embodiment, the above method can be implemented as follows: First, the operating data of the lithium battery, such as voltage, current, temperature, number of cycles, etc., are received. Based on these operating data, the state parameters are extracted, such as calculating the capacity decay rate of the battery in the past period of time, or counting its cumulative operating time in a high temperature environment (for example, above 45°C). At the same time, the current life cycle stage identifier (for example, "use stage") and application scenario identifier (for example, "electric vehicle") of the battery are obtained. Then, based on the two identifiers of "use stage" and "electric vehicle", a preset weight table or rule base is consulted to determine the importance weights of the two state parameters of capacity decay rate and high temperature cumulative operating time for the cascade utilization potential analysis in the current situation. For example, the weight of capacity decay rate may be set to 0.7, and the weight of high temperature cumulative operating time may be set to 0.5. Normalize each state parameter. Finally, combine the calculated normalized state parameter value and the corresponding importance weight to calculate the potential value of the operating data. For example, a weighted summation method can be used: potential value = normalized capacity decay rate value * capacity decay rate weight + normalized high-temperature cumulative operating time value * high-temperature cumulative operating time weight to obtain a quantitative value score.

[0086] Through the above technical solution, the present application can extract key parameters reflecting the battery status and operating conditions based on the lithium battery's operating data, life cycle stage identification, and application scenario identification, and dynamically determine the importance of these parameters in combination with the current situation, thereby calculating the potential value of the operating data for the cascade utilization potential analysis. This makes the value assessment of data no longer static and coarse-grained, but can finely distinguish the importance of different data items in different situations, and more accurately measure the true value of the data. Therefore, it provides a reliable basis for the subsequent adjustment of the collection strategy according to the actual value of the data, helps to optimize the data collection process, reduce the collection of redundant data, and ensure the effective capture of key information, thereby more efficiently and accurately supporting the cascade utilization potential analysis of lithium batteries.

[0087] Preferably, step A201 may include:

[0088] Preprocessing the operating data;

[0089] Calculating the capacity attenuation rate and internal resistance change rate of the lithium battery according to the preprocessed operating data;

[0090] According to the pre-processed operating data, the operating time or accumulated power of the lithium battery in different temperature ranges and different charge and discharge rate ranges is counted;

[0091] The capacity attenuation rate, the internal resistance change rate, and the operating time or the accumulated power are used as the state parameters.

[0092] Among them, preprocessing refers to the process of cleaning, converting and normalizing the original operating data, which can be achieved by using technologies such as filtering, interpolation, smoothing, and outlier removal, with the aim of improving data quality and reliability.

[0093] Among them, the capacity decay rate refers to the speed or percentage at which the capacity of a lithium battery decreases over time or the number of cycles. It can be calculated by taking the ratio of the difference between the current capacity and the initial capacity or a certain benchmark capacity to the initial capacity or the benchmark capacity.

[0094] The internal resistance change rate refers to the rate or percentage at which the internal resistance of a lithium battery increases with time or the number of cycles. It can be calculated by taking the ratio of the difference between the current internal resistance and the initial internal resistance or a certain reference internal resistance to the initial internal resistance or the reference internal resistance.

[0095] Among them, different temperature ranges and different charge and discharge rate ranges refer to multiple sub-ranges divided according to the numerical ranges of temperature and charge and discharge rate during the operation of the lithium battery. For example, the temperature can be divided into low temperature, normal temperature, and high temperature ranges, and the charge and discharge rate can be divided into low rate, medium rate, and high rate ranges. The operating time or cumulative power refers to the cumulative operating time or cumulative power of the lithium battery within a specific temperature range or charge and discharge rate range.

[0096] This solution preprocesses raw operating data to ensure the reliability of the data used in subsequent calculations and statistics. Based on this, it calculates the capacity decay rate and internal resistance change rate, important performance indicators that reflect the speed and extent of battery aging. These rates can capture performance trends that are difficult to capture using only instantaneous capacity or internal resistance values. Furthermore, statistics are collected on the battery's operating time or cumulative charge at different temperature ranges and charge / discharge rate ranges. This quantifies the load experienced by the battery under different stress levels and reflects the impact of historical operating conditions on battery health. Using the capacity decay rate, internal resistance change rate, and a combination of these operating condition statistics as state parameters provides a more predictive and representative feature set than raw data or simple statistics. These comprehensive and detailed state parameters accurately reflect the battery's actual performance and operating stress. Using these accurate state parameters as input improves the accuracy and reliability of subsequent steps in determining the importance of state parameters and calculating the potential value of operating data. For example, when evaluating the potential value of operating data for cascade utilization analysis, these state parameters, which better reflect battery health and historical stress, can accurately determine the contribution of current data to predicting the battery's remaining life and assessing its reuse value. This accurate potential value assessment can effectively guide the adjustment of data collection behavior parameters, realize intelligent and efficient data collection, avoid collecting low-value data, and ensure that important information is not missed, thereby optimizing the data management process of the entire life cycle of lithium batteries and providing a solid data foundation for the analysis of cascade utilization potential.

[0097] Preferably, step A203 may include:

[0098] B1. Obtaining batch operation data of the lithium battery to which the batch belongs; the batch operation data includes historical operation data, historical life cycle stage identification, and historical application scenario identification of other lithium batteries in the same batch as the lithium battery;

[0099] B2. Analyze the performance degradation trends of the lithium battery batches at different historical life cycle stages and different historical application scenarios based on the batch operation data as the overall performance degradation trend;

[0100] B3. Determine the batch correction coefficient for each parameter in the state parameter based on the overall performance degradation trend, the current life cycle stage identifier, and the current application scenario identifier;

[0101] B4. Correcting the importance weight using the batch correction coefficient to obtain a corrected importance weight;

[0102] B5. Based on the state parameters and the modified importance weights, calculate the potential value of the operating data for cascade utilization potential analysis.

[0103] Among them, batch operation data refers to the historical operation data, historical life cycle stage identification and historical application scenario identification generated by other lithium batteries that belong to the same production batch as the current lithium battery during manufacturing during the entire or partial life cycle. It can be obtained by querying and extracting from the database according to the batch number.

[0104] Among them, the overall performance degradation trend refers to the pattern or model reflecting the common degradation law of batteries in the batch obtained by statistical analysis, modeling or curve fitting of the performance data (such as capacity, internal resistance, etc.) of multiple batteries in the same batch at different historical stages and application scenarios. It can be analyzed and represented by statistical regression analysis, machine learning algorithms or the establishment of empirical degradation models.

[0105] Among them, the batch correction coefficient refers to the multiplicative or additive factor that adjusts the original importance weight of the state parameter according to the overall attenuation trend of the batch and the specific stage and scenario of the current battery. It can be determined based on a preset rule table, lookup table or dynamic calculation model.

[0106] Among them, the corrected importance weight refers to the new weight value obtained by combining the original importance weight with the batch correction coefficient, which is used to more accurately reflect the actual information value of the state parameter after considering the common attenuation law of batches. It can be calculated by multiplying the original weight by the correction coefficient or adding the original weight to the correction coefficient.

[0107] This solution incorporates the overall performance degradation information of the battery batch to which it belongs, modifying the importance weights used to calculate potential value. This allows for a more accurate assessment of the value of current operating data for cascade utilization potential analysis. Specifically, batch operating data for the current lithium battery batch is first obtained. This data includes historical operating data, historical lifecycle stage identifiers, and historical application scenario identifiers for other batteries in the same batch. This historical batch data provides the foundation for subsequent analysis of common degradation patterns across batches, enabling assessments to move beyond the current state of individual batteries and incorporate the historical performance of the group. Next, based on this acquired batch operating data, the performance degradation trends of the batch's batteries at different historical lifecycle stages and application scenarios are analyzed to derive an overall performance degradation trend. This step, by mining the batch's historical data, extracts common degradation patterns among batteries from the same batch under similar conditions, capturing the overall behavioral characteristics of the batch and providing an important reference for subsequent batch corrections. Then, based on this overall performance degradation trend, along with the current battery's lifecycle stage identifier and application scenario identifier, batch correction coefficients are determined for each of the state parameters. This means that the determination of the correction coefficient is dynamic. It not only takes into account the overall attenuation law of the batch, but also combines the specific stage and scenario of the current battery, so that the correction coefficient can be more in line with the actual situation of the current battery, reflecting how the relative importance of the current state parameters should be adjusted after considering the commonality of the batch. Subsequently, the importance weights determined according to the current stage and scenario are corrected using the determined batch correction coefficient to obtain the corrected importance weights. Through this correction process, the overall attenuation information of the batch is incorporated into the determination of the importance weights, so that the weights can more accurately reflect the actual contribution of the current state parameters to the cascade utilization potential analysis after combining the common laws of the batch. Finally, based on the extracted state parameters and the corrected importance weights obtained, the potential value of the operating data to the cascade utilization potential analysis is calculated.

[0108] By using the importance weights corrected by batch information, the calculated potential value can more accurately reflect the true information value of the current operating data after taking into account the common attenuation laws of the batches, thereby providing a more reliable data basis for subsequent more accurate cascade utilization potential analysis. Through the above technical solution, this application can incorporate the overall performance attenuation laws of the batch to which the lithium battery belongs into the evaluation process of the potential value of the operating data, making the evaluation results more accurate and detailed. This helps to more effectively identify data with high information value for cascade utilization potential analysis, thereby optimizing data collection strategies, reducing redundant data, reducing data management costs, and providing a more reliable data basis for subsequent cascade utilization potential analysis.

[0109] Preferably, after step B3 and before step B4, the following steps may also be included:

[0110] B6. Obtain the individual historical operating data, individual historical life cycle stage identification, and individual historical application scenario identification of the lithium battery;

[0111] B7. Analyze the individual performance attenuation trend of the lithium battery based on the individual historical operating data, the individual historical life cycle stage identifier, and the individual application scenario identifier;

[0112] B8. Calculate an individualized adjustment for the batch correction coefficient based on the difference between the individual performance degradation trend and the overall performance degradation trend, the current lifecycle stage identifier, and the current application scenario identifier;

[0113] B9. Adjust the batch correction coefficient according to the individualized adjustment amount.

[0114] Among them, individual historical operation data refers to the collection of various data related to the operating status of a specific lithium battery recorded at different time points or different stages of use since it was put into use, which may include historical records of at least one of voltage, current, temperature, number of cycles, capacity, internal resistance, state of charge and health status. Individual historical life cycle stage identification refers to the mark of the life cycle stage of the specific lithium battery in different time periods in the past, which may include historical records of manufacturing stage identification, use stage identification, retirement stage identification and evaluation stage identification. Individual historical application scenario identification refers to the mark of the specific application environment in which the specific lithium battery was used in different time periods in the past, which may include historical records of electric vehicle application scenario identification, energy storage system application scenario identification, backup power application scenario identification and consumer electronics application scenario identification.

[0115] Among them, the individual performance degradation trend refers to the law or pattern of battery performance changes over time or usage intensity revealed by analyzing the historical operating data, historical life cycle stage identification and historical application scenario identification of a specific lithium battery itself. It can be manifested as a capacity degradation curve, an internal resistance growth curve, performance drift under specific working conditions, etc.

[0116] The individualized adjustment refers to the value or factor used to correct the batch correction coefficient, calculated based on the degree of deviation between the individual performance degradation trend of a specific lithium battery and the overall performance degradation trend of the batch, combined with the current life cycle stage and application scenario of the battery. The batch correction coefficient is a coefficient preliminarily determined to correct the importance weight of the state parameter based on the overall performance degradation trend of the batch, the current life cycle stage identifier, and the application scenario identifier.

[0117] This solution, based on determining a batch correction factor based on batch data, further incorporates analysis of individual lithium battery historical data to enable personalized adjustment of the batch correction factor, thereby more accurately assessing the potential value of individual battery operating data. Specifically, by obtaining the individual historical operating data, individual lifecycle stage identifier, and individual application scenario identifier for a specific lithium battery, this provides the foundational data for subsequent personalized analysis. This individual historical data records the battery's unique usage trajectory and performance, and is key to understanding its current status and future trends. Based on this acquired individual historical data, the individual performance degradation trend of the lithium battery is analyzed. Unlike relying solely on the batch average trend, the individual performance degradation trend more accurately reflects the aging characteristics of the battery in actual use. By comparing the difference between the individual performance degradation trend and the overall performance degradation trend, the unique characteristics of the individual battery relative to the batch average are identified. This difference, combined with the current lifecycle stage and application scenario, is used to calculate a personalized adjustment. This adjustment quantifies the extent to which the batch correction factor needs to be adjusted due to individual differences. For example, if the decay rate of an individual battery is significantly faster than the batch average, the importance of some of its operating data (such as the internal resistance change rate) may need to be further emphasized, and the corresponding adjustment amount will reflect this. The batch correction coefficient obtained based on batch analysis is adjusted using the calculated individualized adjustment amount. The correction coefficient after individual adjustment can more accurately reflect the true importance of each parameter in the operating data of this specific lithium battery in its current state to the cascade utilization potential analysis. Ultimately, using this individually adjusted correction coefficient to correct the importance weight can make the potential value of the subsequently calculated operating data more in line with the actual situation of the individual battery, thereby improving the accuracy of the cascade utilization potential analysis.

[0118] By acquiring and analyzing the historical operating data, life cycle stage identifiers, and application scenario identifiers of individual lithium batteries, the unique performance attenuation trend of the individual battery can be analyzed. By comparing the difference between the individual performance attenuation trend and the overall performance attenuation trend of the batch, and combining the current life cycle stage and application scenario, the individualized adjustment amount of the batch correction coefficient is calculated. The batch correction coefficient is adjusted using this individualized adjustment amount so that the corrected coefficient can more accurately reflect the true importance of the specific battery operating data to the cascade utilization potential analysis. This overcomes the shortcomings of relying solely on the batch average trend for correction, improves the accuracy of the potential value assessment of individual battery operating data, and thus provides a more accurate data basis for subsequent cascade utilization potential analysis.

[0119] In one embodiment, obtaining a lithium battery's individual historical operating data, individual historical life cycle stage identifier, and individual historical application scenario identifier can be accomplished by querying historical data records associated with the battery's unique identifier stored in a cloud database or local storage system. Analyzing the lithium battery's individual performance degradation trend based on the individual historical operating data, individual historical life cycle stage identifier, and individual application scenario identifier can be accomplished by performing regression analysis on the battery's historical capacity data to obtain a capacity degradation curve, or by analyzing the rate of change of its internal resistance at a specific temperature or current as a function of cycle number. Based on the difference between the individual performance degradation trend and the overall performance degradation trend, the current life cycle stage identifier, and the current application scenario identifier, calculating an individualized adjustment for the batch correction factor can be accomplished by comparing the slope of the individual capacity degradation curve with the slope of the batch average capacity degradation curve. This difference is then input into a pre-set lookup table or a machine learning-based model, which, combined with the current life cycle stage and application scenario, outputs an adjustment factor or adjustment value. Adjusting the batch correction factor based on the individualized adjustment factor can be accomplished by multiplying the original batch correction factor by the adjustment factor, or by adding the adjustment value to the original batch correction factor.

[0120] In some embodiments, in step A3, the adjusted acquisition behavior parameter includes acquisition frequency, acquisition accuracy, and at least one item in the acquisition data item list.

[0121] The acquisition frequency refers to the time interval for data acquisition or the number of acquisitions per unit time, which can be achieved by fixed time interval acquisition, event-triggered acquisition, or periodic acquisition.

[0122] The acquisition accuracy refers to the quantization granularity or resolution of data acquisition, which can be achieved by adjusting the number of bits of the analog-to-digital converter, adjusting the accuracy of the sampling resistor, or adjusting the accuracy level of the sensor itself.

[0123] The collected data item list refers to the specific data type or parameter set that needs to be collected, which can be achieved by configuring the input channel of the data collection module, modifying the configuration list of the data collection software, or selectively reading specific registers through the communication protocol.

[0124] By dynamically adjusting the collection frequency, collection accuracy, and list of collected data items, this solution can effectively address the challenges of massive data, reduce redundant data collection, and prioritize obtaining the most valuable key information for cascade utilization potential analysis, thereby improving the efficiency and pertinence of data collection and better supporting subsequent feature extraction and potential analysis.

[0125] Specifically, step A3 may include:

[0126] A301 obtains the network status information of the transmission channel of each data item in the operation data;

[0127] A302. Determine the importance metric of each data item in the operating data for the cascade utilization potential analysis based on the potential value, the life cycle stage identifier, and the application scenario identifier;

[0128] A303. Adjust the collection frequency, collection accuracy and at least one item in the collection data item list of the operation data according to the importance metric and the network status information.

[0129] The network status information refers to the performance indicators of the data transmission channel, which can be measured by at least one of the parameters such as bandwidth, delay, packet loss rate, signal strength, etc.

[0130] Among them, importance measurement refers to the contribution of each data item in the operating data to the accurate assessment of the potential for cascade utilization, which can be expressed by a numerical value or level calculated based on rules, expert knowledge, statistical analysis or machine learning models.

[0131] This solution further optimizes the adjustment process of data collection behavior parameters by introducing a detailed assessment of the importance of each data item in the operating data and considering the data transmission network status. This makes the collection strategy more intelligent, robust, and targeted, thereby more effectively supporting the cascade utilization potential analysis. Specifically, step A301 obtains network status information of the transmission channel for each data item in the operating data, such as bandwidth, latency, and stability, to provide actual network environment constraints and basis for subsequent collection parameter adjustments. This allows the system to consider the feasibility and cost of data transmission when formulating the collection strategy, avoiding the problem of the collection strategy being unable to be effectively executed due to network conditions. Step A302, based on the overall potential value assessment, further refines the importance of each data item in the operating data (such as voltage, current, temperature, etc.) to the cascade utilization potential analysis based on the potential value, lifecycle stage identifier, and application scenario identifier. This means that the system can identify which specific data items are most critical and which are less important for accurately assessing the cascade utilization potential under the current battery state, stage, and application scenario. This differentiated importance assessment provides the basis for subsequent refined collection adjustments. Step A303 combines the importance metric determined in step A302 with the network status information obtained in step A301 to adjust the frequency and accuracy of operational data collection, as well as at least one item in the list of collected data items. Based on the importance metric, the system can prioritize the collection quality and frequency of important data items. Simultaneously, combined with network status information, the system can balance and optimize data importance requirements with actual network transmission capacity. For example, for data items with high importance but poor network status (importance and network status can be determined based on thresholds), the collection frequency or accuracy can be appropriately reduced while ensuring minimum requirements. For data items with average importance but good network status, the collection requirements can be maintained or appropriately increased. This combined approach enables the data collection strategy to dynamically adapt to battery status, application scenarios, assessment requirements, and the actual network environment, achieving more refined, intelligent, and robust data collection optimization. This ensures that the most valuable data is acquired first within limited resources, or improves the collection quality of key data when network conditions permit, thereby more effectively supporting subsequent tiered utilization potential analysis. Adjusting the collection data item list can directly control which data items are collected, further optimizing the data volume and transmission burden. For example, for data items of low importance, they can be temporarily removed from the collection list and collection can be resumed after the network is restored.

[0132] By obtaining the network status information of the transmission channel for each data item in the operating data, the data collection strategy can dynamically adapt to the actual transmission environment, avoiding data loss or delay due to network congestion or instability, and improving the robustness of the collection process. By determining the importance metric of each data item in the operating data for the cascade utilization potential analysis, the collection strategy can distinguish the value of different data, prioritize the collection quality and frequency of key data items, and improve the precision and pertinence of the collection. Combining the importance metric with network status information for adjustment, it is possible to balance and optimize the data value requirements and actual transmission capacity, ensuring that the most valuable data is obtained first under limited resources, or improving the collection quality of key data when network conditions permit, thereby improving the intelligence level of data collection and resource utilization efficiency, and more effectively supporting subsequent cascade utilization potential analysis.

[0133] Preferably, step A302 may include:

[0134] identifying, based on the operating data, the lifecycle stage identifier, and the application scenario identifier, a combination of associated data items in the operating data to obtain a combination of associated data items;

[0135] Evaluating the combined value of the associated data item combination for cascade utilization potential analysis based on the associated data item combination, the life cycle stage identifier, and the application scenario identifier;

[0136] The importance metric of each data item in the operation data to the cascade utilization potential analysis is determined based on the potential value, the life cycle stage identifier, the application scenario identifier, the combination of associated data items, and the combination value.

[0137] Identifying associated data item combinations in the operating data refers to analyzing the relationships between different data items in the operating data, such as statistical correlation, physical coupling, or logical associations based on domain knowledge, to discover sets of data items that, as a whole, better reflect the battery status or performance trends than individual data items. This can be achieved using correlation analysis algorithms, clustering algorithms, or rule-based expert systems. Associated data item combinations refer to sets of data items obtained through the above identification process, where the data items in these sets are technically associated with each other.

[0138] Among them, evaluating the combined value of the combination of related data items for the cascade utilization potential analysis refers to further quantifying the contribution of these combinations as a whole to the accurate analysis of the battery cascade utilization potential in specific life cycle stages and application scenarios after identifying the combination of related data items. This can be achieved using machine learning models, multivariate statistical analysis, or scoring models based on expert experience. The combined value refers to the numerical value or indicator obtained through the above evaluation process, which is used to measure the information value of the combination of related data items as a whole for the cascade utilization potential analysis.

[0139] For example, a method based on the improvement of the performance of the prediction model can be used to evaluate the value of the combination. Construct a model for predicting the potential for cascade utilization based on historical data (for example, predicting the remaining capacity or cycle life of the battery when it is retired). First, use a single data item in the combination (such as only using voltage) as input to train the model and evaluate the performance. Then, use the combination of {voltage, current, temperature} as input to train the model and evaluate the performance. If the prediction accuracy (for example, the root mean square error is reduced) or robustness (for example, the performance stability under different working conditions is improved) of the model is significantly improved when using the combination as input, the combination is considered to have a higher combination value. Repeat this process for all identified combinations of associated data items to obtain a combination value score for each combination.

[0140] Among them, determining the importance metric of each data item in the operating data for the cascade utilization potential analysis refers to comprehensively considering the potential value of the individual data item itself, the current life cycle stage and application scenario, and the combined value of the associated data item combination to which the data item belongs, to calculate or assign each data item's final importance weight or score for the cascade utilization potential analysis in the current context. For example, for any data item, its importance metric can be obtained by weighted summation of its own potential value, the combined value contribution of the associated combination to which it belongs, and other relevant factors. The specific weights can be determined based on experience or through training using machine learning methods; for example, the importance metric of a data item = w1*(the potential value of the data item)+w2*(the combined value of the associated combination to which the data item belongs)+w3*(other factors), where w1, w2, and w3 are weights. If the data item does not belong to any associated combination, the combined value of the associated combination to which the data item belongs is zero. Other factors that need to be considered can be determined by looking up the table based on the life cycle stage identifier and the application scenario identifier.

[0141] To determine the importance of each data item in the operating data for the cascade utilization potential analysis, this solution first identifies sets of technically relevant data items within the operating data based on the current operating data, lifecycle stage identifier, and application scenario identifier, forming a combination of related data items. For example, voltage, current, and temperature are often closely related during the battery charging and discharging process and can be identified as a combination of related data items. This step, by discovering the inherent connections between data items, provides more informative units for subsequent value assessment. Next, for each identified combination of related data items, the combined value of these combinations as a whole for the cascade utilization potential analysis is evaluated, taking into account the current lifecycle stage identifier and application scenario identifier. For example, in the electric vehicle application scenario, during the battery retirement phase, the combined data of voltage, current, and temperature may better reflect the battery's power performance degradation at a specific discharge rate, and its combined value may be higher than analyzing voltage, current, or temperature alone. By evaluating combined value, synergistic information that cannot be captured by individual data items can be captured. Finally, when determining the importance of each data item, not only is the potential value of the data item itself, as well as its current lifecycle stage and application scenario, considered, but also the combined value of the associated data item combination to which it belongs. This means that if a data item belongs to an associated combination with high combination value for cascade utilization potential analysis, even if it appears to be of low value individually, its importance measure will be improved. This method makes the assessment of the importance of each data item more comprehensive and accurate by considering the association and combination value of data items, and can more accurately reflect its actual contribution to cascade utilization potential analysis in different contexts, including association with other data items. This more accurate importance measure can provide a more targeted basis for the subsequent adjustment of the collection behavior parameters of the operating data, such as giving priority to collecting data items that belong to high-value associated combinations, or increasing the collection frequency and accuracy of data items in these associated combinations, so as to obtain the most valuable data for cascade utilization potential analysis with limited resources and optimize data collection efficiency and effectiveness.

[0142] Through the above technical solution, the present application can more accurately determine the importance measure of each data item in the operating data to the cascade utilization potential analysis. Since the correlation between data items and their combined value are taken into account, the evaluation results can more comprehensively reflect the actual contribution of data items to the cascade utilization potential analysis in different life cycle stages and application scenarios. This more accurate importance measure provides a more reliable basis for the subsequent adjustment of the collection behavior parameters of the operating data, making the data collection strategy smarter and more targeted, thereby optimizing the efficiency and cost of data collection while ensuring data quality, and better supporting the cascade utilization potential analysis of lithium batteries.

[0143] refer to Figure 2This application provides a lithium battery full life cycle data management system for managing lithium battery full life cycle data to support cascade utilization potential analysis. The system includes:

[0144] Acquisition module 1 is used to obtain the operating data of the lithium battery and the corresponding life cycle stage identifier and application scenario identifier in real time (for the specific process, refer to step A1 above);

[0145] Value assessment module 2, for assessing the potential value of the operation data for cascade utilization potential analysis based on the operation data, the life cycle stage identifier, and the application scenario identifier (for a specific process, refer to step A2 above);

[0146] Collection adjustment module 3, used to adjust the collection behavior parameters of the operation data according to the potential value, the life cycle stage identifier and the application scenario identifier (for the specific process, refer to step A3 above);

[0147] The associated storage module 4 is used to associate and store the operating data obtained after adjusting the collection behavior parameters with the existing full life cycle data of the lithium battery to obtain updated full life cycle data (for the specific process, refer to step A4 above);

[0148] The feature extraction module 5 is used to extract feature data for cascade utilization potential analysis based on the updated full life cycle data (for the specific process, please refer to step A5 above).

[0149] The above description is merely an embodiment of the present application and is not intended to limit the scope of protection of the present application. For those skilled in the art, various modifications and variations of the present application are possible. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present application shall be included in the scope of protection of the present application.

Claims

1. A lithium battery full life cycle data management method for managing lithium battery full life cycle data to support cascade utilization potential analysis, characterized in that: The steps of the method include: A1. Real-time acquisition of lithium battery operating data and corresponding life cycle stage identification and application scenario identification; A2. Based on the operational data, the lifecycle stage identifier, and the application scenario identifier, evaluate the potential value of the operational data for the cascade utilization potential analysis; A3. Adjust the collection behavior parameters of the operating data according to the potential value, the life cycle stage identifier, and the application scenario identifier; A4. The operating data obtained after adjusting the collection behavior parameters is associated with the existing full life cycle data of the lithium battery to obtain updated full life cycle data; A5. Based on the updated full life cycle data, extract characteristic data for cascade utilization potential analysis.

2. A lithium battery full life cycle data management method according to claim 1, characterized in that: The operating data includes at least one of voltage, current, temperature, number of cycles, capacity, internal resistance, state of charge, and health status; The life cycle stage identification includes a manufacturing stage identification, a use stage identification, a retirement stage identification and an evaluation stage identification; The application scenario identification includes an electric vehicle application scenario identification, an energy storage system application scenario identification, a backup power supply application scenario identification and a consumer electronics application scenario identification.

3. The lithium battery full life cycle data management method according to claim 1, characterized in that: Step A2 includes: A201. Based on the operating data, extracting state parameters reflecting the current performance state and operating characteristics of the lithium battery; A202. Determine the importance weight of the state parameter for the cascade utilization potential analysis at the current stage and current application scenario based on the life cycle stage identifier and the application scenario identifier; A203. Combine the state parameters and the importance weights to calculate the potential value of the operating data for cascade utilization potential analysis.

4. A lithium battery full life cycle data management method according to claim 3, characterized in that: Step A201 includes: Preprocessing the operating data; Calculating the capacity attenuation rate and internal resistance change rate of the lithium battery according to the preprocessed operating data; According to the pre-processed operating data, the operating time or accumulated power of the lithium battery in different temperature ranges and different charge and discharge rate ranges is counted; The capacity attenuation rate, the internal resistance change rate, and the operating time or the accumulated power are used as the state parameters.

5. The lithium battery full life cycle data management method according to claim 3, characterized in that: Step A203 includes: B1. Obtaining batch operation data of the lithium battery to which the batch belongs; the batch operation data includes historical operation data, historical life cycle stage identification, and historical application scenario identification of other lithium batteries in the same batch as the lithium battery; B2. Analyze the performance degradation trends of the lithium battery batches at different historical life cycle stages and different historical application scenarios based on the batch operation data as the overall performance degradation trend; B3. Determine the batch correction coefficient for each parameter in the state parameter based on the overall performance degradation trend, the current life cycle stage identifier, and the current application scenario identifier; B4. Correcting the importance weight using the batch correction coefficient to obtain a corrected importance weight; B5. Based on the state parameters and the modified importance weights, calculate the potential value of the operating data for cascade utilization potential analysis.

6. A lithium battery full life cycle data management method according to claim 5, characterized in that: After step B3 and before step B4, the method further includes: B6. Obtain the individual historical operating data, individual historical life cycle stage identification, and individual historical application scenario identification of the lithium battery; B7. Analyze the individual performance attenuation trend of the lithium battery based on the individual historical operating data, the individual historical life cycle stage identifier, and the individual application scenario identifier; B8. Calculate an individualized adjustment amount for the batch correction coefficient based on the difference between the individual performance degradation trend and the overall performance degradation trend, the current lifecycle stage identifier, and the current application scenario identifier; B9. Adjust the batch correction coefficient according to the individualized adjustment amount.

7. The method for managing data of a lithium battery throughout its life cycle according to claim 1, wherein: In step A3, the adjusted acquisition behavior parameters include acquisition frequency, acquisition accuracy, and at least one item in the acquisition data item list.

8. A lithium battery full life cycle data management method according to claim 7, characterized in that: Step A3 includes: A301 obtains the network status information of the transmission channel of each data item in the operation data; A302. Determine the importance metric of each data item in the operating data for the cascade utilization potential analysis based on the potential value, the life cycle stage identifier, and the application scenario identifier; A303. Adjust the collection frequency, collection accuracy and at least one item in the collection data item list of the operation data according to the importance metric and the network status information.

9. A lithium battery full life cycle data management method according to claim 8, characterized in that: Step A302 includes: identifying, based on the operating data, the lifecycle stage identifier, and the application scenario identifier, a combination of associated data items in the operating data to obtain a combination of associated data items; Evaluating the combined value of the associated data item combination for cascade utilization potential analysis based on the associated data item combination, the life cycle stage identifier, and the application scenario identifier; An importance metric of each data item in the operating data to the cascade utilization potential analysis is determined based on the potential value, the life cycle stage identifier, the application scenario identifier, the combination of associated data items, and the combination value.

10. A lithium battery full life cycle data management system for managing lithium battery full life cycle data to support cascade utilization potential analysis, characterized in that: The system includes: An acquisition module is used to obtain the operating data of the lithium battery and the corresponding life cycle stage identification and application scenario identification in real time; a value assessment module, configured to assess the potential value of the operation data for cascade utilization potential analysis based on the operation data, the life cycle stage identifier, and the application scenario identifier; a collection adjustment module, configured to adjust collection behavior parameters of the operation data according to the potential value, the life cycle stage identifier, and the application scenario identifier; An associated storage module, configured to associate and store the operating data obtained after adjusting the collection behavior parameters with the existing full life cycle data of the lithium battery to obtain updated full life cycle data; The feature extraction module is used to extract feature data for cascade utilization potential analysis based on the updated full life cycle data.

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