Battery Life Evaluation System and Method Based on Different Battery Degradation Modes

Abstract

The present invention discloses a battery life evaluation system and method based on different battery degradation modes, belonging to the technical field of battery management evaluation. Through the combination of a data acquisition module and an information management module, the present invention comprehensively collects battery manufacturing, operation and historical information, realizes the precise management of the entire battery life cycle. The data analysis module and the performance evaluation module deeply analyze the battery operation data, classify and identify the degradation modes and key influencing factors, dynamically evaluate the current performance and remaining life of the battery in combination with a prediction model, and simplify the decision-making through a health score to improve the battery management efficiency. The adaptive performance optimization module adjusts the battery parameters and temperature control strategy according to the operation scenario, balances performance and life, and realizes active optimization and safety improvement. The carbon footprint evaluation module covers all stages of battery manufacturing, use and recycling, calculates the carbon emissions of the entire life cycle, traces the high-emission links, and provides low-carbon optimization suggestions to help enterprises achieve the goal of green development.

Description

Technical Field

[0001] The present invention relates to the technical field of battery management evaluation, and particularly to a battery life evaluation system and method based on different battery degradation modes. Background Art

[0002] The performance of a battery is inevitably affected by degradation behaviors during use, such as capacity fade, internal resistance increase, and material aging. These degradation behaviors directly affect the performance, safety, and life of the battery, and also pose great challenges to the operation and maintenance of equipment.

[0003] Existing battery management technologies mostly focus on the monitoring and analysis of single performance parameters, lacking the comprehensive utilization and in-depth analysis of battery full-life cycle data, and are unable to accurately predict the performance change trend and life of the battery. At the same time, in diverse application scenarios, it is difficult to dynamically adjust battery operating parameters, resulting in the inability to balance performance requirements and life optimization in different scenarios. In addition, with the increasing global attention to carbon emission issues, there are still significant gaps in the carbon emission accounting and optimization of the battery full-life cycle, and traditional methods are difficult to achieve comprehensive tracking and targeted improvement of carbon emissions. Summary of the Invention

[0004] The purpose of the present invention is to provide a battery life evaluation system and method based on different battery degradation modes to solve the problems raised in the above background art.

[0005] To achieve the above purpose, the present invention provides the following technical solution: A battery life evaluation system based on different battery degradation modes, comprising:

[0006] A data acquisition module, used for:

[0007] Real-time collecting multi-dimensional operation data of the battery, and at the same time docking with the traceability information database to associate with the initial data provided by the battery manufacturer, and recording the initial information of materials, processes, and batches during battery production;

[0008] A data analysis module, used for:

[0009] Analyzing the multi-source data provided by the acquisition module through a multi-dimensional data analysis model, classifying the degradation behaviors of the battery, where the degradation behaviors include capacity fade, internal resistance increase, and material aging, establishing a characteristic mapping relationship of the degradation mode, and identifying the degradation mode and key influencing factors of the battery;

[0010] A performance evaluation module, used for:

[0011] Based on the results of the data analysis module, combined with the battery design parameters and operating conditions, comprehensively evaluating the current performance and remaining life of the battery through a prediction model;

[0012] An adaptive performance optimization module, configured to:

[0013] Dynamically adjust battery parameters based on the actual state of the battery and the evaluation results of the performance evaluation module, and adaptively adjust the power output and temperature control strategy according to the usage scenarios, where the usage scenarios include energy storage and high-power applications;

[0014] A carbon footprint assessment module, configured to:

[0015] Account for the carbon emission data throughout the entire life cycle of the battery, where the carbon emission data includes the carbon emission data in the stages of raw material extraction, manufacturing, transportation, use, and recycling;

[0016] An information management module, configured to:

[0017] Establish a distributed ledger-based storage architecture, interface with the data acquisition module and other functional modules, digitally manage and trace the information of the entire life cycle of the battery, and provide a user interface for querying the battery status, historical information, and carbon footprint.

[0018] Furthermore, the data acquisition module includes:

[0019] A data sensing unit, configured to:

[0020] Obtain multi-dimensional real-time operation data of the battery in real time by interacting with sensors, where the real-time operation data includes the charge and discharge voltage, current, temperature, environmental conditions, and cycle count of the battery, and the sensors include electrochemical sensors, temperature sensors, and current detection units;

[0021] A data traceability unit, configured to:

[0022] Interface with the traceability information database, match the factory information of the battery through a unique identifier, associate the initial data provided by the battery manufacturer, record the initial information of materials, processes, and batches during battery production, generate the initial battery manufacturing data, where the unique identifier is generated by the manufacturer during the battery production stage and is bound to the battery entity through an embedded method and marking technology;

[0023] During data acquisition, dynamically bind the real-time acquired data to the initial production data.

[0024] Furthermore, the data analysis module includes:

[0025] A data integration unit, configured to:

[0026] Standardize data from different sources through a unified coding rule and data template, eliminate duplicate data by checking the unique identifier of the data, and ensure the integrity and accuracy of the data through verification rules;

[0027] Perform multi-dimensional mapping and association on the real-time operation data of the battery and the initial manufacturing data of the battery to form a comprehensive data set;

[0028] A degradation mode classification unit, configured to:

[0029] Analyze the key indicators in the comprehensive data set and extract the feature vectors reflecting the battery state changes, where the key indicators include the capacity change rate and the internal resistance growth rate;

[0030] Obtain the battery historical data and operating conditions, establish a data-driven model, classify the degradation behaviors, classify the degradation modes into main categories, and generate quantifiable mode feature data, where the main categories include capacity attenuation, internal resistance increase, and material aging;

[0031] A key influencing factor identification unit, configured to:

[0032] Conduct a correlation analysis on the operation data and the degradation mode features, and according to the correlation results, screen out the key influencing factors that have a significant impact on the degradation mode, where the key influencing factors include the high-temperature operation frequency and the charging rate;

[0033] Establish a mapping relationship between the key influencing factors and the degradation modes, and generate a feature map describing the coupling relationship between the degradation modes and the influencing factors.

[0034] Furthermore, the performance evaluation module includes:

[0035] A current performance analysis unit, configured to:

[0036] Receive the feature data output by the data analysis module, where the feature data includes the degradation mode classification result and the key influencing factors, and at the same time import the initial design parameters of the battery;

[0037] Calculate the current capacity retention rate, internal resistance value, and power output level of the battery through dynamic modeling, compare the current operating parameters with the initial design parameters and industry standards, and generate a performance deviation degree evaluation result;

[0038] A life prediction unit, configured to:

[0039] Based on the historical data, degradation mode features, and battery operating conditions, construct a data-driven model for predicting the future performance changes of the battery, and through multi-condition simulations, predict the life of the battery under different scenarios, where the multi-condition simulations include different temperatures, rates, and usage frequencies;

[0040] According to the simulation results, calculate the remaining service life of the battery and generate a performance degradation trend graph, where the remaining service life includes the cycle number and the calendar life;

[0041] Update the prediction model and life estimation results regularly according to the real-time input data;

[0042] A health status assessment unit for:

[0043] Integrate the output results of the current performance analysis unit and the life prediction unit, and comprehensively convert the key performance parameters and remaining life of the battery into a health status score by using weight analysis;

[0044] Divide the battery health status into three levels: normal, attention required, and replacement required according to the range of the health status score.

[0045] Furthermore, the life prediction unit predicts the life of the battery under different scenarios through multi-condition simulation, including:

[0046] Determine the simulation conditions, conduct single-condition simulations for each simulation condition respectively, and obtain single-condition simulation result information;

[0047] Combine the simulation conditions according to the scenarios, determine the combined simulation conditions, and conduct simulations based on the combined simulation conditions to obtain multi-condition simulation result information;

[0048] Conduct simulation condition analysis based on the combined simulation conditions to determine the correlation relationship between sub-conditions in the combined simulation conditions;

[0049] Analyze whether the multi-condition simulation result information is abnormal according to the single-condition simulation result information combined with the correlation relationship between sub-conditions in the combined simulation conditions, and obtain the analysis and judgment result;

[0050] When the analysis and judgment result shows that the multi-condition simulation result information is abnormal, conduct simulations again based on the combined simulation conditions to obtain multi-condition secondary simulation result information;

[0051] Determine the simulation result according to the multi-condition secondary simulation result information.

[0052] Furthermore, the adaptive performance optimization module includes:

[0053] A dynamic parameter adjustment unit for:

[0054] Receive the real-time performance deviation assessment result, health status score, and remaining service life prediction result provided by the performance assessment module, and receive external operation scenario instructions, where the operation scenarios include energy storage mode and high-power mode;

[0055] Dynamically adjust the charge and discharge current limit, cut-off voltage, and temperature control threshold parameters according to the health status level of the battery, and monitor the performance changes of the battery after adjustment;

[0056] Among them, in high-power application scenarios, the output power is preferentially increased and the battery temperature is actively controlled. In the energy storage mode, the battery life is preferentially extended, and the electrochemical stress is reduced by reducing the charging rate and optimizing the current fluctuation;

[0057] The temperature control strategy optimization unit is used for:

[0058] Receiving battery temperature data in real time, where the battery temperature data includes the cell temperature and the overall temperature difference, and evaluating the effectiveness of the current temperature control state based on the results of the performance evaluation module;

[0059] When the current temperature control state deviates from the expectation, a temperature control strategy is generated based on different temperature control scenarios;

[0060] Among them, in high-power application scenarios, the battery temperature rise is controlled by increasing the cooling intensity. The method of increasing the cooling intensity includes increasing the fan speed and enabling the active liquid cooling system. In low-temperature environments, the optimal working temperature range of the battery is maintained by adjusting the working cycle of the heating device. In the energy storage mode, the energy consumption and temperature control accuracy of the temperature control system are optimized and the overall energy consumption is reduced;

[0061] Sending the optimized temperature control strategy to an external execution unit;

[0062] Tracking the temperature change in real time. If the temperature control effect deviates from the expectation, the strategy optimization process is triggered again.

[0063] Furthermore, the temperature control strategy optimization unit generates temperature control strategies based on different temperature control scenarios, including:

[0064] When the current temperature control state deviates from the expectation, information acquisition is performed for the current temperature control state to obtain the current temperature control information, the current battery temperature data, and the expected data information;

[0065] Analyzing the deviation data between the current battery temperature data and the expected data information to obtain deviation analysis data;

[0066] Combining the current temperature control information with the current temperature control scenario for influencing factor analysis to determine the temperature control scenario influencing factors;

[0067] Combining the deviation analysis data and the current temperature control information for comprehensive analysis of the temperature control scenario influencing factors to obtain the temperature control scenario influence factors;

[0068] Analyzing the scenario characteristics of the temperature control scenario to obtain the temperature control scenario characteristics;

[0069] Based on the temperature control scenario characteristics and the battery temperature data, a temperature control plan is initially determined, and the regulation factors are determined by analyzing the temperature control plan;

[0070] Match the regulation factors with the influencing factors of the temperature control scenario, including: perform vectorization processing on the regulation factors and the influencing factors of the temperature control scenario respectively to obtain the processed information of the regulation factors and the processed information of the influencing factors of the temperature control scenario; disassemble the processed information of the regulation factors and extract the effective information to obtain the sub-processed information of the regulation factors; perform matching analysis on the processed information of the influencing factors of the temperature control scenario according to the following formula based on the sub-processed information of the regulation factors:

[0071] Among them, is the matching value between the regulation factor and the influencing factor of the temperature control scenario, is the information component of the sub-processed information of the regulation factor, is the information component of the information intercepted starting from the th bit in the processed information of the influencing factors of the temperature control scenario; determine the matching analysis result according to the matching value between the regulation factor and the influencing factor of the temperature control scenario. When the matching value between the regulation factor and the influencing factor of the temperature control scenario is greater than the preset threshold, the matching analysis result is that there are the same factors between the regulation factor and the influencing factor of the temperature control scenario; otherwise, the matching analysis result is that there are no same factors between the regulation factor and the influencing factor of the temperature control scenario;

[0072] When the matching analysis result is that there are the same factors, retrieve the influencing factors of the temperature control scenario for the same factors to obtain the target influencing factors, and use the target influencing factors to correct the regulation information for the corresponding regulation factors to obtain the regulated correction information of the regulation factors;

[0073] Revise the temperature control plan based on the regulated correction information of the regulation factors to obtain the temperature control strategy.

[0074] Furthermore, the adaptive performance optimization module further includes:

[0075] A scene switching control unit, used for:

[0076] Receive the scene switching signal sent by the external control system, analyze the scene requirements, and extract the switched operation target and constraint conditions;

[0077] According to the operation target of the new scene, recalculate the adaptation parameters, and the adaptation parameters include the power output range, depth of discharge, and temperature control strategy;

[0078] Send the switched operation parameters to the external execution unit;

[0079] After the scene switching is completed, verify whether the actual performance of the battery meets the expectations. If not, trigger the secondary adjustment process.

[0080] Furthermore, the carbon footprint assessment module includes:

[0081] A data accounting unit, used for:

[0082] Receive the carbon emission data generated during the raw material extraction, processing, and manufacturing provided by raw material suppliers and manufacturers, obtain the fuel consumption and corresponding carbon emission data generated during the battery transportation, record the operating efficiency during the battery usage stage and the carbon emission information corresponding to the energy consumption of the temperature control system. During the recycling stage, obtain the carbon emission data related to the recycling, disassembly, and material reuse of used batteries;

[0083] Perform standardized processing on the collected raw data, calculate the total carbon emissions by life cycle stage respectively, and generate stage carbon footprint data;

[0084] Merge the stage carbon emission data to generate a total carbon emission report for the entire life cycle of the battery including the carbon emission accounting results;

[0085] The carbon emission traceability unit is used for:

[0086] Through a unique identifier, multi-dimensionally associate the carbon emission data of the battery with the battery production materials, manufacturing processes, transportation methods, and usage conditions recorded in the traceability information database, and extract the high-energy-consuming processes and high-transportation-distance factors strongly related to carbon emissions;

[0087] Compare the carbon emission ratios in different life cycle stages to obtain the link with the largest carbon emission proportion, compare materials, processes, and operation modes, identify the main sources and influencing factors of carbon emissions, and generate a carbon emission traceability report;

[0088] The carbon emission optimization suggestion unit is used for:

[0089] Based on the carbon emission accounting results and the traceability report, generate low-carbon optimization suggestions for the battery production, usage, and recycling stages.

[0090] Furthermore, a battery life assessment method based on different battery degradation modes is applied to the above-mentioned battery life assessment system based on different battery degradation modes, including the following steps:

[0091] Step 1: Real-time obtain the battery operation data through sensors, connect to the traceability information database, match the battery factory information through the unique identifier, and dynamically bind the real-time collected data with the production initial data;

[0092] Step 2: Standardize the data from different sources, construct a comprehensive data set to ensure data integrity and accuracy, extract key indicators, analyze the battery degradation behavior, classify it into capacity fade, internal resistance increase, or material aging, identify the key influencing factors related to the degradation mode, and establish a characteristic mapping relationship between the degradation mode and the influencing factors;

[0093] Step 3: Analyze the current performance, generate a performance deviation assessment by comparing with the initial design parameters, build a data-driven model, predict the battery life by simulating different operating scenarios, comprehensively evaluate the current performance and remaining life, generate a state-of-health score, and classify it into normal, attention-required, and replacement-required levels;

[0094] Step 4: Adjust the charge and discharge current, cut-off voltage, and temperature control threshold according to the operating scenario, optimize the temperature control strategy, control the battery temperature by cooling, heating, or reducing energy consumption to ensure the best operating state, re-adapt the parameters when the scenario switches, and verify and optimize the battery performance;

[0095] Step 5: Collect carbon emission data in the stages of raw material extraction, manufacturing, transportation, use, and recycling, generate a full-life-cycle carbon emission report, associate the production process and usage conditions through a unique identifier, trace the main sources of carbon emissions, identify high-emission links, and based on the analysis results, propose low-carbon optimization suggestions;

[0096] Step 6: Use a distributed ledger storage architecture to upload battery data to the blockchain and provide a user interface for querying battery status, historical records, and carbon emission information.

[0097] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0098] 1. Through the combination of the data acquisition module and the information management module, the present invention realizes the accurate acquisition, storage, and tracking of battery full-life-cycle data, integrates real-time operation data and manufacturing traceability information, dynamically binds battery production materials, processes, and actual operation performance, provides comprehensive data support for battery performance evaluation, fault diagnosis, and carbon footprint calculation, and based on distributed ledger technology, ensures the security and transparency of data storage. At the same time, it provides a convenient query interface to support users to view the battery status, historical information, and carbon footprint data in real time, which not only improves the scientificity and reliability of battery data management, but also provides technical support for production optimization, quality traceability, and low-carbon decision-making.

[0099] 2. Through the collaborative work of the data analysis module, performance evaluation module, and adaptive performance optimization module, the present invention realizes the accurate evaluation and dynamic optimization of battery performance and state of health. Through multi-dimensional data integration and degradation mode classification, it identifies the key influencing factors of battery degradation, establishes a pattern feature mapping relationship, improves the depth of understanding of the battery state, combines the real-time data and design parameters of the battery, dynamically calculates the current performance and predicts the remaining life, simplifies user decision-making through health scoring, improves management efficiency, dynamically adjusts battery operation parameters and optimizes the temperature control strategy, and achieves the best balance between performance and life according to different usage scenarios. At the same time, it significantly improves the battery operation safety and energy efficiency, makes battery management shift from passive response to active optimization, extends the battery life, and improves the reliability of applications.

[0100] 3. The present invention provides comprehensive quantitative support and optimization suggestions for carbon emission management throughout the life cycle of the battery through the carbon footprint assessment module. The data accounting unit covers the carbon emission data accounting from raw material extraction, manufacturing, transportation, use to recycling stages of the battery, generates a detailed carbon footprint report, identifies high-energy-consuming links and main carbon emission sources through traceability analysis, provides a basis for optimizing production, transportation and operation strategies, combines the carbon emission accounting and traceability results, generates a targeted low-carbon optimization plan, combines the environmental protection management of the battery with the actual operation, promotes the battery industry to develop in a low-carbon and sustainable direction, and provides technical support for the green economy. BRIEF DESCRIPTION OF THE DRAWINGS

[0101] Figure 1 It is a schematic diagram of the modules of the battery life assessment system of the present invention;

[0102] Figure 2 It is a schematic flowchart of the battery life assessment method of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0103] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0104] Please refer to Figure 1-2 , the present invention provides the following technical solutions:

[0105] A battery life assessment system based on different battery degradation modes, including:

[0106] A data acquisition module, used for:

[0107] Real-time collect multi-dimensional operation data of the battery, and at the same time connect to the traceability information database to associate with the initial data provided by the battery manufacturer, and record the initial information of materials, processes, and batches during battery production;

[0108] A data analysis module, used for:

[0109] Analyze the multi-source data provided by the acquisition module through a multi-dimensional data analysis model, classify the degradation behaviors of the battery, the degradation behaviors include capacity attenuation, internal resistance increase, and material aging, establish a characteristic mapping relationship of the degradation mode, and identify the degradation mode and key influencing factors of the battery;

[0110] A performance evaluation module, used for:

[0111] Based on the results of the data analysis module, combined with the battery design parameters and operating conditions, comprehensively evaluate the current performance and remaining life of the battery through a prediction model;

[0112] An adaptive performance optimization module, configured to:

[0113] Dynamically adjust the battery parameters according to the actual state of the battery and the evaluation results of the performance evaluation module, and adaptively adjust the power output and temperature control strategy according to the usage scenarios, where the usage scenarios include energy storage and high-power applications;

[0114] A carbon footprint assessment module, configured to:

[0115] Account for the carbon emission data during the entire life cycle of the battery, where the carbon emission data includes the carbon emission data in the stages of raw material extraction, manufacturing, transportation, use, and recycling;

[0116] An information management module, configured to:

[0117] Establish a storage architecture based on a distributed ledger, interface with the data acquisition module and other functional modules, digitally manage and trace the information of the entire life cycle of the battery, and provide a user interface for querying the battery status, historical information, and carbon footprint.

[0118] In the above embodiments, through accurate degradation mode identification and performance prediction, dynamically adjust the operating parameters, extend the battery life, monitor and analyze the key parameters in real time, detect anomalies in a timely manner, reduce the potential risk of battery failure, adapt to various operating scenarios such as energy storage and high power, dynamically optimize the battery performance, meet diverse requirements, reduce the carbon emissions during the entire life cycle of the battery through carbon footprint assessment and low-carbon optimization suggestions, and achieve information transparency and traceability of the entire life cycle of the battery through a distributed ledger, improving the management efficiency.

[0119] The data acquisition module includes:

[0120] A data sensing unit, configured to:

[0121] Obtain the multi-dimensional real-time operating data of the battery in real time by interacting with sensors, where the real-time operating data includes the charge and discharge voltage, current, temperature, environmental conditions, and cycle times of the battery, and the sensors include electrochemical sensors, temperature sensors, and current detection units;

[0122] A data traceability unit, configured to:

[0123] Dock with the traceability information database, match the factory information of the battery through the unique identifier, associate the initial data provided by the battery manufacturer, record the initial information of materials, processes, and batches during battery production, and generate the initial battery manufacturing data. The unique identifier is generated by the manufacturer during the battery production stage and is bound to the battery entity through an embedded method and marking technology;

[0124] During data collection, dynamically bind the real-time collected data with the initial production data.

[0125] In the above embodiment, through the electrochemical sensor, temperature sensor, and current detection unit, the core parameters during the battery operation process are captured in real time, including charge and discharge voltage, current, temperature, environmental conditions, and cycle times. These data can reflect the immediate performance and health status of the battery, providing a high-precision basis for subsequent analysis.

[0126] In the above embodiment, by docking with the traceability information database, the traceability unit combines the unique identifier to dynamically bind the battery operation data with the initial production data (including material, process, and batch information), ensuring the traceability of the full life cycle information of each battery and laying a data foundation for performance analysis, fault diagnosis, and carbon footprint assessment. At the same time, the unique identifier makes the data collection process highly reliable, avoiding subsequent analysis deviations caused by battery confusion or information loss.

[0127] The data analysis module includes:

[0128] The data integration unit is used for:

[0129] Standardize the data from different sources through a unified coding rule and data template, eliminate duplicate data by checking the unique identifier of the data, and ensure the integrity and accuracy of the data through the verification rule;

[0130] Perform multi-dimensional mapping and association between the real-time battery operation data and the initial battery manufacturing data to form a comprehensive data set;

[0131] The degradation mode classification unit is used for:

[0132] Analyze the key indicators in the comprehensive data set and extract the feature vectors reflecting the battery state changes. The key indicators include the capacity change rate and the internal resistance growth rate;

[0133] Obtain the battery historical data and operating conditions, establish a data-driven model, classify the degradation behavior, classify the degradation mode into main categories, and generate quantifiable mode feature data. The main categories include capacity attenuation, internal resistance increase, and material aging;

[0134] The key influencing factor identification unit is used for:

[0135] Perform a correlation analysis on the operation data and the characteristics of the degradation mode. According to the correlation results, screen out the key influencing factors that have a significant impact on the degradation mode. The key influencing factors include the high-temperature operation frequency and the charge rate;

[0136] Establish a mapping relationship between the key influencing factors and the degradation mode, and generate a characteristic map describing the coupling relationship between the degradation mode and the influencing factors.

[0137] In the above embodiment, the data from different sources is standardized through a unified coding rule and data template, ensuring the integrity and consistency of the data quality. In a complex battery operation environment, the data collected under different batches, scenarios, and conditions may be repetitive, missing, or incorrect. Through the deduplication and verification rules of this unit, the data reliability can be significantly improved. In addition, this unit correlates the real-time operation data with the initial production data in multiple dimensions to form a comprehensive data set. This data set not only contains the instant status information of the battery, but also carries its production process and material characteristics, providing more comprehensive data support for the classification of the degradation mode.

[0138] In the above embodiment, the battery degradation behavior is classified through a data-driven model and divided into three main categories: capacity attenuation, internal resistance increase, and material aging, which helps users understand the essential reasons for the changes in battery performance. For example, by analyzing key indicators such as the capacity change rate and the internal resistance growth rate, specific behaviors such as high-temperature operation or excessive charge rate can be identified, and their long-term impacts on battery performance can be determined.

[0139] In the above embodiment, by establishing the coupling relationship between the key factors and the degradation mode, a characteristic map is generated, which intuitively shows the impact of different operation conditions on battery performance. These characteristic maps provide a scientific basis for users to optimize the battery operation conditions and also provide data support for the parameter adjustment of the adaptive optimization module.

[0140] The performance evaluation module includes:

[0141] The current performance analysis unit is used for:

[0142] Receiving the characteristic data output by the data analysis module, the characteristic data including the degradation mode classification result and the key influencing factors, and at the same time importing the initial design parameters of the battery;

[0143] Calculating the current capacity retention rate, internal resistance value, and power output level of the battery through dynamic modeling, comparing the current operation parameters with the initial design parameters and industry standards, and generating a performance deviation degree evaluation result;

[0144] The life prediction unit is used for:

[0145] Based on historical data, degradation mode characteristics, and battery operating conditions, a data-driven model is constructed to predict the future performance changes of the battery. Through multi-condition simulation, the battery life under different scenarios is predicted, and the multi-condition simulation includes different temperatures, rates, and usage frequencies;

[0146] According to the simulation results, calculate the remaining service life of the battery and generate a performance degradation trend graph. The remaining service life includes the number of cycles and calendar life;

[0147] According to the real-time input data, regularly update the prediction model and life estimation results;

[0148] The state of health assessment unit is used for:

[0149] Integrate the output results of the current performance analysis unit and the life prediction unit, and comprehensively convert the key performance parameters and remaining life of the battery into a state of health score by using weight analysis;

[0150] According to the range of the state of health score, divide the battery state of health into three levels: normal, attention required, and replacement required.

[0151] In the above embodiment, the current performance analysis unit uses the degradation mode classification result and key influencing factor data transmitted from the data analysis module, combines with the battery design parameters, quantifies the current operating state of the battery, calculates the capacity retention rate, internal resistance value, and power output level of the battery through dynamic modeling, and compares with the initial design parameters and industry standards to generate a performance deviation degree assessment report. For example, if the battery capacity retention rate is lower than the standard level, the user will be reminded to adjust the operating conditions, significantly enhancing the user's control ability over the current state of the battery and facilitating timely maintenance measures.

[0152] In the above embodiment, the performance change trend of the battery under different usage scenarios is simulated through a data-driven model, and the remaining life of the battery, including cycle life and calendar life, is estimated, providing scientific support for the user to plan battery replacement and operation strategies. For example, for the battery in a high-power application scenario, its life change under specific rate charging conditions can be predicted, so as to optimize its usage frequency and charge-discharge strategy. By setting the grading standard of the state of health, the user can more efficiently evaluate whether the battery needs maintenance or replacement.

[0153] Furthermore, the life prediction unit predicts the battery life under different scenarios through multi-condition simulation, including:

[0154] Determine the simulation conditions, and conduct single-condition simulations for the simulation conditions respectively to obtain single-condition simulation result information;

[0155] Combine according to scenarios for the simulation conditions, determine the combined simulation conditions, and perform simulations based on the combined simulation conditions to obtain multi-condition simulation result information;

[0156] Perform simulation condition analysis based on the combined simulation conditions to determine the correlation between sub-conditions in the combined simulation conditions;

[0157] Analyze whether the multi-condition simulation result information is abnormal according to the single-condition simulation result information in combination with the correlation between sub-conditions in the combined simulation conditions to obtain an analysis and judgment result;

[0158] When the analysis and judgment result indicates that the multi-condition simulation result information is abnormal, perform simulations again based on the combined simulation conditions to obtain multi-condition secondary simulation result information;

[0159] Determine the simulation result according to the multi-condition secondary simulation result information.

[0160] In the above embodiments, through single-condition simulations, the influence of a single simulation condition on the simulation result is made clear, so that after multi-condition simulations, the multi-condition simulation result information is tested, avoiding the high probability of simulation result errors caused by the high difficulty of multi-condition simulations, which may affect the analysis based on the simulation results. Moreover, when analyzing whether the multi-condition simulation result information is abnormal according to the single-condition simulation result information, the analysis is carried out in combination with the correlation between sub-conditions in the combined simulation conditions, improving the accuracy of testing the multi-condition simulation result information, enhancing the accuracy of the analysis and judgment result, and thus ensuring the accuracy of the simulation result. And when the analysis and judgment result indicates that the multi-condition simulation result information is abnormal, simulations are performed again to avoid the influence of accidental phenomena, providing a guarantee for determining the simulation result.

[0161] Adaptive performance optimization module, including:

[0162] Dynamic parameter adjustment unit, for:

[0163] Receive the real-time performance deviation degree evaluation result, health status score, and remaining service life prediction result provided by the performance evaluation module, and receive external operation scenario instructions, where the operation scenarios include energy storage mode and high-power mode;

[0164] Dynamically adjust the charge and discharge current limit, cut-off voltage, and temperature control threshold parameters according to the health status level of the battery, and monitor the performance changes of the battery after adjustment;

[0165] Among them, in high-power application scenarios, give priority to increasing the output power and actively control the battery temperature. In the energy storage mode, give priority to extending the service life of the battery, and reduce the electrochemical stress by reducing the charging rate and optimizing the current fluctuation;

[0166] Temperature control strategy optimization unit, for:

[0167] Receive battery temperature data in real time. The battery temperature data includes the temperature of individual cells and the overall temperature difference. Based on the results of the performance evaluation module, evaluate the effectiveness of the current temperature control state.

[0168] When the current temperature control state deviates from the expected value, generate a temperature control strategy based on different temperature control scenarios.

[0169] Among them, in the high-power application scenario, control the battery temperature rise by increasing the cooling intensity. The method of increasing the cooling intensity includes increasing the fan speed and enabling the active liquid cooling system. In a low-temperature environment, maintain the optimal working temperature range of the battery by adjusting the working cycle of the heating device. In the energy storage mode, optimize the energy consumption and temperature control accuracy of the temperature control system and reduce the overall energy consumption.

[0170] Send the optimized temperature control strategy to an external execution unit.

[0171] Track the temperature change in real time. If the temperature control effect deviates from the expected value, trigger the policy optimization process again.

[0172] Furthermore, the temperature control strategy optimization unit generates a temperature control strategy based on different temperature control scenarios, including:

[0173] When the current temperature control state deviates from the expected value, obtain the current temperature control information, the current battery temperature data, and the expected data information by acquiring information about the current temperature control state.

[0174] Analyze the deviation data between the current battery temperature data and the expected data information to obtain deviation analysis data.

[0175] Analyze the influencing factors by combining the current temperature control information with the current temperature control scenario to determine the influencing factors of the temperature control scenario.

[0176] Combine the deviation analysis data and the current temperature control information to conduct a comprehensive analysis of the influencing factors of the temperature control scenario to obtain the influencing factor of the temperature control scenario.

[0177] Analyze the scenario characteristics of the temperature control scenario to obtain the temperature control scenario characteristics.

[0178] Based on the temperature control scenario characteristics and the battery temperature data, preliminarily determine a temperature control plan, and analyze the temperature control plan to determine the control factors.

[0179] Match the control factors with the influencing factors of the temperature control scenario, including: perform vectorization processing on the control factors and the influencing factors of the temperature control scenario respectively to obtain the processed information of the control factors and the processed information of the influencing factors of the temperature control scenario; disassemble the processed information of the control factors and extract the effective information to obtain the sub-processed information of the control factors; perform matching analysis on the processed information of the influencing factors of the temperature control scenario according to the following formula based on the sub-processed information of the control factors:

[0180] Wherein, is the matching value between the regulation factor and the temperature control scenario influencing factor, is the information component of the sub-information processed by the regulation factor, is the information component of the information intercepted starting from the th bit in the information processed by the temperature control scenario influencing factor; The matching analysis result is determined according to the matching value between the regulation factor and the temperature control scenario influencing factor. When the matching value between the regulation factor and the temperature control scenario influencing factor is greater than the preset threshold, the matching analysis result is that there are identical factors between the regulation factor and the temperature control scenario influencing factor; otherwise, the matching analysis result is that there are no identical factors between the regulation factor and the temperature control scenario influencing factor;

[0181] When the matching analysis result is that there are identical factors, the temperature control scenario influencing factors are retrieved for the identical factors to obtain the target influencing factors, and the regulation information of the corresponding regulation factor is corrected by using the target influencing factors to obtain the regulation correction information of the regulation factor;

[0182] The temperature control scheme is revised based on the regulation correction information of the regulation factor to obtain the temperature control strategy.

[0183] In the above embodiment, the accuracy of the regulation strategy is improved by correcting the temperature control scheme, the expected deviation caused by the regulation strategy is reduced, so that the regulation result is closer to the expectation. Moreover, the temperature control scenario influencing factors are comprehensively analyzed and determined by combining the deviation analysis data and the current temperature control information, which comprehensively considers all aspects and improves the accuracy of the influencing factors. And by determining the target influencing factors, when using the target influencing factors to correct the regulation information of the corresponding regulation factor, only the corresponding regulation factor in the temperature control scheme is corrected, without correcting the whole temperature control scheme, which reduces the time consumed by ineffective correction and improves the efficiency of correction.

[0184] The scenario switching control unit is used for:

[0185] Receiving the scenario switching signal sent by the external control system, parsing the scenario requirements, and extracting the switched operation target and constraint conditions;

[0186] Recalculating the adaptation parameters according to the operation target of the new scenario, where the adaptation parameters include the power output range, depth of discharge, and temperature control strategy;

[0187] Sending the switched operation parameters to the external execution unit;

[0188] After the scenario switching is completed, verifying whether the actual performance of the battery meets the expectation, and if not, triggering the secondary adjustment process.

[0189] In the above-mentioned embodiment, the dynamic parameter adjustment unit analyzes the battery's real-time performance deviation, health status score, and remaining life prediction results, and combines them with the requirements of the external operating scenario (such as energy storage mode or high-power mode) to adjust the battery's key operating parameters (such as charge and discharge current, cutoff voltage, and temperature control threshold) in real time. For example, in high-power applications, the unit prioritizes increasing the battery's power output while actively controlling the temperature to reduce overheating risks. In energy storage scenarios, the unit focuses more on extending the battery's life, reducing electrochemical stress by lowering the charge rate and optimizing current fluctuations.

[0190] In the above embodiment, in high-temperature scenarios, the unit rapidly reduces battery temperature by increasing cooling intensity (e.g., fan speed or active liquid cooling). In low-temperature scenarios, the unit adjusts the heating device's duty cycle to maintain the battery temperature within the optimal range. Furthermore, by optimizing energy consumption strategies, energy efficiency is maximized in energy storage mode, effectively extending battery life and significantly improving operational safety.

[0191] In the above embodiment, the scenario switching control unit supports rapid transitions between various usage scenarios. For example, when switching between energy storage and high-power application scenarios, the unit can recalculate adaptation parameters based on the new operating target and send them to the execution unit, ensuring that battery performance quickly stabilizes at the target state, significantly enhancing the battery's adaptability in complex application scenarios.

[0192] Carbon footprint assessment module, including:

[0193] Data accounting unit, used for:

[0194] Receive carbon emission data generated during the mining, processing and manufacturing of raw materials from raw material suppliers and manufacturers, obtain fuel consumption and corresponding carbon emission data generated during battery transportation, record the operating efficiency of the battery during use and the carbon emission information corresponding to the energy consumption of the temperature control system, and obtain carbon emission data related to the recycling, disassembly and reuse of used batteries during the recycling stage;

[0195] Standardize the collected raw data, calculate the total carbon emissions by life cycle stage, and generate phased carbon footprint data;

[0196] Merge the phased carbon emission data to generate a total carbon emission report for the entire battery life cycle, including carbon emission accounting results;

[0197] Carbon emission traceability unit, used for:

[0198] Through a unique identifier, the carbon emission data of the battery is multi-dimensionally associated with the battery production materials, manufacturing processes, transportation methods, and usage conditions recorded in the traceability information database, and high-energy-consuming processes and factors with long transportation distances that are strongly correlated with carbon emissions are extracted;

[0199] Compare the carbon emission ratios in different life cycle stages to obtain the stage with the largest carbon emission proportion. Compare materials, processes, and operation modes to identify the main sources and influencing factors of carbon emissions, and generate a carbon emission traceability report;

[0200] A carbon emission optimization suggestion unit is used for:

[0201] Based on the carbon emission accounting results and the traceability report, generate low-carbon optimization suggestions for the battery production, usage, and recycling stages.

[0202] In the above embodiment, the data accounting unit covers the carbon emission data of the entire life cycle of the battery from raw material extraction to manufacturing, transportation, usage, and recycling. By analyzing the extraction and processing data provided by raw material suppliers, users can identify high-emission links in the production stage; by recording fuel consumption during transportation and temperature control energy consumption during the usage stage, users can quantify the carbon footprint during operation. In addition, this unit accounts for carbon emission data in segments according to the life cycle stage and generates a total carbon emission report, providing data support for the green certification and environmental protection design of the battery.

[0203] In the above embodiment, through multi-dimensional correlation analysis, the main sources and key influencing factors of carbon emissions are identified. For example, by analyzing high-energy-consuming links in the material production process, users can optimize the process flow; by evaluating the operation efficiency during the usage stage, users can improve the battery management strategy to reduce energy consumption. This unit also generates a carbon emission traceability path, providing an intuitive basis for users to deeply understand the sources of the carbon footprint.

[0204] In the above embodiment, based on the carbon emission data and user requirements, low-carbon optimization suggestions are provided for the production, operation, and recycling stages. For example, in the production stage, it is recommended to adopt process technologies with lower energy consumption; in the usage stage, optimize the charging strategy to reduce the grid load and improve the battery efficiency; in the recycling stage, it is recommended to preferentially adopt efficient regeneration technologies to reduce material waste.

[0205] A battery life assessment method based on different battery degradation modes is applied to the above battery life assessment system based on different battery degradation modes, and includes the following steps:

[0206] Step 1: Real-time obtain battery operation data through sensors, connect to the traceability information database, match the factory information of the battery through a unique identifier, and dynamically bind the real-time collected data with the production initial data;

[0207] Step 2: Standardize data from different sources, construct a comprehensive dataset to ensure data integrity and accuracy, extract key metrics, analyze battery degradation behavior, classify it into capacity decay, internal resistance increase, or material aging, identify key influencing factors related to the degradation mode, and establish a feature mapping relationship between the degradation mode and the influencing factors;

[0208] Step 3: Analyze the current performance, generate a performance deviation assessment by comparing with the initial design parameters, construct a data-driven model, predict the battery life by simulating different operating scenarios, comprehensively evaluate the current performance and remaining life, generate a health status score, and classify it into normal, attention required, and replacement required levels;

[0209] Step 4: Adjust the charge and discharge current, cut-off voltage, and temperature control threshold according to the operating scenario, optimize the temperature control strategy, control the battery temperature by cooling, heating, or reducing energy consumption to ensure the best operating state, re-adapt the parameters when the scenario changes, and verify and optimize the battery performance;

[0210] Step 5: Collect carbon emission data in the stages of raw material extraction, manufacturing, transportation, use, and recycling, generate a full-life cycle carbon emission report, associate the production process and usage conditions through a unique identifier, trace the main sources of carbon emissions, identify high-emission links, and based on the analysis results, propose low-carbon optimization suggestions;

[0211] Step 6: Use a distributed ledger storage architecture to upload battery data to the blockchain and provide a user interface for querying battery status, historical records, and carbon emission information.

[0212] As mentioned above, it is only the preferred specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art within the technical scope disclosed by the present invention, according to the technical solution of the present invention and its inventive concept, makes equivalent substitutions or changes, and all should be covered by the protection scope of the present invention.

Claims

1. A battery life evaluation system based on different battery degradation modes, characterized in that, Including: A data acquisition module, configured to: Collect multi-dimensional operation data of the battery in real time, and at the same time connect to the traceability information database to associate with the initial data provided by the battery manufacturer, and record the initial information of materials, processes, and batches during battery production; A data analysis module, configured to: Analyze the multi-source data provided by the acquisition module through a multi-dimensional data analysis model, classify the degradation behaviors of the battery, where the degradation behaviors include capacity attenuation, internal resistance increase, and material aging, establish a characteristic mapping relationship of the degradation mode, and identify the degradation mode and key influencing factors of the battery; A performance evaluation module, configured to: Based on the results of the data analysis module, combined with the battery design parameters and operating conditions, comprehensively evaluate the current performance and remaining life of the battery through a prediction model; An adaptive performance optimization module, configured to: Dynamically adjust the battery parameters according to the actual state of the battery and the evaluation results of the performance evaluation module, and adaptively adjust the power output and temperature control strategy according to the usage scenario, where the usage scenario includes energy storage and high-power applications; A carbon footprint evaluation module, configured to: Account for the carbon emission data during the entire life cycle of the battery, where the carbon emission data includes carbon emission data in the stages of raw material extraction, manufacturing, transportation, use, and recycling; An information management module, configured to: Establish a storage architecture based on a distributed ledger, connect to the data acquisition module and other functional modules, digitally manage and track the information of the entire life cycle of the battery, and provide a user interface for querying the battery status, historical information, and carbon footprint; Among them, the adaptive performance optimization module includes: A scenario switching control unit, configured to: Receive a scenario switching signal sent by an external control system, parse the scenario requirements, and extract the switched operation target and constraint conditions; Recalculate the adaptation parameters according to the operation target of the new scenario, where the adaptation parameters include the power output range, depth of discharge, and temperature control strategy; Send the switched operation parameters to an external execution unit; After the scenario switching is completed, verify whether the actual performance of the battery meets the expectations, and if not, trigger a secondary adjustment process; A dynamic parameter adjustment unit, configured to: Receive the real-time performance deviation evaluation result, health status score, and remaining service life prediction result provided by the performance evaluation module, and receive an external operation scenario instruction, where the operation scenario includes an energy storage mode and a high-power mode; Dynamically adjust the charge and discharge current limit, cut-off voltage, and temperature control threshold parameters according to the health status level of the battery, and monitor the performance change of the battery after adjustment; A temperature control strategy optimization unit, configured to: Receive the battery temperature data in real time, where the battery temperature data includes the cell temperature and the overall temperature difference, and evaluate the effectiveness of the current temperature control state based on the results of the performance evaluation module; Generate a temperature control strategy based on different temperature control scenarios when the current temperature control state deviates from the expectation; Send the optimized temperature control strategy to an external execution unit; Track the temperature change in real time, and if the temperature control effect deviates from the expectation, re-trigger the strategy optimization process.

2. The battery life evaluation system based on different battery degradation modes according to claim 1, characterized in that: The data acquisition module includes: A data sensing unit, configured to: By interacting with sensors, multi-dimensional real-time operation data of the battery is obtained in real time. The real-time operation data includes the charge and discharge voltage, current, temperature, environmental conditions, and cycle count of the battery. The sensors include an electrochemical sensor, a temperature sensor, and a current detection unit; A data traceability unit, configured to: Connect to the traceability information database, match the factory information of the battery through a unique identifier, associate the initial data provided by the battery manufacturer, record the initial information of materials, processes, and batches during battery production, and generate initial battery manufacturing data. The unique identifier is generated by the manufacturer during the battery production stage and is bound to the battery entity through an embedded method and marking technology; During data acquisition, dynamically bind the real-time acquired data to the initial production data.

3. The battery life evaluation system based on different battery degradation modes according to claim 2, characterized in that: The data analysis module includes: A data integration unit, configured to: Standardize data from different sources through a unified coding rule and data template, and eliminate duplicate data by checking the unique identifier of the data; Perform multi-dimensional mapping and association between the real-time operation data of the battery and the initial battery manufacturing data to form a comprehensive data set; A degradation mode classification unit, configured to: Analyze key indicators in the comprehensive data set and extract feature vectors reflecting the battery state change. The key indicators include the capacity change rate and the internal resistance growth rate; Obtain the battery historical data and operating conditions, establish a data-driven model, classify the degradation behavior, classify the degradation mode into main categories, and generate quantifiable mode feature data. The main categories include capacity attenuation, internal resistance increase, and material aging; A key influencing factor identification unit, configured to: Perform a correlation analysis on the operation data and the degradation mode characteristics, and according to the correlation results, screen out the key influencing factors that have a significant impact on the degradation mode. The key influencing factors include the high-temperature operation frequency and the charging rate; Establish a mapping relationship between the key influencing factors and the degradation mode, and generate a feature map describing the coupling relationship between the degradation mode and the influencing factors.

4. The battery life evaluation system based on different battery degradation modes according to claim 3, characterized in that: The performance evaluation module includes: A current performance analysis unit, configured to: Receive the feature data output by the data analysis module. The feature data includes the degradation mode classification result and the key influencing factors, and at the same time import the initial design parameters of the battery; Calculate the current capacity retention rate, internal resistance value, and power output level of the battery through dynamic modeling, compare the current operating parameters with the initial design parameters and industry standards, and generate a performance deviation degree evaluation result; A life prediction unit, configured to: Based on the historical data, degradation mode characteristics, and battery operating conditions, construct a data-driven model for predicting the future performance change of the battery, and predict the life of the battery under different scenarios through multi-condition simulation. The multi-condition simulation includes different temperatures, rates, and usage frequencies; According to the simulation results, calculate the remaining service life of the battery and generate a performance degradation trend graph. The remaining service life includes the cycle count and the calendar life; Regularly update the prediction model and the life estimation result according to the real-time input data; A health state evaluation unit, configured to: Integrate the output results of the current performance analysis unit and the remaining life prediction unit, and comprehensively convert the key performance parameters and remaining life of the battery into a health status score by using weight analysis; According to the range of the health status score, divide the battery health status into three levels: normal, attention required, and replacement required.

5. The battery life evaluation system based on different battery degradation modes according to claim 4, characterized in that: The remaining life prediction unit predicts the remaining life of the battery under different scenarios through multi-condition simulation, including: Determine the simulation conditions, and conduct single-condition simulations for the simulation conditions respectively to obtain single-condition simulation result information; Combine the simulation conditions according to the scenarios, determine the combined simulation conditions, and conduct simulations based on the combined simulation conditions to obtain multi-condition simulation result information; Conduct simulation condition analysis based on the combined simulation conditions to determine the correlation between sub-conditions in the combined simulation conditions; Analyze whether the multi-condition simulation result information is abnormal according to the single-condition simulation result information combined with the correlation between sub-conditions in the combined simulation conditions to obtain an analysis and judgment result; When the analysis and judgment result is that the multi-condition simulation result information is abnormal, conduct simulations again based on the combined simulation conditions to obtain multi-condition secondary simulation result information; Determine the simulation result according to the multi-condition secondary simulation result information.

6. The battery life evaluation system based on different battery degradation modes according to claim 1, characterized in that: The carbon footprint assessment module includes: The data accounting unit is used for: Receive the carbon emission data generated during the raw material extraction, processing, and manufacturing provided by the raw material suppliers and manufacturers, obtain the fuel consumption and corresponding carbon emission data generated during the battery transportation, record the carbon emission information corresponding to the operation efficiency and temperature control system energy consumption during the battery usage stage, and in the recycling stage, obtain the carbon emission data related to the recycling, dismantling, and material reuse of waste batteries; Perform standardized processing on the collected raw data, calculate the total carbon emissions by life cycle stage respectively, and generate stage carbon footprint data; Merge the stage carbon emission data to generate a total carbon emission report for the entire life cycle of the battery including the carbon emission accounting results; The carbon emission traceability unit is used for: Through the unique identifier, conduct multi-dimensional association of the battery's carbon emission data with the battery production materials, manufacturing processes, transportation methods, and usage conditions recorded in the traceability information database, and extract the high-energy-consuming processes and high-transportation-distance factors strongly related to carbon emissions; Compare the carbon emission ratios of different life cycle stages to obtain the link with the largest carbon emission proportion, compare materials, processes, and operation modes, identify the main sources and influencing factors of carbon emissions, and generate a carbon emission traceability report; The carbon emission optimization suggestion unit is used for: Generate low-carbon optimization suggestions for the battery production, usage, and recycling stages based on the carbon emission accounting results and the traceability report.

7. The battery life evaluation system based on different battery degradation modes according to claim 1, wherein In high-power application scenarios, the dynamic parameter adjustment unit preferentially improves the output power and actively controls the battery temperature. In the energy storage mode, it preferentially extends the battery life by reducing the charging rate and optimizing the current fluctuation to reduce the electrochemical stress; In high-power application scenarios, the temperature control strategy optimization unit controls the battery temperature rise by increasing the cooling intensity. The methods for increasing the cooling intensity include increasing the fan speed and enabling the active liquid cooling system. In low-temperature environments, the optimal operating temperature range of the battery is maintained by adjusting the duty cycle of the heating device. In the energy storage mode, the energy consumption and temperature control accuracy of the temperature control system are optimized and the overall energy consumption is reduced.

8. The battery life evaluation system based on different battery degradation modes according to claim 1, wherein The temperature control strategy optimization unit generates temperature control strategies based on different temperature control scenarios, including: When the current temperature control state deviates from the expectation, information acquisition is performed for the current temperature control state to obtain the current temperature control information, the current battery temperature data, and the expected data information; Analyze the deviation data between the current battery temperature data and the expected data information to obtain the deviation analysis data; Analyze the influencing factors by combining the current temperature control information with the current temperature control scenario to determine the temperature control scenario influencing factors; Perform a comprehensive analysis of the temperature control scenario influencing factors by combining the deviation analysis data and the current temperature control information to obtain the temperature control scenario influencing factors; Analyze the scenario characteristics of the temperature control scenario to obtain the temperature control scenario characteristics; Preliminarily determine the temperature control scheme according to the temperature control scenario characteristics combined with the battery temperature data, and analyze the temperature control scheme to determine the adjustment factors; Match the adjustment factors with the temperature control scenario influencing factors, including: perform vectorization processing on the adjustment factors and the temperature control scenario influencing factors respectively to obtain the adjustment factor processing information and the temperature control scenario influencing factor processing information; disassemble the adjustment factor processing information and extract the effective information to obtain the adjustment factor processing sub-information, and perform matching analysis according to the adjustment factor processing sub-information under the temperature control scenario influencing factor processing information: Among them, is the matching value of the regulation factor and the temperature control scenario influencing factor, is the information component of the regulation factor processing sub-information, is the information component of the information intercepted starting from the th bit in the temperature control scenario influencing factor processing information; the matching analysis result is determined according to the matching value of the regulation factor and the temperature control scenario influencing factor. When the matching value of the regulation factor and the temperature control scenario influencing factor is greater than the preset threshold, the matching analysis result is that there are identical factors between the regulation factor and the temperature control scenario influencing factor; otherwise, the matching analysis result is that there are no identical factors between the regulation factor and the temperature control scenario influencing factor. When the matching analysis result shows that there are the same factors, retrieve the temperature control scenario influencing factors for the same factors to obtain the target influencing factors, and use the target influencing factors to correct the adjustment information for the corresponding adjustment factors to obtain the adjustment correction information of the adjustment factors; Revise the temperature control scheme based on the adjustment correction information of the adjustment factors to obtain the temperature control strategy.

9. A battery life assessment method based on different battery degradation modes, in the battery life assessment system based on different battery degradation modes as described in claim 8, characterized in that, It includes the following steps: Step 1: Real-time obtain the battery operation data through sensors, connect to the traceability information database, match the factory information of the battery through the unique identifier, and dynamically bind the real-time collected data with the production initial data; Step 2: Standardize the data from different sources, construct a comprehensive data set to ensure the integrity and accuracy of the data, extract key indicators, analyze the battery degradation behavior, classify it into capacity attenuation, internal resistance increase or material aging, identify the key influencing factors related to the degradation mode, and establish the characteristic mapping relationship between the degradation mode and the influencing factors; Step 3: Analyze the current performance, generate a performance deviation degree evaluation by comparing with the initial design parameters, construct a data-driven model, predict the battery life by simulating different operating scenarios, comprehensively evaluate the current performance and the remaining life, generate a health status score, and divide it into normal, attention required, replacement required levels; Step 4: Adjust the charge and discharge current, cut-off voltage, and temperature control threshold according to the operating scenario, optimize the temperature control strategy, control the battery temperature by cooling, heating or reducing energy consumption, ensure the best operating state, re-adapt the parameters when the scenario switches, and verify and optimize the battery performance; Step 5: Collect carbon emission data in the raw material extraction, manufacturing, transportation, use, and recycling stages, generate a full-life cycle carbon emission report, associate production processes and usage conditions through unique identifiers, trace the main sources of carbon emissions, identify high-emission links, and based on the analysis results, propose low-carbon optimization suggestions; Step 6: Use a distributed ledger storage architecture to upload battery data to the blockchain and provide a user interface for querying battery status, historical records, and carbon emission information.

Images