Battery life evaluation system and method based on different recession modes of battery

Abstract

The invention discloses a battery life evaluation system and method based on different decline modes of a battery, and belongs to the technical field of battery management evaluation. Through combination of the data acquisition module and the information management module, battery manufacturing, operation and historical information is comprehensively acquired, accurate management of the whole life cycle of the battery is realized, the data analysis module and the performance evaluation module deeply analyze battery operation data, classify and identify recession modes and key influence factors, and the reliability of the battery is improved. The current performance and the remaining life of the battery are dynamically evaluated by combining a prediction model, decision making is simplified through health scoring, the battery management efficiency is improved, the adaptive performance optimization module adjusts battery parameters and temperature control strategies according to operation scenes, the performance and the life are balanced, active optimization and safety improvement are achieved, and the safety is improved. The carbon footprint evaluation module covers all stages of battery manufacturing, using and recycling, checks full-life-cycle carbon emission, traces the source of high emission links, provides low-carbon optimization suggestions, and assists enterprises in achieving the goal of green development.

Description

Technical Field

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

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

[0003] Existing battery management technologies mostly focus on the monitoring and analysis of a single performance parameter, lacking comprehensive utilization and in-depth analysis of battery data throughout its life cycle, and are unable to accurately predict battery performance trends and lifespan. At the same time, in a variety of application scenarios, battery operating parameters are difficult to dynamically adjust, resulting in an inability to balance performance requirements and lifespan optimization in different scenarios. In addition, with the world's growing concern about carbon emissions, there are still significant gaps in the accounting and optimization of carbon emissions throughout the battery life cycle, and traditional methods make it difficult to achieve comprehensive tracking and targeted improvements to carbon emissions. Summary of the invention

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

[0005] To achieve the above object, the present invention provides the following technical solution: a battery life evaluation system based on different battery degradation modes, comprising: Data acquisition module for: Collect multi-dimensional operating data of batteries in real time, and connect to the traceability information database to associate the initial data provided by the battery manufacturer, and record the initial information of materials, processes, and batches during battery production; Data analysis module for: Analyze the multi-source data provided by the acquisition module through a multi-dimensional data analysis model, classify the battery's degradation behavior, which includes capacity decay, internal resistance increase, and material aging, establish a characteristic mapping relationship of the degradation mode, and identify the battery's degradation mode and key influencing factors; Performance evaluation module for: Based on the results of the data analysis module, combined with battery design parameters and operating conditions, a prediction model is used to comprehensively evaluate the current performance and remaining life of the battery; Adaptive performance optimization module for: Dynamically adjust battery parameters based on the actual state of the battery and the evaluation results of the performance evaluation module, and adaptively adjust power output and temperature control strategies based on usage scenarios, including energy storage and high-power applications; Carbon footprint assessment module for: Calculate the carbon emissions data over the entire life cycle of the battery, including the carbon emissions data from the raw material mining, manufacturing, transportation, use and recycling stages; Information management module for: Establish a storage architecture based on distributed ledger, connect data collection modules and other functional modules, digitally manage and track battery life cycle information, and provide a user interface for querying battery status, historical information and carbon footprint.

[0006] Furthermore, the data acquisition module includes: Data sensing unit for: By interacting with sensors, multi-dimensional real-time operation data of the battery is obtained in real time, and the real-time operation data includes the battery's charge and discharge voltage, current, temperature, environmental conditions and cycle times. The sensors include electrochemical sensors, temperature sensors and current detection units; Data traceability unit, used for: Connect to 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 data of battery manufacturing. 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; While data is being collected, the real-time collected data is dynamically bound to the initial production data.

[0007] Furthermore, the data analysis module includes: Data integration unit for: Standardize data from different sources through unified coding rules and data templates, eliminate duplicate data by checking the unique identifiers of the data, and ensure data integrity and accuracy through verification rules; Map and correlate real-time battery operation data with initial battery manufacturing data in multiple dimensions to form a comprehensive data set; Decline pattern classification unit for: Analyze key indicators in the comprehensive data set and extract feature vectors reflecting battery state changes, wherein the key indicators include capacity change rate and internal resistance growth rate; Obtain battery historical data and operating conditions, establish a data-driven model, classify degradation behaviors, classify degradation patterns into major categories, and generate quantifiable pattern feature data, wherein the major categories include capacity attenuation, internal resistance increase, and material aging; Key influencing factors identification unit, used to: Performing a correlation analysis on the operating data and the decay mode characteristics, and screening out key influencing factors that have a significant impact on the decay mode based on the correlation results, wherein the key influencing factors include high temperature operating frequency and charging rate; A mapping relationship between key influencing factors and decay patterns is established, and a characteristic map describing the coupling relationship between decay patterns and influencing factors is generated.

[0008] Furthermore, the performance evaluation module includes: Current performance analysis units, used for: Receiving characteristic data output by a data analysis module, the characteristic data including a degradation mode classification result and key influencing factors, and importing initial design parameters of the battery; Calculate the current capacity retention rate, internal resistance 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 performance deviation evaluation results; Life prediction unit for: Based on historical data, decay pattern characteristics and battery operating conditions, a data-driven model is constructed to predict future performance changes of the battery, and the battery life under different scenarios is predicted through multi-condition simulation, including different temperatures, rates and usage frequencies; According to the simulation results, the remaining service life of the battery is calculated and a performance decay trend graph is generated, wherein the remaining service life includes the number of cycles and the calendar life; Regularly update the prediction model and life estimation results based on real-time input data; Health status assessment unit for: Integrate the output results of the current performance analysis unit and the life prediction unit, and use weight analysis to convert the key performance parameters and remaining life of the battery into a health status score; Based on the range of the health status score, the battery health status is divided into three levels: normal, need attention, and need replacement.

[0009] Furthermore, the life prediction unit predicts the battery life in different scenarios through multi-condition simulation, including: Determine simulation conditions, perform single condition simulations on the simulation conditions respectively, and obtain single condition simulation result information; Combining simulation conditions according to scenarios, determining combined simulation conditions, and performing simulation based on the combined simulation conditions to obtain multi-condition simulation result information; Perform simulation condition analysis based on the combined simulation conditions to determine the correlation between the sub-conditions in the combined simulation conditions; Analyze whether the multi-condition simulation result information is abnormal based on the single-condition simulation result information combined with the correlation between the sub-conditions in the combined simulation condition, and obtain the analysis and judgment result; When the analysis and judgment result shows that the multi-condition simulation result information is abnormal, the simulation is performed again based on the combined simulation conditions to obtain the multi-condition secondary simulation result information; The simulation results are determined based on the multi-condition secondary simulation result information.

[0010] Furthermore, the adaptive performance optimization module includes: Dynamic parameter adjustment unit, used for: 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 external operation scenario instructions, wherein the operation scenario includes energy storage mode and high power mode; Dynamically adjust the charge and discharge current limit, cut-off voltage, and temperature control threshold parameters according to the battery's health status level, and monitor the performance changes of the adjusted battery; Among them, in high-power application scenarios, priority is given to increasing output power and actively controlling battery temperature. In energy storage mode, priority is given to extending battery life and reducing electrochemical stress by reducing charging rate and optimizing current fluctuations. Temperature control strategy optimization unit, used for: Receive battery temperature data in real time, the battery temperature data including single cell temperature and overall temperature difference, and evaluate the effectiveness of the current temperature control state based on the results of the performance evaluation module; If the current temperature control status deviates from expectations, a temperature control strategy is generated based on different temperature control scenarios; Among them, in high-power application scenarios, the temperature rise of the battery 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 a low-temperature environment, the optimal operating 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; Send the optimized temperature control strategy to the external execution unit; Track temperature changes in real time. If the temperature control effect deviates from expectations, the strategy optimization process will be re-triggered.

[0011] Furthermore, 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 expectations, information is acquired for the current temperature control state to obtain current temperature control information, current battery temperature data, and expected data information; Analyze the deviation data between the current battery temperature data and the expected data information to obtain deviation analysis data; Analyze the influencing factors of the current temperature control scene by combining the current temperature control information with the current temperature control scene to determine the influencing factors of the temperature control scene; 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 and obtain the temperature control scenario influencing factors; Analyze the scene characteristics of the temperature control scene to obtain the temperature control scene characteristics; Preliminarily determine the temperature control plan based on the temperature control scenario characteristics and battery temperature data, and analyze the temperature control plan to determine the control factors; Matching the control factors and the temperature control scene influencing factors includes: performing vector processing on the control factors and the temperature control scene influencing factors respectively to obtain control factor processing information and temperature control scene influencing factor processing information; disassembling the control factor processing information and extracting effective information to obtain control factor processing sub-information; matching and analyzing the temperature control scene influencing factor processing information according to the control factor processing sub-information through the following formula: in, is the matching value between the control factor and the influencing factor of the temperature control scene, The information component of the sub-information for the regulatory factor processing, The first one in the temperature control scene influencing factor processing information The information component of the information intercepted at the beginning of the bit; the matching analysis result is determined according to the matching value of the control factor and the temperature control scene influencing factor. When the matching value of the control factor and the temperature control scene influencing factor is greater than the preset threshold, the matching analysis result is that the control factor and the temperature control scene influencing factor have the same factors, otherwise, the matching analysis result is that there are no same factors between the control factor and the temperature control scene influencing factor; When the matching analysis result shows that the same factors exist, the temperature control scenario influencing factors are retrieved for the same factors to obtain the target influencing factors, and the target influencing factors are used to correct the control information for the corresponding control factors to obtain the control correction information of the control factors; The temperature control scheme is revised based on the control correction information of the control factors to obtain the temperature control strategy.

[0012] Furthermore, the adaptive performance optimization module further includes: Scene switching control unit, used for: Receive the scene switching signal sent by the external control system, analyze the scene requirements, and extract the operation goals and constraints after switching; Recalculate adaptation parameters according to the operation objectives of the new scenario, wherein the adaptation parameters include power output range, discharge depth and temperature control strategy; Send the switched operating parameters to the external execution unit; After the scene switching is completed, verify whether the actual performance of the battery meets expectations. If not, trigger the secondary adjustment process.

[0013] Furthermore, the carbon footprint assessment module includes: Data accounting unit, used for: 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 carbon emission information corresponding to the operating efficiency and temperature control system energy consumption during the battery use phase, and obtain carbon emission data related to the recycling, disassembly and material reuse of used batteries during the recycling phase; Standardize the collected raw data, calculate the total carbon emissions according to the life cycle stage, and generate phased carbon footprint data; Combine the phased carbon emission data to generate a total carbon emission report for the entire battery life cycle including carbon emission accounting results; Carbon emission traceability unit, used for: 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 consumption processes and high-transportation distance factors that are strongly correlated with carbon emissions are extracted; Compare the carbon emission ratios at different life cycle stages to identify the link with the largest carbon emission share, compare materials, processes and operation modes, identify the main sources and influencing factors of carbon emissions, and generate a carbon emission traceability report; Carbon Emission Optimization Recommendation Unit for: Based on the carbon emission accounting results and traceability reports, low-carbon optimization suggestions are generated for the battery production, use and recycling stages.

[0014] 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, and includes the following steps: Step 1: Obtain battery operation data in real time through sensors, connect to the traceability information database, match the battery's factory information through a unique identifier, and dynamically bind real-time collected data with initial production data; Step 2: Standardize data from different sources, build a comprehensive data set, ensure data integrity and accuracy, extract key indicators, 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; Step 3: Analyze the current performance, compare the initial design parameters to generate a performance deviation assessment, build 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 divide it into normal, attention-needed, and replacement-needed 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 to ensure the best operating state, re-adapt parameters when switching scenarios, and verify and optimize battery performance; Step 5: Collect carbon emission data from the mining, manufacturing, transportation, use, and recycling stages of raw materials, 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 propose low-carbon optimization suggestions based on the analysis results; Step 6: Use the distributed ledger storage architecture to upload battery data to the chain and provide a user interface for querying battery status, history records and carbon emission information.

[0015] Compared with the prior art, the present invention has the following beneficial effects: 1. The present invention realizes the accurate collection, storage and tracking of battery life cycle data through the combination of data acquisition module and information management module, integrates real-time operation data and manufacturing traceability information, dynamically binds battery production materials and processes with actual operation performance, and provides comprehensive data support for battery performance evaluation, fault diagnosis and carbon footprint calculation. Based on distributed ledger technology, it ensures the security and transparency of data storage, and 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.

[0016] 2. The present invention realizes accurate evaluation and dynamic optimization of battery performance and health status through the collaborative work of data analysis module, performance evaluation module and adaptive performance optimization module. Through multi-dimensional data integration and decay pattern classification, the key influencing factors of battery decay are identified, and pattern feature mapping relationship is established, which improves the depth of understanding of battery status. Combined with the real-time data and design parameters of the battery, the current performance is dynamically calculated and the remaining life is predicted. The health score simplifies user decision-making and improves management efficiency. By dynamically adjusting battery operating parameters and optimizing temperature control strategies, the optimal balance between performance and life is achieved according to different usage scenarios. At the same time, the battery operation safety and energy efficiency are significantly improved, so that battery management is transformed from passive response to active optimization, extending battery life and improving application reliability.

[0017] 3. The present invention provides comprehensive quantitative support and optimization suggestions for carbon emission management throughout the battery life cycle through the carbon footprint assessment module. The data accounting unit covers the carbon emission data accounting of the battery from raw material mining, manufacturing, transportation, use to recycling stages, and generates a detailed carbon footprint report. Through traceability analysis, it identifies high-energy consumption links and major sources of carbon emissions, providing a basis for optimizing production, transportation and operation strategies. Combining carbon emission accounting and traceability results, it generates targeted low-carbon optimization solutions, combining the environmental management of batteries with actual operations, promoting the battery industry to develop in a low-carbon and sustainable direction, and providing technical support for the green economy. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 It is a module schematic diagram of the battery life evaluation system of the present invention; Figure 2 It is a schematic diagram of the flow of the battery life evaluation method of the present invention. DETAILED DESCRIPTION

[0019] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0020] See also Figure 1-2 , the present invention provides the following technical solutions: Battery life assessment system based on different battery degradation modes, including: Data acquisition module for: Collect multi-dimensional operating data of batteries in real time, and connect to the traceability information database to associate the initial data provided by the battery manufacturer, and record the initial information of materials, processes, and batches during battery production; Data analysis module for: Analyze the multi-source data provided by the acquisition module through a multi-dimensional data analysis model, classify the battery's degradation behavior, which includes capacity decay, internal resistance increase, and material aging, establish a characteristic mapping relationship of the degradation mode, and identify the battery's degradation mode and key influencing factors; Performance evaluation module for: Based on the results of the data analysis module, combined with battery design parameters and operating conditions, a prediction model is used to comprehensively evaluate the current performance and remaining life of the battery; Adaptive performance optimization module for: Dynamically adjust battery parameters based on the actual state of the battery and the evaluation results of the performance evaluation module, and adaptively adjust power output and temperature control strategies based on usage scenarios, including energy storage and high-power applications; Carbon footprint assessment module for: Calculate the carbon emissions data over the entire life cycle of the battery, including the carbon emissions data from the raw material mining, manufacturing, transportation, use and recycling stages; Information management module for: Establish a storage architecture based on distributed ledger, connect data collection modules and other functional modules, digitally manage and track battery life cycle information, and provide a user interface for querying battery status, historical information and carbon footprint.

[0021] In the above embodiments, accurate decay pattern recognition and performance prediction are used to dynamically adjust operating parameters, extend battery life, monitor and analyze key parameters in real time, detect anomalies in a timely manner, reduce the risk of potential battery failure, adapt to various operating scenarios such as energy storage and high power, dynamically optimize battery performance, meet diverse needs, reduce carbon emissions throughout the battery life cycle through carbon footprint assessment and low-carbon optimization recommendations, and achieve information transparency and traceability throughout the battery life cycle through distributed ledgers to improve management efficiency.

[0022] Data acquisition module, including: Data sensing unit for: By interacting with sensors, multi-dimensional real-time operation data of the battery is obtained in real time, and the real-time operation data includes the battery's charge and discharge voltage, current, temperature, environmental conditions and cycle times. The sensors include electrochemical sensors, temperature sensors and current detection units; Data traceability unit, used for: Connect to 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 data of battery manufacturing. 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; While data is being collected, the real-time collected data is dynamically bound to the initial production data.

[0023] In the above embodiment, the core parameters of the battery operation process are captured in real time through electrochemical sensors, temperature sensors and current detection units, including charging and discharging voltage, current, temperature, environmental conditions and number of cycles. These data can reflect the instantaneous performance and health status of the battery, providing a high-precision basis for subsequent analysis.

[0024] In the above embodiment, by connecting to the traceability information database, the traceability unit combines the unique identifier to dynamically bind the battery operation data with the initial production data (including materials, processes and batch information), ensuring the traceability of the entire life cycle information of each battery, 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.

[0025] Data analysis modules, including: Data integration unit for: Standardize data from different sources through unified coding rules and data templates, eliminate duplicate data by checking the unique identifiers of the data, and ensure data integrity and accuracy through verification rules; Map and correlate real-time battery operation data with initial battery manufacturing data in multiple dimensions to form a comprehensive data set; Decline pattern classification unit for: Analyze key indicators in the comprehensive data set and extract feature vectors reflecting battery state changes, wherein the key indicators include capacity change rate and internal resistance growth rate; Obtain battery historical data and operating conditions, establish a data-driven model, classify degradation behaviors, classify degradation patterns into major categories, and generate quantifiable pattern feature data, wherein the major categories include capacity attenuation, internal resistance increase, and material aging; Key influencing factors identification unit, used to: Performing a correlation analysis on the operating data and the decay mode characteristics, and screening out key influencing factors that have a significant impact on the decay mode based on the correlation results, wherein the key influencing factors include high temperature operating frequency and charging rate; A mapping relationship between key influencing factors and decay patterns is established, and a characteristic map describing the coupling relationship between decay patterns and influencing factors is generated.

[0026] In the above embodiment, data from different sources are standardized through unified coding rules and data templates to ensure the integrity and consistency of data quality. In a complex battery operating environment, data collected in different batches, scenarios and conditions may be repeated, missing or erroneous. The deduplication and verification rules of this unit can significantly improve data reliability. In addition, the unit associates real-time operating data with initial production data in multiple dimensions to form a comprehensive data set. This data set not only contains the real-time status information of the battery, but also carries its production process and material properties, providing more comprehensive data support for the classification of decay modes.

[0027] In the above embodiment, the battery degradation behavior is classified into three main categories: capacity decay, internal resistance increase, and material aging through a data-driven model, which helps users understand the essential reasons for changes in battery performance. For example, by analyzing key indicators such as capacity change rate and internal resistance growth rate, the long-term impact of specific behaviors such as high temperature operation or excessive charging rate on battery performance can be identified.

[0028] In the above embodiment, by establishing a coupling relationship between key factors and decay modes, characteristic maps are generated, which intuitively show the impact of different operating conditions on battery performance. These characteristic maps provide a scientific basis for users to optimize battery operating conditions and also provide data support for parameter adjustment of the adaptive optimization module.

[0029] Performance evaluation modules, including: Current performance analysis units, used for: Receiving characteristic data output by a data analysis module, the characteristic data including a degradation mode classification result and key influencing factors, and importing initial design parameters of the battery; Calculate the current capacity retention rate, internal resistance 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 performance deviation evaluation results; Life prediction unit for: Based on historical data, decay pattern characteristics and battery operating conditions, a data-driven model is constructed to predict future performance changes of the battery, and the battery life under different scenarios is predicted through multi-condition simulation, including different temperatures, rates and usage frequencies; According to the simulation results, the remaining service life of the battery is calculated and a performance decay trend graph is generated, wherein the remaining service life includes the number of cycles and the calendar life; Regularly update the prediction model and life estimation results based on real-time input data; Health status assessment unit for: Integrate the output results of the current performance analysis unit and the life prediction unit, and use weight analysis to convert the key performance parameters and remaining life of the battery into a health status score; Based on the range of the health status score, the battery health status is divided into three levels: normal, need attention, and need replacement.

[0030] In the above embodiment, the current performance analysis unit uses the degradation mode classification results and key influencing factor data transmitted from the data analysis module, combined with the battery design parameters, to quantify the current operating state of the battery, calculate the battery capacity retention rate, internal resistance value and power output level through dynamic modeling, and compare them with the initial design parameters and industry standards to generate a performance deviation evaluation 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, which significantly enhances the user's ability to control the current state of the battery and facilitates timely maintenance measures.

[0031] In the above embodiments, the data-driven model is used to simulate the performance change trend of the battery in different usage scenarios, and the remaining life of the battery, including cycle life and calendar life, is estimated, providing scientific support for users to plan battery replacement and operation strategies. For example, for batteries in high-power application scenarios, their life changes under specific charging conditions can be predicted, thereby optimizing their usage frequency and charging and discharging strategies. By setting the grading standards for health status, users can more efficiently evaluate whether the battery needs maintenance or replacement.

[0032] Furthermore, the life prediction unit predicts the life of the battery in different scenarios through multi-condition simulation, including: Determine simulation conditions, perform single condition simulations on the simulation conditions respectively, and obtain single condition simulation result information; Combining simulation conditions according to scenarios, determining combined simulation conditions, and performing simulation based on the combined simulation conditions to obtain multi-condition simulation result information; Perform simulation condition analysis based on the combined simulation conditions to determine the correlation between the sub-conditions in the combined simulation conditions; Analyze whether the multi-condition simulation result information is abnormal based on the single-condition simulation result information combined with the correlation between the sub-conditions in the combined simulation condition, and obtain the analysis and judgment result; When the analysis and judgment result shows that the multi-condition simulation result information is abnormal, the simulation is performed again based on the combined simulation conditions to obtain the multi-condition secondary simulation result information; The simulation results are determined based on the multi-condition secondary simulation result information.

[0033] In the above embodiment, a single-condition simulation is used to clarify the influence of a single simulation condition on the simulation result, so that the multi-condition simulation result information is checked after the multi-condition simulation, thereby avoiding the high difficulty of the multi-condition simulation resulting in a high possibility of error in the simulation result, which affects the analysis based on the simulation result. Moreover, when analyzing whether the multi-condition simulation result information is abnormal based on the single-condition simulation result information, the correlation between the sub-conditions in the combined simulation condition is analyzed, which improves the accuracy of the inspection of the multi-condition simulation result information and the accuracy of the analysis and judgment results, thereby ensuring the accuracy of the simulation results. Moreover, when the analysis and judgment result shows that the multi-condition simulation result information is abnormal, the simulation is performed again to avoid the influence of accidental phenomena, thereby providing a guarantee for the determination of the simulation results.

[0034] Adaptive performance optimization module, including: Dynamic parameter adjustment unit, used for: 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 external operation scenario instructions, wherein the operation scenario includes energy storage mode and high power mode; Dynamically adjust the charge and discharge current limit, cut-off voltage, and temperature control threshold parameters according to the battery's health status level, and monitor the performance changes of the adjusted battery; Among them, in high-power application scenarios, priority is given to increasing output power and actively controlling battery temperature. In energy storage mode, priority is given to extending battery life and reducing electrochemical stress by reducing charging rate and optimizing current fluctuations. Temperature control strategy optimization unit, used for: Receive battery temperature data in real time, the battery temperature data including single cell temperature and overall temperature difference, and evaluate the effectiveness of the current temperature control state based on the results of the performance evaluation module; If the current temperature control status deviates from expectations, a temperature control strategy is generated based on different temperature control scenarios; Among them, in high-power application scenarios, the temperature rise of the battery 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 a low-temperature environment, the optimal operating 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; Send the optimized temperature control strategy to the external execution unit; Track temperature changes in real time. If the temperature control effect deviates from expectations, the strategy optimization process will be re-triggered. Furthermore, 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 expectations, information is acquired for the current temperature control state to obtain current temperature control information, current battery temperature data, and expected data information; Analyze the deviation data between the current battery temperature data and the expected data information to obtain deviation analysis data; Analyze the influencing factors of the current temperature control scene by combining the current temperature control information with the current temperature control scene to determine the influencing factors of the temperature control scene; 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 and obtain the temperature control scenario influencing factors; Analyze the scene characteristics of the temperature control scene to obtain the temperature control scene characteristics; Preliminarily determine the temperature control plan based on the temperature control scenario characteristics and battery temperature data, and analyze the temperature control plan to determine the control factors; Matching the control factors and the temperature control scene influencing factors includes: performing vector processing on the control factors and the temperature control scene influencing factors respectively to obtain control factor processing information and temperature control scene influencing factor processing information; disassembling the control factor processing information and extracting effective information to obtain control factor processing sub-information; matching and analyzing the temperature control scene influencing factor processing information according to the control factor processing sub-information through the following formula: in, is the matching value between the control factor and the influencing factor of the temperature control scene, The information component of the sub-information for the regulatory factor processing, The first one in the temperature control scene influencing factor processing information The information component of the information intercepted at the beginning of the bit; the matching analysis result is determined according to the matching value of the control factor and the temperature control scene influencing factor. When the matching value of the control factor and the temperature control scene influencing factor is greater than the preset threshold, the matching analysis result is that the control factor and the temperature control scene influencing factor have the same factors, otherwise, the matching analysis result is that there are no same factors between the control factor and the temperature control scene influencing factor; When the matching analysis result shows that the same factors exist, the temperature control scenario influencing factors are retrieved for the same factors to obtain the target influencing factors, and the target influencing factors are used to correct the control information for the corresponding control factors to obtain the control correction information of the control factors; The temperature control scheme is revised based on the control correction information of the control factors to obtain the temperature control strategy.

[0035] In the above embodiment, the accuracy of the control strategy is improved by correcting the temperature control scheme, and the expected deviation caused by the control strategy is reduced, so that the control result is closer to the expectation. In addition, the deviation analysis data and the current temperature control information are combined for comprehensive analysis to determine the temperature control scenario influencing factors, and comprehensive consideration is given to improve the accuracy of the influencing factors. In addition, by determining the target influencing factors, when the control information is corrected for the corresponding control factors using the target influencing factors, only the corresponding control factors in the temperature control scheme are corrected, and there is no need to correct the temperature control scheme as a whole, thereby reducing the time consumed by invalid corrections and improving the efficiency of corrections.

[0036] Scene switching control unit, used for: Receive the scene switching signal sent by the external control system, analyze the scene requirements, and extract the operation goals and constraints after switching; Recalculate adaptation parameters according to the operation objectives of the new scenario, wherein the adaptation parameters include power output range, discharge depth and temperature control strategy; Send the switched operating parameters to the external execution unit; After the scene switching is completed, verify whether the actual performance of the battery meets expectations. If not, trigger the secondary adjustment process.

[0037] In the above embodiment, the dynamic parameter adjustment unit analyzes the battery's real-time performance deviation, health status score, and remaining life prediction results, and combines external operating scenario requirements (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 improving the battery's power output while actively controlling the temperature to reduce the risk of overheating; while in energy storage scenarios, the unit is more concerned with extending life and reducing electrochemical stress by reducing the charge rate and optimizing current fluctuations.

[0038] In the above embodiment, in high temperature scenarios, the unit quickly reduces the battery temperature by increasing the cooling intensity (such as fan speed or active liquid cooling system); in low temperature scenarios, the battery temperature is kept in the optimal range by adjusting the working cycle of the heating device. In addition, by optimizing the energy consumption strategy, energy efficiency is maximized in the energy storage mode, effectively extending the battery life and significantly improving the operational safety.

[0039] In the above embodiment, the scene switching control unit supports fast switching between multiple usage scenarios. For example, when switching between energy storage and high-power application scenarios, the unit can recalculate the adaptation parameters according to the new operation target and send them to the execution unit to ensure that the battery performance quickly stabilizes at the target state, significantly enhancing the adaptability of the battery in complex application scenarios.

[0040] Carbon footprint assessment module, including: Data accounting unit, used for: 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 carbon emission information corresponding to the operating efficiency and temperature control system energy consumption during the battery use phase, and obtain carbon emission data related to the recycling, disassembly and material reuse of used batteries during the recycling phase; Standardize the collected raw data, calculate the total carbon emissions according to the life cycle stage, and generate phased carbon footprint data; Combine the phased carbon emission data to generate a total carbon emission report for the entire battery life cycle including carbon emission accounting results; Carbon emission traceability unit, used for: 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 consumption processes and high-transportation distance factors that are strongly correlated with carbon emissions are extracted; Compare the carbon emission ratios at different life cycle stages to identify the link with the largest carbon emission share, compare materials, processes and operation modes, identify the main sources and influencing factors of carbon emissions, and generate a carbon emission traceability report; Carbon Emission Optimization Recommendation Unit for: Based on the carbon emission accounting results and traceability reports, low-carbon optimization suggestions are generated for the battery production, use and recycling stages.

[0041] In the above embodiment, the data accounting unit covers the carbon emission data of the entire life cycle of the battery, from raw material mining to manufacturing, transportation, use and recycling. By analyzing the mining and processing data provided by the raw material supplier, the user can identify the high-emission links in the production stage; by recording the fuel consumption during transportation and the temperature control energy consumption during the use stage, the user can quantify the carbon footprint during operation. In addition, the unit calculates the carbon emission data by segment according to the life cycle stage and generates a total carbon emission report to provide data support for the green certification and environmental protection design of the battery.

[0042] In the above embodiment, the main sources of carbon emissions and key influencing factors are identified through multi-dimensional correlation analysis. For example, by analyzing the high energy consumption links in the material production process, users can optimize the process flow; by evaluating the operating efficiency of the use phase, users can improve the battery management strategy to reduce energy consumption. The unit also generates a carbon emission traceability path, which provides an intuitive basis for users to gain a deeper understanding of the source of carbon footprints.

[0043] In the above embodiment, based on carbon emission data and user needs, 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 use stage, the charging strategy is optimized to reduce grid load and improve battery efficiency; in the recycling stage, it is recommended to give priority to high-efficiency regeneration technology to reduce material waste.

[0044] The battery life evaluation method based on different battery degradation modes is applied to the above-mentioned battery life evaluation system based on different battery degradation modes, and includes the following steps: Step 1: Obtain battery operation data in real time through sensors, connect to the traceability information database, match the battery's factory information through a unique identifier, and dynamically bind real-time collected data with initial production data; Step 2: Standardize data from different sources, build a comprehensive data set, ensure data integrity and accuracy, extract key indicators, 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; Step 3: Analyze the current performance, compare the initial design parameters to generate a performance deviation assessment, build 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 divide it into normal, attention-needed, and replacement-needed 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 to ensure the best operating state, re-adapt parameters when switching scenarios, and verify and optimize battery performance; Step 5: Collect carbon emission data from the mining, manufacturing, transportation, use, and recycling stages of raw materials, 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 propose low-carbon optimization suggestions based on the analysis results; Step 6: Use the distributed ledger storage architecture to upload battery data to the chain and provide a user interface for querying battery status, history records and carbon emission information.

[0045] The above description is only a preferred specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can make equivalent replacements or changes according to the technical scheme and inventive concept of the present invention within the technical scope disclosed by the present invention, which 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: include: Data acquisition module for: Collect multi-dimensional operating data of batteries in real time, and connect to the traceability information database to associate the initial data provided by the battery manufacturer, and record the initial information of materials, processes, and batches during battery production; Data analysis module for: Analyze the multi-source data provided by the acquisition module through a multi-dimensional data analysis model, classify the battery's degradation behavior, which includes capacity decay, internal resistance increase, and material aging, establish a characteristic mapping relationship of the degradation mode, and identify the battery's degradation mode and key influencing factors; Performance evaluation modules for: Based on the results of the data analysis module, combined with battery design parameters and operating conditions, a prediction model is used to comprehensively evaluate the current performance and remaining life of the battery; Adaptive performance optimization module for: Dynamically adjust battery parameters based on the actual state of the battery and the evaluation results of the performance evaluation module, and adaptively adjust power output and temperature control strategies based on usage scenarios, including energy storage and high-power applications; Carbon footprint assessment module for: Calculate the carbon emissions data over the entire life cycle of the battery, including the carbon emissions data from the raw material mining, manufacturing, transportation, use and recycling stages; Information management module for: Establish a storage architecture based on distributed ledger, connect data collection modules and other functional modules, digitally manage and track battery life cycle information, and provide a user interface for querying battery status, historical information and carbon footprint.

2. The battery life assessment system based on different battery degradation modes as claimed in claim 1, characterized in that: The data acquisition module comprises: Data sensing unit for: By interacting with sensors, multi-dimensional real-time operation data of the battery is obtained in real time, and the real-time operation data includes the battery's charge and discharge voltage, current, temperature, environmental conditions and cycle times. The sensors include electrochemical sensors, temperature sensors and current detection units; Data traceability unit, used for: Connect to 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 data of battery manufacturing. 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; While data is being collected, the real-time collected data is dynamically bound to the initial production data.

3. The battery life assessment system based on different battery degradation modes as claimed in claim 2, characterized in that: The data analysis module comprises: Data integration unit for: Standardize data from different sources through unified coding rules and data templates, and eliminate duplicate data by checking the unique identifier of the data; Map and correlate real-time battery operation data with initial battery manufacturing data in multiple dimensions to form a comprehensive data set; Decline pattern classification unit for: Analyze key indicators in the comprehensive data set and extract feature vectors reflecting battery state changes, wherein the key indicators include capacity change rate and internal resistance growth rate; Obtain battery historical data and operating conditions, establish a data-driven model, classify degradation behaviors, classify degradation patterns into major categories, and generate quantifiable pattern feature data, wherein the major categories include capacity attenuation, internal resistance increase, and material aging; Key influencing factors identification unit, used to: Performing a correlation analysis on the operating data and the decay mode characteristics, and screening out key influencing factors that have a significant impact on the decay mode based on the correlation results, wherein the key influencing factors include high temperature operating frequency and charging rate; A mapping relationship between key influencing factors and decay patterns is established, and a characteristic map describing the coupling relationship between decay patterns and influencing factors is generated.

4. The battery life assessment system based on different battery degradation modes as claimed in claim 3, characterized in that: The performance evaluation module comprises: Current performance analysis units, used for: Receiving characteristic data output by a data analysis module, the characteristic data including a degradation mode classification result and key influencing factors, and importing initial design parameters of the battery; Calculate the current capacity retention rate, internal resistance 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 performance deviation evaluation results; Life prediction unit for: Based on historical data, decay pattern characteristics and battery operating conditions, a data-driven model is constructed to predict future performance changes of the battery, and the battery life under different scenarios is predicted through multi-condition simulation, including different temperatures, rates and usage frequencies; According to the simulation results, the remaining service life of the battery is calculated and a performance decay trend graph is generated, wherein the remaining service life includes the number of cycles and the calendar life; Regularly update the prediction model and life estimation results based on real-time input data; Health status assessment unit for: Integrate the output results of the current performance analysis unit and the life prediction unit, and use weight analysis to convert the key performance parameters and remaining life of the battery into a health status score; Based on the range of the health status score, the battery health status is divided into three levels: normal, need attention, and need replacement.

5. The battery life assessment system based on different battery degradation modes as claimed in claim 4, characterized in that: The life prediction unit predicts the battery life in different scenarios through multi-condition simulation, including: Determine simulation conditions, perform single condition simulations on the simulation conditions respectively, and obtain single condition simulation result information; Combining simulation conditions according to scenarios, determining combined simulation conditions, and performing simulation based on the combined simulation conditions to obtain multi-condition simulation result information; Perform simulation condition analysis based on the combined simulation conditions to determine the correlation between the sub-conditions in the combined simulation conditions; Analyze whether the multi-condition simulation result information is abnormal based on the single-condition simulation result information combined with the correlation between the sub-conditions in the combined simulation condition, and obtain the analysis and judgment result; When the analysis and judgment result shows that the multi-condition simulation result information is abnormal, the simulation is performed again based on the combined simulation conditions to obtain the multi-condition secondary simulation result information; The simulation results are determined based on the multi-condition secondary simulation result information.

6. The battery life assessment system based on different battery degradation modes as claimed in claim 4, characterized in that: The adaptive performance optimization module comprises: Dynamic parameter adjustment unit for: 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 external operation scenario instructions, wherein the operation scenario includes energy storage mode and high power mode; Dynamically adjust the charge and discharge current limit, cut-off voltage, and temperature control threshold parameters according to the battery's health status level, and monitor the performance changes of the adjusted battery; Among them, in high-power application scenarios, priority is given to increasing output power and actively controlling battery temperature. In energy storage mode, priority is given to extending battery life and reducing electrochemical stress by reducing charging rate and optimizing current fluctuations. Temperature control strategy optimization unit, used for: Receive battery temperature data in real time, the battery temperature data including single cell temperature and overall temperature difference, and evaluate the effectiveness of the current temperature control state based on the results of the performance evaluation module; If the current temperature control status deviates from expectations, a temperature control strategy is generated based on different temperature control scenarios; Among them, in high-power application scenarios, the temperature rise of the battery 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 a low-temperature environment, the optimal operating 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; Send the optimized temperature control strategy to the external execution unit; Track temperature changes in real time. If the temperature control effect deviates from expectations, the strategy optimization process will be re-triggered.

7. The battery life assessment system based on different battery degradation modes as claimed in claim 6, characterized in that: 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 expectations, information is acquired for the current temperature control state to obtain current temperature control information, current battery temperature data, and expected data information; Analyze the deviation data between the current battery temperature data and the expected data information to obtain deviation analysis data; Analyze the influencing factors of the current temperature control scene by combining the current temperature control information with the current temperature control scene to determine the influencing factors of the temperature control scene; 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 and obtain the temperature control scenario influencing factors; Analyze the scene characteristics of the temperature control scene to obtain the temperature control scene characteristics; Preliminarily determine the temperature control plan based on the temperature control scenario characteristics and battery temperature data, and analyze the temperature control plan to determine the control factors; Matching the control factors and the temperature control scene influencing factors includes: performing vector processing on the control factors and the temperature control scene influencing factors respectively to obtain control factor processing information and temperature control scene influencing factor processing information; disassembling the control factor processing information and extracting effective information to obtain control factor processing sub-information; matching and analyzing the temperature control scene influencing factor processing information according to the control factor processing sub-information through the following formula: in, is the matching value between the control factor and the influencing factor of the temperature control scene, The information component of the sub-information for the regulatory factor processing, The first one in the temperature control scene influencing factor processing information The information component of the information intercepted at the beginning of the bit; the matching analysis result is determined according to the matching value of the control factor and the temperature control scene influencing factor. When the matching value of the control factor and the temperature control scene influencing factor is greater than the preset threshold, the matching analysis result is that the control factor and the temperature control scene influencing factor have the same factors, otherwise, the matching analysis result is that there are no same factors between the control factor and the temperature control scene influencing factor; When the matching analysis result shows that the same factors exist, the temperature control scenario influencing factors are retrieved for the same factors to obtain the target influencing factors, and the target influencing factors are used to correct the control information for the corresponding control factors to obtain the control correction information of the control factors; The temperature control scheme is revised based on the control correction information of the control factors to obtain the temperature control strategy.

8. The battery life assessment system based on different battery degradation modes as claimed in claim 6, characterized in that: The adaptive performance optimization module further includes: Scene switching control unit, used for: Receive the scene switching signal sent by the external control system, analyze the scene requirements, and extract the operation goals and constraints after switching; Recalculate adaptation parameters according to the operation objectives of the new scenario, wherein the adaptation parameters include power output range, discharge depth and temperature control strategy; Send the switched operating parameters to the external execution unit; After the scene switching is completed, verify whether the actual performance of the battery meets expectations. If not, trigger the secondary adjustment process.

9. The battery life assessment system based on different battery degradation modes as claimed in claim 8, characterized in that: The carbon footprint assessment module includes: Data accounting unit, used for: 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 carbon emission information corresponding to the operating efficiency and temperature control system energy consumption during the battery use phase, and obtain carbon emission data related to the recycling, disassembly and material reuse of used batteries during the recycling phase; Standardize the collected raw data, calculate the total carbon emissions according to the life cycle stage, and generate phased carbon footprint data; Combine the phased carbon emission data to generate a total carbon emission report for the entire battery life cycle including carbon emission accounting results; Carbon emission traceability unit, used for: 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 consumption processes and high-transportation distance factors that are strongly correlated with carbon emissions are extracted; Compare the carbon emission ratios at different life cycle stages to identify the link with the largest carbon emission share, compare materials, processes and operation modes, identify the main sources and influencing factors of carbon emissions, and generate a carbon emission traceability report; Carbon Emission Optimization Recommendation Unit for: Based on the carbon emission accounting results and traceability reports, low-carbon optimization suggestions are generated for the battery production, use and recycling stages.

10. A battery life assessment method based on different battery degradation modes, based on the battery life assessment system based on different battery degradation modes as claimed in claim 9, characterized in that: The steps include: Step 1: Obtain battery operation data in real time through sensors, connect to the traceability information database, match the battery's factory information through a unique identifier, and dynamically bind real-time collected data with initial production data; Step 2: Standardize data from different sources, build a comprehensive data set, ensure data integrity and accuracy, extract key indicators, 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; Step 3: Analyze the current performance, compare the initial design parameters to generate a performance deviation assessment, build 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 divide it into normal, attention-needed, and replacement-needed 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 to ensure the best operating state, re-adapt parameters when switching scenarios, and verify and optimize battery performance; Step 5: Collect carbon emission data from the mining, manufacturing, transportation, use, and recycling stages of raw materials, 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 propose low-carbon optimization suggestions based on the analysis results; Step 6: Use the distributed ledger storage architecture to upload battery data to the chain and provide a user interface for querying battery status, history records and carbon emission information.

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