Energy storage battery life prediction method and system and medium

Abstract

The invention relates to the technical field of energy storage battery life prediction, in particular to an energy storage battery life prediction method and system and a medium. The method comprises the following steps: dividing energy storage battery performance data by obtaining energy storage battery design data, respectively extracting performance characteristic parameters of a lithium battery and a lead-acid battery, performing identification analysis on the gradient degradation situation of the operation performance of the lithium battery based on an operation load overload trend, and determining the performance of the lithium battery according to the identification analysis result. Carrying out trend prediction on the dynamic load response imbalance condition of the lead-acid battery; through analysis of abnormal behaviors of the two types of batteries, dynamic degradation coupling data of the overall performance of the energy storage battery is obtained, the trend of battery operation stability attenuation and electric energy transmission quality attenuation is determined, then the local degradation and operation risk of a battery cell are predicted, and finally accurate prediction of the service life of the energy storage battery is achieved. By predicting the service life of the energy storage battery, the operation of the energy storage battery is safer and more efficient.

Description

Technical Field

[0001] This invention relates to the field of energy storage battery life prediction technology, and in particular to an energy storage battery life prediction method, system and medium. Background Technology

[0002] Energy storage batteries, as core equipment for achieving energy regulation, load balancing, and stable power quality, have been widely deployed in various scenarios such as the grid side, user side, and microgrids. There are many types of energy storage batteries, commonly including lithium-ion batteries and lead-acid batteries. Given the differences in performance degradation paths between different types of energy storage batteries (such as lithium-ion and lead-acid batteries), existing prediction mechanisms mostly use uniform index modeling, failing to distinguish key degradation factors such as lithium-ion insertion / extraction rate fluctuations, lattice collapse trends, plate sulfation reactions, and electrolyte circulation obstacles. They also lack targeted lifetime prediction path decomposition and response characteristic coupling analysis. Especially in scenarios with mixed deployments of various batteries, if the coupling relationship between the dynamic degradation of heterogeneous battery performance is not considered, it can easily lead to problems such as distorted overall system capacity assessment, severe fluctuations in power output, and unbalanced equipment load scheduling. However, traditional energy storage battery lifetime prediction suffers from inaccurate prediction of battery dynamic load response imbalance and inaccurate prediction of the battery's power transmission quality degradation trend. Summary of the Invention

[0003] Therefore, it is necessary to provide a method, system, and medium for predicting the lifespan of energy storage batteries to solve at least one of the aforementioned technical problems.

[0004] To achieve the above objective, a method for predicting the lifespan of an energy storage battery includes the following steps: Step S1: Obtain energy storage battery design data; evaluate energy storage battery performance data based on energy storage battery design data; divide energy storage battery performance data to obtain energy storage lithium battery performance data and energy storage lead-acid battery performance data. Step S2: Obtain energy storage battery operation log data; determine the overload trend of energy storage battery operation based on energy storage battery operation log data; detect the gradient degradation trend of lithium battery operation performance based on energy storage battery performance data based on energy storage battery overload trend; predict the dynamic load response imbalance of energy storage lead-acid battery based on energy storage lead-acid battery performance data based on energy storage battery overload trend. Step S3: Couple the dynamic load response misalignment of the energy storage lead-acid battery and the gradient degradation of the lithium battery's operating performance to obtain coupled data on the dynamic degradation of the energy storage battery's performance; determine the degradation of the energy storage battery's operating stability based on the coupled data on the dynamic degradation of the energy storage battery's performance; predict the degradation trend of the energy quality of the energy storage battery's transmission based on the degradation of the energy storage battery's operating stability. Step S4: Predict the local degradation of the energy storage battery cells based on the energy transmission quality degradation trend; detect the operational risk of the energy storage battery based on the local degradation of the energy storage battery cells; predict the lifespan of the energy storage battery based on the operational risk and the local degradation of the energy storage battery cells, and obtain the lifespan prediction data of the energy storage battery.

[0005] This method achieves precise classification and evaluation of the performance of lithium-ion and lead-acid batteries by comprehensively acquiring and meticulously analyzing energy storage battery design data and operational log data, thus improving the accuracy and relevance of performance data. Combined analysis of dynamic load response and operational performance gradients effectively reveals the intrinsic relationship between battery performance degradation, enhancing the ability to determine the stability degradation of energy storage batteries and accurately predicting the changing trend of transmitted power quality. Based on the prediction of localized degradation according to power quality degradation trends, it achieves high-precision identification of the location and extent of cell degradation, further improving the accuracy and timeliness of operational risk identification. Through multi-dimensional risk assessment and deviation accumulation detection, it can systematically quantify the operational risk status of energy storage batteries, providing solid data support for lifespan prediction and ensuring the scientific nature and refined management level of lifespan assessment. The overall process effectively integrates design data and operational status, realizing dynamic tracking and precise control of energy storage battery lifespan prediction, promoting the safe operation and efficient maintenance of energy storage systems. Therefore, this invention is an optimization of traditional energy storage battery life prediction, solving the problems of inaccurate prediction of battery dynamic load response imbalance and inaccurate prediction of battery power transmission quality degradation trend. It improves the accuracy of predicting battery dynamic load response imbalance and battery power transmission quality degradation trend.

[0006] The present invention also provides an energy storage battery life prediction system for performing the energy storage battery life prediction method described above, the energy storage battery life prediction system comprising: The battery performance evaluation module is used to acquire energy storage battery design data; evaluate energy storage battery performance data based on the energy storage battery design data; and divide the energy storage battery performance data to obtain energy storage lithium battery performance data and energy storage lead-acid battery performance data. The load response mismatch prediction module is used to acquire energy storage battery operation log data; determine the overload trend of energy storage battery operation based on the energy storage battery operation log data; detect the gradient degradation of lithium battery operation performance based on the overload trend of energy storage battery operation; and predict the dynamic load response mismatch of energy storage lead-acid battery based on the energy storage battery operation log data. The power quality degradation prediction module is used to couple and determine the dynamic load response imbalance of the energy storage lead-acid battery and the gradient degradation of the lithium battery's operating performance to obtain dynamic degradation coupling data of the energy storage battery; determine the energy storage battery's operating stability degradation based on the dynamic degradation coupling data of the energy storage battery; and predict the energy quality degradation trend of the energy storage battery based on the energy storage battery's operating stability degradation. The battery life prediction module is used to predict the local degradation of the battery cells based on the degradation trend of the energy transmission quality of the energy storage battery; detect the operational risk status of the energy storage battery based on the local degradation status of the battery cells; and predict the life of the energy storage battery based on the operational risk status and the local degradation status of the battery cells, thereby obtaining the energy storage battery life prediction data.

[0007] This invention relates to an energy storage battery life prediction system. This system can implement the energy storage battery life prediction method of this invention. It is used to combine the operation and signal transmission medium between various modules to complete the energy storage battery life prediction method. The internal modules of the system cooperate with each other and accurately identify the performance degradation path and operational risks of energy storage batteries based on multi-dimensional coupled analysis of design data and operating data, thereby predicting the life of energy storage batteries with high accuracy.

[0008] A computer-readable storage medium storing a computer program, wherein the computer program is used to perform the energy storage battery life prediction method. Attached Figure Description

[0009] Figure 1 This is a flowchart illustrating the steps of a method for predicting the lifespan of an energy storage battery. Figure 2 for Figure 1 A detailed flowchart illustrating the implementation steps of step S3. Figure 3 for Figure 1 A detailed flowchart illustrating the implementation steps of step S4. The realization of the objective, functional features and advantages of the present invention will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0010] The technical method of the present invention will now be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without inventive effort are within the scope of protection of the present invention.

[0011] Furthermore, the accompanying drawings are merely illustrative of the invention and are not necessarily drawn to scale. The same reference numerals in the drawings denote the same or similar parts, and therefore repeated descriptions of them will be omitted. Some block diagrams shown in the drawings are functional entities and do not necessarily correspond to physically or logically independent entities. These functional entities can be implemented in software, in one or more hardware modules or integrated circuits, or in different network and / or processor methods and / or microcontroller methods.

[0012] It should be understood that although the terms "first," "second," etc., may be used herein to describe various units, these units should not be limited by these terms. These terms are used merely to distinguish one unit from another. For example, without departing from the scope of the exemplary embodiments, a first unit may be referred to as a second unit, and similarly, a second unit may be referred to as a first unit. The term "and / or" as used herein includes any and all combinations of one or more of the associated listed items.

[0013] To achieve the above objectives, please refer to Figures 1 to 3 A method for predicting the lifespan of an energy storage battery includes the following steps: Step S1: Obtain energy storage battery design data; evaluate energy storage battery performance data based on energy storage battery design data; divide energy storage battery performance data to obtain energy storage lithium battery performance data and energy storage lead-acid battery performance data. In this embodiment of the invention, a data access channel is constructed, and the Battery Management Controller (BMS) interface in the industrial control system is invoked to extract the factory design data of the energy storage battery from the battery design document database via the standard Modbus TCP protocol. This design data includes complete parameters such as energy storage battery type, rated capacity (in Ah), cell configuration (series and parallel structure), rated operating voltage (in V), battery chemistry system, maximum charge / discharge rate, recommended temperature range, and electrolyte ratio. Based on the above design data, the technical indicators are reviewed using the built-in computing module of the industrial control server according to the performance evaluation rules for various energy storage battery designs. Specifically, a performance scoring table based on rated discharge capacity is established, the maximum energy throughput is calculated by corresponding the rated charge and discharge rates, and the thermal management efficiency level is obtained by matching the heat capacity ratio with the heat dissipation module design parameters. Through a multi-dimensional parameter table, key performance indicators are extracted and mapped to standardized energy storage battery performance data, and the battery number, technical indicator group, and structure number are labeled accordingly. For performance evolution analysis of batteries with different chemical systems, based on the "positive electrode active material composition" and "battery structure description" fields in the design data, feature word matching rules are used to divide the data into lithium battery data groups and lead-acid battery data groups. For example, when the design parameters contain descriptions such as LiFePO4, NCM, and graphite anode, the system automatically classifies the group into energy storage lithium battery performance data; if it contains keywords such as PbO2 anode and spongy lead anode, it is marked as energy storage lead-acid battery performance data. The two data groups are cached separately in the performance data cache area, with a unique data number for subsequent retrieval.

[0014] Step S2: Obtain energy storage battery operation log data; determine the overload trend of energy storage battery operation based on energy storage battery operation log data; detect the gradient degradation trend of lithium battery operation performance based on energy storage battery performance data based on energy storage battery overload trend; predict the dynamic load response imbalance of energy storage lead-acid battery based on energy storage lead-acid battery performance data based on energy storage battery overload trend. In this embodiment of the invention, communication is established with an operation log data acquisition device deployed at the energy storage system site via an RS485 interface to acquire energy storage battery operation log data at an hourly time resolution. This log data includes parameters such as real-time charging and discharging current, voltage change trends, temperature curves, cycle count, load call frequency, ambient humidity records, battery SOC (State of Charge), and battery SOH (State of Health). The above operation log data is imported into the operation load analysis module. By analyzing the ratio of the charging and discharging current fluctuation amplitude to the battery's maximum rated current over a continuous 6-hour period, excessive operation load behavior is identified. If calls exceeding 1.1 times the rated current occur in three cycles (each cycle representing a high-load period within a day), it is determined to be an overloaded trend. The persistence of this trend is further corroborated by the battery thermal management module's operating frequency, historical discharge rate trend chart, and temperature rise rate. The energy storage lithium battery performance data cached in the previous step is retrieved, and the design capacity, charging and discharging efficiency, and structural number corresponding to each set of data are extracted. Using the rate of decline of the ratio of "actual output capacity" to "design capacity" in the operation log at different time points as the core indicator, combined with the daily cycle depth curve changes, the gradient degradation trend of lithium battery operating performance is analyzed. For example, when the daily capacity decline rate exceeds 3% and fails to recover to above 95% capacity level within 5 consecutive days, it is marked as gradient degradation; if accompanied by increased internal resistance and voltage plateau collapse, it is further confirmed as moderate performance gradient degradation. For the performance data of energy storage lead-acid batteries, the dynamic load response misalignment is calculated using the abnormal ratio between load call frequency and recovery voltage time. Specifically, the recovery time curve after the battery discharges to 10.5V in the original log is called. If the recovery voltage time exceeds 30 minutes and the load is frequently started (more than 5 times / hour), the load response function is judged to be abnormal. By observing the upward trend of the average response delay over 7 consecutive days, a dynamic response misalignment data package is constructed, and the corresponding battery number and structural location are recorded.

[0015] Step S3: Couple the dynamic load response misalignment of the energy storage lead-acid battery and the gradient degradation of the lithium battery's operating performance to obtain coupled data on the dynamic degradation of the energy storage battery's performance; determine the degradation of the energy storage battery's operating stability based on the coupled data on the dynamic degradation of the energy storage battery's performance; predict the degradation trend of the energy quality of the energy storage battery's transmission based on the degradation of the energy storage battery's operating stability. In this embodiment of the invention, the obtained "dynamic load response misalignment status of energy storage lead-acid batteries" data package and "gradient degradation trend of lithium battery operating performance" data are cross-mapped to establish a unified coupled data structure. This structure uses battery ID as the primary key field and includes battery type, location number, response misalignment level, gradient degradation level, cumulative abnormal call count, voltage platform stability index, and internal resistance change trend. A performance coupling strategy based on time-series linkage is adopted to synchronously compare the abnormal trends exhibited by the two types of batteries within the same time period. For example, if the lithium battery's performance degradation level rises from level 1 to level 3 within a week, and the lead-acid battery's response misalignment level rises to level 2 or above during the same period, it is marked as an enhanced coupling degradation mode; conversely, if the changes are not synchronized, it is marked as a weakly coupled degradation mode. This determination is completed through a logical judgment formula and written into the dynamic degradation coupling data of energy storage battery performance. Based on the cumulative performance decline trend, the relationship between coupling level and power allocation behavior in the coupled data, the degree of operational stability degradation of the energy storage battery is calculated. Using output power deviation coefficient, temperature stability curve fluctuation value, and charge / discharge control response delay as input variables, the percentage decrease in stability is calculated. If the stability degradation rate exceeds 15% within one week, it is marked as a rapid decline in stability. Based on this stability degradation result, the changes in power quality transmitted by the energy storage system are assessed. Specifically, during the analysis, voltage fluctuation values, current harmonic content (THD), and frequency drift values ​​(Hz) during the energy storage system's external power supply are extracted and compared with the equipment design tolerances to determine the power quality degradation trend. For example, when THD consistently exceeds 3% and the frequency deviation exceeds ±0.5Hz, it is recorded as a severe degradation trend. This trend data is stored in the system quality database and bound to the coupling data tags.

[0016] Step S4: Predict the local degradation of the energy storage battery cells based on the energy transmission quality degradation trend; detect the operational risk of the energy storage battery based on the local degradation of the energy storage battery cells; predict the lifespan of the energy storage battery based on the operational risk and the local degradation of the energy storage battery cells, and obtain the lifespan prediction data of the energy storage battery.

[0017] In this embodiment of the invention, based on the data on the degradation trend of transmitted power quality, the focus is on analyzing the microscopic behaviors of each battery cluster during periods of power supply anomalies, such as uneven internal heat distribution, sudden local temperature increases, and abnormal voltage drops. Infrared thermal image data recorded every 5 minutes by the on-site deployed thermal imaging monitoring system is used, with the cell region coordinate matrix and local temperature rise threshold (e.g., a temperature rise rate exceeding 10°C / minute) as input parameters, to identify local degradation locations. The evolution data of these local degradation locations are then combined to statistically analyze their frequency, duration, and regional correlation. For example, if cell number C12 exhibits 5 degradation phenomena in the past 3 days, all located in the upper left quadrant, it is recorded as a local degradation cluster area. By calling the cell structure mapping map, the corresponding locations are marked as high-risk areas, and the thermal stress concentration trend is recorded. Based on the above local degradation information, the thermal runaway risk index, voltage drop trend, and cycle frequency change are integrated to calculate the operational risk status of the energy storage battery. This process employs a failure rate growth rate algorithm driven by physical formulas: It sets the normal operating voltage range and the threshold voltage difference, and combines this with the integral value of the thermal accumulation curve to derive a cell risk score, which is then categorized into risk levels (e.g., I to IV). After risk level classification, it retrieves runtime records and cycle count data, combining the risk level, local degradation intensity, and average temperature change rate to perform a lifespan assessment. The lifespan estimate is then fitted using the cell's thermal failure critical condition and discharge capacity degradation rate to generate an estimated remaining lifespan for the energy storage battery. The data format includes battery ID + remaining cycles (in cycles) + corresponding cell location + current risk level, summarized in a table.

[0018] Preferably, step S1 includes the following steps: Step S11: Obtain energy storage battery design data; In this embodiment of the invention, design data of the energy storage battery is acquired during the life prediction process. This design data includes, but is not limited to, cell material type, electrode structure, electrolyte type, cell size parameters, cell arrangement structure, rated capacity, voltage level, design temperature range, safety threshold, battery management system (BMS) configuration specifications, current carrying capacity, and cycle design life. The design data is acquired by accessing the original design database provided by the manufacturer. This database is read using an industrial-grade Structured Query Language (SQL) interface. During the reading process, a data extraction engine (such as Apache NiFi) is used for field filtering and tagging to ensure the consistency and completeness of the extracted data fields. All design data is stored in a local distributed database (such as ClickHouse) and provides a set of static basic parameters for subsequent steps involving the retrieval of the energy storage battery's internal structure data and performance evaluation. The output of this step is a complete dataset of energy storage battery design parameters.

[0019] Step S12: Collect internal structural data of the energy storage battery based on the energy storage battery design data; In this embodiment of the invention, structural information from the energy storage battery design data is used to collect detailed parameters of the battery's internal structure, including the number of cell stacking layers, electrode winding density, separator material thickness, internal temperature distribution parameters, and conductive path length. Specifically, through design document analysis and structural measurement techniques, a data set containing multi-dimensional structural features of the battery cell is obtained.

[0020] Step S13: Evaluate the performance data of the energy storage battery based on the design data and internal structure data of the energy storage battery; In this embodiment of the invention, the electrochemical performance indicators of the energy storage battery are evaluated by combining the design parameters obtained in step S11 and the structural features collected in step S12. Using the design capacity, electrode area, and active ingredient content of the electrode material, the deviation range between the theoretical and actual battery capacity is calculated. Internal resistance, ion diffusion resistance, and thermal resistance are calculated based on the internal structural parameters. Through derivation using physical formulas and empirical parameters, a set of performance data, including capacity retention rate, internal resistance change rate, and thermal performance indicators, is obtained.

[0021] Step S14: Divide the energy storage battery performance data to obtain energy storage lithium battery performance data and energy storage lead-acid battery performance data.

[0022] In this embodiment of the invention, based on the performance data obtained in step S13, the overall performance data is divided into two groups according to the battery type identification information: performance data for energy storage lithium batteries and performance data for energy storage lead-acid batteries. The lithium battery performance data includes characteristic indicators such as capacity retention, cycle life, and internal resistance change; the lead-acid battery performance data includes indicators such as discharge capacity, plate sulfation degree, and cycle performance. The two groups of data are used independently for subsequent lifespan analysis and prediction of different types of energy storage batteries.

[0023] Preferably, step S13 includes the following steps: Step S131: Identify the arrangement of individual energy storage battery cells based on the internal structure data of the energy storage battery; In this embodiment of the invention, the internal structural data of the energy storage battery includes the spatial distribution parameters of individual battery cells. By parsing this data, the arrangement of the battery cells in the battery pack is identified. The specific process includes: reading the coordinate information of each cell in the internal structural data, determining the arrangement of the cells in the battery pack (e.g., series, parallel, or mixed arrangement), and analyzing the arrangement pattern between cells based on their relative positions. A structural analysis algorithm is used to classify the hierarchical relationships and arrangement of the cells, generating structural description data of the battery cell arrangement, providing basic information for subsequent analysis of heat conduction paths and electrical characteristics. This step outputs the structural parameter data of the energy storage battery cell arrangement.

[0024] Step S132: Collect the cell arrangement structure of the energy storage battery based on the internal structure data of the energy storage battery; In this embodiment of the invention, detailed cell arrangement information is extracted from the internal structure data of the energy storage battery, including the number of cells, arrangement direction, spacing, and number of layers. A three-dimensional arrangement model of the cells is obtained by analyzing the cell position information and spatial parameters. Specific operations include: calculating the cell spacing and connection relationships between adjacent cells based on their spatial coordinates to determine the geometric structure of the cell arrangement (e.g., planar arrangement, stacked arrangement, wound arrangement, etc.). This step generates data information containing the characteristics of the cell arrangement structure, which serves as input data for subsequent evaluation of heat conduction paths and heat dissipation capabilities.

[0025] Step S133: Determine the internal heat conduction path of the energy storage battery based on the spacing of the battery cell arrangement method when it exceeds 30mm and the battery cell arrangement structure. In this embodiment of the invention, the internal heat conduction path of the energy storage battery is analyzed by combining the cell arrangement obtained in step S131 and the cell arrangement structure obtained in step S132. When the distance between adjacent cells in the cell arrangement exceeds 30 mm, based on the physical laws of heat conduction, it is determined that heat conduction mainly occurs through air conduction and contact conduction. Using the heat conduction path calculation method, combined with the cell arrangement density and contact surface conditions, a heat conduction path diagram inside the energy storage battery is drawn. This path diagram describes the conduction path of heat from the heat-generating cells to the heat dissipation area and outputs the data information of the internal heat conduction path of the energy storage battery, providing key parameters for subsequent heat dissipation capacity evaluation.

[0026] Step S134: Collect thermal conductivity data of energy storage battery electrode materials based on energy storage battery design data; In this embodiment of the invention, when extracting the thermal conductivity parameters of electrode materials from the energy storage battery design data, the structural design documents and material list of the energy storage battery product are first retrieved to determine the specific types of positive and negative electrode materials used, such as lithium iron phosphate, lithium nickel cobalt manganese oxide (NCM), lithium cobalt oxide, etc., as positive electrode materials, and graphite or silicon-based composite materials as negative electrode materials. After identifying the material types, the corresponding detailed material technical specifications are further obtained. These specifications are generally provided by the material supplier or battery manufacturer and cover the thermophysical performance indicators of the material under standard test conditions, including but not limited to thermal conductivity, specific heat capacity, density, and thermal diffusivity. For the extraction of thermal conductivity parameters, the steady-state thermal conductivity test results of the material at 25°C or within a specified operating temperature range (e.g., 25°C to 60°C) should be consulted first. This data is obtained using the steady-state heat flow method or laser flash method, and the unit is watts per meter per Kelvin (W / m·K). When reading the specification file, the thermal conductivity values ​​of the positive and negative electrode materials should be recorded separately. If the material is a composite structure, such as a coated positive electrode or a doped negative electrode, the effective thermal conductivity should be weighted according to the proportional rule, combining the thermal conductivity and mass fraction of each component. For multi-layer coated electrode materials, the thermal conductivity of the current collector and the active material layer should be distinguished and processed separately in the subsequent heat dissipation path analysis. All collected thermal conductivity parameters should be labeled with their corresponding test conditions, including temperature, material density, and compaction density, to ensure consistency in subsequent calculations. After the data collection is completed, the thermal conductivity values ​​of various electrode materials should be compiled into a table as the basic input for heat conduction path modeling and heat transfer efficiency analysis. The output of this step is a set of thermal conductivity parameter values ​​of various electrode materials in the energy storage battery under specific temperature conditions, with the unit uniformly set as W / m·K, providing physical quantity boundary inputs for subsequent thermal diffusion capacity assessment and battery thermo-electric coupling analysis.

[0027] Step S135: Evaluate the heat dissipation capacity of the energy storage battery based on the thermal conductivity data of the battery electrode material exceeding 2.5 W / m·K and the internal heat conduction path of the energy storage battery; In this embodiment of the invention, the thermal conductivity data of the electrode material obtained in step S134 is rigorously compared with a set critical threshold of 2.5 W / m·K. If the thermal conductivity value is higher than this threshold, it indicates that the material has strong thermal conductivity. Then, the overall heat dissipation capacity of the battery is quantitatively evaluated by combining the heat conduction path information obtained in step S133. In the specific evaluation process, firstly, the main heat transfer route from the inside of the electrode material to the outside of the battery package is determined based on the heat conduction path diagram. The structural dimensions, material types, and interface connection methods of each path segment are marked, and the length scale, cross-sectional area distribution, and positions of different contact interfaces in the path are clarified. Secondly, temperature data between different path nodes are collected. This data is measured by thermocouple arrays arranged inside the cell and around the casing to ensure a true reflection of the temperature gradient. Based on the thermal conductivity of different material segments on the path and the contact thermal resistance data formed at the connection points between materials, the heat transfer efficiency is calculated segment by segment. In this process, the thermal resistance influence of each segment during the heat conduction from the heating area of ​​the cell to the packaging shell is fully considered, and the convective heat transfer characteristics between the shell surface and the environment are combined to evaluate whether the heat can be effectively released within the set working time. Furthermore, for locations with discontinuous heat conduction in the connection structure, such as solder joints, adhesive interfaces, or insulating gasket locations, it is necessary to add equivalent thermal resistance correction based on their material thermophysical properties and actual contact conditions to accurately reflect their impact on the heat transfer process. The thermal resistance along all heat transfer paths is summarized to obtain an index value reflecting the overall ease of heat dissipation of the battery. This value is then converted into the heat power that can be effectively transferred from the inside of the cell to the outer casing and further dissipated per unit time. Combined with the heating rate under actual operating current, it is determined whether the battery's thermal management capability meets the heat dissipation requirements. The evaluation results are output in numerical form, including the total heat released per second (in watts), the heat dissipation ratio per unit volume, and temperature distribution data at each path node, providing clear boundary inputs and references for subsequent comprehensive analysis of thermo-electric coupling performance.

[0028] Step S136: Test the electrical characteristics of the energy storage battery based on the energy storage battery design data; In this embodiment of the invention, when conducting battery electrical characteristic tests based on the target electrical parameters specified in the energy storage battery design data, a standardized operating procedure is required to comprehensively and systematically measure and record multiple electrical performance indicators to ensure that all data are representative and traceable. The core testing task is to obtain static and dynamic electrical behavior data of the battery, focusing on key items such as internal resistance measurement, voltage response analysis, charge / discharge rate capability assessment, and transient current response capability quantification. The specific testing work is carried out using a high-precision electrochemical integrated testing system. This system has a current control accuracy better than ±0.01A, a voltage sampling resolution of 1mV, and constant current source and AC impedance testing functions. The testing process first involves a static constant current discharge experiment, setting multiple constant current discharge conditions at different rates (such as 0.5C, 1C, 2C, 5C). By monitoring the battery terminal voltage change process, the voltage plateau change trend and output stability parameters are extracted. Simultaneously, the working internal resistance value at different rates is calculated using the ΔV / ΔI formula, and the internal resistance growth slope is obtained through linear fitting. Following this, electrochemical impedance spectroscopy (EIS) testing was performed, with a frequency scan range of 1 mHz to 10 kHz. By obtaining the high-frequency intercept and semi-circular diameter from the Nyquist plot, key parameters such as ohmic resistance (Rs), charge transfer resistance (Rct), and diffusion impedance were extracted to reflect the energy transfer efficiency and internal structural uniformity during the battery's electrochemical process. In the transient response test, a square wave current impact was applied, and the voltage change curve was recorded. The response time constant and dynamic response coefficient were extracted to determine the battery's voltage recovery capability and polarization degree under sudden load conditions. Throughout the test cycle, the ambient temperature was kept constant within 25 ± 1 °C. By comparing the differences in voltage response and the dynamic change trajectory of internal resistance under different current loads, a complete electrical characteristic data set was established, including voltage response curves, current density-voltage relationship graphs, frequency domain impedance spectra, and transient response curves.

[0029] Step S137: Evaluate the performance data of the energy storage battery based on its electrical characteristics and heat dissipation capacity.

[0030] In this embodiment of the invention, when combining the heat dissipation capacity data obtained in step S135 and the electrical characteristic data obtained in step S136 for comprehensive performance evaluation, a thermal-electric coupling analysis process is used to perform in-depth calculation and analysis of the multi-physics interaction effects of the energy storage battery under actual operating conditions, so as to achieve quantitative evaluation of the battery's comprehensive performance and prediction of degradation trends. In specific operation, the heat dissipation capacity data obtained in step S135, including surface heat flux density (in W / m²), thermal resistivity (in K / W), temperature gradient distribution (in K / m), etc., are first input as boundary thermal conditions to the thermal field analysis module. This module calculates the temperature distribution and heat flux density path of each node based on the thermal conductivity and structural parameters of the battery shell material using the steady-state heat conduction equation. Simultaneously, the electrical characteristic data obtained in step S136, including current density distribution per unit time (A / m²), internal voltage response characteristics, polarization resistance, and ohmic internal resistance (mΩ), are input as electrical boundary conditions into the electric field solution module. The current distribution path in the multilayer electrode structure is established using the charge continuity equation and Ohm's law. The thermal-electric coupling analysis process employs a finite element analysis framework, iteratively solving the mutual influence of thermal stress changes and current distribution changes within the three-dimensional battery structure mesh. By calculating the peak thermal stress (MPa) generated by hotspots with concentrated current, it analyzes whether local thermal runaway causes occur. Further analysis examines the degree of influence of electrical performance on the heat dissipation path, identifying whether local temperature rise causes changes in material resistivity, leading to current density redistribution. Simultaneously, feedback analysis analyzes the dynamic impact of heat dissipation capacity on electrical parameters, identifying changes in voltage plateau stability under different heat dissipation efficiencies, electrochemical reaction rate drift, and regions of increased polarization. The comprehensive energy storage battery performance data output by this process includes three main parts: first, thermal performance indicators, including maximum temperature rise rate, thermal resistance increment, and steady-state heat distribution uniformity; second, electrical performance indicators, including unit voltage drop rate, current density gradient, and resistance growth rate; and third, parameters related to thermal-electric coupling effects, such as voltage fluctuation frequency under thermal stress, dynamic coefficient of resistance with temperature, and changes in the size of the hot zone induced by current concentration. Through the fusion of these multi-parameter results, a comprehensive and quantitative technical assessment of the overall battery performance stability and future degradation trends can be achieved, providing a high-precision input data foundation for subsequent control strategy optimization, heat dissipation structure adjustment, and lifetime prediction algorithms.

[0031] Preferably, the determination of the overload trend of the energy storage battery in step S2 includes: 48 hours of energy storage battery operation log data were collected for energy storage battery equipment operation time series analysis to obtain energy storage battery equipment operation time series data. In this embodiment of the invention, equipment operation log data for the most recent 48 consecutive hours is collected from the energy storage battery system operation management module. The log data includes equipment start-up and shutdown times, current, voltage, power load, and charging / discharging status. The log data is sorted by timestamp to construct a time-series data set of equipment operation. The time-series data is presented in minutes, forming a time series of operating status and load parameters for each minute. Specifically, data preprocessing techniques are used to clean the raw logs, including removing outliers, aligning timestamps, and imputing missing data, ensuring the continuity and accuracy of the time series. This results in time-series data reflecting the equipment's 48-hour operating status, including time nodes, corresponding load power, and status markers, providing foundational data for subsequent energy transfer analysis.

[0032] Identify changes in the electrical energy transmitted by energy storage batteries based on the operating time series data of energy storage battery devices; In this embodiment of the invention, the operating time series data of the energy storage battery device obtained in step 1 is used to calculate the magnitude of power transmission variation within a continuous time period. Specifically, the power data at each time point in the time series is used to calculate the power difference between adjacent time points to obtain the rate of change in power transmission. The rate of change data is statistically analyzed, and a sliding window technique is used to calculate the power transmission variation curve over 48 hours, reflecting the trend of battery load changes. Through numerical calculation, the maximum magnitude of change and the average value of change are extracted as a quantitative description of power variation. This step outputs a numerical indicator of the energy storage battery's power transmission variation, which is used for assessing fluctuations in charging and discharging demand.

[0033] Assess the fluctuation of energy storage battery charging and discharging demand when the change in the energy transmitted by the energy storage battery exceeds 41.4%; In this embodiment of the invention, a threshold of 41.4% is set for the obtained rate of change in electrical energy transmission. When the rate of change in electrical energy exceeds this threshold within a certain time period, the energy storage battery is determined to be in a state of high charge-discharge fluctuation. The fluctuation characteristics of charge-discharge demand are evaluated by statistically analyzing the number and distribution of time points exceeding the threshold. Specific operations include calculating the duration, frequency, and continuity of the time points exceeding the threshold, and using fluctuation statistical analysis methods to quantify the intensity and amplitude of changes in charge-discharge demand. This evaluation result forms a quantitative index of the fluctuation of energy storage battery charge-discharge demand, providing a basis for subsequent detection of sudden demand. Output data includes the number of fluctuations, average fluctuation amplitude, and fluctuation duration.

[0034] Detect sudden surges in energy storage battery demand based on fluctuations in energy storage battery charging and discharging demand. In this embodiment of the invention, a charge-discharge demand fluctuation index is used to identify sudden surges in energy storage battery demand. A sudden surge is defined as a rapid change in energy demand within a short period (e.g., within 10 consecutive minutes) with a fluctuation amplitude exceeding a specific increase. Specifically, a time-series anomaly detection algorithm is used to filter extreme load change points within a short period. Through threshold judgment and change rate calculation, sudden demand increase events are identified, and the frequency and intensity of these events are statistically analyzed. The results of this step output a list of sudden demand surge events and corresponding surge amplitude data, providing an event basis for predicting capacity overruns.

[0035] Predict the extent to which the carrying capacity of energy storage batteries exceeds limits based on sudden surges in demand for energy storage batteries. In this embodiment of the invention, based on the obtained information on sudden surge events, and combined with the design capacity and rated load-bearing capacity parameters of the energy storage battery, the degree of exceeding the load-bearing capacity limit is calculated. The degree of exceeding the limit is expressed as the ratio of the sudden surge load to the rated maximum load. The specific calculation formula is the peak value of the sudden surge load divided by the rated load-bearing capacity, outputting a percentage value of exceeding the limit. By statistically analyzing the percentage of exceeding the limit for all sudden events, the instantaneous load-bearing pressure of the energy storage battery under high load is determined. The result of this step is a numerical assessment of the energy storage battery's load-bearing capacity exceeding the limit, clarifying the degree of overload risk.

[0036] The overload trend of energy storage batteries is determined based on the degree of overload of the energy storage battery and the sudden surge in demand for energy storage batteries.

[0037] In this embodiment of the invention, a comprehensive judgment on the overload trend of energy storage batteries is formed by combining the obtained data on the degree of exceeding the load limit with the frequency of sudden increases in event frequency. A time-series description of the overload trend is constructed by statistically analyzing the changing trends of the degree of exceeding the limit and the frequency of sudden increases. Specific operations include time-series analysis of the overload rate to determine the intensity and persistence of the overload trend. This step outputs the evaluation results of the overload trend, including the trend change rate, trend duration, and intensity indicators of the overload, providing key trend data for predicting the lifespan of energy storage batteries.

[0038] Preferably, the lithium battery performance degradation detection in step S2 includes: Based on the trend of excessive load during operation of energy storage batteries, high-rate discharge testing of energy storage lithium batteries is conducted on the performance data of energy storage lithium batteries to obtain the high-rate discharge situation of energy storage lithium batteries. In this embodiment of the invention, the overload trend data collected by the energy storage battery system is used to locate the high-load period of the energy storage lithium battery. During this period, high-rate discharge detection is performed on the current, voltage, temperature, and power data recorded in the energy storage lithium battery performance monitoring module. High-rate discharge detection refers to the analysis of discharge processes where the discharge current reaches or exceeds the rated discharge rate of 1C or more. Specifically, the current time series during the high-load period is extracted, the instantaneous discharge rate (instantaneous current divided by the battery's rated capacity) is calculated, and all time points and duration intervals with discharge rates greater than 1C are screened out. The frequency, duration, and depth of discharge of high-rate discharge events are statistically analyzed to form a detailed dataset of high-rate discharge conditions. This step, through precise time synchronization technology, ensures the accurate location and time segmentation of high-rate discharge data, providing reliable data support for subsequent lithium-ion insertion / extraction rate analysis.

[0039] Predicting an abnormally high lithium-ion deintercalation rate in energy storage lithium batteries based on high-rate discharge conditions; In this embodiment of the invention, based on the obtained high-rate discharge events, the lithium-ion intercalation / deintercalation rate is calculated within the corresponding discharge period. Specifically, the relationship between the lithium battery discharge current and battery capacity is used, and the number of lithium-ions intercalated / deintercalated per unit time is calculated using the coulomb counting method. The intercalation / deintercalation rate is defined as the rate at which lithium ions are intercalated / deintercalated from the positive electrode lattice per unit time, converted into the rate of change of the number of moles of intercalated / deintercalated lithium ions. Statistical analysis of the intercalation / deintercalation rate is performed on the high-rate discharge period to identify abnormally high rate segments that are significantly higher than those in the normal discharge phase. By comparing with the normal discharge rate threshold, the time interval and magnitude of the increased intercalation / deintercalation rate are quantitatively determined. The time series and amplitude data of the abnormally high lithium-ion intercalation / deintercalation rate are output, providing data basis for identifying abnormal vacancy in the crystal lattice layer.

[0040] An abnormal increase in the lithium-ion intercalation / deintercalation rate can be used to identify abnormal vacuum conditions in the lithium battery lattice layer. In this embodiment of the invention, the time period of abnormally increased insertion / extraction rate is used as input, and combined with the lattice structure characteristics of the positive electrode material of the energy storage lithium battery, the abnormal vacancy state of the lattice layer is identified. Abnormal vacancy refers to the situation where lithium ion insertion / extraction is too rapid, resulting in a significant decrease in the lithium ion content inside the lattice layer, causing local instability of the lattice layer. During implementation, by comparing the electrochemical characteristics of the normal lithium ion insertion state, combined with experimental detection data such as X-ray diffraction (XRD) or electrochemical impedance spectroscopy (EIS), the region of abnormally reduced lithium ion concentration in the lattice layer corresponding to the abnormal insertion / extraction rate is identified. Specific operations include analyzing the changes in lattice peak positions and the decrease in peak intensity to determine the degree of lithium ion voiding in the lattice layer. The spatial distribution and temporal variation data of abnormal vacancy in the lattice layer are output to assist in the subsequent prediction of lattice structure collapse trends.

[0041] Predict the collapse trend of the lithium battery lattice structure based on the abnormal evacuation of the lithium battery lattice layer. In this embodiment of the invention, the identified abnormally evacuated regions of the crystal lattice are the focus. Based on changes in lithium-ion concentration distribution and electrochemical reaction kinetics, the trend of crystal lattice collapse is predicted. Structural collapse refers to the instability of the crystal lattice caused by the large-scale insertion and extraction of lithium ions, resulting in lattice fracture, distortion, or collapse. Specific technical operations include using lattice mechanical parameters obtained in the laboratory, combined with load cycling data, to calculate the cumulative stress and strain values ​​of the crystal lattice. By quantitatively analyzing whether the stress threshold exceeds the material strength limit, the probability and trend of structural collapse are determined. The predicted time curve and spatial distribution of crystal lattice collapse are output, reflecting the dynamic process of gradual structural deterioration.

[0042] Predicting lithium-ion migration channel blockage based on the lattice structure collapse trend of energy storage lithium batteries; In this embodiment of the invention, the obstruction of lithium-ion migration channels is analyzed based on the obtained lattice structure collapse trend. Lithium-ion migration channels are the pathways through which lithium ions migrate between lattice layers within a lithium battery. Structural collapse leads to partial blockage or narrowing of these channels, affecting lithium-ion migration efficiency. Specifically, the process involves combining observation data of the material's microstructure using scanning electron microscopy (SEM) or transmission electron microscopy (TEM) to identify changes in channel morphology in lattice-collapsed regions. Simultaneously, electrochemical impedance spectroscopy (EIS) is used to measure changes in lithium-ion migration impedance, quantitatively assessing the degree of channel blockage. This step outputs data on the blockage rate and location of lithium-ion migration channels, serving as the basis for subsequent determination of charge transfer path interruption.

[0043] Predict the interruption of charge transfer paths in lithium batteries based on the blockage of lithium-ion migration channels and the collapse trend of the lattice structure of energy storage lithium batteries; In this embodiment of the invention, data on lithium-ion migration channel blockage rate and lattice structure collapse are combined to analyze whether the charge transfer path is interrupted. The charge transfer path includes the migration pathways of lithium ions and electrons within the positive electrode active material; structural collapse and channel blockage can lead to path breakage. Technical methods include using electrochemical impedance spectroscopy data to identify characteristic signals of sudden increases in charge transfer resistance, and combining this with the location data of microstructural break points to determine path integrity. Numerical analysis methods are used to calculate path connectivity, outputting the time point and break location of the path interruption, forming a detailed dataset of charge transfer path interruptions.

[0044] Determine the failure status of lithium battery active materials based on the interruption of lithium battery charge transfer path; In this embodiment of the invention, the failure status of the positive electrode active material of a lithium battery is assessed based on the identified charge transfer path interruption data. Failure manifests as the active material's inability to effectively participate in electrochemical reactions, leading to capacity decay. By comparing the battery capacity change curve and the time of path interruption, the proportion and distribution of failed active materials are determined. Chemical analysis methods are used to characterize the sampled active materials, and the failure rate of the active materials is calculated in conjunction with the capacity loss rate. Data on the active material failure rate, failure area, and failure evolution trend are output to form a detailed failure assessment report.

[0045] Predict the increase in lithium battery transport resistance based on the interruption of lithium battery charge transfer path and the failure status of lithium battery active materials. In this embodiment of the invention, the overall transport resistance trend of a lithium battery is calculated based on charge transfer path interruption and active material failure. Specifically, this involves using electrochemical impedance spectroscopy to measure the transport resistance value, and combining the path interruption rate and the proportion of failed active material to construct a quantitative model for the increase in transport resistance. Transport resistance data from different operating cycles are compared to analyze the curve shape of resistance increasing over time. The percentage increase and rate of change of transport resistance are output, providing resistance change parameters for subsequent performance degradation detection.

[0046] The gradient deterioration of lithium battery performance is detected based on the increase in lithium battery transmission resistance and the failure status of lithium battery active materials.

[0047] In this embodiment of the invention, the gradient degradation trend of lithium battery operating performance is comprehensively evaluated by combining transmission resistance change data and active material failure status. By establishing a gradient degradation index system, the data on increased transmission resistance and active material failure are weighted and fused to form a quantitative index of performance degradation degree. Time series analysis technology is used to monitor the trend of index changes, forming a dynamic monitoring curve of the gradient degradation trend. This detection result covers the spatial and temporal distribution of performance degradation, clearly showing the degree of degradation in different gradient regions, providing accurate performance degradation basis data for predicting the lifespan of energy storage batteries.

[0048] Preferably, the prediction of dynamic load response misalignment of the energy storage lead-acid battery in step S2 includes: Based on the trend of excessive load during the operation of energy storage batteries, the performance data of energy storage lead-acid batteries are analyzed to determine the situation of excessive current output of energy storage lead-acid batteries. In this embodiment of the invention, real-time current data of the energy storage lead-acid battery is acquired through a current sensor during actual operation. The current sensor is a high-precision Hall effect sensor, capable of monitoring the battery's output current with milliampere-level accuracy. Time series analysis is performed on the acquired current data, and a sliding window method is used to extract the current peak value. Specifically, a time window length of 10 seconds and a sampling frequency of 100Hz per second are used to ensure accurate capture of short-term high-load conditions. By setting a threshold for the battery's rated maximum discharge current, periods exceeding this threshold are determined to be in a "current output too high" state. The result of this process is a set of current overload time points and durations, forming a data set of "energy storage lead-acid battery current output too high conditions," providing a data foundation for the next step of estimating lead sulfate crystal formation.

[0049] Based on the excessive current output of the energy storage lead-acid battery, the performance data of the energy storage lead-acid battery are used to predict the formation of lead sulfate crystals in the battery, and the formation of lead sulfate crystals in the battery is obtained. In this embodiment of the invention, the obtained data on excessive current output during certain periods are analyzed simultaneously with changes in battery operating temperature, open-circuit voltage, and internal resistance. The formation process of lead sulfate crystals inside the battery is closely related to local overvoltage and temperature rise during high-current discharge. Specifically, electrochemical impedance spectroscopy (EIS) is used to collect impedance data of the battery under different loads. The EIS spectrometer uses a frequency scanning range from 1 mHz to 100 kHz. After acquiring the impedance data, the degree of crystal formation is reflected by the change in the semicircle diameter in the low-frequency region of the impedance spectrum. Based on the EIS data corresponding to excessive current output, the formation rate and distribution of lead sulfate crystals are inferred, forming quantitative data on "battery lead sulfate crystal formation," including indicators such as crystal coverage area and estimated thickness.

[0050] Detecting abnormal electrolyte circulation within a battery based on the formation of lead sulfate crystals in the battery; In this embodiment of the invention, the obtained crystal formation information, combined with the internal temperature field distribution and liquid flow velocity monitoring data of the battery, is used to detect abnormal electrolyte circulation. Temperature sensors are arranged at multiple key locations within the battery, and the electrolyte flow state is inferred through multi-point temperature difference data. Combined with measurement data from flow rate sensors or ultrasonic flow meters, the electrolyte flow velocity and direction information are obtained. Areas with severe crystal formation are usually accompanied by electrolyte flow obstruction, leading to abnormally high local temperatures and decreased flow rates. By comparing the temperature and flow rate distribution under normal operating conditions, abnormal zones are identified, forming "abnormal electrolyte circulation status within the battery" data, including the location of the abnormal area, the temperature difference value, and the percentage decrease in flow rate.

[0051] Predict the abnormal deterioration of the battery plates based on the abnormal circulation of the electrolyte inside the battery. In this embodiment of the invention, the degree of electrode degradation is predicted by analyzing the impact of abnormal electrolyte circulation areas on battery electrode materials. Electrode degradation is mainly manifested as active material shedding, structural deformation, and reduced conductivity. Microstructural changes in the battery electrodes are obtained through resistivity measurements and scanning electron microscopy (SEM) image acquisition. The resistivity distribution on the electrode surface and inside is measured using a four-probe method, and combined with the porosity and crack density data obtained from SEM, the degree of electrode degradation is quantitatively assessed. The accelerated local corrosion rate caused by abnormal electrolyte circulation is used to infer the degradation rate and spatial distribution, forming a dataset of "abnormal degradation of energy storage battery electrodes."

[0052] The bridging and short circuit of the positive and negative electrodes of the lead-acid battery can be determined based on the softening of the battery plates and the abnormal circulation of the electrolyte inside the battery. In this embodiment of the invention, mechanical contact and bridging short circuits between battery plates are identified by combining battery plate softening assessment with electrolyte circulation anomaly data. Plate softening is determined through plate material hardness testing and deformation measurement. A nanoindenter is used to detect localized hardness changes in the plates, and a laser scanner is used to measure plate thickness and deformation to quantify the degree of softening. Simultaneously, an internal battery conductivity detection device is used to meticulously measure the conductivity in different regions of the battery. Bridging short circuit areas are characterized by abnormally high localized conductivity. Combining these two sets of data, multi-parameter fusion is used to determine the "positive and negative electrode bridging short circuit situation," outputting the specific location, length, and affected range of the bridging short circuit.

[0053] Predict the degradation of electrode plate conductivity based on abnormal deterioration of energy storage battery plates. In this embodiment of the invention, the degradation of conductivity efficiency is quantified by combining the obtained electrode plate degradation data with conductivity tests of the electrode plate material. A four-probe resistivity testing device is used to perform detailed measurements of the resistance values ​​of the electrode plate at different depths and regions. The reduction percentage of the effective conductive area of ​​the electrode plate and the degree of local resistance increase are calculated to obtain the overall conductivity efficiency degradation curve of the electrode plate. Simultaneously, considering the changes in contact resistance caused by electrode plate degradation, a contact resistance tester is used for multi-point measurements to further refine the conductivity efficiency data. The result of this step is the "electrode plate conductivity efficiency degradation" data, including the percentage decrease in conductivity and a spatial distribution map.

[0054] The dynamic load response imbalance of energy storage lead-acid batteries can be predicted based on the degradation of electrode plate conductivity and the bridging short circuit between the positive and negative electrodes.

[0055] In this embodiment of the invention, a predictive basis for dynamic load response mismatch is constructed. The decay of electrode plate conductivity leads to uneven current distribution within the battery, while bridging short circuits cause localized short-circuit currents; both factors jointly affect the battery's load response. Using a high-precision current distribution measurement device and a battery equivalent circuit tester, the current response curve of the battery under dynamic load conditions is obtained. By comparing the current response with that of a healthy battery, and combining electrode plate conductivity and bridging short-circuit data, the degree to which the load response deviates from the normal state is analyzed to derive indices of dynamic load response mismatch in energy storage lead-acid batteries, including response time delay, peak current deviation, and the degree of decrease in load adaptability.

[0056] Preferably, step S3 includes the following steps: Step S31: Determine the dynamic load response misalignment of the energy storage lead-acid battery and the gradient degradation of the lithium battery's operating performance by coupling them together to obtain coupled data on the dynamic degradation of the energy storage battery's performance. In this embodiment of the invention, key performance indicators are extracted from the dynamic load response misalignment of energy storage lead-acid batteries, including data such as current response time delay, load response peak deviation, and electrode plate conductivity degradation. This data originates from quantitative values ​​obtained using current sensors and electrode material testing equipment in the preceding steps. Simultaneously, for parallel-connected lithium battery systems, performance gradient degradation data is collected, including capacity deceleration rate, internal resistance increase curve, and performance degradation data corresponding to the number of cycles. Data acquisition is performed using a constant current charge-discharge tester and an electrochemical impedance spectroscopy (EIS). The two types of data are synchronized along a time axis and compared using a multidimensional data fusion method. This process employs matrix operations and statistical analysis tools, specifically including normalization, correlation analysis, and covariance calculation, to determine the coupling relationship between the performance degradation of the two battery systems. During the coupling process, by calculating the co-variance parameters of the dynamic load response misalignment indicators and the lithium battery gradient degradation indicators, "energy storage battery performance dynamic degradation coupling data" is generated. The data format includes multidimensional performance indicator vectors and their temporal evolution characteristics, used for subsequent performance stability and capacity prediction.

[0057] Step S32: Determine the degradation of the operational stability of the energy storage battery based on the coupled data of dynamic degradation of energy storage battery performance; In this embodiment of the invention, the coupled dataset obtained in step S31 is used to focus on analyzing the stability indicators of the energy storage battery operation. Specific operations include extracting statistical features from parameters such as current response consistency, voltage fluctuation range, and system internal resistance changes in the dynamic degradation coupled data. A spectrum analysis method is used to perform Fourier transform on the voltage and current fluctuation signals to extract frequency component changes and quantify the unstable frequency bands in system operation. Based on the time-series data of resistance and capacity, a stability degradation index, such as the Stability Degradation Index (SSI), is calculated. This index is obtained by weighted summation of the standard deviation of the operating voltage and the proportion of the increase in internal resistance. This step outputs "Energy Storage Battery Operation Stability Degradation" data, including the stability degradation index value and its time evolution curve, which reflects the trend of decreasing dynamic stability during battery operation.

[0058] Step S33: Determine the capacity decay trend of the energy storage battery based on the coupled data of dynamic degradation of energy storage battery performance; In this embodiment of the invention, by conducting in-depth analysis of capacity-related parameters in the coupled data, the changing trend of energy storage battery capacity decay is quantified. Core data such as the battery's nominal capacity, actual discharge capacity, and charging efficiency are extracted from the coupled data. Data acquisition is completed using a capacity tester and current / voltage acquisition devices. Through time series regression analysis, the capacity decay rate and its acceleration are calculated. The least squares method is used to fit the curve of capacity changing with the number of cycles, determining the capacity decay function. Temperature compensation technology is also used during the analysis to eliminate the influence of external temperature changes on capacity measurement, ensuring the accuracy of the decay trend data and forming "energy storage battery capacity decay trend" data, including capacity decay curve equation parameters and future decay prediction values, providing data support for battery life assessment.

[0059] Step S34: Predict the degradation trend of energy transmission quality of the energy storage battery based on the degradation of the energy storage battery's operational stability and capacity.

[0060] In this embodiment of the invention, the output voltage and current waveforms of the energy storage battery are monitored in real time using power quality analysis equipment, collecting power quality indicators such as harmonic content, transient voltage drop, and fluctuation amplitude. Combining the operational stability degradation index and capacity degradation curve, frequency response analysis is used to assess the distortion level of the battery output signal. The power quality degradation index is calculated, specifically constructed from harmonic distortion rate (THD) and voltage deviation statistical parameters. This index shows an increasing trend with the degradation of capacity and stability. During the prediction process, based on time series analysis, the future changing trends of power quality indicators are derived, forming "energy storage battery power quality degradation trend" data. This data includes time-series prediction curves of power quality indicators and key node values, providing a quantitative basis for evaluating the power transmission performance of the energy storage system.

[0061] Of particular importance, step S34 includes the following steps: Step S341: Estimate the instantaneous current distribution imbalance based on the degradation of the energy storage battery's operational stability and the degradation trend of its capacity. In this embodiment of the invention, data on the degradation of the operational stability of the energy storage battery is acquired. This data includes the range of voltage fluctuations, voltage change rate, duration of voltage drop, and the number of charge / discharge control failures recorded by the system controller per unit time. Simultaneously, data on the capacity degradation trend of the energy storage battery is acquired. This data consists of the initial rated capacity of the cell group, the current maximum usable capacity, the number of charge / discharge cycles, and the capacity decrease rate per unit time. A cell voltage-capacity balance map is constructed using the voltage fluctuation rate and the capacity degradation ratio. The current carrying capacity of each cell under multiple load conditions is reconstructed over time. Based on the cell current change rate and the deviation from the standard mean within adjacent time windows, an instantaneous current distribution deviation index is calculated. This index is expressed as the variance of the current difference between cells, in milliamperes per second (mAh / s), providing instantaneous current distribution imbalance data for the cell cluster under the current operating conditions, thus providing direct data input for subsequent local overcurrent calculations.

[0062] Step S342: Determine the local overcurrent situation in battery transmission based on the instantaneous current distribution imbalance; In this embodiment of the invention, based on the instantaneous current distribution imbalance data output in step S341, step S342 is implemented. The real-time collected cell current data is compared with the system-calibrated rated safe current limit to identify cell units exceeding the rated current limit. The deviation ratio between the current current value of each cell and the historically statistically analyzed normal operating current range is calculated, and cell groups with a current deviation rate greater than 15% are selected. Using a Hall sensor array deployed within the battery module, the peak current of each channel is synchronized according to the timestamp, and sudden overcurrent events are identified by combining the current load fluctuation spectrum, determining the local overcurrent situation of battery transmission in each detection cycle. The overcurrent data is output in the form of a triplet consisting of cell number, peak current value, and current duration, and is recorded for subsequent contact resistance heating calculations.

[0063] Step S343: Predict the degree of contact resistance heating overload based on the local overcurrent situation of battery transmission; In this embodiment of the invention, the actual contact resistance value of each cell terminal connection position is retrieved by looking up a table. This resistance value is measured using a four-wire method during factory testing and stored in the Battery Management System (BMS) database. Based on the current-heat power relationship P=I²R, the heat power generated during the overcurrent stage of each cell is calculated, and the accumulated heat energy is obtained by combining this with the overcurrent duration. Then, using the temperature rise conduction time constant, the heat generation is converted into the corresponding contact point temperature rise amplitude. The heat power value, duration, and temperature rise rate data are output as parameters of the contact resistance overload degree, generating a contact resistance overload degree record table. The recorded content includes the cell number, overcurrent point, heat generation power, temperature rise value, and its rate of change.

[0064] Step S344: Determine the thermal diffusion of the energy storage battery based on the local overcurrent situation of battery transmission and the degree of overload caused by contact resistance heating; In this embodiment of the invention, thermal diffusion simulation is performed on the battery cell and its adjacent units based on a thermal conduction path structure model. A two-dimensional thermal field mapping matrix is ​​used, with the temperature rise of each heat source as the initial heat source. Under the premise of a fixed thermal conductivity coefficient, the diffusion path and heat flux density distribution of heat to surrounding battery cells and structural components are calculated according to Fourier's law. The thermal diffusion simulation uses measured thermal conductivity coefficients and the heat capacity parameters of the battery cell packaging material, without using any fitting model. The temperature change process of each grid point is calculated iteratively through physical rules to obtain the average temperature rise, the location of the maximum temperature rise point, the diameter of the thermal diffusion range, and other indicators for each battery cell in the current time period, forming a thermal diffusion map of the energy storage battery.

[0065] Step S345: Predict the battery operation scheduling imbalance based on the thermal diffusion of the energy storage battery; In this embodiment of the invention, by analyzing the temperature gradient distribution of the battery cells within the heat diffusion range, the charging and discharging current regulation behavior caused by excessive temperature difference is identified. During operation, the BMS system adjusts its scheduling strategy based on the battery cell temperature state, limiting the current of high-temperature cells to prevent thermal runaway, thereby increasing the load on some low-temperature cells. By calling the scheduling log to record information, indicators such as the number of current regulation command changes, the magnitude of individual cell power adjustment, and the frequency change of scheduling between cells are read in each time period. Combined with the number of cells distributed in the heat diffusion area and the temperature deviation ratio, an operational scheduling load imbalance index is constructed. This index is expressed in time series form and output as a battery operational scheduling imbalance data table, which includes parameters such as the scheduling fluctuation coefficient, the number of cells participating in scheduling, and the magnitude of load redistribution.

[0066] Step S346: Predict the trend of energy quality degradation of the energy storage battery based on the battery operation scheduling imbalance and the thermal diffusion of the energy storage battery.

[0067] In this embodiment of the invention, based on the output battery operation scheduling imbalance data and the thermal diffusion map in step S344, step S346 is implemented, employing an energy output waveform quality analysis method to perform high-frequency sampling of the voltage and current output from the energy storage battery system port. The sampling frequency is set to 20kHz, and the sampling period covers the entire charging and discharging process, with key time periods marked when imbalance occurs. The Discrete Fourier Transform (DFT) method is used to extract harmonic components from the output waveform, and indicators such as total harmonic distortion (THD), voltage instantaneous drop count, and current peak density are statistically analyzed. By overlaying the analysis with the thermal diffusion map, the output waveform change trend is synchronously recorded during the temperature rise center region period. The analysis results are used to form an energy storage battery transmission energy quality degradation trend report, which records the energy quality shift amplitude, occurrence frequency, and corresponding thermal-scheduling background data for each abnormal period, providing input data for the lifetime prediction step.

[0068] Preferably, step S4 includes the following steps: Step S41: Predict the local degradation of the energy storage battery cells based on the energy transmission quality degradation trend of the energy storage battery. In this embodiment of the invention, based on the energy quality degradation trend data of the energy storage battery obtained in step S34, abnormal features in the battery output voltage and current waveforms are extracted, such as voltage drop frequency, harmonic distortion, and transient current peak changes. A high-precision oscilloscope and harmonic analyzer are used to continuously monitor the energy storage battery output, collecting time-series data related to energy quality. The collected data is segmented and analyzed, and abnormal bands of energy quality indicators are correlated with the physical location of the battery cells. By establishing a correlation database between energy quality anomalies and changes in cell temperature and internal resistance, local cell degradation can be located. Electrochemical impedance spectroscopy (EIS) is used to further detect changes in electrode reaction impedance of suspected degraded cells, confirming local degradation characteristics and outputting "local cell degradation status" data, including abnormal values ​​of energy quality indicators, impedance change parameters, and corresponding timestamps for each cell, serving as a quantitative basis for the cell's health status.

[0069] Step S42: Identify the accumulation of deviations within the energy storage battery cluster based on the localized degradation of the energy storage battery cells; In this embodiment of the invention, based on the local degradation of each cell determined in step S41, performance difference indicators among the cells within the battery cluster are calculated, covering capacity differences, voltage deviations, and internal resistance distribution ranges. Real-time voltage and temperature monitoring of each cell in the battery cluster is performed using a multi-point voltage sampling system, collecting high-frequency sampling data. The mean, variance, and range of the cell performance indicators within the cluster are calculated using statistical methods to form a deviation accumulation parameter. Further, time series analysis is used to track the changing trend of this deviation parameter to determine the deviation accumulation rate. A large deviation accumulation rate indicates severe uneven degradation within the battery cluster. "Deviation Accumulation Status within the Energy Storage Battery Cluster" data is output, including deviation statistics and trend curves for each performance indicator within the cluster, providing basic data for subsequent risk detection.

[0070] Step S43: Detect the operational risk status of the energy storage battery based on the accumulation of deviations within the energy storage battery cluster and the localized degradation of the energy storage battery cells; In this embodiment of the invention, the cluster deviation accumulation data output in step S42 is jointly analyzed with the local degradation data in step S41, and potential risk points are identified using multi-parameter cross-validation technology. An anomaly detection algorithm is used to determine the boundaries of multi-dimensional performance data, identifying performance extreme values ​​and abnormal clustering areas. The operational risk level is quantified by combining key parameters such as a surge in cell internal resistance, a sharp drop in capacity, and an abnormal rise in temperature. During the risk assessment process, a thermal imager is used to scan the battery cluster to confirm the consistency between abnormal temperature areas and cell degradation data. The risk level is classified according to preset thresholds, including low, medium, and high risk categories, and outputs "energy storage battery operation risk status" data, which includes risk level indicators and corresponding cell numbers, risk occurrence time, and abnormal parameter values, for use in system maintenance and lifespan prediction.

[0071] Step S44: Based on the operational risk status of the energy storage battery and the accumulation of deviations within the energy storage battery cluster, predict the lifespan of the energy storage battery to obtain energy storage battery lifespan prediction data.

[0072] In this embodiment of the invention, a lifespan prediction index system is constructed by integrating the operational risk status data from step S43 and the deviation accumulation trend from step S42. Through time-series correlation analysis of the operational risk level and the deviation accumulation rate, the estimated remaining lifespan of the battery cluster is derived. Lifespan prediction relies on a comprehensive calculation of the battery capacity degradation rate, stability loss rate, and cell degradation acceleration factor. Interpolation and trend extrapolation methods are used to calculate the lifespan termination time. The lifespan prediction results are output as "Energy Storage Battery Lifespan Prediction Data" in numerical form, including the expected lifespan cycle (unit: hours or cycle count), remaining capacity percentage, and a list of parameters for critical risk moments. This data provides a quantitative basis for energy storage system maintenance decisions, enabling precise lifespan management.

[0073] Of particular importance is that step S41 includes the following steps: Step S411: Predict the growth of harmonic content in the battery transmission current based on the energy quality degradation trend of the energy storage battery. In this embodiment of the invention, based on the obtained "energy storage battery power quality degradation trend" data, characterizing indicators such as voltage waveform distortion, frequency drift rate, and total harmonic content (THD) variation curves are extracted. In specific operation, a high-speed data acquisition device (such as a three-phase power analyzer with an accuracy of no less than 0.1% RMS error, like the Yokogawa WT5000) is used to continuously sample the instantaneous current waveform at the energy storage battery output under different load fluctuations at 100ms intervals, obtaining the fundamental wave and harmonic components of each order within each cycle. The sampled data is then sent to a harmonic spectrum analysis module based on Fourier transform, and harmonic sequence calculations are performed according to the IEEE Std 519-2014 standard. Analyzing the 7-day transmission data, a trend chart of total harmonic distortion (THDi) was constructed and compared with the steady-state harmonic baseline data under the initial reference operating conditions. The daily average rise rate of THDi and the absolute proportion of the growth of the 5th, 7th, and 11th harmonic components were identified to obtain the data results of "the growth of battery transmission current harmonic content", which served as the input basis for subsequent steps.

[0074] Step S412: Determine the abnormal operating conditions of the inverter based on the increase in harmonic content of the battery transmission current; In this embodiment of the invention, based on data on the increase in harmonic content of battery transmission current, a high-frequency anomaly triggering monitoring mechanism is used to perform multi-point synchronous waveform acquisition at the inverter output. A synchronous sampling device with microsecond-level time accuracy (such as a high-speed sampling card on the NI PXI platform) is selected to synchronously record the inverter bus voltage, current signal, and their conduction state voltage waveforms. According to IEC 61000-4-7, a harmonic frequency band power spectrum is constructed to quantify the energy proportion of each order of harmonics. Compared with the harmonic tolerance limits provided by the inverter manufacturer (e.g., <5% THDi), when a high-order harmonic exceeds the limit for more than 3 consecutive seconds, the inverter anomaly marking mechanism is triggered. Technical parameters such as the temperature rise of the IGBT switches, peak capacitor current, and number of voltage interruptions and restarts are synchronously collected. A judgment logic combination is used to identify the type of inverter operation anomaly, including three types of operation anomalies: harmonic interference, thermal runaway, and capacitor breakdown, forming an "Inverter Operation Anomaly Status" data packet containing the occurrence time, type classification, duration, and corresponding harmonic sequence data.

[0075] Step S413: Detect the pollution status of the power grid based on the increase in harmonic content of the battery transmission current; In this embodiment of the invention, the data on "increase in harmonic content of battery transmission current" obtained in step S411 and the result of "abnormal operation of inverter" in step S412 are received to establish an assessment process for harmonic injection pollution of the power grid. Time-series data such as voltage distortion, power factor change, and voltage flicker rate before and after the energy storage battery's grid connection point (PCC) are retrieved and collected for 72 hours using a PQ distributed detection unit (e.g., a Fluke 435 II power quality analyzer) to obtain a comprehensive data set including fundamental frequency, THDv, unbalance, and flicker parameters (Pst, Plt). The data is input into a power quality disturbance classifier based on the IEEE 1159 standard, which outputs pollution characteristic indicators such as "neutral point voltage fluctuation," "enhanced voltage distortion," and "frequent rise in zero-sequence current." These indicators are combined with the inverter's output harmonic spectrum to form a correlation judgment, assembling the characteristic data into a "transmission grid pollution status" data structure, which serves as the basis for subsequent fluctuation trend assessment.

[0076] Step S414: Determine the growth trend of power transmission fluctuations based on the pollution status of the power grid; In this embodiment of the invention, the daily THDv fluctuation rate, zero-sequence current peak growth slope, and frequency drift velocity are extracted from the "transmission grid pollution status" data obtained in step S413 as key factors and input into the grid transient disturbance trend extraction module. This module employs a differential voltage sampling and sliding window time-domain analysis algorithm, using voltage amplitude change data with a sampling period of at least 10 seconds to extract a joint data vector of power fluctuation amplitude (unit: ms), frequency (unit: / day), and phase jump (unit: degree). The voltage stability index (e.g., VSI = ΔU_peak / Δt) is calculated at each time point, and trend fitting analysis is performed on the multi-day data to output "power transmission fluctuation growth trend" data. This data is used to reveal the voltage discontinuity and short-term frequency disturbance increase between the power output from the energy storage system and the load, providing a basis for further analysis of the cell operating status.

[0077] Step S415: Detect abnormal conditions of battery cell operation balance based on the growth trend of power transmission fluctuations; In this embodiment of the invention, the "power transmission fluctuation growth trend" data is invoked, and the asynchronous response at the cell level is identified by comparing it with the time-series data of voltage, current, and temperature of each individual cell within the energy storage battery cluster. Specifically, the instantaneous voltage change of each cell during fluctuations is collected (sampling rate not less than 500Hz). An absolute voltage difference comparison algorithm is used to calculate the cell response delay difference (in ms) and peak current response amplitude difference (in A) between adjacent time periods. A multi-node collaborative data association framework is used to map the data between different clusters onto a unified time axis. By judging whether the asynchronous response threshold between different cells exceeds a certain value (e.g., ΔV>50mV or ΔI>0.5A) under the same power disturbance conditions, if voltage-current dual response inconsistency is detected 10 times consecutively, it is marked as an "abnormal cell operation balance condition," forming a "abnormal cell operation balance condition" dataset with location information, response time difference data, and disturbance source synchronization records.

[0078] Step S416: Predict the localized degradation of the energy storage battery cells based on the abnormal balance of the battery cells and the increasing trend of power transmission fluctuations.

[0079] In this embodiment of the invention, based on the "abnormal cell operation balance" obtained in step S415 and the "power transmission fluctuation growth trend" results in step S414, a joint degradation analysis process is constructed. Temperature change curves, current load curves, and internal resistance change sequences are collected for all cells marked as having balance anomalies over the past 24 hours. Distributed data aggregation nodes (deployed in the BMS sub-nodes of each battery cluster) are used to summarize their peak change rates and trends. A comparative analysis method is used to compare the parameter change rates of the abnormal cells with the average value of the entire cluster. If the rate of increase in internal resistance exceeds 50% of the average value, or the rate of temperature rise is higher than 5°C / min, it is confirmed as a local degradation trend. Combining the joint statistical results of power fluctuation frequency and temperature rise frequency, a cell degradation trend map is constructed to output a "local degradation status of energy storage battery cells" data structure, including detailed indicators such as the degraded cell number, temperature rise data, internal resistance growth rate, and correlation with fluctuations, providing input for subsequent life assessment steps.

[0080] The present invention also provides an energy storage battery life prediction system for performing the energy storage battery life prediction method described above, the energy storage battery life prediction system comprising: The battery performance evaluation module is used to acquire energy storage battery design data; evaluate energy storage battery performance data based on the energy storage battery design data; and divide the energy storage battery performance data to obtain energy storage lithium battery performance data and energy storage lead-acid battery performance data. The load response mismatch prediction module is used to acquire energy storage battery operation log data; determine the overload trend of energy storage battery operation based on the energy storage battery operation log data; detect the gradient degradation of lithium battery operation performance based on the overload trend of energy storage battery operation; and predict the dynamic load response mismatch of energy storage lead-acid battery based on the energy storage battery operation log data. The power quality degradation prediction module is used to couple and determine the dynamic load response imbalance of the energy storage lead-acid battery and the gradient degradation of the lithium battery's operating performance to obtain dynamic degradation coupling data of the energy storage battery; determine the energy storage battery's operating stability degradation based on the dynamic degradation coupling data of the energy storage battery; and predict the energy quality degradation trend of the energy storage battery based on the energy storage battery's operating stability degradation. The battery life prediction module is used to predict the local degradation of the battery cells based on the degradation trend of the energy transmission quality of the energy storage battery; detect the operational risk status of the energy storage battery based on the local degradation status of the battery cells; and predict the life of the energy storage battery based on the operational risk status and the local degradation status of the battery cells, thereby obtaining the energy storage battery life prediction data.

[0081] A computer-readable storage medium storing a computer program, wherein the computer program is used to perform the energy storage battery life prediction method.

[0082] The above description is merely a specific embodiment of the present invention, enabling those skilled in the art to understand or implement the invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the invention. Therefore, the present invention is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features of the invention herein.

Claims

1. A method for predicting the lifespan of an energy storage battery, characterized in that, Includes the following steps: Step S1: Obtain energy storage battery design data; Evaluate energy storage battery performance data based on energy storage battery design data; The energy storage battery performance data is divided into energy storage lithium battery performance data and energy storage lead-acid battery performance data. Step S2: Obtain energy storage battery operation log data; determine the overload trend of energy storage battery operation based on energy storage battery operation log data; detect the gradient degradation trend of lithium battery operation performance based on energy storage battery performance data based on energy storage battery overload trend; predict the dynamic load response imbalance of energy storage lead-acid battery based on energy storage lead-acid battery performance data based on energy storage battery overload trend. Step S3: Couple the dynamic load response misalignment of the energy storage lead-acid battery and the gradient degradation of the lithium battery's operating performance to obtain coupled data on the dynamic degradation of the energy storage battery's performance; determine the degradation of the energy storage battery's operating stability based on the coupled data on the dynamic degradation of the energy storage battery's performance; predict the degradation trend of the energy quality of the energy storage battery's transmission based on the degradation of the energy storage battery's operating stability. Step S4: Predict the local degradation of the energy storage battery cells based on the energy transmission quality degradation trend; detect the operational risk of the energy storage battery based on the local degradation of the energy storage battery cells; predict the lifespan of the energy storage battery based on the operational risk and the local degradation of the energy storage battery cells, and obtain the lifespan prediction data of the energy storage battery.

2. The method for predicting the lifespan of an energy storage battery according to claim 1, characterized in that, Step S1 includes the following steps: Step S11: Obtain energy storage battery design data; Step S12: Collect internal structural data of the energy storage battery based on the energy storage battery design data; Step S13: Evaluate the performance data of the energy storage battery based on the design data and internal structure data of the energy storage battery; Step S14: Divide the energy storage battery performance data to obtain energy storage lithium battery performance data and energy storage lead-acid battery performance data.

3. The method for predicting the lifespan of an energy storage battery according to claim 2, characterized in that, Step S13 includes the following steps: Step S131: Identify the arrangement of individual energy storage battery cells based on the internal structure data of the energy storage battery; Step S132: Collect the cell arrangement structure of the energy storage battery based on the internal structure data of the energy storage battery; Step S133: Determine the internal heat conduction path of the energy storage battery based on the spacing of the battery cell arrangement method when it exceeds 30mm and the battery cell arrangement structure. Step S134: Collect thermal conductivity data of energy storage battery electrode materials based on energy storage battery design data; Step S135: Evaluate the heat dissipation capacity of the energy storage battery based on the thermal conductivity data of the battery electrode material exceeding 2.5 W / m·K and the internal heat conduction path of the energy storage battery; Step S136: Test the electrical characteristics of the energy storage battery based on the energy storage battery design data; Step S137: Evaluate the performance data of the energy storage battery based on its electrical characteristics and heat dissipation capacity.

4. The method for predicting the lifespan of an energy storage battery according to claim 1, characterized in that, The determination of the overload trend of the energy storage battery in step S2 includes: 48 hours of energy storage battery operation log data were collected for energy storage battery equipment operation time series analysis to obtain energy storage battery equipment operation time series data. Identify changes in the electrical energy transmitted by energy storage batteries based on the operating time series data of energy storage battery devices; Assess the fluctuation of energy storage battery charging and discharging demand when the change in the energy transmitted by the energy storage battery exceeds 41.4%; Detect sudden surges in energy storage battery demand based on fluctuations in energy storage battery charging and discharging demand. Predict the extent to which the carrying capacity of energy storage batteries exceeds limits based on sudden surges in demand for energy storage batteries. The overload trend of energy storage batteries is determined based on the degree of overload of the energy storage battery and the sudden surge in demand for energy storage batteries.

5. The method for predicting the lifespan of an energy storage battery according to claim 1, characterized in that, Step S2, the detection of the gradient degradation of lithium battery operating performance, includes: Based on the trend of excessive load during operation of energy storage batteries, high-rate discharge testing of energy storage lithium batteries is conducted on the performance data of energy storage lithium batteries to obtain the high-rate discharge situation of energy storage lithium batteries. Predicting an abnormally high lithium-ion deintercalation rate in energy storage lithium batteries based on high-rate discharge conditions; An abnormal increase in the lithium-ion intercalation / deintercalation rate can be used to identify abnormal vacuum conditions in the lithium battery lattice layer. Predict the collapse trend of the lithium battery lattice structure based on the abnormal evacuation of the lithium battery lattice layer. Predicting lithium-ion migration channel blockage based on the lattice structure collapse trend of energy storage lithium batteries; Predict the interruption of charge transfer paths in lithium batteries based on the blockage of lithium-ion migration channels and the collapse trend of the lattice structure of energy storage lithium batteries; Determine the failure status of lithium battery active materials based on the interruption of lithium battery charge transfer path; Predict the increase in lithium battery transport resistance based on the interruption of lithium battery charge transfer path and the failure status of lithium battery active materials. The gradient deterioration of lithium battery performance is detected based on the increase in lithium battery transmission resistance and the failure status of lithium battery active materials.

6. The method for predicting the lifespan of an energy storage battery according to claim 1, characterized in that, The prediction of dynamic load response misalignment of the energy storage lead-acid battery in step S2 includes: Based on the trend of excessive load during the operation of energy storage batteries, the performance data of energy storage lead-acid batteries are analyzed to determine the situation of excessive current output of energy storage lead-acid batteries. Based on the excessive current output of the energy storage lead-acid battery, the performance data of the energy storage lead-acid battery are used to predict the formation of lead sulfate crystals in the battery, and the formation of lead sulfate crystals in the battery is obtained. Detecting abnormal electrolyte circulation within a battery based on the formation of lead sulfate crystals in the battery; Predict the abnormal deterioration of the battery plates based on the abnormal circulation of the electrolyte inside the battery. The bridging and short circuit of the positive and negative electrodes of the lead-acid battery can be determined based on the softening of the battery plates and the abnormal circulation of the electrolyte inside the battery. Predict the degradation of electrode plate conductivity based on abnormal deterioration of energy storage battery plates. The dynamic load response imbalance of energy storage lead-acid batteries can be predicted based on the degradation of electrode plate conductivity and the bridging short circuit between the positive and negative electrodes.

7. The method for predicting the lifespan of an energy storage battery according to claim 1, characterized in that, Step S3 includes the following steps: Step S31: Determine the dynamic load response misalignment of the energy storage lead-acid battery and the gradient degradation of the lithium battery's operating performance by coupling them together to obtain coupled data on the dynamic degradation of the energy storage battery's performance. Step S32: Determine the degradation of the operational stability of the energy storage battery based on the coupled data of dynamic degradation of energy storage battery performance; Step S33: Determine the capacity decay trend of the energy storage battery based on the coupled data of dynamic degradation of energy storage battery performance; Step S34: Predict the degradation trend of energy transmission quality of the energy storage battery based on the degradation of the energy storage battery's operational stability and capacity.

8. The method for predicting the lifespan of an energy storage battery according to claim 1, characterized in that, Step S4 includes the following steps: Step S41: Predict the local degradation of the energy storage battery cells based on the energy transmission quality degradation trend of the energy storage battery. Step S42: Identify the accumulation of deviations within the energy storage battery cluster based on the localized degradation of the energy storage battery cells; Step S43: Detect the operational risk status of the energy storage battery based on the accumulation of deviations within the energy storage battery cluster and the localized degradation of the energy storage battery cells; Step S44: Based on the operational risk status of the energy storage battery and the accumulation of deviations within the energy storage battery cluster, predict the lifespan of the energy storage battery to obtain energy storage battery lifespan prediction data.

9. A battery life prediction system, characterized in that, For performing the energy storage battery life prediction method as described in claim 1, the energy storage battery life prediction system includes: The battery performance evaluation module is used to acquire energy storage battery design data; evaluate energy storage battery performance data based on the energy storage battery design data; and divide the energy storage battery performance data to obtain energy storage lithium battery performance data and energy storage lead-acid battery performance data. The load response mismatch prediction module is used to acquire energy storage battery operation log data; determine the overload trend of energy storage battery operation based on the energy storage battery operation log data; detect the gradient degradation of lithium battery operation performance based on the overload trend of energy storage battery operation; and predict the dynamic load response mismatch of energy storage lead-acid battery based on the energy storage battery operation log data. The power quality degradation prediction module is used to couple and determine the dynamic load response imbalance of the energy storage lead-acid battery and the gradient degradation of the lithium battery's operating performance to obtain dynamic degradation coupling data of the energy storage battery; determine the energy storage battery's operating stability degradation based on the dynamic degradation coupling data of the energy storage battery; and predict the energy quality degradation trend of the energy storage battery based on the energy storage battery's operating stability degradation. The battery life prediction module is used to predict the local degradation of the battery cells based on the degradation trend of the energy transmission quality of the energy storage battery; detect the operational risk status of the energy storage battery based on the local degradation status of the battery cells; and predict the life of the energy storage battery based on the operational risk status and the local degradation status of the battery cells, thereby obtaining the energy storage battery life prediction data.

10. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed, it implements the energy storage battery life prediction method as described in any one of claims 1 to 8.

Images