Battery simulation test method and system and storage medium

Abstract

The invention relates to the technical field of battery testing, in particular to a battery simulation testing method and system and a storage medium. The method comprises the following steps: obtaining the particle size of a single battery; carrying out particle size gradient layering on the particle size of the single battery, and dividing the electrode material into a plurality of particle size intervals according to the particle size; recording the number and distribution density of particles in each particle size interval, and generating a particle size gradient distribution diagram; detecting a single battery material; performing pore topology analysis on the single battery material, and calculating pore connectivity, pore diameter and porosity; and according to the particle size gradient distribution diagram, determining the influence of particle size distribution on the topological characteristics of the pore structure, and generating particle-pore associated characteristic data. Through a pore topology analysis technology and a dynamic response monitoring technology, correlation analysis of the microstructure and the macroscopic performance of the battery is realized, so that the accuracy and the reliability of battery performance prediction are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of battery testing, and in particular to a battery simulation testing method, system and storage medium. Background Art

[0002] As an energy storage and conversion device, batteries are primarily characterized by differences in performance parameters between individual cells, the particle size characteristics of electrode materials, and their pore structure. However, existing battery simulation testing technologies have significant shortcomings in these areas. First, the performance parameters (such as capacity, internal resistance, and self-discharge rate) of individual cells in a battery pack vary, and these differences increase with increasing charge and discharge cycles. Existing technologies struggle to accurately simulate these variations, leading to problems such as overcharge and over-discharge during the battery pack's charge and discharge processes, impacting battery life and safety. Second, the particle size of the electrode material significantly influences lithium-ion transport and electrode structural stability. Smaller particles shorten the lithium-ion diffusion path, but excessively small particle sizes increase the probability of side reactions. Existing technologies struggle to accurately simulate the actual particle size distribution and its dynamic changes during charge and discharge, resulting in inadequate predictions and long-term evaluations of battery performance. Furthermore, the pore structure of the electrode material affects its mechanical strength and chemical stability, which in turn impacts the battery's cycle life and safety. Summary of the Invention

[0003] Based on this, it is necessary to provide a battery simulation test method, system and storage medium to solve at least one of the above technical problems.

[0004] To achieve the above object, a battery simulation test method is provided, the method comprising the following steps: Step S1: Obtain the particle size of the single cell; perform particle size gradient stratification on the particle size of the single cell, and divide the electrode material into multiple particle size intervals according to the particle size; record the number and distribution density of particles in each particle size interval, and generate a particle size gradient distribution map; Step S2: Detecting the single cell material; performing pore topology analysis on the single cell material and calculating pore connectivity, pore size, and porosity; determining the effect of particle size distribution on the topological characteristics of the pore structure based on the particle size gradient distribution map, and generating particle-pore correlation feature data; Step S3: Collecting the capacity and internal resistance of a single cell; fitting a capacity decay curve for the capacity of the single cell, calculating the capacity change trend during multiple charge and discharge cycles, and fitting a capacity decay curve; performing an internal resistance response analysis on the internal resistance of the single cell, and monitoring the real-time change of the internal resistance during the charge and discharge process to generate internal resistance dynamic response data; using the capacity decay curve to evaluate the impact on the internal resistance dynamic response data, and generating capacity-internal resistance correlation data; Step S4: Collect the charge and discharge usage time of the battery; construct a battery performance simulation model based on the capacity-internal resistance correlation data, the particle size gradient distribution map and the particle-pore correlation characteristic data; predict the long-term performance simulation of the battery based on the battery performance simulation model and the number of charge and discharge cycles and usage time to obtain a battery performance prediction report.

[0005] Preferably, step S1 includes the following steps: Step S11: Collecting single cells; pre-processing the single cells, performing preliminary screening of the battery particles using a vibration screening device, and removing single cells with abnormal sizes; Step S12: measuring the particle size of the screened single cells, measuring the length, width, and height of each particle, and recording the particle size measurement data; Step S13: Based on the particle size measurement data, the single battery particles are subjected to a particle size gradient stratification, and the particles are divided into multiple particle size intervals according to the size range to obtain the particle count; the particle size range of each interval is 10% of the particle size of the previous interval; Step S14: Count the number of particles in each particle size range and record the statistical results; calculate the volume percentage of particles in each particle size range to obtain distribution density data; Step S15: Draw a particle size gradient distribution diagram based on the particle quantity and distribution density data.

[0006] Preferably, performing pore topology analysis on the single cell material and calculating pore connectivity, pore size and porosity in step S2 includes: The surface of the single cell material is divided into a plurality of grid units of equal area, and the area of each grid unit is 1 square millimeter; In each grid cell, use an optical microscope to capture images of the pore structure at a perpendicular angle to the surface of the battery material with a resolution of no less than 1000 pixels / mm; The image of each grid cell is digitized to extract the pore contour information, including the boundary coordinates and shape characteristics of the pore; Based on the pore contour information, the pore connectivity within each grid cell is calculated; the number of connected paths between adjacent pores is detected, and pores with more than three connected paths are considered to have high connectivity. Measure the diameter of each pore and calculate the minimum circumscribed circle diameter of the pore outline to determine the pore size; The porosity within each grid cell was calculated, the ratio of the total pore area to the grid cell area was calculated to determine the porosity, and the porosity was recorded as a percentage.

[0007] Preferably, in step S2, determining the influence of the particle size distribution on the topological characteristics of the pore structure according to the particle size gradient distribution diagram includes: The particle size data in the particle size gradient distribution diagram are divided into particle size intervals in the order from small to large. When the particle size data is less than 1 micron, it is a small particle interval; when the particle size data is in the range of 1-5 microns, it is a medium particle interval; when the particle size data is greater than 5 microns, it is a large particle interval; Extract pore connectivity, pore size, and porosity from the topological features of the pore structure; In the particle size range, the pore connectivity is converted into a topological map, and the pore permeability is evaluated by the number and length of branches in the topological map. Within the particle size range, the pore size distribution is divided into three intervals: less than 1 micron, 1-10 microns, and greater than 10 microns. The pore size percentage in each interval is calculated. The pore size concentration is evaluated by the change in the pore size percentage. In the particle size range, the ratio of pore volume to total volume in each particle size range is calculated, and the porosity is evaluated by the pore volume ratio; The pore penetration degree, pore size concentration and porosity are fused and recorded to obtain particle-pore correlation feature data.

[0008] Preferably, in step S3, the capacity decay curve of the single battery is fitted to calculate the change trend of the capacity in multiple charge and discharge cycles. Fitting the capacity decay curve includes: Perform charge and discharge cycle tests on single cells for at least 50 times, and record the discharge capacity data for each cycle; Sort the recorded discharge capacity data according to the number of charge and discharge cycles to form a capacity data sequence; Calculate the capacity retention rate of each charge and discharge cycle, where the capacity retention rate is defined as the ratio of the discharge capacity of the current cycle to the initial discharge capacity; Draw a scatter plot of capacity retention versus charge and discharge cycle number; The charge and discharge cycle stages are divided according to the number of charge and discharge cycles. When the number of charge and discharge cycles is less than 10 times, it is marked as the early stage of the charge and discharge cycle; when the number of charge and discharge cycles is between 11 and 30 times, it is marked as the middle stage of the charge and discharge cycle; when the number of late charge and discharge cycles is greater than 31 times, it is marked as the late stage of the charge and discharge cycle; At the initial stage of charge and discharge cycles, the change in capacity retention rate for each cycle is calculated and the change value for each cycle is recorded to form an initial change sequence; For the mid-term of the charge and discharge cycle, calculate the average capacity retention rate of every 5 cycles, and record the average value of every 5 cycles to form a mid-term average sequence; For the later stage of charge and discharge cycles, the weighted average capacity retention rate of every 10 cycles is calculated, and the weighted average value of every 10 cycles is recorded to form a later stage weighted sequence.

[0009] Preferably, in step S3, the capacity decay curve of the single battery is fitted to calculate the change trend of the capacity during multiple charge and discharge cycles. Fitting the capacity decay curve further includes: For the initial change sequence, calculate the difference in the amount of change between adjacent cycles, and record the difference in the amount of change for each cycle to form an initial difference sequence; For the mid-term average sequence, calculate the difference between the average values of 5 adjacent cycles, and record the difference between the average values of each 5 cycles to form a mid-term difference sequence; For the late weighted sequence, calculate the weighted average difference between 10 adjacent cycles, and record the weighted average difference of each 10 cycles to form a late difference sequence; According to the initial difference sequence, calculate the change trend of each data point and extract the change trend value of each cycle; According to the mid-term difference sequence, the change trend of each data point is calculated, and the change trend value of every 5 cycles is extracted; According to the late difference sequence, the change trend of each data point is calculated and the change trend value of every 10 cycles is extracted; Compare and analyze the change trend values in the early, middle and late stages to determine the capacity decay rate in each stage and obtain the capacity decay characteristics; Mark the capacity decay points according to the capacity decay characteristics of each stage; A capacity decay curve is drawn based on the capacity decay points, where the slope of each straight line in the capacity decay curve represents the capacity decay rate at that stage.

[0010] Preferably, in step S3, evaluating the impact of the capacity decay curve on the internal resistance dynamic response data includes: Extracting the attenuation characteristic points of the capacity attenuation curve, where the attenuation characteristic points include the initial capacity point, the cycle number point at which the capacity retention rate first decreases by 10%, the cycle number point at which the capacity retention rate first decreases by 20%, and the final capacity point; Extracting response characteristic points of the internal resistance dynamic response data, wherein the response characteristic points include the initial internal resistance point, the cycle number point at which the internal resistance first significantly increases, the cycle number point at which the internal resistance first reaches a peak value, and the final internal resistance point; Time-align the attenuation characteristic point with the response characteristic point; calculate the correlation coefficient between the attenuation characteristic point and the response characteristic point at each cycle number point to obtain the attenuation and internal resistance correlation coefficient; According to the correlation coefficient between attenuation and internal resistance, the cycle number points are divided into high correlation interval, medium correlation interval and low correlation interval. Among them, the high correlation interval indicates that there is a strong correlation between capacity attenuation and internal resistance change, and the low correlation interval indicates that the correlation between the two is weak. In the high correlation range, calculate the slope between the capacity retention rate and the internal resistance change. The larger the absolute value of the slope, the more significant the impact of the internal resistance change on the capacity fade. In the medium correlation interval, calculate the curvature of the curve between the capacity retention rate and the internal resistance change. The greater the curvature, the more complex it is to determine the effect of the internal resistance change on the capacity decay. In the low correlation range, the degree of dispersion between the capacity retention rate and the internal resistance change is calculated. The greater the degree of dispersion, the more random the effect of the internal resistance change on the capacity decay. The results corresponding to the high correlation interval, the medium correlation interval, and the low correlation interval are mapped into a correlation data table to generate capacity-internal resistance correlation data.

[0011] Preferably, step S4 includes the following steps: Step S41: Collect the battery charge and discharge time, and use a high-precision timer to record the start and end time of each charge and discharge cycle, accurate to the second; Step S42: Record the total battery usage time, and add up the duration of all charge and discharge cycles to obtain the total battery usage time; Step S43: The capacity-internal resistance correlation data weight is set to 0.4, the particle size gradient distribution map weight is set to 0.3, and the particle-pore correlation feature data weight is set to 0.3, and the weights are weighted and calculated to construct a battery performance simulation model; Step S44: Inputting the battery's charge and discharge usage time into the battery performance simulation model, predicting the battery's capacity retention rate and internal resistance change after 100 cycles, and outputting a battery performance simulation score; Step S45: Determine the battery performance according to the battery performance simulation score and obtain a battery performance prediction report.

[0012] The technical benefits of the present invention are as follows: Step S1 stratifies the particle size of individual cells into a particle size gradient and generates a particle size gradient distribution map, accurately recording the number and distribution density of particles within each particle size range. This detailed particle size distribution information provides fundamental data for subsequent analysis, ensuring a comprehensive understanding of the battery's internal microstructure, thereby providing an accurate structural basis for in-depth research on battery performance. In step S2, pore topology analysis is performed on the individual cell materials to calculate pore connectivity, pore size, and porosity. Combined with the particle size gradient distribution map, particle-pore correlation feature data is generated. This process not only comprehensively assesses the pore structure of the battery material but also clarifies the specific impact of particle size distribution on the pore structure topology, providing key data support for understanding the physical and chemical processes within the battery and revealing the intrinsic connection between battery performance and microstructure. Step S3 collects individual cell capacity and internal resistance data, performs capacity decay curve fitting, and analyzes the internal resistance response to generate capacity decay curves and internal resistance dynamic response data. Furthermore, the capacity decay curve is used to assess the impact of the internal resistance dynamic response data, generating capacity-internal resistance correlation data. This series of operations can accurately monitor the performance changes of the battery during the charge and discharge process, providing comprehensive and accurate data support for the evaluation of battery performance, and ensuring accurate grasp of the trend of battery performance changes. Step S4 constructs a battery performance simulation model based on the capacity-internal resistance correlation data, the particle size gradient distribution map, and the particle-pore correlation characteristic data, and predicts the long-term performance of the battery in combination with the number of charge and discharge cycles and the usage time to generate a battery performance prediction report. This method can comprehensively consider the relationship between the internal microstructure and macroscopic performance of the battery, predict the long-term performance of the battery through the simulation model, provide a reliable basis for the design, optimization and application of the battery, and evaluate the battery life and performance stability in advance.

[0013] This specification also provides a battery simulation test system for executing the battery simulation test method described above, the battery simulation test system comprising: The particle size gradient detection module is used to obtain the particle size of the single cell; perform particle size gradient stratification on the particle size of the single cell and divide the electrode material into multiple particle size intervals according to the particle size; record the number and distribution density of particles in each particle size interval and generate a particle size gradient distribution map; The particle-pore correlation module is used to detect single-cell battery materials; it performs pore topology analysis on single-cell battery materials and calculates pore connectivity, pore size, and porosity; based on the particle size gradient distribution map, it determines the impact of particle size distribution on the topological characteristics of the pore structure and generates particle-pore correlation feature data; The capacity-internal resistance correlation module is used to collect the capacity and internal resistance of a single cell; perform capacity decay curve fitting on the capacity of the single cell, calculate the capacity change trend during multiple charge and discharge cycles, and fit the capacity decay curve; perform internal resistance response analysis on the internal resistance of the single cell, and monitor the real-time changes of the internal resistance during the charge and discharge process to generate internal resistance dynamic response data; use the capacity decay curve to evaluate the impact on the internal resistance dynamic response data, and generate capacity-internal resistance correlation data; The battery performance simulation prediction module is used to collect the battery's charge and discharge usage time; build a battery performance simulation model based on capacity-internal resistance correlation data, particle size gradient distribution map and particle-pore correlation characteristic data; predict the long-term performance of the battery based on the battery performance simulation model and the number of charge and discharge cycles and usage time to obtain a battery performance prediction report.

[0014] The battery simulation test system of the present invention realizes comprehensive detection and accurate prediction from battery microstructure to macroscopic performance through the synergistic effect of the particle size gradient detection module, particle-pore correlation module, capacity-internal resistance correlation module and battery performance simulation prediction module, providing systematic data support and reliable prediction basis for battery performance evaluation and optimization, thereby effectively improving the scientificity and accuracy of battery research and development and application.

[0015] A computer-readable storage medium stores a computer program, which implements the above-mentioned battery simulation test method when executed. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 A schematic flow chart of the steps of a battery simulation test method; Figure 2 for Figure 1 Detailed implementation steps of step S1 in FIG. The purpose, features and advantages of the present invention will be further described with reference to the accompanying drawings and in conjunction with the embodiments. DETAILED DESCRIPTION

[0017] The following is a clear and complete description of the technical method of the present invention in conjunction with the accompanying drawings. It is obvious that the embodiments described are part of the embodiments of the present invention, but not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without making any creative efforts are within the scope of protection of the present invention.

[0018] In addition, the accompanying drawings are merely schematic illustrations of the present invention and are not necessarily drawn to scale. Identical reference numerals in the figures denote identical or similar parts, and thus repetitive descriptions thereof will be omitted. Some of the block diagrams shown in the accompanying drawings are functional entities that do not necessarily correspond to physically or logically separate entities. These functional entities may be implemented in software, in one or more hardware modules or integrated circuits, or in different network and / or processor and / or microcontroller approaches.

[0019] It should be understood that although the terms "first," "second," and the like may be used herein to describe various elements, these elements should not be limited by these terms. These terms are used solely to distinguish one element from another. For example, a first element may be referred to as a second element, and similarly, a second element may be referred to as a first element, without departing from the scope of the exemplary embodiments. The term "and / or" as used herein includes any and all combinations of one or more of the listed associated items.

[0020] To achieve this, please refer to Figures 1 to 2 , a battery simulation test method, the method comprising the following steps: Step S1: Obtain the particle size of the single cell; perform particle size gradient stratification on the particle size of the single cell, and divide the electrode material into multiple particle size intervals according to the particle size; record the number and distribution density of particles in each particle size interval, and generate a particle size gradient distribution map; In an embodiment of the present invention, a laser particle size analyzer is used to accurately measure the particle size of a single cell. A sample of the electrode material from the single cell to be tested is placed in a dedicated sample dispersion device. Ultrasonic dispersion is performed using a frequency of 40 kHz and a time of 3 minutes to fully disperse the electrode material particles and prevent particle agglomeration. Subsequently, the laser particle size analyzer is activated, using optical parameters of a refractive index of 1.59 and an absorptivity of 0.1. Particle size measurements are performed in accordance with ISO 13320 standards, with a measurement range of 0.1 to 300 microns and a measurement time of 120 seconds. The measurement is repeated three times to ensure data accuracy, and the average value is taken as the final particle size measurement result. After the measurement is completed, computer-assisted analysis software is used to perform a particle size gradient stratification based on the measured particle size data. The electrode material particle size range is divided into multiple particle size intervals. For example, using 10-micron intervals as a unit, the first particle size interval is 0.1 to 10 microns, the second particle size interval is 10 to 20 microns, and so on, until the entire measured particle size range is covered. For each particle size range, the software automatically counts the number of particles within that range and calculates the distribution density. The distribution density is determined by the ratio of the number of particles to the volume of the particle size range, with the unit being particles / cubic micron. Finally, the particle number and distribution density data for each particle size range are imported into the mapping software. With the particle size range as the horizontal axis and the particle number and distribution density as the vertical axis, a particle size gradient distribution map is generated. This distribution map intuitively shows the number and distribution of single cell electrode material particles within different particle size ranges.

[0021] Step S2: Detecting the single cell material; performing pore topology analysis on the single cell material and calculating pore connectivity, pore size, and porosity; determining the effect of particle size distribution on the topological characteristics of the pore structure based on the particle size gradient distribution map, and generating particle-pore correlation feature data; In an embodiment of the present invention, pore topology analysis is performed on single-cell battery materials. High-resolution X-ray computed tomography (CT) technology is used for nondestructive testing of single-cell battery materials. A single-cell battery material sample is mounted on the sample stage of a CT scanner. Scan parameters are set as an X-ray tube voltage of 100 kV, a tube current of 100 μA, a scanning resolution of 1 micron, a scanning angle range of 0 to 360 degrees, and a step angle of 0.1 degrees. CT scanning acquires three-dimensional image data of the interior of the single-cell battery material. The image data is stored in a 16-bit grayscale format with a resolution of 2048 × 2048 pixels. Subsequently, image processing software is used to analyze and process the acquired 3D image data. Threshold segmentation is performed on the image, using a grayscale value of 128 as the threshold to separate the pore region from the solid material region in the image. Pore connectivity, pore size, and porosity are calculated based on the segmented image data. The pore connectivity is analyzed by the eight-connected algorithm, and the proportion of the number of interconnected pores in the pore network to the total number of pores is counted; the pore size distribution is determined by calculating the equivalent spherical diameter of each pore, and the pore size range is from 0.1 microns to 100 microns, with 1 micron as an interval unit for statistics; the porosity is determined by calculating the ratio of the pore volume to the total volume of the material, and the result is expressed as a percentage. Then, combined with the particle size gradient distribution map generated in step S1, the effect of the particle size distribution on the topological characteristics of the pore structure is analyzed. The correlation analysis algorithm is used to calculate the correlation coefficient between the particle size distribution and the pore connectivity, pore size distribution and porosity to determine the specific influence of the particle size distribution on the pore structure. The particle size distribution data is correlated with the pore structure characteristic data to generate particle-pore correlation characteristic data, which includes the correspondence between the particle size interval and the corresponding pore connectivity, pore size distribution and porosity, and is stored in a tabular form.

[0022] Step S3: Collecting the capacity and internal resistance of a single cell; fitting a capacity decay curve for the capacity of the single cell, calculating the capacity change trend during multiple charge and discharge cycles, and fitting a capacity decay curve; performing an internal resistance response analysis on the internal resistance of the single cell, and monitoring the real-time change of the internal resistance during the charge and discharge process to generate internal resistance dynamic response data; using the capacity decay curve to evaluate the impact on the internal resistance dynamic response data, and generating capacity-internal resistance correlation data; In this embodiment of the present invention, the capacity of a single battery is collected. The single battery is installed in a high-precision battery testing system and set to constant current charge and discharge mode, with the charge current set to 1C, the discharge current also set to 1C, the charge cut-off voltage set to 4.2V, and the discharge cut-off voltage set to 2.5V. In this mode, the single battery is subjected to multiple charge and discharge cycles, totaling 100 cycles. After each charge and discharge cycle, the system automatically records the battery's discharge capacity in milliampere-hours (mAh). After collection, the recorded capacity data is imported into data analysis software and processed using a nonlinear fitting method, selecting an exponential decay model for fitting. This fitting method calculates a capacity decay curve, which reflects the capacity change trend over multiple charge and discharge cycles. Next, the internal resistance of the single battery is collected and analyzed. The internal resistance of the single battery is measured using the AC impedance method in the same charge and discharge testing system. The AC impedance test parameters are set to an AC signal amplitude of 10 mV and a frequency range of 0.1 Hz to 100 kHz. During the charge and discharge process, the system automatically collects internal resistance data, including the battery's AC impedance spectrum, at every 1% capacity change. The AC impedance spectrum is analyzed using an equivalent circuit model to extract the battery's internal resistance value in ohms (Ω). Simultaneously, the system monitors real-time changes in internal resistance during the charge and discharge process, generating internal resistance dynamic response data. This data is stored as a time series, containing the corresponding relationship between internal resistance values and the corresponding charge and discharge capacity percentages. Finally, the internal resistance dynamic response data is evaluated using the fitted capacity decay curve. A correlation analysis is performed between the capacity decay characteristic parameters in the capacity decay curve and the internal resistance values in the internal resistance dynamic response data, and the correlation between the two is calculated. Based on the strength of the correlation, the degree of impact of capacity decay on the internal resistance dynamic response is determined. The capacity decay data and the internal resistance dynamic response data are correlated to generate capacity-internal resistance correlation data.

[0023] Step S4: Collect the charge and discharge usage time of the battery; construct a battery performance simulation model based on the capacity-internal resistance correlation data, the particle size gradient distribution map and the particle-pore correlation characteristic data; predict the long-term performance simulation of the battery based on the battery performance simulation model and the number of charge and discharge cycles and usage time to obtain a battery performance prediction report.

[0024] In this embodiment of the present invention, the battery's charge and discharge time is first collected. The individual cells are connected to a high-precision battery management system, which is configured to record the start and end times of each charge and discharge process with millisecond-level accuracy. The system automatically calculates the duration of each charge and discharge cycle and stores this time data as a timestamp, along with the corresponding number of charge and discharge cycles. The charge and discharge time data is output in a table format, containing information such as the number of cycles, charge and discharge start and end times, and duration. Subsequently, battery performance simulation operations are performed based on the capacity-internal resistance correlation data, the particle size gradient distribution map, and the particle-pore correlation feature data. These three types of data are imported into professional data analysis software. Using the software's data fusion function, the cycle number, capacity, and internal resistance values in the capacity-internal resistance correlation data are correlated with the particle size range and distribution density in the particle size gradient distribution map, and the pore connectivity, pore size distribution, and porosity in the particle-pore correlation feature data. A correlation analysis algorithm is then configured to analyze the influence of particle size distribution and pore structure characteristics on capacity decay and internal resistance change, thereby determining the interactions between these factors. Based on the above correlation analysis results, combined with the charge and discharge cycle number and usage time data, the long-term performance of the battery is simulated using the software's predictive analysis module. Set the simulation parameters, including the battery's initial capacity, initial internal resistance, particle size distribution range, porosity range, and upper limits for the number of charge and discharge cycles and usage time. Based on the input parameters and existing correlation data, the software predicts the battery's capacity retention rate, internal resistance growth trend, and overall performance changes under different charge and discharge cycle numbers and usage time through calculation and deduction. Finally, a battery performance prediction report is generated. The report is presented in the form of charts and text, including the predicted capacity retention rate curve, internal resistance growth curve, and key node information of battery performance attenuation, providing a basis for battery performance evaluation and life prediction.

[0025] As an example of the present invention, refer to Figure 2 As shown, in this example, step S1 includes: Step S11: Collecting single cells; pre-processing the single cells, performing preliminary screening of the battery particles using a vibration screening device, and removing single cells with abnormal sizes; Step S12: measuring the particle size of the screened single cells, measuring the length, width, and height of each particle, and recording the particle size measurement data; Step S13: Based on the particle size measurement data, the single battery particles are subjected to a particle size gradient stratification, and the particles are divided into multiple particle size intervals according to the size range to obtain the particle count; the particle size range of each interval is 10% of the particle size of the previous interval; Step S14: Count the number of particles in each particle size range and record the statistical results; calculate the volume percentage of particles in each particle size range to obtain distribution density data; Step S15: Draw a particle size gradient distribution diagram based on the particle quantity and distribution density data.

[0026] In this embodiment of the present invention, single-cell battery samples are first collected. The single-cell battery samples are placed in a vibrating screening device set to a vibration frequency of 50 Hz, an amplitude of 5 mm, and a screening time of 10 minutes. The vibrating screening device has a mesh size of 0.5 mm, which is used to remove abnormal particles smaller than 0.5 mm or larger than twice the mesh size (i.e., 1 mm). After screening, the single-cell battery particles that pass through the mesh are collected and the abnormally sized particles are removed. Step S12 is then performed to measure the dimensions of the screened single-cell battery particles. A three-dimensional optical measuring instrument is used to accurately measure the length, width, and height of each particle. The measuring instrument's resolution parameters are set to 0.01 mm, and the measurement range is 0.5 mm to 5 mm. Single-cell battery particles are placed one by one on the measuring instrument's stage. The automatic scanning function acquires the length, width, and height data for each particle. The measurement results are stored in the data acquisition system, creating a particle size measurement data table that records information such as particle number, length, width, and height. In step S13, the single cell particles are stratified by particle size gradient according to the particle size measurement data. The longest dimension of the particle is used as the particle size reference value, and the particles are divided into multiple particle size intervals according to the size range. The first particle size interval is set to 0.5 mm to 0.55 mm, and the particle size range of each subsequent interval is 10% of the particle size of the previous interval. For example, the second interval is 0.55 mm to 0.605 mm, and so on. Through the data processing software, the particle size measurement data is matched with the particle size interval, and the number of particles in each particle size interval is counted. Proceed to step S14, count the number of particles in each particle size interval, and calculate the volume proportion of particles in each particle size interval. The statistical software automatically counts the number of particles in each particle size interval and records the statistical results. When calculating the volume proportion of particles in each particle size interval, first calculate its volume based on the length, width, and height of each particle, and then add up the volumes of all particles in the same particle size interval to obtain the total volume of the interval. Next, the total volume of each interval is divided by the sum of the total volumes of all particles to obtain the volume fraction of particles within that size interval, i.e., the distribution density data. Finally, in step S15, a particle size gradient distribution diagram is plotted using mapping software based on the particle number and distribution density data. A combined bar chart and line graph is generated, with the particle size interval as the horizontal axis and the particle number and distribution density as the vertical axis. The bar chart represents the number of particles within each size interval, and the line graph shows the changing trend of the distribution density, thereby intuitively displaying the size distribution characteristics of the single cell particles.

[0027] Preferably, performing pore topology analysis on the single cell material and calculating pore connectivity, pore size and porosity in step S2 includes: The surface of the single cell material is divided into a plurality of grid units of equal area, and the area of each grid unit is 1 square millimeter; In each grid cell, use an optical microscope to capture images of the pore structure at a perpendicular angle to the surface of the battery material with a resolution of no less than 1000 pixels / mm; The image of each grid cell is digitized to extract the pore contour information, including the boundary coordinates and shape characteristics of the pore; Based on the pore contour information, the pore connectivity within each grid cell is calculated; the number of connected paths between adjacent pores is detected, and pores with more than three connected paths are considered to have high connectivity. Measure the diameter of each pore and calculate the minimum circumscribed circle diameter of the pore outline to determine the pore size; The porosity within each grid cell was calculated, the ratio of the total pore area to the grid cell area was calculated to determine the porosity, and the porosity was recorded as a percentage.

[0028] In an embodiment of the present invention, the surface of a single battery material is first divided into multiple grid cells of equal area, with each grid cell measuring 1 square millimeter. High-precision laser cutting equipment is used to mark the surface of the battery material to ensure the accuracy of the grid division. Subsequently, an optical microscope is used to photograph the pore structure within each grid cell. The microscope's imaging angle is set perpendicular to the battery material surface to ensure image clarity and accuracy. The imaging resolution is set to no less than 1000 pixels / mm to meet the high precision requirements for pore structure detail. After imaging, the image of each grid cell is imported into image processing software for digitization. An image edge detection algorithm is used to extract pore contour information, including pore boundary coordinates and shape characteristics. The software's automatic recognition function accurately locates pore boundaries and records their coordinate data. Simultaneously, the pore shape characteristics are analyzed, including parameters such as pore shape factor and irregularity. Based on the extracted pore contour information, the pore connectivity within each grid cell is calculated. Graph theory algorithms are used to analyze the connectivity between pores and detect the number of connecting paths between adjacent pores. If the number of connected paths of a pore exceeds 3, it will be marked as a highly connected pore, and its position and the number of connected paths will be recorded. Next, the diameter of each pore is measured. The pore size is determined by calculating the minimum circumscribed circle diameter of the pore contour. Using the geometric analysis tool in the image processing software, the minimum circumscribed circle of each pore is automatically calculated, and its diameter value is recorded. At the same time, the diameter distribution of all pores in each grid unit is statistically analyzed. Finally, the porosity in each grid unit is calculated. The porosity is determined by calculating the ratio of the total pore area to the grid unit area and recorded as a percentage. The specific operation is: add up the area of each pore to obtain the total pore area in the grid unit, and then calculate the ratio of this area to the total area of the grid unit (1 square millimeter) to obtain the porosity percentage. The porosity data of each grid unit is stored in the database to provide basic data support for subsequent analysis.

[0029] Preferably, in step S2, determining the influence of the particle size distribution on the topological characteristics of the pore structure according to the particle size gradient distribution diagram includes: The particle size data in the particle size gradient distribution diagram are divided into particle size intervals in the order from small to large. When the particle size data is less than 1 micron, it is a small particle interval; when the particle size data is in the range of 1-5 microns, it is a medium particle interval; when the particle size data is greater than 5 microns, it is a large particle interval; Extract pore connectivity, pore size, and porosity from the topological features of the pore structure; In the particle size range, the pore connectivity is converted into a topological map, and the pore permeability is evaluated by the number and length of branches in the topological map. Within the particle size range, the pore size distribution is divided into three intervals: less than 1 micron, 1-10 microns, and greater than 10 microns. The pore size percentage in each interval is calculated. The pore size concentration is evaluated by the change in the pore size percentage. In the particle size range, the ratio of pore volume to total volume in each particle size range is calculated, and the porosity is evaluated by the pore volume ratio; The pore penetration degree, pore size concentration and porosity are fused and recorded to obtain particle-pore correlation feature data.

[0030] In an embodiment of the present invention, first, the particle size data in the particle size gradient distribution diagram is classified in order from small to large. The particle size data is screened and divided by data processing software, and particles with a size less than 1 micron are classified as small particles, particles with a size between 1 micron and 5 microns are classified as medium particles, and particles with a size greater than 5 microns are classified as large particles. After the classification is completed, the number of particles and distribution density data in each particle size interval are extracted respectively to provide a basis for subsequent analysis. Next, the pore connectivity, pore size and porosity data in the pore structure topological characteristics are extracted. The pore structure image is processed using image analysis software to extract the connectivity information of each pore, including the number and length of connection paths between pores; at the same time, the diameter of each pore is measured, and the diameter distribution of all pores is statistically analyzed; finally, the porosity, that is, the ratio of pore volume to the total volume of the material, is calculated and recorded as a percentage. The pore connectivity is analyzed within the particle size interval. Pore connectivity is converted into a topological map. Using topological map analysis software, a pore connectivity topological map is drawn, with pores represented as nodes and connectivity paths as edges. The number and length of branches for each node in the topological map are counted. A greater number of branches and longer branch lengths indicate a higher degree of pore permeability. For pore size distribution, the pore size data is divided into three intervals: less than 1 micron, 1-10 microns, and greater than 10 microns. Using statistical software, the number of pores within each interval is counted, and the proportion of pores within each interval to the total pore size is calculated. The change in the pore size proportion reflects the pore size concentration; a higher proportion indicates a higher pore size concentration. Furthermore, within each particle size interval, the ratio of pore volume to total volume, i.e., porosity, is calculated. Using a volume calculation formula, the ratio of pore volume to total volume within the particle size interval is calculated to assess porosity. A higher porosity indicates a higher porosity within that particle size interval. Finally, the pore permeability, pore size concentration, and porosity are combined and recorded as feature fusion. The above analysis results are integrated into a data table, with each particle size range corresponding to a row of data, including specific values for pore penetration, pore size concentration, and porosity. This data table is the particle-pore correlation feature data, providing detailed correlation information for subsequent battery performance analysis.

[0031] Preferably, in step S3, the capacity decay curve of the single battery is fitted to calculate the change trend of the capacity in multiple charge and discharge cycles. Fitting the capacity decay curve includes: Perform charge and discharge cycle tests on single cells for at least 50 times, and record the discharge capacity data for each cycle; Sort the recorded discharge capacity data according to the number of charge and discharge cycles to form a capacity data sequence; Calculate the capacity retention rate of each charge and discharge cycle, where the capacity retention rate is defined as the ratio of the discharge capacity of the current cycle to the initial discharge capacity; Draw a scatter plot of capacity retention versus charge and discharge cycle number; The charge and discharge cycle stages are divided according to the number of charge and discharge cycles. When the number of charge and discharge cycles is less than 10 times, it is marked as the early stage of the charge and discharge cycle; when the number of charge and discharge cycles is between 11 and 30 times, it is marked as the middle stage of the charge and discharge cycle; when the number of late charge and discharge cycles is greater than 31 times, it is marked as the late stage of the charge and discharge cycle; At the initial stage of charge and discharge cycles, the change in capacity retention rate for each cycle is calculated and the change value for each cycle is recorded to form an initial change sequence; For the mid-term of the charge and discharge cycle, calculate the average capacity retention rate of every 5 cycles, and record the average value of every 5 cycles to form a mid-term average sequence; For the later stage of charge and discharge cycles, the weighted average capacity retention rate of every 10 cycles is calculated, and the weighted average value of every 10 cycles is recorded to form a later stage weighted sequence.

[0032] In this embodiment of the present invention, a single battery cell is first subjected to a charge-discharge cycle test for no fewer than 50 cycles. A high-precision battery testing system is used, set to constant current charge-discharge mode, with a charge current of 1C, a discharge current of 1C, a charge cut-off voltage of 4.2V, and a discharge cut-off voltage of 2.5V. After each charge-discharge cycle, the system automatically records the discharge capacity data in milliampere-hours (mAh). After the test is complete, the recorded discharge capacity data is sorted by the number of charge-discharge cycles to form a capacity data sequence. Next, the capacity retention rate is calculated for each charge-discharge cycle. The capacity retention rate is defined as the ratio of the discharge capacity in the current cycle to the initial discharge capacity, expressed as a percentage. The specific calculation method is to divide the discharge capacity in each cycle by the initial discharge capacity, and then multiply by 100 to obtain the capacity retention rate. For example, if the initial discharge capacity is 1000mAh and the discharge capacity in the 10th cycle is 950mAh, the capacity retention rate for the 10th cycle is 95%. A scatter plot is then plotted showing the capacity retention rate as a function of the number of charge-discharge cycles. Using graphing software, plot the capacity retention data for each cycle as scatter points on a graph with the number of charge and discharge cycles as the horizontal axis and the capacity retention rate as the vertical axis. The scatter plot allows for a visual observation of the changing trend of the capacity retention rate over the number of cycles. The charge and discharge cycling process is divided into three stages based on the number of charge and discharge cycles. When the number of charge and discharge cycles is less than 10, it is marked as the early stage of the charge and discharge cycling process; when the number of charge and discharge cycles is between 11 and 30, it is marked as the middle stage of the charge and discharge cycling process; and when the number of charge and discharge cycles is greater than 31, it is marked as the late stage of the charge and discharge cycling process. For the early stage of the charge and discharge cycling process, the change in the capacity retention rate for each cycle is calculated. The change is defined as the difference between the capacity retention rates of two adjacent cycles. For example, the difference between the capacity retention rate of the second cycle and the capacity retention rate of the first cycle is the change for the second cycle. The change value for each cycle is recorded to form an initial change sequence. For the middle stage of the charge and discharge cycling process, the average capacity retention rate for every five cycles is calculated. The capacity retention rates for every five cycles are summed and divided by 5 to obtain the average value for every five cycles. For example, the average capacity retention rate for cycles 11 to 15 is the sum of the capacity retention rates for these five cycles divided by 5. The average value for every five cycles is recorded to form a mid-term average sequence. For the later stages of charge and discharge cycling, the weighted average capacity retention rate for every 10 cycles is calculated. Using the weighted average method, different weights are assigned based on the number of cycles. For example, the weighted average capacity retention rate for cycles 32 to 41 is calculated as: (32nd capacity retention rate × Weight 1 + 33rd capacity retention rate × Weight 2 + ... + 41st capacity retention rate × Weight 10) ÷ (Weight 1 + Weight 2 + ... + Weight 10). The weights can be decreased as the number of cycles increases; for example, Weight 1 is 1, Weight 2 is 0.9, Weight 3 is 0.8, and so on. The weighted average value for every 10 cycles is recorded to form a later stage weighted sequence.

[0033] Preferably, in step S3, the capacity decay curve of the single battery is fitted to calculate the change trend of the capacity during multiple charge and discharge cycles. Fitting the capacity decay curve further includes: For the initial change sequence, calculate the difference in the amount of change between adjacent cycles, and record the difference in the amount of change for each cycle to form an initial difference sequence; For the mid-term average sequence, calculate the difference between the average values of 5 adjacent cycles, and record the difference between the average values of each 5 cycles to form a mid-term difference sequence; For the late weighted sequence, calculate the weighted average difference between 10 adjacent cycles, and record the weighted average difference of each 10 cycles to form a late difference sequence; According to the initial difference sequence, calculate the change trend of each data point and extract the change trend value of each cycle; According to the mid-term difference sequence, the change trend of each data point is calculated and the change trend value of every 5 cycles is extracted; According to the late difference sequence, the change trend of each data point is calculated and the change trend value of every 10 cycles is extracted; Compare and analyze the change trend values in the early, middle and late stages to determine the capacity decay rate in each stage and obtain the capacity decay characteristics; Mark the capacity decay points according to the capacity decay characteristics of each stage; A capacity decay curve is drawn based on the capacity decay points, where the slope of each straight line in the capacity decay curve represents the capacity decay rate at that stage.

[0034] In an embodiment of the present invention, the initial change sequence is first processed. Data analysis software is used to calculate the difference in change between adjacent cycles. The specific operation is: subtract the change in the previous cycle from the change in the current cycle to obtain the difference in change. For example, if the change in the second cycle is -5% and the change in the third cycle is -7%, then the difference in change in the third cycle is -2% (-7% - (-5%)). The difference in change for each cycle is recorded to form an initial difference sequence. Next, the mid-term average sequence is processed. The difference in average values between five adjacent cycles is calculated. The average capacity retention rate of the current five cycles is subtracted from the average capacity retention rate of the previous five cycles to obtain the average difference. For example, if the average capacity retention rate for cycles 11-15 is 90% and the average capacity retention rate for cycles 16-20 is 88%, then the difference in average values for cycles 16-20 is -2% (88% - 90%). The difference in average values for each of the five cycles is recorded to form a mid-term difference sequence. Then, the late weighted sequence is processed. Calculate the weighted average difference between 10 consecutive cycles. Subtract the weighted average capacity retention of the previous 10 cycles from the weighted average capacity retention of the current 10 cycles to obtain the weighted average difference. For example, if the weighted average capacity retention of cycles 32-41 is 85% and the weighted average capacity retention of cycles 42-51 is 83%, then the weighted average difference between cycles 42-51 is -2% (83% - 85%). Record the weighted average difference for each 10-cycle period to form a late difference sequence. Subsequently, calculate the trend of each data point based on the early difference sequence. Use linear regression analysis to perform trend analysis on each data point in the early difference sequence and extract the trend value for each cycle. The trend value is expressed as the rate of change of the difference value for that cycle relative to the number of cycles. Use the same linear regression analysis method to calculate the trend of each data point based on the mid-term difference sequence. Extract the trend value for every 5 cycles and express it as the rate of change of the average difference value for those 5 cycles relative to the number of cycles. Based on the later difference sequence, the linear regression analysis method is continued to be used to calculate the change trend of each data point. The change trend value of every 10 cycles is extracted, and the change trend value is expressed as the rate of change of the weighted average difference of the 10 cycles relative to the number of cycles. The change trend values in the early, middle and late stages are compared and analyzed. Through the data analysis software, the change trend values of the three stages are visually compared to determine the capacity decay rate of each stage. The capacity decay rate is measured by the absolute value of the change trend value. The larger the absolute value, the faster the decay rate. The capacity decay point is marked according to the capacity decay characteristics of each stage. Among the change trend values in the early, middle and late stages, find the point where the change trend value changes significantly and mark it as the capacity decay point. For example, if the initial change trend value suddenly changes from -0.5% to -2%, the point is marked as the capacity decay point.Finally, plot the capacity decay curve based on the capacity decay points. Using graphing software, connect the capacity decay points, with the number of charge and discharge cycles as the horizontal axis and the capacity retention rate as the vertical axis, to form the capacity decay curve. The slope of each line in the curve represents the capacity decay rate at that stage; the more negative the slope, the faster the decay rate.

[0035] It is particularly important that the internal resistance response analysis of the single cell internal resistance in step S3 includes: Use an internal resistance tester to measure the internal resistance of the single battery and record the internal resistance value of each measurement; The internal resistance of a single battery is measured three times, at the front, middle, and rear ends of the battery, to obtain internal resistance data at different locations; Add the internal resistance values measured three times and divide by 3 to get the average internal resistance value; Calculate the difference between the internal resistance value measured each time and the average internal resistance value to determine the fluctuation of the internal resistance; With time as the horizontal axis and internal resistance value as the vertical axis, draw the internal resistance change curve; Mark the point where the internal resistance first increases significantly, the point where the internal resistance reaches its peak, and the point where the internal resistance begins to stabilize on the internal resistance change curve; Divide the difference between the internal resistance value of the current measurement point and the internal resistance value of the previous measurement point by the internal resistance value of the previous measurement point to calculate the internal resistance change rate between adjacent measurement points; The trend of internal resistance change is determined based on the internal resistance change rate.

[0036] In an embodiment of the present invention, a high-precision internal resistance tester is first used to measure the internal resistance of a single battery. The single battery is placed in the tester's measuring fixture, ensuring good electrode contact. The internal resistance tester's measurement mode is set to AC impedance, with an AC signal amplitude of 10 millivolts and a measurement frequency of 1 kHz. Internal resistance measurements are performed at the front, middle, and rear ends of the battery. Before each measurement, the tester's probes are carefully aligned with the battery contact points. The internal resistance value for each measurement is recorded in milliohms (mΩ). After completing three internal resistance measurements, the three measured internal resistance values are added together and then divided by 3 to obtain the average internal resistance value. For example, if the front-end measurement value is 50 milliohms, the middle measurement value is 52 milliohms, and the rear-end measurement value is 51 milliohms, then the average internal resistance value is (50 + 52 + 51) ÷ 3 = 51 milliohms. Next, the difference between each measured internal resistance value and the average internal resistance value is calculated to determine the internal resistance fluctuation. For example, the difference between the front-end measurement and the average internal resistance is 50-51 = -1 milliohm, the difference between the middle measurement and the average internal resistance is 52-51 = 1 milliohm, and the difference between the back-end measurement and the average internal resistance is 51-51 = 0 milliohm. These differences are recorded for subsequent analysis of internal resistance fluctuations. Subsequently, a graphing software is used to plot the internal resistance variation curve, with time as the horizontal axis and internal resistance as the vertical axis. The time point and corresponding internal resistance value of each measurement are entered into the graphing software to generate a graph of the internal resistance variation over time. The points where the internal resistance first significantly increases, reaches its peak, and begins to stabilize are marked on the internal resistance variation curve. These points are determined based on the internal resistance trend: the point where the internal resistance first significantly increases is defined as the point where the internal resistance value increases by more than 5% relative to the previous measurement point; the point where the internal resistance reaches its peak is defined as the measurement point where the internal resistance reaches its highest point; and the point where the internal resistance begins to stabilize is defined as the starting point of three consecutive measurement points where the internal resistance value fluctuates by less than 2%. Finally, the rate of change of internal resistance between adjacent measurement points is calculated. Divide the difference between the current and previous resistance values by the previous value to calculate the internal resistance change rate. For example, if the current resistance value is 55 milliohms and the previous value is 50 milliohms, the internal resistance change rate is (55 - 50) ÷ 50 = 10%. The internal resistance change rate can be used to determine the internal resistance trend: a continuously increasing rate indicates an upward trend; a steady or decreasing rate indicates a steady or decreasing trend.

[0037] Of particular importance is that monitoring the real-time changes in internal resistance during the charge and discharge process in step S3 includes: Place the battery in a constant temperature and humidity test environment, set the temperature to maintain at 25±1℃ and the humidity to maintain at 50±5%; Use an internal resistance monitor to connect the positive and negative terminals of the battery; set the internal resistance monitor's sampling frequency to 1 time per second, the measurement current to 100 mA, and the measurement time to 1 second; Start the battery charging and discharging equipment, set the charging current to 1A, the discharging current to 1A, the charging voltage upper limit to 4.2V, and the discharging voltage lower limit to 2.5V; During the charging and discharging process, the internal resistance monitor collects internal resistance data once per second and records the timestamp and internal resistance value of each collection; While collecting internal resistance data, the battery's charge and discharge status is recorded, including charging voltage, discharging voltage, charging current, and discharging current.

[0038] In an embodiment of the present invention, first, the battery is placed in a constant temperature and humidity chamber, which can accurately control the temperature and humidity of the test environment. The temperature parameters of the constant temperature and humidity chamber are set to 25±1°C and the humidity parameters are set to 50±5%, ensuring that the battery is in stable environmental conditions throughout the test process. After the temperature and humidity reach the set values and stabilize, the battery is fixed to the test stand to ensure a stable and reliable connection between the battery and the test equipment. Next, an internal resistance monitor is used to connect the positive and negative poles of the battery. The probes of the internal resistance monitor are tightly connected to the positive and negative poles of the battery to ensure good contact and no loose connections. The sampling frequency of the internal resistance monitor is set to 1 time per second, the measurement current is set to 100 mA, and the measurement time is set to 1 second. These parameter settings ensure that the internal resistance monitor can accurately measure the internal resistance value of the battery in a short time while avoiding excessive current shock to the battery. Subsequently, the battery charging and discharging equipment is started, and the charging current is set to 1 ampere, the discharging current is set to 1 ampere, the charging voltage upper limit is set to 4.2 volts, and the discharge voltage lower limit is set to 2.5 volts. These parameter settings comply with standard battery charge and discharge requirements, ensuring that the battery performs charge and discharge cycle tests within a safe range. During the charge and discharge process, the internal resistance monitor collects internal resistance data once per second at a set sampling frequency. Each acquisition includes a timestamp and internal resistance value. The timestamp is accurate to the millisecond level, and the internal resistance value is expressed in milliohms (mΩ). The collected data is transmitted in real time to a data logging system and stored in a table containing the timestamp, internal resistance value, and the corresponding charge and discharge status indicator. Simultaneously, the battery charging and discharging equipment records the battery's charge and discharge status, including charge voltage, discharge voltage, charge current, and discharge current. The charging and discharge voltages are recorded with an accuracy of 0.01 volt, and the charging and discharge currents are recorded with an accuracy of 0.01 ampere. This data is also transmitted in real time to the data logging system and aligned with the timestamp of the internal resistance data to ensure data synchronization and integrity. This approach provides a comprehensive understanding of the battery's internal resistance changes and the dynamic changes in the charge and discharge status during the charge and discharge process.

[0039] Preferably, in step S3, evaluating the impact of the capacity decay curve on the internal resistance dynamic response data includes: Extracting the attenuation characteristic points of the capacity attenuation curve, where the attenuation characteristic points include the initial capacity point, the cycle number point at which the capacity retention rate first decreases by 10%, the cycle number point at which the capacity retention rate first decreases by 20%, and the final capacity point; Extracting response characteristic points of the internal resistance dynamic response data, wherein the response characteristic points include the initial internal resistance point, the cycle number point at which the internal resistance first significantly increases, the cycle number point at which the internal resistance first reaches a peak value, and the final internal resistance point; Time-align the attenuation characteristic point with the response characteristic point; calculate the correlation coefficient between the attenuation characteristic point and the response characteristic point at each cycle number point to obtain the attenuation and internal resistance correlation coefficient; According to the correlation coefficient between attenuation and internal resistance, the cycle number points are divided into high correlation interval, medium correlation interval and low correlation interval. Among them, the high correlation interval indicates that there is a strong correlation between capacity attenuation and internal resistance change, and the low correlation interval indicates that the correlation between the two is weak. In the high correlation range, calculate the slope between the capacity retention rate and the internal resistance change. The larger the absolute value of the slope, the more significant the impact of the internal resistance change on the capacity fade. In the medium correlation interval, calculate the curvature of the curve between the capacity retention rate and the internal resistance change. The greater the curvature, the more complex it is to determine the effect of the internal resistance change on the capacity decay. In the low correlation range, the degree of dispersion between the capacity retention rate and the internal resistance change is calculated. The greater the degree of dispersion, the more random the effect of the internal resistance change on the capacity decay. The results corresponding to the high correlation interval, the medium correlation interval, and the low correlation interval are mapped into a correlation data table to generate capacity-internal resistance correlation data.

[0040] In an embodiment of the present invention, first, attenuation characteristic points are extracted from the capacity decay curve. Data analysis software is used to analyze the capacity decay curve, and the initial capacity point is determined to be the capacity value of the first charge-discharge cycle; the cycle number point at which the capacity retention rate first decreases by 10% is the cycle number corresponding to when the capacity retention rate decreases from the initial value to 90%; the cycle number point at which the capacity retention rate first decreases by 20% is the cycle number corresponding to when the capacity retention rate decreases from the initial value to 80%; and the final capacity point is the capacity value of the last charge-discharge cycle. The cycle numbers and corresponding capacity retention values of these characteristic points are recorded. Next, response characteristic points are extracted from the internal resistance dynamic response data. The same data analysis software is used to analyze the internal resistance dynamic response data, and the initial internal resistance point is determined to be the internal resistance value of the first charge-discharge cycle; the cycle number point at which the internal resistance first significantly increases is the cycle number corresponding to when the internal resistance value increases by more than 10% relative to the initial internal resistance value; the cycle number point at which the internal resistance first reaches a peak is the cycle number corresponding to when the internal resistance value reaches its highest point; and the final internal resistance point is the internal resistance value of the last charge-discharge cycle. The cycle numbers and corresponding internal resistance values of these characteristic points are recorded. Subsequently, the attenuation characteristic points are time-aligned with the response characteristic points. Using the data alignment tool, the characteristic points in the capacity decay curve and the internal resistance dynamic response data are matched according to the number of cycles to ensure that the capacity retention rate and internal resistance value at each cycle point can correspond one to one. The aligned data are stored in the same data table for subsequent analysis. The correlation coefficient between the attenuation characteristic point and the response characteristic point at each cycle point is calculated. Using statistical analysis software, the capacity retention rate and internal resistance value at each cycle point are correlated and the correlation coefficient is calculated. The range of the correlation coefficient is -1 to 1. The closer the absolute value is to 1, the stronger the correlation between the capacity decay and the internal resistance change. Based on the attenuation and internal resistance correlation coefficient, the cycle points are divided into high correlation intervals, medium correlation intervals, and low correlation intervals. Set the threshold of the correlation coefficient. For example, when the absolute value of the correlation coefficient is greater than 0.8, it is judged as a high correlation interval; when the absolute value of the correlation coefficient is between 0.5 and 0.8, it is judged as a medium correlation interval; when the absolute value of the correlation coefficient is less than 0.5, it is judged as a low correlation interval. In the high correlation interval, calculate the slope between the capacity retention rate and the change in internal resistance. Use the linear regression analysis method to fit the capacity retention rate and the change in internal resistance in the high correlation interval and calculate the slope. The larger the absolute value of the slope, the more significant the effect of the internal resistance change on the capacity attenuation. In the medium correlation interval, calculate the curvature of the curve between the capacity retention rate and the change in internal resistance. Use the curve fitting tool to fit the capacity retention rate and the change in internal resistance in the medium correlation interval and calculate the curvature of the curve. The greater the curvature, the more complex the effect of the internal resistance change on the capacity attenuation. In the low correlation interval, calculate the degree of dispersion between the capacity retention rate and the change in internal resistance. Use the statistical analysis tool to perform a dispersion analysis on the capacity retention rate and the change in internal resistance in the low correlation interval and calculate the degree of dispersion.The greater the degree of dispersion, the more random the effect of internal resistance changes on capacity decay. Finally, the results corresponding to the high correlation interval, medium correlation interval, and low correlation interval are mapped into a correlation data table. The cycle number range, correlation coefficient range, slope value, curve curvature value, and dispersion value of each interval are recorded in a correlation data table to generate capacity-internal resistance correlation data. This data table is stored in tabular form and contains information such as cycle number range, correlation coefficient range, slope value, curve curvature value, and dispersion value, providing detailed data support for subsequent battery performance analysis.

[0041] Preferably, step S4 includes the following steps: Step S41: Collect the battery charge and discharge time, and use a high-precision timer to record the start and end time of each charge and discharge cycle, accurate to the second; Step S42: Record the total battery usage time, and add up the duration of all charge and discharge cycles to obtain the total battery usage time; Step S43: The capacity-internal resistance correlation data weight is set to 0.4, the particle size gradient distribution map weight is set to 0.3, and the particle-pore correlation feature data weight is set to 0.3, and the weights are weighted and calculated to construct a battery performance simulation model; Step S44: Inputting the battery's charge and discharge usage time into the battery performance simulation model, predicting the battery's capacity retention rate and internal resistance change after 100 cycles, and outputting a battery performance simulation score; Step S45: Determine the battery performance according to the battery performance simulation score and obtain a battery performance prediction report.

[0042] In this embodiment of the present invention, a high-precision timer is used to collect battery charge and discharge time. The timer is connected to the battery charging and discharging equipment and set to second-level accuracy. At the beginning of each charge and discharge cycle, the timer is started to record the start time; at the end of each charge and discharge cycle, the timer is stopped to record the end time. The timer stores the start and end times of each cycle in a data recording system as timestamps with second accuracy. The process proceeds to step S42, where the total battery life is recorded. Data processing software extracts the duration data of all charge and discharge cycles from the data recording system. The duration of each cycle (in seconds) is accumulated to obtain the total battery life. The total life is stored in seconds in a database and converted to hours for subsequent analysis. In step S43, a battery performance simulation model is constructed. The capacity-internal resistance correlation data, particle size gradient distribution map, and particle-pore correlation feature data are imported into the data analysis software. The weight of the capacity-internal resistance correlation data is set to 0.4, the weight of the particle size gradient distribution map to 0.3, and the weight of the particle-pore correlation feature data to 0.3. Through weighted calculation, the three types of data are integrated to construct a battery performance simulation model. The specific operation of weighted calculation is: multiply the influencing factor of each data by its corresponding weight, and then add the results to obtain a comprehensive performance index. Go to step S44, and input the battery's charge and discharge usage time into the battery performance simulation model. Set the simulation parameters in the model to predict the capacity retention rate and internal resistance change of the battery after 100 cycles. The model predicts performance based on the input charge and discharge usage time, combined with the weighted results of the capacity-internal resistance correlation data, the particle size gradient distribution map and the particle-pore correlation characteristic data. The prediction results are output in the form of a percentage of the capacity retention rate and a numerical form of the internal resistance change, and a battery performance simulation score is generated. The simulation score is calculated based on the predicted capacity retention rate and internal resistance change, and is obtained through preset scoring rules, with a score range of 0 to 100 points. Finally, in step S45, the battery performance is judged according to the battery performance simulation score. Set scoring thresholds. For example, a score above 80 indicates good battery performance, a score between 60 and 80 indicates moderate performance, and a score below 60 indicates poor performance. Generate a battery performance prediction report based on the scoring results. The report includes the predicted capacity retention, internal resistance change, and performance simulation score after 100 cycles, providing a comprehensive evaluation of battery performance.

[0043] This specification also provides a battery simulation test system for executing the battery simulation test method described above, the battery simulation test system comprising: The particle size gradient detection module is used to obtain the particle size of the single cell; perform particle size gradient stratification on the particle size of the single cell and divide the electrode material into multiple particle size intervals according to the particle size; record the number and distribution density of particles in each particle size interval and generate a particle size gradient distribution map; The particle-pore correlation module is used to detect single-cell battery materials; it performs pore topology analysis on single-cell battery materials and calculates pore connectivity, pore size, and porosity; based on the particle size gradient distribution map, it determines the impact of particle size distribution on the topological characteristics of the pore structure and generates particle-pore correlation feature data; The capacity-internal resistance correlation module is used to collect the capacity and internal resistance of a single cell; perform capacity decay curve fitting on the capacity of the single cell, calculate the capacity change trend during multiple charge and discharge cycles, and fit the capacity decay curve; perform internal resistance response analysis on the internal resistance of the single cell, and monitor the real-time changes of the internal resistance during the charge and discharge process to generate internal resistance dynamic response data; use the capacity decay curve to evaluate the impact on the internal resistance dynamic response data, and generate capacity-internal resistance correlation data; The battery performance simulation prediction module is used to collect the battery's charge and discharge usage time; build a battery performance simulation model based on capacity-internal resistance correlation data, particle size gradient distribution map and particle-pore correlation characteristic data; predict the long-term performance of the battery based on the battery performance simulation model and the number of charge and discharge cycles and usage time to obtain a battery performance prediction report.

[0044] The present invention is therefore intended to be illustrative and non-restrictive in all respects, with the scope of the invention being defined by the appended claims rather than the foregoing description, and all changes that come within the meaning and range of equivalents of the application documents are intended to be embraced therein.

[0045] The foregoing description is intended only to provide specific embodiments of the present invention, which will enable those skilled in the art to understand and implement the present invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention is not intended to be limited to the embodiments shown herein, but is to be construed in the widest possible manner consistent with the principles and novel features disclosed herein.

Claims

1. A battery simulation test method, characterized in that: The following steps are involved: Step S1: Obtain the particle size of the single cell; perform particle size gradient stratification on the particle size of the single cell, and divide the electrode material into multiple particle size intervals according to the particle size; record the number and distribution density of particles in each particle size interval, and generate a particle size gradient distribution map; Step S2: Detecting the single cell material; performing pore topology analysis on the single cell material and calculating pore connectivity, pore size, and porosity; determining the effect of particle size distribution on the topological characteristics of the pore structure based on the particle size gradient distribution map, and generating particle-pore correlation feature data; Step S3: Collecting the capacity and internal resistance of a single cell; fitting a capacity decay curve for the capacity of the single cell, calculating the capacity change trend during multiple charge and discharge cycles, and fitting a capacity decay curve; performing an internal resistance response analysis on the internal resistance of the single cell, and monitoring the real-time change of the internal resistance during the charge and discharge process to generate internal resistance dynamic response data; using the capacity decay curve to evaluate the impact on the internal resistance dynamic response data, and generating capacity-internal resistance correlation data; Step S4: collecting the battery charge and discharge time; Based on the capacity-internal resistance correlation data, particle size gradient distribution map and particle-pore correlation characteristic data, a battery performance simulation model is constructed; based on the battery performance simulation model and the number of charge and discharge cycles and usage time, the long-term performance simulation of the battery is predicted to obtain a battery performance prediction report.

2. The battery simulation test method according to claim 1, characterized in that: Step S1 includes the following steps: Step S11: Collecting single cells; pre-processing the single cells, performing preliminary screening of the battery particles using a vibration screening device, and removing single cells with abnormal sizes; Step S12: measuring the particle size of the screened single cells, measuring the length, width, and height of each particle, and recording the particle size measurement data; Step S13: Based on the particle size measurement data, the single battery particles are subjected to a particle size gradient stratification, and the particles are divided into multiple particle size intervals according to the size range to obtain the particle count; the particle size range of each interval is 10% of the particle size of the previous interval; Step S14: Count the number of particles in each particle size range and record the statistical results; calculate the volume percentage of particles in each particle size range to obtain distribution density data; Step S15: Draw a particle size gradient distribution diagram based on the particle quantity and distribution density data.

3. The battery simulation test method according to claim 1, characterized in that: In step S2, the pore topology analysis of the single cell material is performed, and the pore connectivity, pore size, and porosity are calculated, including: The surface of the single cell material is divided into a plurality of grid units of equal area, and the area of each grid unit is 1 square millimeter; In each grid cell, use an optical microscope to capture images of the pore structure at a perpendicular angle to the surface of the battery material with a resolution of no less than 1000 pixels / mm; The image of each grid cell is digitized to extract the pore contour information, including the boundary coordinates and shape characteristics of the pore; Based on the pore contour information, the pore connectivity within each grid cell is calculated; the number of connected paths between adjacent pores is detected, and pores with more than three connected paths are considered to have high connectivity. Measure the diameter of each pore and calculate the minimum circumscribed circle diameter of the pore outline to determine the pore size; The porosity within each grid cell was calculated, the ratio of the total pore area to the grid cell area was calculated to determine the porosity, and the porosity was recorded as a percentage.

4. The battery simulation test method according to claim 1, characterized in that: In step S2, determining the effect of particle size distribution on the topological characteristics of the pore structure based on the particle size gradient distribution diagram includes: The particle size data in the particle size gradient distribution diagram are divided into particle size intervals in the order from small to large. When the particle size data is less than 1 micron, it is a small particle interval; when the particle size data is in the range of 1-5 microns, it is a medium particle interval; when the particle size data is greater than 5 microns, it is a large particle interval; Extract pore connectivity, pore size, and porosity from the topological features of the pore structure; In the particle size range, the pore connectivity is converted into a topological map, and the pore permeability is evaluated by the number and length of branches in the topological map. Within the particle size range, the pore size distribution is divided into three intervals: less than 1 micron, 1-10 microns, and greater than 10 microns. The pore size percentage in each interval is calculated. The pore size concentration is evaluated by the change in the pore size percentage. In the particle size range, the ratio of pore volume to total volume in each particle size range is calculated, and the porosity is evaluated by the pore volume ratio; The pore penetration degree, pore size concentration and porosity are fused and recorded to obtain particle-pore correlation feature data.

5. The battery simulation test method according to claim 1, characterized in that: In step S3, a capacity decay curve is fitted for the capacity of the single battery to calculate the change trend of the capacity during multiple charge and discharge cycles. The capacity decay curve is fitted including: Perform charge and discharge cycle tests on single cells for at least 50 times, and record the discharge capacity data for each cycle; Sort the recorded discharge capacity data according to the number of charge and discharge cycles to form a capacity data sequence; Calculate the capacity retention rate of each charge and discharge cycle, where the capacity retention rate is defined as the ratio of the discharge capacity of the current cycle to the initial discharge capacity; Draw a scatter plot of capacity retention versus charge and discharge cycle number; The charge and discharge cycle stages are divided according to the number of charge and discharge cycles. When the number of charge and discharge cycles is less than 10 times, it is marked as the early stage of the charge and discharge cycle; when the number of charge and discharge cycles is between 11 and 30 times, it is marked as the middle stage of the charge and discharge cycle; when the number of late charge and discharge cycles is greater than 31 times, it is marked as the late stage of the charge and discharge cycle; At the initial stage of charge and discharge cycles, the change in capacity retention rate for each cycle is calculated and the change value for each cycle is recorded to form an initial change sequence; For the mid-term of the charge and discharge cycle, calculate the average capacity retention rate of every 5 cycles, and record the average value of every 5 cycles to form a mid-term average sequence; For the later stage of charge and discharge cycles, the weighted average capacity retention rate of every 10 cycles is calculated, and the weighted average value of every 10 cycles is recorded to form a later stage weighted sequence.

6. The battery simulation test method according to claim 5, characterized in that: In step S3, the capacity decay curve of the single battery is fitted to calculate the change trend of the capacity during multiple charge and discharge cycles. Fitting the capacity decay curve also includes: For the initial change sequence, calculate the difference in the amount of change between adjacent cycles, and record the difference in the amount of change for each cycle to form an initial difference sequence; For the mid-term average sequence, calculate the difference between the average values of 5 adjacent cycles, and record the difference between the average values of each 5 cycles to form a mid-term difference sequence; For the late weighted sequence, calculate the weighted average difference between 10 adjacent cycles, and record the weighted average difference of each 10 cycles to form a late difference sequence; According to the initial difference sequence, calculate the change trend of each data point and extract the change trend value of each cycle; According to the mid-term difference sequence, the change trend of each data point is calculated, and the change trend value of every 5 cycles is extracted; According to the late difference sequence, the change trend of each data point is calculated and the change trend value of every 10 cycles is extracted; Compare and analyze the change trend values in the early, middle and late stages to determine the capacity decay rate in each stage and obtain the capacity decay characteristics; Mark the capacity decay points according to the capacity decay characteristics of each stage; A capacity decay curve is drawn based on the capacity decay points, where the slope of each straight line in the capacity decay curve represents the capacity decay rate at that stage.

7. The battery simulation test method according to claim 1, characterized in that: In step S3, the impact of the capacity decay curve on the dynamic response data of the internal resistance is evaluated by: Extracting the attenuation characteristic points of the capacity attenuation curve, where the attenuation characteristic points include the initial capacity point, the cycle number point at which the capacity retention rate first decreases by 10%, the cycle number point at which the capacity retention rate first decreases by 20%, and the final capacity point; Extracting response characteristic points of the internal resistance dynamic response data, wherein the response characteristic points include the initial internal resistance point, the cycle number point at which the internal resistance first significantly increases, the cycle number point at which the internal resistance first reaches a peak value, and the final internal resistance point; Time-align the attenuation characteristic point with the response characteristic point; calculate the correlation coefficient between the attenuation characteristic point and the response characteristic point at each cycle number point to obtain the attenuation and internal resistance correlation coefficient; According to the correlation coefficient between attenuation and internal resistance, the cycle number points are divided into high correlation interval, medium correlation interval and low correlation interval. Among them, the high correlation interval indicates that there is a strong correlation between capacity attenuation and internal resistance change, and the low correlation interval indicates that the correlation between the two is weak. In the high correlation range, calculate the slope between the capacity retention rate and the internal resistance change. The larger the absolute value of the slope, the more significant the impact of the internal resistance change on the capacity fade. In the medium correlation interval, calculate the curvature of the curve between the capacity retention rate and the internal resistance change. The greater the curvature, the more complex it is to determine the effect of the internal resistance change on the capacity decay. In the low correlation range, the degree of dispersion between the capacity retention rate and the internal resistance change is calculated. The greater the degree of dispersion, the more random the effect of the internal resistance change on the capacity decay. The results corresponding to the high correlation interval, the medium correlation interval, and the low correlation interval are mapped into a correlation data table to generate capacity-internal resistance correlation data.

8. The battery simulation test method according to claim 1, characterized in that: Step S4 includes the following steps: Step S41: Collect the battery charge and discharge time, and use a high-precision timer to record the start and end time of each charge and discharge cycle, accurate to the second; Step S42: Record the total battery usage time, and add up the duration of all charge and discharge cycles to obtain the total battery usage time; Step S43: The capacity-internal resistance correlation data weight is set to 0.4, the particle size gradient distribution map weight is set to 0.3, and the particle-pore correlation feature data weight is set to 0.3, and the weights are weighted and calculated to construct a battery performance simulation model; Step S44: Inputting the battery's charge and discharge usage time into the battery performance simulation model, predicting the battery's capacity retention rate and internal resistance change after 100 cycles, and outputting a battery performance simulation score; Step S45: Determine the battery performance according to the battery performance simulation score and obtain a battery performance prediction report.

9. A battery simulation test system, characterized in that: For executing the battery simulation test method according to claim 1, the battery simulation test system comprises: The particle size gradient detection module is used to obtain the particle size of the single cell; perform particle size gradient stratification on the particle size of the single cell and divide the electrode material into multiple particle size intervals according to the particle size; record the number and distribution density of particles in each particle size interval and generate a particle size gradient distribution map; The particle-pore correlation module is used to detect single-cell battery materials; it performs pore topology analysis on single-cell battery materials and calculates pore connectivity, pore size, and porosity; based on the particle size gradient distribution map, it determines the impact of particle size distribution on the topological characteristics of the pore structure and generates particle-pore correlation feature data; The capacity-internal resistance correlation module is used to collect the capacity and internal resistance of a single cell; perform capacity decay curve fitting on the capacity of the single cell, calculate the capacity change trend during multiple charge and discharge cycles, and fit the capacity decay curve; perform internal resistance response analysis on the internal resistance of the single cell, and monitor the real-time changes of the internal resistance during the charge and discharge process to generate internal resistance dynamic response data; use the capacity decay curve to evaluate the impact on the internal resistance dynamic response data, and generate capacity-internal resistance correlation data; The battery performance simulation prediction module is used to collect the battery's charge and discharge usage time; build a battery performance simulation model based on capacity-internal resistance correlation data, particle size gradient distribution map and particle-pore correlation characteristic data; predict the long-term performance of the battery based on the battery performance simulation model and the number of charge and discharge cycles and usage time to obtain a battery performance prediction report.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed, the battery simulation test method according to any one of claims 1 to 8 is implemented.

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