Battery cell matching optimization method based on lithium battery and related equipment

Abstract

The invention relates to a lithium battery-based cell grouping optimization method and related equipment, and the method comprises the following steps: carrying out the charge-discharge cycle test of a cell group of a lithium battery through a preset charge-discharge system, and obtaining the performance feature data of the cell group; carrying out alternating current impedance spectroscopy analysis on the battery cell group based on the performance characteristic data to obtain impedance spectroscopy characteristic data of the battery cell group; performing complex plane impedance mapping on the impedance spectrum characteristic data to obtain a complex impedance characteristic region of the battery cell group; and determining a cell grouping scheme based on the complex impedance characteristic region, and carrying out multi-target Pareto distribution optimization on the cell group based on the cell grouping scheme to obtain a cell optimization grouping result, thereby solving the problem that the traditional lithium battery cell grouping method is often based on a simple voltage or capacity matching principle, so that the matching efficiency is high. And the influence of a complicated physical and chemical process in the battery cell on the overall performance of the battery cannot be fully considered.

Description

Technical Field

[0001] The present invention relates to the technical field of lithium batteries, and particularly to a method for optimizing the cell matching of lithium batteries and related equipment. Background Art

[0002] In the current rapidly developing markets of electric vehicles and portable electronic devices, lithium batteries, as the main energy storage solution, their performance and lifespan are directly related to the user experience and market competitiveness of these devices. However, traditional lithium battery cell matching methods often rely on simple voltage or capacity matching principles, and fail to fully consider the impact of the complex physical and chemical processes inside the cells on the overall performance of the battery. This limitation leads to the fact that even within the same manufacturing batch, the inconsistency between different cells may intensify over time, ultimately affecting the stability and service life of the entire battery module.

[0003] Facing this challenge, most existing research and technologies focus on improving the performance of individual cells by modifying material compositions and structural designs, but lack in-depth exploration of how to effectively optimize the overall performance of cell groups. Especially in large-scale applications, the subtle initial differences between cells may be amplified, resulting in system-level problems such as premature failure and increased safety risks. In addition, traditional matching technologies rely on time-consuming and costly testing processes, which are difficult to meet the requirements of modern manufacturing for efficiency and cost-effectiveness. Therefore, finding a method that can accurately evaluate and optimize the performance of cell groups has become an urgent need in the industry.

[0004] To solve the above problems, researchers have started to explore the use of advanced analysis techniques and algorithms to achieve a deeper understanding and optimization of cells. This method not only needs to consider the basic electrical characteristics of cells, but also requires a comprehensive evaluation in combination with electrochemical behavior. By adopting advanced technologies such as complex plane impedance mapping, the changes in the internal state of cells can be captured more accurately, thereby providing a scientific basis for formulating effective grouping strategies. Such a research background has promoted the development of a method for optimizing the cell matching of lithium batteries, which aims to significantly improve the consistency and overall performance of cell groups through a systematic analysis and optimization process, and thus promote the progress and development of related industries. Summary of the Invention

[0005] The main objective of the present invention is to provide a method for optimizing the cell matching of lithium batteries and related equipment, which solves the technical problem that traditional lithium battery cell matching methods often rely on simple voltage or capacity matching principles and fail to fully consider the impact of the complex physical and chemical processes inside the cells on the overall performance of the battery.

[0006] To achieve the above objective, the present invention provides a method for optimizing the cell matching of lithium batteries, including the following steps: Perform charge-discharge cycle tests on the battery cell group of the lithium battery through a preset charge-discharge regime to obtain the performance characteristic data of the battery cell group; Perform AC impedance spectroscopy analysis on the battery cell group based on the performance characteristic data to obtain the impedance spectrum characteristic data of the battery cell group; Perform complex plane impedance mapping on the impedance spectrum characteristic data of the battery cell group to obtain the complex impedance characteristic region of the battery cell group; Determine the battery cell grouping scheme based on the complex impedance characteristic region, and perform multi-objective Pareto allocation optimization on the battery cell group based on the battery cell grouping scheme to obtain the optimized battery cell grouping result.

[0007] Furthermore, the performing charge-discharge cycle tests on the battery cell group of the lithium battery through a preset charge-discharge regime to obtain the performance characteristic data of the battery cell group includes: Perform constant current and constant voltage charging on the battery cell group of the lithium battery through a preset charge-discharge regime to obtain the charging curve data, and perform constant current discharge on the battery cell group of the lithium battery to the cut-off voltage based on the charging curve data to obtain the discharge curve data; Calculate the actual capacity of each battery cell in the battery cell group through the discharge curve data to obtain the battery cell capacity data; wherein, the battery cell capacity data includes the initial capacity and the discharge cut-off capacity of each battery cell; Perform multiple charge-discharge cycles on the battery cell group of the lithium battery based on the battery cell capacity data and a preset number of cycles to obtain the cycle test data, and perform performance statistical analysis on each battery cell through the cycle test data to obtain the battery cell performance characteristic data; wherein, the performance characteristic data includes the capacity attenuation rate, the internal resistance growth rate, the energy efficiency, and the thermal stability parameter.

[0008] Furthermore, the performing AC impedance spectroscopy analysis on the battery cell group based on the performance characteristic data to obtain the impedance spectrum characteristic data of the battery cell group includes: Apply a small-amplitude sinusoidal AC signal to the battery cell group based on the performance characteristic data to obtain the electrical response signal of the battery cell group, and perform Fourier transform on the electrical response signal to obtain the frequency-domain impedance data; wherein, the electrical response signal includes a voltage response signal and a current response signal; Construct the first Nyquist plot of the battery cell group through the frequency-domain impedance data, and perform high-frequency region semicircle fitting on the first Nyquist plot to obtain the high-frequency equivalent resistance parameter; Perform mid-frequency region Warburg impedance fitting on the first Nyquist plot based on the high-frequency equivalent resistance parameter to obtain the Warburg coefficient, and calculate the diffusion coefficient of lithium ions for the battery cell group based on the Warburg coefficient to obtain the lithium ion diffusion coefficient; Performing a linear fitting on the low-frequency region linear part of the first Nyquist plot based on the lithium-ion diffusion coefficient to obtain low-frequency linear fitting parameters, and calculating the double-layer capacitance of the battery cell group based on the low-frequency linear fitting parameters to obtain the double-layer capacitance value; Performing an equivalent circuit modeling on the impedance spectrum of the battery cell group based on the double-layer capacitance value to obtain the equivalent circuit parameters of the battery cell group, and extracting impedance spectrum characteristics from the equivalent circuit parameters to obtain impedance spectrum characteristic data.

[0009] Further, performing a complex plane impedance mapping on the impedance spectrum characteristic data of the battery cell to obtain the complex impedance characteristic region of the battery cell group, including: Performing a complex plane impedance projection and mapping on the battery cell group based on the impedance spectrum characteristic data of the battery cell to obtain the second Nyquist plot of the battery cell group, and extracting a discrete point set from the second Nyquist plot to obtain the impedance discrete point set of the battery cell group; Performing a density clustering analysis on the battery cell group based on the impedance discrete point set to obtain the impedance density peak points of the battery cell group, and performing a convex hull algorithm processing on the impedance density peak points to obtain the impedance convex hull region of the battery cell group; Performing a DBSCAN clustering analysis on the battery cell group based on the impedance convex hull region to obtain the impedance clustering clusters of the battery cell group, and identifying the boundary points of the impedance clustering clusters to obtain the impedance clustering boundary of the battery cell group; Performing a self-organizing mapping neural network analysis on the battery cell group based on the impedance clustering boundary to obtain the characteristic topology map of the battery cell group, and performing a region division on the characteristic topology map to obtain the complex impedance characteristic region of the battery cell group.

[0010] Further, determining a battery cell grouping scheme based on the complex impedance characteristic region, and performing a multi-objective Pareto allocation optimization on the battery cell group based on the battery cell grouping scheme to obtain the optimized battery cell grouping result, including: Performing a feature space mapping on the battery cell group based on the complex impedance characteristic region to obtain the battery cell feature vector, and performing a multi-dimensional scaling analysis on the battery cell feature vector to obtain the battery cell distance matrix; Performing a hierarchical clustering process on the battery cell group based on the battery cell distance matrix to obtain the battery cell hierarchical structure tree, and performing a dynamic pruning on the battery cell hierarchical structure tree to obtain the battery cell grouping scheme; wherein, there are multiple and mutually different battery cell grouping schemes; Setting constraint conditions for the battery cell grouping scheme to obtain a feasible scheme space, and constructing a multi-objective function for the feasible scheme space to obtain a set of objective functions, Performing a non-dominated solution search on the battery cell group based on the set of objective functions to obtain the Pareto optimal solution, and determining a scheme for the battery cell group based on the Pareto optimal solution to obtain the optimized battery cell grouping result.

[0011] Further, performing feature space mapping on the battery cell group based on the complex impedance feature region to obtain a battery cell feature vector includes: Performing contour boundary extraction on the complex impedance feature region to obtain a polar coordinate curve sequence of the battery cell group, and performing polar coordinate transformation on the battery cell group based on the polar coordinate curve sequence to obtain a complex plane mapping point set of the battery cell group; Performing geometric feature calculation on the complex plane mapping point set to obtain shape description parameters of the battery cell group; wherein, the shape description parameters include contour perimeter, area ratio, and curvature distribution; Performing principal direction analysis on the shape description parameters through curvature distribution to obtain a characteristic principal axis vector of the battery cell group, and performing orthogonal basis decomposition on the characteristic principal axis vector to obtain a base matrix of the battery cell group; Performing projection coordinate transformation on the battery cell group based on the base matrix to obtain a normalized coordinate set of the battery cell group, and performing density distribution calculation on the normalized coordinate set to obtain a characteristic distribution function of the battery cell group; Performing moment statistical analysis on the characteristic distribution function to obtain statistical moment characteristics of the battery cell group, and performing dimensionless processing on the statistical moment characteristics to obtain a standardized characteristic quantity of the battery cell group; Performing vector assembly on the battery cell group based on the standardized characteristic quantity to obtain a battery cell feature vector.

[0012] Further, performing polar coordinate transformation on the battery cell group based on the polar coordinate curve sequence to obtain a complex plane mapping point set of the battery cell group includes: Performing phase decomposition on the polar coordinate curve sequence to obtain an angle distribution sequence of the battery cell group, and performing harmonic analysis on the angle distribution sequence to obtain a phase spectrum component of the battery cell group; Performing radial coordinate reconstruction on the battery cell group based on the phase spectrum component to obtain a radial distance sequence of the battery cell group, and performing smooth spline interpolation on the radial distance sequence to obtain a continuous curve function of the battery cell group; Performing differential operation on the continuous curve function to obtain a derivative sequence of the battery cell group, and performing singular point detection on the derivative sequence to obtain a characteristic point set of the battery cell group; Performing curve segment division on the battery cell group based on the characteristic point set to obtain a piecewise function set of the battery cell group, and performing polar coordinate mapping transformation on the piecewise function set to obtain complex plane distribution points of the battery cell group; Performing density aggregation degree calculation on the complex plane distribution points to obtain a regional density map of the battery cell group, and performing contour line extraction on the regional density map to obtain a density contour line of the battery cell group; Perform point set resampling on the battery cell group based on the density contour line to obtain the complex plane mapping point set of the battery cell group, and perform normalization processing on the complex plane mapping point set to obtain the standardized complex plane point set.

[0013] The present invention also provides an optimization device for battery cell matching based on a lithium battery, including: A test module, configured to perform charge and discharge cycle tests on the battery cell group of the lithium battery through a preset charge and discharge regime to obtain the performance characteristic data of the battery cell group; An analysis module, configured to perform AC impedance spectroscopy analysis on the battery cell group based on the performance characteristic data to obtain the impedance spectrum characteristic data of the battery cell group; A mapping module, configured to perform complex plane impedance mapping on the impedance spectrum characteristic data of the battery cells in the battery cell group to obtain the complex impedance characteristic region of the battery cell group; An optimization module, configured to determine a battery cell grouping scheme based on the complex impedance characteristic region, and perform multi-objective Pareto allocation optimization on the battery cell group based on the battery cell grouping scheme to obtain the optimized battery cell matching result.

[0014] The present invention also provides a computer device, including a memory and a processor. A computer program is stored in the memory, and when the processor executes the computer program, the steps of the method described in any one of the above are implemented.

[0015] The present invention also provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the steps of the method described in any one of the above are implemented.

[0016] An optimization method for battery cell matching based on a lithium battery provided by the present invention includes the following steps: performing charge and discharge cycle tests on the battery cell group of the lithium battery through a preset charge and discharge regime to obtain the performance characteristic data of the battery cell group; performing AC impedance spectroscopy analysis on the battery cell group based on the performance characteristic data to obtain the impedance spectrum characteristic data of the battery cell group; performing complex plane impedance mapping on the impedance spectrum characteristic data to obtain the complex impedance characteristic region of the battery cell group; determining a battery cell grouping scheme based on the complex impedance characteristic region, and performing multi-objective Pareto allocation optimization on the battery cell group based on the battery cell grouping scheme to obtain the optimized battery cell matching result, which solves the technical problem that traditional battery cell matching methods for lithium batteries often rely on simple voltage or capacity matching principles and fail to fully consider the influence of complex physical and chemical processes inside the battery cells on the overall performance of the battery, improves the overall stability of the battery module, reduces the safety risk caused by the failure of individual battery cells, and improves the reliability of the entire battery system. Description of the Drawings

[0017] Figure 1 is a schematic diagram of the steps of an optimization method for battery cell matching based on a lithium battery in an embodiment of the present invention; Figure 2 It is a structural block diagram of a lithium - battery - based cell matching optimization device in an embodiment of the present invention; Figure 3 It is a structural schematic block diagram of a computer device in an embodiment of the present invention.

[0018] The realization of the object, functional characteristics and advantages of the present invention will be further described with reference to the embodiments and the accompanying drawings. Detailed implementation manners

[0019] In order to make the object, technical solution and advantages of the present invention clearer, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described here are only used to explain the present invention and are not used to limit the present invention.

[0020] As Figure 1 shown, Figure 1 It is a schematic diagram of the steps of a lithium - battery - based cell matching optimization method in an embodiment of the present invention; An embodiment of the present invention provides a lithium - battery - based cell matching optimization method, including the following steps: Step S1, perform charge - discharge cycle tests on the cell group of the lithium battery through a preset charge - discharge regime to obtain the performance characteristic data of the cell group.

[0021] Specifically, a charge-discharge cycle test is performed on the battery cell group of the lithium battery through a preset charge-discharge regime. This is the first and crucial step in the entire optimization method because this step directly determines the quality of the data relied on in subsequent steps. Specifically, in this process, a reasonable charge-discharge regime needs to be designed according to the requirements of the target application scenario. For example, in the application scenario of an electric vehicle battery, considering different working conditions that may be encountered during actual use, such as high-speed driving, urban congestion, etc., a series of different charge-discharge rates and cycles need to be set to simulate these situations. These preset charge-discharge regimes should not only cover typical modes of daily use but also include tests under extreme conditions to comprehensively evaluate the performance of the battery cell group under various conditions. Next, the charge-discharge cycle test is implemented on the battery cell group according to these preset regimes, which means each battery cell will go through multiple charging and discharging processes. During this period, key parameters in each cycle are recorded in detail, such as voltage, current, temperature, and the rate of change of capacity, etc. These are all important indicators for measuring the performance of the battery cell. For example, in a complete test, if the voltage fluctuation of a certain battery cell exceeds the expected range at a specific charge-discharge rate, or its capacity decay rate is significantly faster than that of other battery cells, this indicates that there may be internal problems with this battery cell or it is incompatible with other battery cells. The data collected in this way can not only reflect the basic electrical characteristics of a single battery cell but also reveal its stability and durability during long-term use. Then, based on the above-obtained performance characteristic data, the working state of each battery cell and its potential problem points can be further analyzed. For example, in the actual application of an electric vehicle battery, if it is found that the capacity retention rate of several battery cells in a group is significantly lower than the average level in a high-temperature environment, the reason can be further explored through subsequent means such as alternating current impedance spectroscopy analysis. This meticulous preliminary test provides a solid foundation for subsequent steps, enabling subsequent complex plane impedance mapping and Pareto distribution optimization to be carried out based on accurate and detailed data, thereby ensuring that the finally formed battery cell grouping scheme is both scientific and practical, maximizing the overall performance and service life of the entire battery module. In short, this initial step is the key starting point of the entire optimization process, ensuring that each subsequent step can be based on a reliable data foundation, and thus realizing the effective management and optimal configuration of the lithium battery cell group.

[0022] Step S2: Perform an alternating current impedance spectroscopy analysis on the battery cell group based on the performance characteristic data to obtain impedance spectrum characteristic data of the battery cell group.

[0023] Specifically, an AC impedance spectroscopy analysis is performed on the battery cell group based on the performance characteristic data, which is an important step after initially obtaining the performance characteristic data of the battery cell group. First of all, this process requires the use of a professional electrochemical workstation to apply a small-amplitude sine wave voltage or current signal to the battery cell group and measure its response. These signals usually cover a wide frequency band range to comprehensively capture the dynamic behavior of each component and interface inside the battery cell. In this way, the complex impedance values of the battery cell at different frequencies can be obtained, and then an AC impedance spectrogram can be constructed. For example, in the application scenario of electric vehicle batteries, by performing AC impedance spectroscopy analysis on multiple battery cells under the same charge-discharge regime, subtle differences between different battery cells can be found, and these differences may reflect problems such as the aging degree of the materials inside the battery cell and uneven electrolyte distribution. In actual operation, the collected raw data will go through a series of complex mathematical processes, including noise reduction and smoothing, etc., to improve the reliability and accuracy of the data. Then, specialized software tools are used to fit these processed data to extract parameters representing the characteristics of each part inside the battery cell, such as ohmic resistance, charge transfer resistance, and double-layer capacitance, etc. These parameters together constitute the impedance spectrum characteristic data of the battery cell group, which can not only reflect the basic electrical characteristics of the battery cell but also reveal the complex physical and chemical processes inside it. For example, if it is found during the analysis that the charge transfer resistance of a certain battery cell is significantly higher than that of other battery cells, this may mean that there is poor contact or loss of active substances in this battery cell. If these problems are not solved, they will seriously affect the long-term stable operation of the entire battery module. Further, by comparing the impedance spectrum characteristic data of different battery cells, those battery cells with similar internal states can be identified, which is crucial for subsequent grouping optimization. For example, in a large-scale production electric vehicle battery system, only when all battery cells have highly consistent internal states can the efficient operation and long life of the entire system be ensured. Therefore, based on the impedance spectrum characteristic data obtained above, researchers can formulate a more scientific and reasonable grouping strategy, thus providing solid data support for subsequent complex plane impedance mapping and multi-objective Pareto allocation optimization. In short, this step is not only the key bridge connecting the initial performance test and subsequent in-depth analysis but also the basis for realizing the optimal matching of lithium battery cell groups, ensuring that the finally formed battery cell grouping scheme not only meets the actual application requirements but also maximally improves the overall performance and service life.

[0024] Step S3: Perform complex plane impedance mapping on the impedance spectrum characteristic data of the battery cell group to obtain the complex impedance characteristic region of the battery cell group.

[0025] Specifically, complex plane impedance mapping is performed on the impedance spectrum characteristic data of the battery cell group. This is a crucial step after obtaining the impedance spectrum characteristic data of the battery cell group, and its purpose is to reveal the complex characteristics of the internal state of the battery cell through a visual way. First of all, in this process, it is necessary to convert the previously obtained complex impedance values from the frequency domain to the complex plane, that is, to represent the impedance values at each frequency point in the form of real and imaginary parts. Specifically, the impedance value at each frequency point can be represented as a complex number, where the real part corresponds to the ohmic resistance, and the imaginary part reflects the capacitance and inductance effects. In this way, the impedance values at different frequencies can be mapped onto the complex plane to form a series of discrete points, and these points together constitute the complex impedance characteristic region of the battery cell. Then, using professional electrochemistry analysis software, these discrete points are connected to form a continuous curve or region, which can visually display the characteristics of each component inside the battery cell and their interactions. For example, in the application scenario of electric vehicle batteries, by performing complex plane impedance mapping on multiple battery cells under the same charge-discharge regime, the impedance characteristic differences between different battery cells can be clearly seen. If the complex impedance characteristic region of a certain battery cell significantly deviates from that of other battery cells, this may indicate that there are internal problems in this battery cell, such as uneven electrolyte distribution, shedding of active materials, etc. If these problems are not identified and solved, they will seriously affect the performance and lifespan of the entire battery module. Furthermore, complex plane impedance mapping can not only help identify problems inside a single battery cell, but also be used to evaluate the consistency between a group of battery cells. For example, in a large-scale production electric vehicle battery system, only when all battery cells have highly consistent complex impedance characteristic regions can the efficient operation and long lifespan of the entire system be ensured. Therefore, based on the above-obtained complex impedance characteristic regions, researchers can formulate a more scientific and reasonable grouping strategy. By comparing the complex impedance characteristic regions of different battery cells, those battery cells with similar internal states can be identified and grouped together, so as to reduce the overall system performance degradation caused by individual differences. In addition, complex plane impedance mapping can also provide important information about the aging degree of the battery cell. As the usage time increases, the complex impedance characteristic region of the battery cell will change, and this change can be used to track the aging process of the battery cell through regular monitoring. For example, in a long-term operating electric vehicle battery system, by performing periodic complex plane impedance mapping on the battery cells, early aging signs can be detected in a timely manner, and corresponding maintenance measures can be taken, such as replacing the battery cells that are about to fail, so as to extend the service life of the entire battery module. At the same time, this method can also help researchers better understand various physical and chemical processes inside the battery cell, providing valuable data support for further optimizing battery design and production processes.In summary, this step is not only a key bridge connecting the AC impedance spectroscopy analysis with the subsequent multi-objective Pareto allocation optimization, but also the foundation for realizing the optimal grouping of lithium battery cell packs. It ensures that the finally formed cell grouping scheme not only meets the actual application requirements, but also maximally improves the overall performance and service life.

[0026] Step S4: Determine the cell grouping scheme based on the complex impedance characteristic region, and perform multi-objective Pareto allocation optimization on the cell pack based on the cell grouping scheme to obtain the optimized cell grouping result.

[0027] Specifically, a battery cell grouping scheme is determined based on the complex impedance characteristic regions, and multi-objective Pareto allocation optimization is performed on the battery cell groups based on the battery cell grouping scheme. This is the last step in the entire battery cell matching optimization method, aiming to achieve the optimal combination of battery cells through scientific methods to improve the overall performance and service life of the battery system. First of all, in this process, it is necessary to evaluate the state of each battery cell according to the complex impedance characteristic regions obtained in the previous steps. These complex impedance characteristic regions not only reflect the basic electrical characteristics of the battery cells, but also reveal the complex physical and chemical processes inside them. For example, in the application scenario of electric vehicle batteries, by comparing and analyzing the complex impedance characteristic regions of multiple battery cells under the same charge and discharge regime, those battery cells with similar internal states can be identified, providing a basis for subsequent grouping. Next, based on the above-obtained complex impedance characteristic regions, researchers can formulate a more scientific and reasonable battery cell grouping scheme. Specifically, this step involves grouping battery cells with similar complex impedance characteristic regions together to ensure that the battery cells within each group are as consistent as possible in performance. For example, in a large-scale production electric vehicle battery system, only when all battery cells have highly consistent internal states can the efficient operation and long life of the entire system be ensured. Therefore, through an accurate grouping scheme, the risk of overall system performance degradation caused by individual differences can be significantly reduced. In addition, this grouping strategy can also help identify potential problem battery cells, such as those units whose complex impedance characteristic regions deviate significantly from other battery cells, so as to take corresponding maintenance measures or replacement strategies. Then, based on the battery cell grouping scheme, multi-objective Pareto allocation optimization is further carried out. This process involves balancing multiple objective functions, such as maximizing the energy density of the battery module, minimizing the internal resistance, and extending the service life, etc. By applying advanced optimization algorithms, the best battery cell matching scheme can be found on the premise of meeting these objectives. For example, in the design process of electric vehicle batteries, multiple factors such as the energy density, power density, and cycle life of the battery may need to be considered simultaneously. In this case, using the multi-objective Pareto allocation optimization method, a set of non-dominated solutions (i.e., the Pareto front) can be found, and the most practical battery cell matching scheme can be selected from them. For example, in a specific electric vehicle battery application scenario, assume that we have a batch of battery cells that have been preliminarily screened and tested, and they exhibit different complex impedance characteristic regions at different charge and discharge rates. Through careful analysis and comparison, we can group those battery cells with similar complex impedance characteristic regions together and perform multi-objective Pareto allocation optimization based on these grouping results. Finally, we obtain a set of optimized battery cell matching schemes, which not only improve the consistency and stability of the entire battery module, but also effectively extend its service life.In summary, this step is not only a crucial link connecting the previous data collection and analysis but also the core step in achieving the optimal grouping of lithium - battery cells. It ensures that the finally formed cell grouping scheme not only meets the actual application requirements but also maximally improves the overall performance and service life. In this way, not only can the overall performance of the battery system be improved, but also valuable data support and technical guidance can be provided for future battery design and production.

[0028] In a specific embodiment, the charging and discharging cycle test of the lithium - battery cell group is carried out through a preset charging and discharging regime to obtain the performance characteristic data of the cell group, including: The lithium - battery cell group is charged with constant current and constant voltage through a preset charging and discharging regime to obtain charging curve data, and based on the charging curve data, the lithium - battery cell group is discharged with constant current to the cut - off voltage to obtain discharge curve data; The actual capacity of each cell in the cell group is calculated through the discharge curve data to obtain cell capacity data; wherein, the cell capacity data includes the initial capacity and the discharge cut - off capacity of each cell; Based on the cell capacity data and a preset number of cycles, the lithium - battery cell group is subjected to multiple charging and discharging cycles to obtain cycle test data, and through the cycle test data, performance statistical analysis is carried out on each cell to obtain cell performance characteristic data; wherein, the performance characteristic data includes the capacity attenuation rate, the internal resistance growth rate, the energy efficiency, and the thermal stability parameter.

[0029] Specifically, the charging and discharging cycle test of the lithium battery cell group is carried out through a preset charging and discharging regime to obtain the performance characteristic data of the cell group, which is the basic step of the whole optimization method. First of all, in this process, a reasonable charging and discharging regime needs to be designed according to specific application scenarios. For example, in the application scenario of electric vehicle batteries, considering the different working conditions that may be encountered in the actual use process, such as high-speed driving, urban congestion, etc., a series of different charging and discharging rates and cycles need to be set to simulate these situations. Specifically, in this step, the cell group of the lithium battery is first charged at a constant current and constant voltage through the preset charging and discharging regime to obtain the charging curve data, and the cell group of the lithium battery is discharged at a constant current to the cut-off voltage based on the charging curve data to obtain the discharging curve data. After obtaining the charging and discharging curve data, the next step is to calculate the actual capacity of each cell. Through the discharging curve data, the actual capacity of each cell in the cell group can be accurately calculated, including the initial capacity and the discharging cut-off capacity. This process not only provides the basic electrical characteristic information of the cell, but also provides key data support for subsequent analysis. For example, in the application scenario of electric vehicle batteries, if the initial capacity of a certain cell is significantly lower than that of other cells, this may mean that there are manufacturing defects or early aging phenomena in this cell. If these problems are not identified and solved, they will seriously affect the performance and life of the entire battery module. Based on the obtained cell capacity data and the preset number of cycles, the cell group of the lithium battery is further subjected to multiple charging and discharging cycles to obtain the cycle test data. This process usually involves hundreds or even thousands of charging and discharging cycles, aiming to comprehensively evaluate the stability and durability of the cell in long-term use. Through the cycle test data, detailed performance statistical analysis can be carried out on each cell, so as to obtain the cell performance characteristic data, including the capacity attenuation rate, the internal resistance growth rate, the energy efficiency and the thermal stability parameters, etc. For example, in an electric vehicle battery system, after multiple charging and discharging cycles, if it is found that the capacity attenuation rate of a certain cell is significantly higher than that of other cells, this may indicate that the aging speed of the internal materials of this cell is relatively fast, and its service life needs to be particularly concerned. In addition, by comparing and analyzing the cycle test data of multiple cells under the same charging and discharging regime, it is also possible to identify those cells with similar performance characteristics, providing a basis for subsequent grouping optimization. For example, in a large-scale production electric vehicle battery system, only when all cells have highly consistent performance characteristics can the efficient operation and long life of the entire system be ensured. Therefore, based on the obtained cell performance characteristic data, researchers can formulate a more scientific and reasonable grouping strategy, thereby reducing the risk of overall system performance degradation caused by individual differences. Further, this detailed performance statistical analysis can not only help identify the problems of individual cells, but also provide valuable data support for improving battery design and production processes.For example, in a long-term operating electric vehicle battery system, by performing periodic performance tests and data analysis on the battery cells, signs of early aging can be detected in a timely manner, and corresponding maintenance measures can be taken, such as replacing the soon-to-fail battery cells, thereby extending the service life of the entire battery module. In short, this step is not only a key bridge connecting the preliminary test and subsequent in-depth analysis, but also the basis for realizing the optimal matching of lithium battery cell groups. It ensures that the finally formed cell grouping scheme not only meets the actual application requirements, but also maximally improves the overall performance and service life. In this way, not only can the overall performance of the battery system be improved, but also valuable data support and technical guidance can be provided for future battery design and production.

[0030] In a specific embodiment, performing an alternating current impedance spectroscopy analysis on the cell group based on the performance characteristic data to obtain impedance spectrum characteristic data of the cell group, including: Applying a small-amplitude sinusoidal alternating current signal to the cell group based on the performance characteristic data to obtain an electrical response signal of the cell group, and performing a Fourier transform on the electrical response signal to obtain frequency-domain impedance data; wherein, the electrical response signal includes a voltage response signal and a current response signal; Constructing a first Nyquist plot of the cell group through the frequency-domain impedance data, and performing a high-frequency region semicircle fitting on the first Nyquist plot to obtain a high-frequency equivalent resistance parameter; Performing a Warburg impedance fitting on the Nyquist plot in the intermediate frequency region based on the high-frequency equivalent resistance parameter to obtain a Warburg coefficient, and calculating a diffusion coefficient of the cell group based on the Warburg coefficient to obtain a lithium-ion diffusion coefficient; Performing a low-frequency region linear part fitting on the first Nyquist plot based on the lithium-ion diffusion coefficient to obtain a low-frequency linear fitting parameter, and calculating a double-layer capacitance of the cell group based on the low-frequency linear fitting parameter to obtain a double-layer capacitance value; Performing an equivalent circuit modeling on the impedance spectrum of the cell group based on the double-layer capacitance value to obtain equivalent circuit parameters of the cell group, and performing impedance spectrum characteristic extraction on the equivalent circuit parameters to obtain impedance spectrum characteristic data.

[0031] Specifically, based on the performance characteristic data, an AC impedance spectroscopy analysis is performed on the battery cell group to obtain impedance spectrum characteristic data of the battery cell group. This is a key step after initially obtaining the basic performance characteristics of the battery cell group, aiming to reveal the internal state of the battery cell and its changes through detailed electrochemical analysis. First, in this process, a small-amplitude sinusoidal AC signal needs to be applied to the battery cell group according to the previously obtained performance characteristic data, and its electrical response signals, including voltage response signals and current response signals, are recorded. These signals usually cover a wide frequency band range to comprehensively capture the dynamic behavior of each component and interface inside the battery cell. For example, in the application scenario of electric vehicle batteries, by performing AC impedance spectroscopy analysis on multiple battery cells under the same charge-discharge regime, subtle differences between different battery cells can be found, and these differences may reflect problems such as the aging degree of the materials inside the battery cell and uneven electrolyte distribution. Next, the recorded electrical response signals are subjected to Fourier transform to convert them from the time domain to the frequency domain, thereby obtaining frequency-domain impedance data. This step can not only provide the complex impedance values of the battery cell at different frequencies but also lay the foundation for the subsequent construction of the Nyquist plot. Specifically, by constructing the first Nyquist plot of the battery cell group and fitting the semicircular part in the high-frequency region, high-frequency equivalent resistance parameters can be obtained. These parameters reflect the ohmic resistance and charge transfer resistance inside the battery cell and are crucial for evaluating the internal resistance characteristics of the battery cell. For example, in an electric vehicle battery system, if the high-frequency equivalent resistance of a certain battery cell is significantly higher than that of other battery cells, this may indicate problems such as poor contact or loss of active substances in this battery cell. Based on the above-obtained high-frequency equivalent resistance parameters, further Warburg impedance fitting is performed on the middle-frequency region of the Nyquist plot to calculate the Warburg coefficient. This process involves a detailed analysis of the diffusion process of the battery cell, and the lithium-ion diffusion coefficient is calculated to evaluate the lithium-ion transport efficiency inside the battery cell. For example, in the application scenario of electric vehicle batteries, the lithium-ion diffusion coefficient is directly related to the charge-discharge rate and energy density of the battery, so accurately measuring this parameter is crucial for optimizing the battery performance. Then, based on the obtained lithium-ion diffusion coefficient, the linear part in the low-frequency region of the first Nyquist plot is fitted to obtain low-frequency linear fitting parameters, and these parameters are used for double-layer capacitance calculation to obtain the double-layer capacitance value. This parameter reflects the capacitance characteristics of the double layer on the electrode surface and is of great significance for understanding the electrode interface reaction mechanism. Finally, based on the above-obtained double-layer capacitance value, an equivalent circuit model of the impedance spectrum of the battery cell group is established to obtain the equivalent circuit parameters of the battery cell group, and impedance spectrum feature extraction is performed on these parameters to finally obtain impedance spectrum characteristic data. The equivalent circuit model can not only intuitively display the characteristics of each component inside the battery cell and their interactions but also provide a scientific basis for subsequent grouping optimization. For example, in a large-scale production electric vehicle battery system, only when all battery cells have highly consistent equivalent circuit parameters can the efficient operation and long life of the entire system be ensured.Therefore, based on the impedance spectrum characteristic data obtained above, researchers can formulate a more scientific and reasonable grouping strategy, thereby reducing the risk of overall system performance degradation caused by individual differences. In short, this step is not only the key bridge connecting the preliminary test and the subsequent in-depth analysis, but also the basis for realizing the optimal grouping of lithium battery cells. It ensures that the finally formed cell grouping scheme not only meets the actual application requirements, but also maximizes the overall performance and service life. In this way, not only can the overall performance of the battery system be improved, but also valuable data support and technical guidance can be provided for future battery design and production.

[0032] In a specific embodiment, the complex plane impedance mapping of the cell impedance spectrum characteristic data to obtain the complex impedance characteristic region of the cell group includes: Based on the cell impedance spectrum characteristic data, perform complex plane impedance projection and mapping on the cell group to obtain the second Nyquist plot of the cell group, and extract the discrete point set of the second Nyquist plot to obtain the impedance discrete point set of the cell group; Based on the impedance discrete point set, perform density clustering analysis on the cell group to obtain the impedance density peak points of the cell group, and perform convex hull algorithm processing on the impedance density peak points to obtain the impedance convex hull region of the cell group; Based on the impedance convex hull region, perform DBSCAN clustering analysis on the cell group to obtain the impedance clustering clusters of the cell group, and identify the boundary points of the impedance clustering clusters to obtain the impedance clustering boundary of the cell group; Based on the impedance clustering boundary, perform self-organizing mapping neural network analysis on the cell group to obtain the characteristic topology map of the cell group, and perform region division on the characteristic topology map to obtain the complex impedance characteristic region of the cell group.

[0033] Specifically, based on the impedance spectrum characteristic data, perform complex plane impedance projection and mapping on the battery cell group to obtain the second Nyquist plot of the battery cell group, and extract the discrete point set from the second Nyquist plot. This is a key step after obtaining the impedance spectrum characteristic data of the battery cell group. First, in this process, it is necessary to convert the previously obtained complex impedance values from the frequency domain to the complex plane, that is, represent the impedance values at each frequency point in the form of real and imaginary parts. Specifically, the impedance value at each frequency point can be represented as a complex number, where the real part corresponds to the ohmic resistance, and the imaginary part reflects the capacitance and inductance effects. In this way, the impedance values at different frequencies can be mapped to the complex plane to form a series of discrete points, and these points together constitute the second Nyquist plot of the battery cell. For example, in the application scenario of electric vehicle batteries, by performing complex plane impedance mapping on multiple battery cells under the same charge-discharge regime, the impedance characteristic differences between different battery cells can be clearly seen. If the second Nyquist plot of a certain battery cell deviates significantly from other battery cells, this may indicate that there are internal problems in this battery cell, such as uneven electrolyte distribution, shedding of active materials, etc. Next, based on the second Nyquist plot obtained above, further extract the discrete point set from it to obtain the impedance discrete point set of the battery cell group. These discrete point sets can not only intuitively display the characteristics of each component inside the battery cell and their interactions, but also provide basic data for further analysis. For example, in a large-scale production electric vehicle battery system, only when all battery cells have a highly consistent internal state can the efficient operation and long life of the entire system be ensured. Therefore, based on the impedance discrete point set obtained above, researchers can formulate a more scientific and reasonable grouping strategy. By comparing the discrete point sets of different battery cells, those battery cells with similar internal states can be identified and grouped together, thereby reducing the risk of overall system performance degradation caused by individual differences. Then, based on the impedance discrete point set, perform density clustering analysis on the battery cell group to obtain the impedance density peak points of the battery cell group, and perform convex hull algorithm processing on the impedance density peak points to obtain the impedance convex hull region of the battery cell group. Density clustering analysis is an unsupervised learning method that identifies dense and sparse regions by calculating the density around each point. For example, in the application scenario of electric vehicle batteries, assume that we have a batch of battery cells that have been preliminarily screened and tested, and they show different impedance discrete point sets at different charge-discharge rates. Through density clustering analysis, we can identify those impedance density peak points with high density, and these points represent regions with relatively consistent internal states of the battery cells. Then, using the convex hull algorithm to process these impedance density peak points, a minimum convex polygon region that encloses these points can be obtained, that is, the impedance convex hull region. This region can not only help identify the internal state consistency of the battery cells, but also provide a basis for further clustering analysis.Perform DBSCAN clustering analysis on the battery cell group based on the impedance convex hull region to obtain impedance clustering clusters of the battery cell group, and identify boundary points of the impedance clustering clusters to obtain the impedance clustering boundary of the battery cell group. DBSCAN (Density-Based Spatial Clustering of Applications with Noise) is a density-based spatial clustering algorithm that can effectively handle noise points and discover clusters of arbitrary shapes. For example, in an electric vehicle battery system, through DBSCAN clustering analysis, battery cells with similar impedance characteristics can be grouped into the same clustering cluster. Suppose we have 100 battery cells. After DBSCAN clustering analysis, 5 clustering clusters are obtained, with each cluster containing 20 battery cells. By identifying the boundary points of these clustering clusters, the boundaries between the clusters can be further clarified, providing basic data for subsequent self-organizing map neural network analysis. Finally, perform self-organizing map neural network (SOM) analysis on the battery cell group based on the impedance clustering boundary to obtain the characteristic topology map of the battery cell group, and perform region division on the characteristic topology map to obtain the complex impedance characteristic region of the battery cell group. The self-organizing map neural network is an unsupervised learning algorithm that can map high-dimensional data onto a two-dimensional or three-dimensional grid to form a characteristic topology map. For example, in the application scenario of an electric vehicle battery, through SOM analysis, the impedance clustering boundary obtained above can be mapped onto a two-dimensional grid to form a characteristic topology map. In this topology map, each node represents a battery cell, and the distance between the nodes reflects the similarity between the battery cells. Through region division, battery cells with similar impedance characteristics can be grouped into the same region, thereby achieving more refined grouping optimization. Suppose among 100 battery cells, after SOM analysis, 10 different complex impedance characteristic regions are obtained, with each region containing 10 battery cells. This refined grouping scheme not only improves the consistency and stability of the entire battery module but also effectively extends its service life. In short, this step is not only a key bridge connecting the AC impedance spectrum analysis and subsequent multi-objective Pareto allocation optimization but also the basis for realizing the optimal matching of lithium battery cell groups. Through detailed complex plane impedance mapping, density clustering analysis, DBSCAN clustering analysis, and self-organizing map neural network analysis, the complex characteristics of the internal state of the battery cells can be comprehensively revealed, providing solid data support for formulating a scientific and reasonable grouping strategy. For example, in the design process of an electric vehicle battery, through the detailed analysis of the above steps, not only can the consistency and stability of the battery module be improved, but its service life can also be significantly extended, providing valuable data support and technical guidance for future battery design and production. In this way, not only can the overall performance of the battery system be improved, but also strong guarantees can be provided for maintenance and management in practical applications.

[0034] In a specific embodiment, determining a battery cell grouping scheme based on the complex impedance characteristic region, and performing multi-objective Pareto allocation optimization on the battery cell group based on the battery cell grouping scheme to obtain an optimized battery cell grouping result, including: Performing feature space mapping on the battery cell group based on the complex impedance characteristic region to obtain a battery cell feature vector, and performing multi-dimensional scaling analysis on the battery cell feature vector to obtain a battery cell distance matrix; Performing hierarchical clustering processing on the battery cell group based on the battery cell distance matrix to obtain a battery cell hierarchical structure tree, and performing dynamic pruning on the battery cell hierarchical structure tree to obtain a battery cell grouping scheme; wherein, there are multiple and mutually different battery cell grouping schemes; Setting constraint conditions for the battery cell grouping scheme to obtain a feasible solution space, and constructing a multi-objective function for the feasible solution space to obtain a set of objective functions, Searching for non-dominated solutions for the battery cell group based on the set of objective functions to obtain Pareto optimal solutions, and determining a scheme for the battery cell group based on the Pareto optimal solutions to obtain an optimized battery cell grouping result.

[0035] Specifically, based on the complex impedance characteristic region, a battery cell grouping scheme is determined, and based on this grouping scheme, multi-objective Pareto allocation optimization is performed on the battery cell group to obtain the optimized battery cell grouping result, which is a key step in the entire lithium battery cell grouping optimization process. First of all, in this process, it is necessary to perform feature space mapping on the battery cell group based on the previously obtained complex impedance characteristic region to obtain the battery cell feature vector. Specifically, the complex impedance characteristic region of each battery cell can be represented as a high-dimensional feature vector, which not only reflects the basic electrical characteristics of the battery cell, but also reveals the complex physical and chemical processes inside it. For example, in the application scenario of electric vehicle batteries, by comparing and analyzing the complex impedance characteristic regions of multiple battery cells under the same charge and discharge regime, those battery cells with similar internal states can be identified. Next, multidimensional scaling analysis (MDS) is performed on the battery cell feature vector to obtain the battery cell distance matrix. Multidimensional scaling analysis is a dimensionality reduction technique that preserves the spatial structure of the original data by calculating the distance between each pair of battery cells. Suppose we have 100 battery cells. After MDS analysis, we can obtain a 100x100 distance matrix, where each element represents the distance between two battery cells. This distance matrix can not only intuitively show the similarity or difference between battery cells, but also provide basic data for further clustering analysis. Then, based on the battery cell distance matrix, hierarchical clustering processing is performed on the battery cell group to obtain the battery cell hierarchical structure tree, and dynamic pruning is performed on the battery cell hierarchical structure tree to obtain the battery cell grouping scheme. Hierarchical clustering is a common clustering method that represents the relationship between battery cells by constructing a hierarchical structure tree. For example, in an electric vehicle battery system, through hierarchical clustering analysis, those battery cells with similar impedance characteristics can be grouped into the same level. Suppose among 100 battery cells, after hierarchical clustering processing, a hierarchical structure tree with multiple levels is obtained. Then, using the dynamic pruning algorithm to prune this tree, the hierarchical structure tree can be divided into multiple independent branches according to the set threshold, so as to obtain multiple different battery cell grouping schemes. For example, after dynamic pruning, 5 different grouping schemes can be obtained, and each scheme contains 20 battery cells. Further, constraint conditions are set for the battery cell grouping scheme to obtain the feasible solution space, and a multi-objective function is constructed for the feasible solution space to obtain the objective function set. The constraint conditions usually include requirements for performance indicators such as battery cell capacity, internal resistance, and energy efficiency. For example, in the design process of electric vehicle batteries, multiple factors such as the energy density, power density, and cycle life of the battery may need to be considered simultaneously. In this case, a multi-objective function set can be constructed, which includes objectives such as maximizing the energy density of the battery module, minimizing the internal resistance, and extending the service life.Suppose we set three objective functions: F1 (maximizing energy density), F2 (minimizing internal resistance), and F3 (maximizing cycle life). Through these objective functions, the optimal cell grouping scheme can be found while meeting various performance requirements. Based on the set of objective functions, a non-dominated solution search is performed on the cell group to obtain the Pareto optimal solution, and based on the Pareto optimal solution, the cell group is determined to obtain the optimized cell grouping result. Non-dominated solution search is an important step in multi-objective optimization, aiming to find a set of non-inferior solutions (i.e., the Pareto front), from which the cell grouping scheme that best meets the actual needs is selected. Specifically, the Pareto optimal solution refers to a solution that cannot improve any one objective without compromising at least one other objective under a given set of objective functions. For example, in the above electric vehicle battery application scenario, suppose we obtain a set of 10 Pareto optimal solutions through non-dominated solution search. Each solution represents a different cell grouping scheme, which shows different degrees of optimization effects in terms of energy density, internal resistance, and cycle life. Suppose we have a batch of 100 cells that have been preliminarily screened and tested, and they show different complex impedance characteristic regions at different charge and discharge rates. First, we convert these complex impedance characteristic regions into feature vectors and use multidimensional scaling analysis (MDS) to calculate the distance matrix between the cells. Suppose the obtained distance matrix is 100x100, and each element represents the Euclidean distance between two cells. Next, a hierarchical clustering algorithm is used to construct a hierarchical structure tree of the cells. Initially, each cell is a separate cluster, and the clusters with the closest distance are gradually merged until a preset stopping condition is reached. Suppose we finally obtain a hierarchical structure tree with multiple levels. Then, a dynamic pruning algorithm is used to prune the tree, and according to the set threshold, the hierarchical structure tree is divided into multiple independent branches, thus obtaining multiple different cell grouping schemes. For example, after dynamic pruning, 5 different grouping schemes are obtained, and each scheme contains 20 cells. For each cell grouping scheme, we set a series of constraint conditions, such as requirements for performance indicators such as cell capacity, internal resistance, and energy efficiency. Then, a multi-objective function set is constructed, including objectives such as maximizing the energy density of the battery module, minimizing the internal resistance, and extending the service life. For example, suppose we set three objective functions: F1 (maximizing energy density), F2 (minimizing internal resistance), and F3 (maximizing cycle life). Through these objective functions, the optimal cell grouping scheme can be found while meeting various performance requirements. Suppose in the actual application of electric vehicle batteries, there are 100 cells, and after hierarchical clustering and dynamic pruning, 5 different grouping schemes are obtained, and each scheme contains 20 cells. Each scheme has different performances in terms of energy density, internal resistance, and cycle life.For example: Solution 1: The average energy density is 250 Wh / kg, the average internal resistance is 5 mΩ, and the average cycle life is 2000 times. Solution 2: The average energy density is 240 Wh / kg, the average internal resistance is 4.5 mΩ, and the average cycle life is 2100 times. Solution 3: The average energy density is 260 Wh / kg, the average internal resistance is 5.5 mΩ, and the average cycle life is 1900 times. Solution 4: The average energy density is 245 Wh / kg, the average internal resistance is 4.8 mΩ, and the average cycle life is 2050 times. Solution 5: The average energy density is 255 Wh / kg, the average internal resistance is 5.2 mΩ, and the average cycle life is 1950 times. Based on these solutions, we construct a set of multi-objective functions and perform non-dominated solution search. Suppose we obtain a set of 10 Pareto optimal solutions through non-dominated solution search. Each solution represents a different cell grouping scheme, and they show different degrees of optimization effects in terms of energy density, internal resistance, and cycle life. For example: Solution 1: The energy density is 250 Wh / kg, the internal resistance is 4.8 mΩ, and the cycle life is 2050 times. Solution 2: The energy density is 245 Wh / kg, the internal resistance is 4.7 mΩ, and the cycle life is 2100 times. Solution 3: The energy density is 260 Wh / kg, the internal resistance is 5.2 mΩ, and the cycle life is 1950 times. In this way, not only can the consistency and stability of the battery module be improved, but also its service life can be significantly extended, thus providing valuable data support and technical guidance for future battery design and production. In addition, this detailed analysis and optimization method can also provide a strong guarantee for maintenance and management in practical applications, ensuring the efficient operation and long life of the electric vehicle battery system. In short, this step is not only a key link connecting the previous data collection and analysis, but also the core step to realize the optimal grouping of lithium battery cells, which ensures that the finally formed cell grouping scheme not only meets the actual application requirements, but also maximizes the overall performance and service life. In this way, not only can the overall performance of the battery system be improved, but also valuable data support and technical guidance can be provided for future battery design and production. For example, in the design process of electric vehicle batteries, through the detailed analysis of the above steps, not only can the consistency and stability of the battery module be improved, but also its service life can be significantly extended, thus providing valuable data support and technical guidance for future battery design and production. In this way, not only can the overall performance of the battery system be improved, but also a strong guarantee can be provided for maintenance and management in practical applications.

[0036] In a specific embodiment, the mapping of the cell group to the feature space based on the complex impedance feature region to obtain a cell feature vector includes: Extract the contour boundary of the complex impedance characteristic region to obtain a sequence of polar coordinate curves of the battery cell group. Based on the sequence of polar coordinate curves, perform a polar coordinate transformation on the battery cell group to obtain a set of complex plane mapping points of the battery cell group; Calculate the geometric features of the set of complex plane mapping points to obtain the shape description parameters of the battery cell group; wherein, the shape description parameters include the contour perimeter, area ratio, and curvature distribution; Perform a principal direction analysis on the shape description parameters through a preset Hu invariant moment algorithm to obtain the characteristic principal axis vector of the battery cell group, and perform an orthogonal basis decomposition on the characteristic principal axis vector to obtain the base matrix of the battery cell group; Based on the base matrix, perform a projection coordinate transformation on the battery cell group to obtain a set of normalized coordinates of the battery cell group, and calculate the density distribution of the set of normalized coordinates to obtain the characteristic distribution function of the battery cell group; Perform a moment statistical analysis on the characteristic distribution function to obtain the statistical moment characteristics of the battery cell group, and perform a dimensionless processing on the statistical moment characteristics to obtain the standardized characteristic quantity of the battery cell group; Based on the standardized characteristic quantity, perform a vector assembly on the battery cell group to obtain the battery cell characteristic vector.

[0037] Specifically, based on the complex impedance characteristic region, a feature space mapping is performed on the battery cell group to obtain the battery cell feature vector, which is an important step in the entire optimization process of lithium battery cell matching. First of all, in this process, it is necessary to extract the contour boundary of the complex impedance characteristic region to obtain the polar coordinate curve sequence of the battery cell group. Specifically, the complex impedance characteristic region of each battery cell can be represented by its contour boundary, and these contour boundaries can be converted into a series of polar coordinate curve sequences. For example, in the application scenario of electric vehicle batteries, by comparing and analyzing the complex impedance characteristic regions of multiple battery cells under the same charge and discharge regime, those battery cells with similar internal states can be identified. Next, based on the polar coordinate curve sequence, a polar coordinate transformation is performed on the battery cell group to obtain the complex plane mapping point set of the battery cell group. Polar coordinate transformation is a method of converting a polar coordinate curve sequence into a point set on the complex plane. Suppose we have 100 battery cells. After polar coordinate transformation, we can obtain a set of mapping points of these battery cells on the complex plane. These mapping points can not only intuitively show the similarity or difference between battery cells, but also provide basic data for further geometric feature calculation. Then, geometric feature calculation is performed on the complex plane mapping point set to obtain the shape description parameters of the battery cell group. The shape description parameters include contour perimeter, area ratio, curvature distribution, etc. For example, in an electric vehicle battery system, through geometric feature calculation, the shape characteristics of the complex impedance characteristic region of each battery cell can be quantified. Suppose among 100 battery cells, after geometric feature calculation, the contour perimeter, area ratio, and curvature distribution of each battery cell are obtained. For example, the contour perimeter of a certain battery cell is 50 unit lengths, the area ratio is 0.8, and the curvature distribution is 0.6. These parameters help to further analyze the internal state consistency of the battery cells. Further, through the preset Hu invariant moment algorithm, a principal direction analysis is performed on the shape description parameters to obtain the characteristic principal axis vector of the battery cell group, and an orthogonal basis decomposition is performed on the characteristic principal axis vector to obtain the basis matrix of the battery cell group. The Hu invariant moment algorithm is a technology used in image processing and pattern recognition, which can perform principal direction analysis while maintaining shape characteristics unchanged. Suppose we use the Hu invariant moment algorithm to analyze the shape description parameters of the above 100 battery cells and obtain the characteristic principal axis vector of each battery cell. Then, through orthogonal basis decomposition, these characteristic principal axis vectors can be converted into a set of basis matrices. For example, suppose after orthogonal basis decomposition of the characteristic principal axis vector of each battery cell, a 3x3 basis matrix is obtained. Based on the basis matrix, a projection coordinate transformation is performed on the battery cell group to obtain the normalized coordinate set of the battery cell group, and a density distribution calculation is performed on the normalized coordinate set to obtain the characteristic distribution function of the battery cell group. Projection coordinate transformation is a method of mapping high-dimensional data to a low-dimensional space. Suppose we perform a projection coordinate transformation on the basis matrix of the above 100 battery cells to obtain the normalized coordinate set of each battery cell.For example, the normalized coordinate set of a certain battery cell is (0.2, 0.4, 0.6). Then, through density distribution calculation, the characteristic distribution function of each battery cell can be obtained. Suppose the characteristic distribution function of a certain battery cell shows that its density is concentrated in a specific area, which indicates that this battery cell is more concentrated in certain characteristics. Then, moment statistical analysis is performed on the characteristic distribution function to obtain the statistical moment characteristics of the battery cell group, and the statistical moment characteristics are dimensionless processed to obtain the standardized characteristic quantities of the battery cell group. Moment statistical analysis is a method for describing the distribution characteristics of data. Suppose we perform moment statistical analysis on the characteristic distribution functions of the above 100 battery cells and obtain the statistical moment characteristics of each battery cell. For example, the first moment of a certain battery cell is 0.3, the second moment is 0.5, and the third moment is 0.7. Then, through dimensionless processing, these statistical moment characteristics can be converted into standardized characteristic quantities. For example, the standardized characteristic quantity of a certain battery cell is (0.3, 0.5, 0.7). Finally, based on the standardized characteristic quantities, vector assembly is performed on the battery cell group to obtain the battery cell characteristic vector. Vector assembly is the process of combining multiple standardized characteristic quantities into a characteristic vector. Suppose we perform vector assembly on the standardized characteristic quantities of the above 100 battery cells and obtain the characteristic vector of each battery cell. For example, the characteristic vector of a certain battery cell is (0.3, 0.5, 0.7, 0.2, 0.4, 0.6), and this characteristic vector not only contains the basic electrical characteristics of this battery cell but also reveals the complex internal physical and chemical processes. To illustrate this process in more detail, here is a specific example: Suppose we have a batch of 100 battery cells that have undergone preliminary screening and testing, and they exhibit different complex impedance characteristic regions at different charge and discharge rates. First, we extract the contour boundaries of these complex impedance characteristic regions to obtain the polar coordinate curve sequence of each battery cell. Suppose the polar coordinate curve sequence of each battery cell contains 100 points. After polar coordinate transformation, the mapped point set of each battery cell on the complex plane is obtained. Next, geometric feature calculation is performed on these mapped point sets on the complex plane to obtain the shape description parameters of each battery cell. For example, the contour perimeter of a certain battery cell is 50 unit lengths, the area ratio is 0.8, and the curvature distribution is 0.6. These parameters help to further analyze the internal state consistency of the battery cell. Then, through the preset Hu invariant moment algorithm, principal direction analysis is performed on these shape description parameters to obtain the characteristic principal axis vector of each battery cell, and orthogonal basis decomposition is performed on it to obtain the basis matrix of each battery cell. Suppose the characteristic principal axis vector of each battery cell is orthogonally decomposed to obtain a 3x3 basis matrix. Based on these basis matrices, projection coordinate transformation is performed on the battery cell group to obtain the normalized coordinate set of each battery cell, and density distribution calculation is performed on these normalized coordinate sets to obtain the characteristic distribution function of each battery cell. For example, the normalized coordinate set of a certain battery cell is (0.2, 0.4, 0.6), and its characteristic distribution function shows that the density is concentrated in a specific area.Next, perform moment statistical analysis on these characteristic distribution functions to obtain the statistical moment characteristics of each battery cell, and perform dimensionless processing on them to obtain the standardized characteristic quantities of each battery cell. For example, the first moment of a certain battery cell is 0.3, the second moment is 0.5, and the third moment is 0.7. After dimensionless processing, the standardized characteristic quantity is (0.3, 0.5, 0.7). Finally, based on these standardized characteristic quantities, vector assembly is performed on the battery cell group to obtain the characteristic vector of each battery cell. For example, the characteristic vector of a certain battery cell is (0.3, 0.5, 0.7, 0.2, 0.4, 0.6). This characteristic vector not only contains the basic electrical characteristics of the battery cell, but also reveals the complex physical and chemical processes inside it. In this way, not only can the consistency and stability of the battery module be improved, but its service life can also be significantly extended, thus providing valuable data support and technical guidance for future battery design and production. In addition, this detailed analysis and optimization method can also provide a strong guarantee for maintenance and management in practical applications, ensuring the efficient operation and long life of the electric vehicle battery system. In short, this step is not only a key link connecting the previous data collection and analysis, but also the core step to realize the optimal grouping of lithium battery cells, which ensures that the finally formed battery cell grouping scheme not only meets the actual application requirements, but also maximizes the overall performance and service life. In this way, not only can the overall performance of the battery system be improved, but also valuable data support and technical guidance can be provided for future battery design and production. For example, in the design process of electric vehicle batteries, through the detailed analysis of the above steps, not only can the consistency and stability of the battery module be improved, but its service life can also be significantly extended, thus providing valuable data support and technical guidance for future battery design and production. In this way, not only can the overall performance of the battery system be improved, but also a strong guarantee can be provided for maintenance and management in practical applications.

[0038] In a specific embodiment, the performing polar coordinate transformation on the battery cell group based on the polar coordinate curve sequence to obtain a complex plane mapping point set of the battery cell group includes: Performing phase decomposition on the polar coordinate curve sequence to obtain an angle distribution sequence of the battery cell group, and performing harmonic analysis on the angle distribution sequence to obtain phase spectrum components of the battery cell group; Based on the phase spectrum components, performing radial coordinate reconstruction on the battery cell group to obtain a radial distance sequence of the battery cell group, and performing smooth spline interpolation on the radial distance sequence to obtain a continuous curve function of the battery cell group; Performing differential operation on the continuous curve function to obtain a derivative sequence of the battery cell group, and performing singular point detection on the derivative sequence to obtain a set of characteristic points of the battery cell group; Based on the set of feature points, the battery cell group is divided into curve segments to obtain a set of piecewise functions for the battery cell group, and a polar coordinate mapping transformation is performed on the set of piecewise functions to obtain the complex plane distribution points of the battery cell group; The density aggregation degree of the complex plane distribution points is calculated to obtain the regional density map of the battery cell group, and contour lines are extracted from the regional density map to obtain the density contour lines of the battery cell group; Based on the density contour lines, point set resampling is performed on the battery cell group to obtain the complex plane mapping point set of the battery cell group.

[0039] Specifically, based on the sequence of polar coordinate curves, perform a polar coordinate transformation on the battery cell group to obtain a set of complex plane mapping points of the battery cell group, which is one of the key steps in the entire lithium battery cell matching optimization process. First, in this process, it is necessary to perform phase decomposition on the sequence of polar coordinate curves to obtain the angle distribution sequence of the battery cell group, and perform harmonic analysis on the angle distribution sequence to obtain the phase spectrum components of the battery cell group. Specifically, the sequence of polar coordinate curves of each battery cell can be represented by its angle distribution, and these angle distributions can be further decomposed into a series of phase spectrum components. For example, in the application scenario of electric vehicle batteries, by comparing and analyzing the sequences of polar coordinate curves of multiple battery cells under the same charge-discharge regime, battery cells with similar internal states can be identified. Next, based on the phase spectrum components, perform radial coordinate reconstruction on the battery cell group to obtain the radial distance sequence of the battery cell group, and perform smooth spline interpolation on the radial distance sequence to obtain the continuous curve function of the battery cell group. Radial coordinate reconstruction is a method of converting phase spectrum components into radial distances. Suppose we have 100 battery cells. After radial coordinate reconstruction, we can obtain the radial distance sequence of each battery cell. Then, through smooth spline interpolation, these radial distance sequences can be converted into continuous curve functions. For example, the radial distance sequence of a certain battery cell is (0.2, 0.4, 0.6, 0.8). After smooth spline interpolation, a smooth continuous curve function is obtained. Then, perform a differential operation on the continuous curve function to obtain the derivative sequence of the battery cell group, and perform singular point detection on the derivative sequence to obtain the set of characteristic points of the battery cell group. Differential operation is a method used to describe the rate of change of a curve. Suppose we perform a differential operation on the continuous curve functions of the above 100 battery cells to obtain the derivative sequence of each battery cell. For example, the derivative sequence of a certain battery cell is (0.3, 0.5, 0.7). Then, through singular point detection, the singular points in these derivative sequences can be identified, thereby obtaining the set of characteristic points of each battery cell. For example, the set of characteristic points of a certain battery cell contains three significant singular points, located at the 2nd, 5th, and 8th positions of the derivative sequence respectively. Further, based on the set of characteristic points, perform curve segment division on the battery cell group to obtain the set of piecewise functions of the battery cell group, and perform polar coordinate mapping transformation on the set of piecewise functions to obtain the distribution points of the battery cell group in the complex plane. Curve segment division is a method of dividing a continuous curve into multiple piecewise functions. Suppose we perform curve segment division on the set of characteristic points of the above 100 battery cells to obtain the set of piecewise functions of each battery cell. For example, the set of piecewise functions of a certain battery cell contains three piecewise functions, corresponding to the three singular points in its set of characteristic points respectively. Then, through polar coordinate mapping transformation, these sets of piecewise functions can be converted into distribution points in the complex plane. For example, the distribution points of a certain battery cell in the complex plane are (0.2 + 0.4i, 0.6 + 0.8i, 1.0 + 1.2i).Then, calculate the density aggregation degree of the complex plane distribution points to obtain the regional density map of the battery cell group, and extract the contour lines from the regional density map to obtain the density contour lines of the battery cell group. Density aggregation degree calculation is a method for quantifying the degree of aggregation of data points in space. Suppose we have calculated the density aggregation degree of the complex plane distribution points of the above 100 battery cells and obtained the regional density map of each battery cell. For example, the regional density map of a certain battery cell shows that its density is concentrated in a specific area, indicating that this battery cell is more concentrated in certain characteristics. Then, through contour line extraction, the density contour lines can be extracted from the regional density map, so as to more intuitively display the characteristic distribution of the battery cells. Finally, based on the density contour lines, perform point set resampling on the battery cell group to obtain the complex plane mapping point set of the battery cell group. Point set resampling is a method of resampling the original point set to improve the uniformity and representativeness of the data. Suppose we have performed point set resampling on the density contour lines of the above 100 battery cells and obtained the complex plane mapping point set of each battery cell. For example, the complex plane mapping point set of a certain battery cell contains 100 evenly distributed points, which not only contain the basic electrical characteristics of this battery cell, but also reveal the complex physical and chemical processes inside it. To illustrate this process in more detail, here is a specific example: Suppose we have a batch of 100 battery cells that have been preliminarily screened and tested, and they exhibit different polar coordinate curve sequences at different charge and discharge rates. First, we decompose these polar coordinate curve sequences into phases to obtain the angular distribution sequence of each battery cell. For example, the angular distribution sequence of a certain battery cell is (0.1, 0.2, 0.3, 0.4). Then, perform harmonic analysis on these angular distribution sequences to obtain the phase spectrum components of each battery cell. Suppose the phase spectrum components of a certain battery cell are (0.1, 0.2, 0.3). Then, based on these phase spectrum components, reconstruct the radial coordinates of the battery cell group to obtain the radial distance sequence of each battery cell. For example, the radial distance sequence of a certain battery cell is (0.2, 0.4, 0.6, 0.8). Then, through smooth spline interpolation, convert these radial distance sequences into continuous curve functions. Suppose the continuous curve function of a certain battery cell is a smooth curve, and its derivative sequence is (0.3, 0.5, 0.7). Next, perform differential operations on these continuous curve functions to obtain the derivative sequence of each battery cell. For example, the derivative sequence of a certain battery cell is (0.3, 0.5, 0.7). Then, through singularity detection, identify the singular points in these derivative sequences, so as to obtain the set of characteristic points of each battery cell. For example, the set of characteristic points of a certain battery cell contains three significant singular points, which are located at the 2nd, 5th, and 8th positions of the derivative sequence respectively. Then, based on these sets of characteristic points, divide the battery cell group into curve segments to obtain the set of piecewise functions of each battery cell. For example, the set of piecewise functions of a certain battery cell contains three piecewise functions, which correspond to the three singular points in its set of characteristic points respectively.Next, through polar coordinate mapping transformation, these piecewise function sets are converted into distribution points on the complex plane. For example, the distribution points of a certain battery cell on the complex plane are (0.2 + 0.4i, 0.6 + 0.8i, 1.0 + 1.2i). Further, density aggregation calculation is performed on these distribution points on the complex plane to obtain the regional density map of each battery cell. For example, the regional density map of a certain battery cell shows that its density is concentrated in a specific area, indicating that this battery cell is more concentrated in certain characteristics. Then, through contour line extraction, density contour lines are extracted from the regional density map to more intuitively display the characteristic distribution of the battery cell. Finally, based on these density contour lines, point set resampling is performed on the battery cell group to obtain the complex plane mapping point set of each battery cell. For example, the complex plane mapping point set of a certain battery cell contains 100 uniformly distributed points, which not only contain the basic electrical characteristics of this battery cell but also reveal the complex physical and chemical processes inside it. In this way, not only can the consistency and stability of the battery module be improved, but its service life can also be significantly extended, thus providing valuable data support and technical guidance for future battery design and production. In addition, this detailed analysis and optimization method can also provide strong guarantee for maintenance and management in practical applications, ensuring the efficient operation and long life of the electric vehicle battery system. In short, this step is not only a key link connecting the previous data collection and analysis but also the core step in realizing the optimized grouping of lithium battery cells, ensuring that the finally formed battery cell grouping scheme not only meets the actual application requirements but also maximally improves the overall performance and service life. In this way, not only can the overall performance of the battery system be improved, but valuable data support and technical guidance can also be provided for future battery design and production. For example, in the design process of electric vehicle batteries, through the detailed analysis of the above steps, not only can the consistency and stability of the battery module be improved, but its service life can also be significantly extended, thus providing valuable data support and technical guidance for future battery design and production. In this way, not only can the overall performance of the battery system be improved, but strong guarantee can also be provided for maintenance and management in practical applications.

[0040] The above describes the optimized method for battery cell grouping based on lithium batteries in the embodiments of the present invention. Next, the optimized device for battery cell grouping based on lithium batteries in the embodiments of the present invention will be described. Please refer to Figure 2 , an embodiment of the optimized device for battery cell grouping based on lithium batteries in the embodiments of the present invention includes: A test module 21, configured to perform charge and discharge cycle tests on a battery cell group of a lithium battery through a preset charge and discharge regime to obtain performance characteristic data of the battery cell group; An analysis module 22, configured to perform AC impedance spectroscopy analysis on the battery cell group based on the performance characteristic data to obtain impedance spectrum characteristic data of the battery cell group; A mapping module 23, configured to perform complex plane impedance mapping on the impedance spectrum feature data of the battery cells to obtain a complex impedance feature region of the battery cell group; An optimization module 24, configured to determine a battery cell grouping scheme based on the complex impedance feature region, and perform multi-objective Pareto allocation optimization on the battery cell group based on the battery cell grouping scheme to obtain an optimized battery cell grouping result.

[0041] In this embodiment, for the specific implementation of each unit in the above device embodiment, please refer to the above method embodiment, and details are not described herein again.

[0042] Refer to Figure 3 , in an embodiment of the present invention, a computer device is further provided. The internal structure of the computer device may be as Figure 3 shown. The computer device includes a processor, a memory, a display screen, an input device, a network interface, and a database connected through a system bus. Among them, the processor of the computer design is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program, and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store the corresponding data in this embodiment. The network interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, the above method is implemented.

[0043] Those skilled in the art can understand that Figure 3 the structure shown in

[0044] is only a block diagram of a part of the structure related to the solution of the present invention, and does not constitute a limitation on the computer device to which the solution of the present invention is applied.

[0045] Those of ordinary skill in the art can understand that all or part of the processes in the methods of the above embodiments can be completed by instructing relevant hardware through a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above methods. Among them, any reference to a memory, storage, database, or other medium provided by the present invention and used in the embodiments can include non-volatile and / or volatile memories. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (SSRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), Rambus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM, etc.

[0046] It should be noted that in this article, the terms "including", "comprising", or any other variant thereof are intended to cover non-exclusive inclusion, such that a process, apparatus, article, or method that includes a series of elements includes not only those elements but also other elements not expressly listed, or elements that are inherent to such process, apparatus, article, or method. Without further limitation, an element defined by the statement "including one..." does not exclude the presence of additional identical elements in the process, apparatus, article, or method that includes such element.

[0047] The above are only the preferred embodiments of the present invention, and do not limit the patent scope of the present invention accordingly. Any equivalent structural or equivalent process transformation made by using the specification and drawings of the present invention, or directly or indirectly applied in other related technical fields, shall be equally included in the patent protection scope of the present invention.

Claims

1. A method for optimizing the cell grouping of lithium batteries, characterized in that: The following steps are involved: Perform charge and discharge cycle tests on the lithium battery cell group through a preset charge and discharge system to obtain performance characteristic data of the cell group; Performing an AC impedance spectrum analysis on the battery cell group based on the performance characteristic data to obtain impedance spectrum characteristic data of the battery cell group; Performing complex plane impedance mapping on the impedance spectrum characteristic data of the battery cell group to obtain a complex impedance characteristic region of the battery cell group; A cell grouping scheme is determined based on the complex impedance characteristic region, and a multi-objective Pareto allocation optimization is performed on the cell groups based on the cell grouping scheme to obtain a cell optimization grouping result.

2. The method for optimizing the battery cell assembly based on lithium batteries according to claim 1, characterized in that: The charging and discharging cycle test of the lithium battery cell group is performed through a preset charging and discharging system to obtain performance characteristic data of the cell group, including: The battery cell group of the lithium battery is charged at a constant current and constant voltage through a preset charge and discharge system to obtain charging curve data, and the battery cell group of the lithium battery is discharged at a constant current to a cut-off voltage based on the charging curve data to obtain discharge curve data; Calculating the actual capacity of each battery cell in the battery cell group through the discharge curve data to obtain battery cell capacity data; wherein the battery cell capacity data includes the initial capacity and the discharge cut-off capacity of each battery cell; Based on the cell capacity data and a preset number of cycles, the cell group of the lithium battery is subjected to multiple charge and discharge cycles to obtain cycle test data, and performance statistical analysis is performed on each cell through the cycle test data to obtain cell performance characteristic data; wherein the performance characteristic data includes capacity attenuation rate, internal resistance growth rate, energy efficiency and thermal stability parameters.

3. The method for optimizing the cell assembly of lithium batteries according to claim 1, characterized in that: The performing AC impedance spectrum analysis on the battery cell group based on the performance characteristic data to obtain impedance spectrum characteristic data of the battery cell group includes: Applying a small-amplitude sinusoidal AC signal to the battery cell group based on the performance characteristic data to obtain an electrical response signal of the battery cell group, and performing Fourier transform on the electrical response signal to obtain frequency domain impedance data; wherein the electrical response signal includes a voltage response signal and a current response signal; Constructing a first Nyquist diagram of the battery cell group through the frequency domain impedance data, and performing high-frequency semicircle fitting on the first Nyquist diagram to obtain high-frequency equivalent resistance parameters; Performing a mid-frequency Warburg impedance fitting on the first Nyquist plot based on the high-frequency equivalent resistance parameter to obtain a Warburg coefficient, and calculating a diffusion coefficient on the battery cell group based on the Warburg coefficient to obtain a lithium ion diffusion coefficient; Fitting the linear part of the low-frequency region of the first Nyquist plot based on the lithium ion diffusion coefficient to obtain low-frequency linear fitting parameters, and calculating the double-layer capacitance of the battery cell group based on the low-frequency linear fitting parameters to obtain a double-layer capacitance value; Based on the double-layer capacitance value, an equivalent circuit model is performed on the impedance spectrum of the battery cell group to obtain equivalent circuit parameters of the battery cell group, and impedance spectrum feature extraction is performed on the equivalent circuit parameters to obtain impedance spectrum feature data.

4. The method for optimizing the cell assembly of lithium batteries according to claim 1, characterized in that: The performing complex plane impedance mapping on the impedance spectrum characteristic data of the battery cell group to obtain the complex impedance characteristic region of the battery cell group includes: Performing complex plane impedance projection and mapping on the battery cell group based on the impedance spectrum characteristic data of the battery cell group to obtain a second Nyquist diagram of the battery cell group, and performing discrete point set extraction on the second Nyquist diagram to obtain an impedance discrete point set of the battery cell group; Based on the impedance discrete point set, density cluster analysis is performed on the battery cell group to obtain the impedance density peak point of the battery cell group, and convex hull algorithm processing is performed on the impedance density peak point to obtain the impedance convex hull area of ​​the battery cell group; Performing DBSCAN cluster analysis on the battery cell group based on the impedance convex hull area to obtain impedance clusters of the battery cell group, and performing boundary point identification on the impedance clusters to obtain impedance cluster boundaries of the battery cell group; Based on the impedance clustering boundary, a self-organizing mapping neural network analysis is performed on the battery cell group to obtain a characteristic topological map of the battery cell group, and the characteristic topological map is divided into regions to obtain a complex impedance characteristic region of the battery cell group.

5. The method for optimizing the cell assembly of lithium batteries according to claim 1, characterized in that: The determining of the cell grouping scheme based on the complex impedance characteristic region, and performing multi-objective Pareto allocation optimization on the cell groups based on the cell grouping scheme to obtain a cell optimization grouping result, includes: Performing feature space mapping on the battery cell group based on the complex impedance characteristic region to obtain a battery cell feature vector, and performing multidimensional scaling analysis on the battery cell feature vector to obtain a battery cell distance matrix; Performing hierarchical clustering processing on the cell groups based on the cell distance matrix to obtain a cell hierarchy structure tree, and dynamically pruning the cell hierarchy structure tree to obtain a cell grouping scheme; wherein the cell grouping schemes have multiple and different schemes; Setting constraints on the cell grouping scheme to obtain a feasible solution space, and constructing a multi-objective function on the feasible solution space to obtain an objective function set. Based on the objective function set, a non-dominated solution search is performed on the battery cell group to obtain a Pareto optimal solution, and based on the Pareto optimal solution, a solution is determined for the battery cell group to obtain a battery cell optimization grouping result.

6. The method for optimizing the cell assembly of lithium batteries according to claim 5, characterized in that: The performing feature space mapping on the battery cell group based on the complex impedance feature region to obtain a battery cell feature vector includes: Extracting contour boundaries of the complex impedance feature region to obtain a polar coordinate curve sequence of the battery cell group, and performing polar coordinate transformation on the battery cell group based on the polar coordinate curve sequence to obtain a complex plane mapping point set of the battery cell group; Calculating geometric features of the complex plane mapping point set to obtain shape description parameters of the battery cell group; wherein the shape description parameters include contour perimeter, area ratio and curvature distribution; Performing a main direction analysis on the shape description parameters by using a preset Hu invariant moment algorithm to obtain a characteristic principal axis vector of the battery cell group, and performing an orthogonal basis decomposition on the characteristic principal axis vector to obtain a basis matrix of the battery cell group; Performing a projection coordinate transformation on the battery cell group based on the basis matrix to obtain a normalized coordinate set of the battery cell group, and performing density distribution calculation on the normalized coordinate set to obtain a characteristic distribution function of the battery cell group; The characteristic distribution function is subjected to moment statistical analysis to obtain statistical moment characteristics of the battery cell group, and the statistical moment characteristics are dimensionlessly processed to obtain standardized characteristic quantities of the battery cell group, and the battery cell group is vector-assembled based on the standardized characteristic quantities to obtain a battery cell characteristic vector.

7. The method for optimizing the cell assembly of lithium batteries according to claim 6, characterized in that: The step of performing polar coordinate transformation on the battery cell group based on the polar coordinate curve sequence to obtain a complex plane mapping point set of the battery cell group includes: Performing phase decomposition on the polar coordinate curve sequence to obtain an angle distribution sequence of the battery cell group, and performing harmonic analysis on the angle distribution sequence to obtain a phase spectrum component of the battery cell group; Reconstructing radial coordinates of the battery cell group based on the phase spectrum component to obtain a radial distance sequence of the battery cell group, and performing smooth spline interpolation on the radial distance sequence to obtain a continuous curve function of the battery cell group; Performing a differential operation on the continuous curve function to obtain a derivative sequence of the battery cell group, and performing singular point detection on the derivative sequence to obtain a characteristic point set of the battery cell group; Dividing the battery cell group into curve segments based on the characteristic point set to obtain a piecewise function set of the battery cell group, and performing polar coordinate mapping transformation on the piecewise function set to obtain complex plane distribution points of the battery cell group; Calculating the density concentration of the complex plane distribution points to obtain a regional density map of the battery cell group, and extracting contour lines of the regional density map to obtain a density contour line of the battery cell group; The point set of the battery cell group is resampled based on the density contour line to obtain a complex plane mapping point set of the battery cell group.

8. A lithium battery-based cell group optimization device, characterized in that: include: The test module is used to perform a charge and discharge cycle test on the lithium battery cell group through a preset charge and discharge system to obtain performance characteristic data of the cell group; An analysis module, configured to perform an AC impedance spectrum analysis on the battery cell group based on the performance characteristic data to obtain impedance spectrum characteristic data of the battery cell group; A mapping module, used for performing complex plane impedance mapping on the impedance spectrum characteristic data of the battery cell group to obtain a complex impedance characteristic region of the battery cell group; The optimization module is used to determine the battery cell grouping scheme based on the complex impedance characteristic area, and perform multi-objective Pareto allocation optimization on the battery cell group based on the battery cell grouping scheme to obtain the battery cell optimization grouping result.

9. A computer device comprising a memory and a processor, wherein a computer program is stored in the memory, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.

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