A technique and method for repairing by lithium battery bms equalization algorithm

Abstract

The application relates to the technical field of lithium battery repair, and particularly discloses a technology and method for repairing through a lithium battery BMS equalization algorithm, an OCV-SOC mapping model adapted to cell aging is constructed in combination with historical working conditions of the cells and spatial arrangement of the module, the SOC dispersion of the module is measured based on a differential voltage characteristic peak, the SOC dispersion is coupled with a thermal topology power boundary to realize time-sharing differential equalization of adjacent cells, the mapping model is iteratively corrected through periodic static heat steady-state re-measurement, finally, two-dimensional repair standard judgment is completed according to an SOC range and a normalized full-module thermal stability coefficient, a complete closed-loop BMS equalization repair process for retired iron phosphate lithium batteries is formed, and the problems of large SOC estimation deviation of a traditional scheme, easy heat accumulation during synchronous equalization, easy rebound of consistency after repair and single judgment standard are effectively solved, while the SOC estimation accuracy of the aged cells, the equalization operation safety and the repair efficiency are improved, and the repair quality and the safety level of gradient utilization of the retired battery module are improved.

Description

Technical Field

[0001] This invention relates to the field of lithium battery repair technology, and in particular to a technology and method for repairing lithium batteries using a BMS equalization algorithm. Background Technology

[0002] Retired lithium iron phosphate battery modules consist of multiple cells connected in series. After long-term vehicle use, the self-discharge rate and aging degree of the cells differ, the state of charge of each cell gradually shifts, and the voltage difference between individual cells continues to widen, making it difficult to directly apply to energy storage, low-speed electric vehicles, and other cascade utilization scenarios. Lithium battery BMS equalization repair relies on the battery management system to collect the voltage, current, and temperature operating parameters of the module cells. Through the built-in algorithm, it drives the equalization circuit to release energy to high SOC cells and replenish energy to low SOC cells, eliminating the difference in charge between cells. Consistency repair is completed solely through software calculations, without the need to disassemble or modify the cell hardware. This is the standard processing procedure in the cascade repair production line of retired power batteries.

[0003] In existing technologies, conventional BMS balancing equipment uses the static voltage difference of individual cells as the balancing criterion. In lithium iron phosphate batteries, the voltage plateau in the charge range is flat, and the terminal voltage is not sensitive to changes in SOC. Coupled with the differential shift in the OCV-SOC curve of individual cells caused by the aging of retired cells, relying solely on voltage for balancing can only achieve a superficial uniformity of the cell terminal voltage and cannot eliminate the discrete error of the cell's true SOC. After the module is put into dynamic operation of energy storage, the latent SOC deviation will continue to accumulate and expand, causing rapid deterioration of module consistency, decrease in usable capacity, and deterioration of cycle stability, making it difficult to meet the long-term stable operation requirements of cascaded batteries.

[0004] Therefore, there is an urgent need for a technology and method that uses a lithium battery BMS equalization algorithm to solve the above problems. Summary of the Invention

[0005] The purpose of this invention is to provide a method for repairing lithium battery BMS equalization algorithms, comprising the following steps: Real-time temperature field data, historical cell operating condition data, and module spatial arrangement parameters of the retired lithium iron phosphate battery module to be repaired are obtained to obtain the active lithium loss of each cell and the thermal conduction distance parameters between cells in the module. Based on the active lithium loss of each cell and the thermal conduction distance parameters between cells in the module, an initial aging adaptation OCV-SOC mapping model for each cell is constructed, and the thermal constraint equalization power boundary is calibrated. The stationary terminal voltage data and total circuit current data of each cell in the retired lithium iron phosphate battery module to be repaired are obtained to obtain the differential voltage characteristic peak sequence of each cell, and the module SOC dispersion of each cell is obtained according to the differential voltage characteristic peak sequence of each cell and the initial aging adaptation OCV-SOC mapping model. Based on the single-cell deviation amplitude in the module SOC dispersion, the corresponding equalization power and running time are matched. Combined with the spatial arrangement of the deviation cells, the time-sharing start-stop sequence of adjacent cells is determined. Within the limit of the thermal constraint equalization power boundary, the time-sharing equalization control command of adjacent cells is generated, driving the BMS equalization circuit to perform differentiated SOC consistency adjustment on each single cell and collect equalization running time data in real time. Based on the equalization runtime data and the preset equalization iteration interval parameters, an equalization pause trigger signal is generated to control the cessation of equalization operation until the cell voltage reaches thermal steady state. Then, the differential voltage characteristics are re-acquired to iteratively correct the initial aging adaptation OCV-SOC mapping model of each cell to obtain the real-time SOC value of each cell after correction. The module's individual unit SOC range and overall module thermal stability coefficient are obtained based on the real-time SOC value and real-time temperature field data of each unit. The equalization repair is terminated and the repair result is output based on the comparison results of the individual unit SOC range and the preset repair threshold, as well as the comparison results of the overall module thermal stability coefficient and the preset safety threshold.

[0006] Furthermore, the step of obtaining the resting terminal voltage data and total circuit current data of each cell in the retired lithium iron phosphate battery module to be repaired to obtain the differential voltage characteristic peak sequence of each cell, and obtaining the module SOC dispersion of each cell based on the differential voltage characteristic peak sequence of each cell and the initial aging adaptation OCV-SOC mapping model, includes: Collect the static terminal voltage data of each cell of the retired lithium iron phosphate battery module to be repaired and the total current data of the module circuit to perform timing alignment and filtering and noise reduction processing to obtain the basic data of the module's original electrical signal. Differential operation processing is performed on the original electrical signal data of the module to obtain the voltage differential data sequence of each unit, and peak characteristic parameters are extracted from the voltage differential data sequence of each unit to construct the voltage characteristic peak sequence of each unit. The peak positions of each individual differential voltage characteristic peak sequence are aligned one by one with the reference characteristic peaks in the initial aging adaptation OCV-SOC mapping model in ascending order of SOC, and the SOC reference anchor point corresponding to each characteristic peak is locked. Between two adjacent SOC reference anchor points, linear interpolation is performed based on the current measured static terminal voltage to obtain the initial interpolated SOC value. The ampere-hour integral correction of the total circuit current before static is introduced to correct the initial interpolated SOC value, and the real-time SOC value of each individual cell is obtained. Based on the real-time SOC values ​​of each individual cell, discrete calculations are performed to obtain the SOC dispersion parameters of the battery module individual cells.

[0007] Furthermore, the steps of matching the equalization power and operating time corresponding to the single-cell deviation amplitude in the module's SOC dispersion, determining the time-sharing start-stop sequence of adjacent cells based on the spatial arrangement of the deviation cells, generating time-sharing equalization control commands for adjacent cells within the limits of the thermally constrained equalization power boundary, driving the BMS equalization circuit to perform differentiated SOC consistency adjustment for each single cell and collecting equalization operating time data in real time; include: The module SOC dispersion parameters and battery thermal topology parameters are normalized and preprocessed to obtain the basic data for determining the equilibrium boundary. Based on the basic data for determining the equilibrium boundary, the single-cell equilibrium power constraint interval is calibrated. After temperature-power mapping, the thermal topology-constrained equilibrium power boundary parameters are obtained. The time-sharing equalization configuration parameters of adjacent cells in a single unit are obtained based on the module SOC dispersion parameters and the thermal topology-constrained equalization power boundary parameters. Based on the time-sharing equalization configuration parameters of multiple adjacent cells, corresponding adjacent cell time-sharing equalization control commands are generated. The BMS equalization circuit is driven according to the adjacent cell time-sharing equalization control commands to complete the differential SOC consistency adjustment drive output, and real-time equalization runtime data of each cell are collected synchronously during the adjustment process.

[0008] Furthermore, the step of generating an equalization pause trigger signal based on the equalization runtime data and preset equalization iteration interval parameters to control the cessation of equalization operation until the cell voltage reaches thermal steady state, and then re-acquiring differential voltage characteristics to iteratively correct the initial aging adaptation OCV-SOC mapping model of each cell to obtain the corrected real-time SOC value of each cell, includes: Collect real-time balancing runtime data and preset balancing iteration interval parameters, compare the difference with the threshold to obtain the balancing iteration judgment base data, and perform duration comparison judgment based on the balancing iteration judgment base data to generate a balancing pause trigger signal. The BMS equalization operation is shut down according to the equalization pause trigger signal to maintain the battery module in a static state and continuously monitor the cell voltage fluctuation data during the static process. Based on the cell voltage fluctuation data, the voltage thermal steady state is determined and locked. Individual differential voltage characteristic data under static conditions are collected, and the initial aging adaptation OCV-SOC mapping model is iteratively corrected based on the individual differential voltage characteristic data to obtain the real-time SOC value of each individual cell after correction.

[0009] Furthermore, the step of obtaining the module's individual unit SOC range and the overall module thermal stability coefficient based on the real-time SOC values ​​of each individual unit after correction and the real-time temperature field data, and terminating the equalization repair and outputting the repair results based on the comparison results of the module's individual unit SOC range with a preset repair threshold and the comparison results of the overall module thermal stability coefficient with a preset safety threshold, includes: Collect the real-time SOC value of each individual unit after correction and the real-time temperature field data of the whole module, unify the time series and integrate multi-dimensional information to obtain the basic data of module SOC and thermal state; The module's SOC and thermal state data are processed separately to obtain the module's individual unit SOC range parameter and the overall module's thermal stability coefficient. Based on the SOC range parameter of the individual module and the thermal stability coefficient of the whole module, and by comparing the SOC range parameter of the individual module and the thermal stability coefficient of the whole module with the preset repair threshold and the preset safety threshold respectively, a two-dimensional safety compliance comparison result is obtained. Based on the dual-dimensional safety compliance comparison results, the module balance repair is comprehensively determined to be up to standard and the overall thermal state is safe, resulting in the final repair judgment status. The BMS balance repair operation is terminated based on the final repair judgment status, and the module balance repair result instruction is output synchronously.

[0010] Furthermore, the step of processing the module's SOC and thermal state data to obtain the individual module SOC range parameter and the overall module thermal stability coefficient includes: Extract real-time temperature data and temperature point layout information of each unit from the module SOC and thermal state basic data, complete coordinate binding and anomaly filtering, and obtain the module temperature sampling basic dataset. Based on the analysis of the module temperature sampling dataset, the maximum and minimum temperature difference amplitude of each unit and the global temperature standard deviation are obtained, and the module temperature distribution characteristic parameters are constructed based on the global temperature standard deviation. The module's temperature distribution characteristic parameters are combined with the module's SOC and thermal state basic data to perform weighted fusion, resulting in a module weighted thermal state index. The module's weighted thermal state index is then normalized to obtain the overall module thermal stability coefficient, which characterizes the overall safety status.

[0011] Furthermore, this invention also discloses a repair system using a lithium battery BMS equalization algorithm, comprising: The initial calibration module is used to acquire real-time temperature field data, historical cell operating condition data and module spatial arrangement parameters of the retired lithium iron phosphate battery module to be repaired, calculate the loss of active lithium in a single cell and the thermal conduction distance between cells, construct an initial aging adaptation OCV-SOC mapping model based on the above parameters, and calibrate the thermal constraint equalization power boundary. The feature calculation module is used to collect the static terminal voltage data of each cell in the battery module and the total circuit current data, process and generate the differential voltage feature peak sequence of each cell, and calculate the SOC dispersion of the module cell by combining the initial aging adaptation OCV-SOC mapping model. The equalization control module is used to generate time-sharing equalization control commands for adjacent cells based on the module's SOC dispersion and thermal constraint equalization power boundary, drive the BMS equalization circuit to complete the individual cell differentiated SOC consistency adjustment, and collect equalization runtime data in real time. The iterative correction module is used to generate an equalization pause trigger signal based on the equalization runtime data and the preset equalization iteration interval parameter, shut down the equalization operation and place it in the cell thermal steady state, re-acquire the differential voltage characteristics iteratively correct the OCV-SOC mapping model, and obtain the real-time SOC value of each cell after correction. The result determination module is used to calculate the module SOC range and the overall module thermal stability coefficient based on the real-time SOC values ​​of each individual unit and the real-time temperature field data of the module. By comparing the repair threshold and the safety threshold in two dimensions, it determines and terminates the equalization repair operation and outputs the module repair result instruction.

[0012] Furthermore, the equalization control module includes: The equalization calculation unit is used to obtain the module SOC dispersion parameters and thermal constraint equalization power boundary parameters to obtain the basic parameters of the module equalization working condition. The equalization configuration unit is used to perform coupling calculations based on the basic parameters of the module's equalization operating conditions to match and obtain the time-sharing equalization configuration parameters of each adjacent cell in a single unit. The instruction generation unit is used to generate corresponding adjacent cell time-sharing control instructions based on the time-sharing configuration parameters of each individual cell. The equalization execution unit is used to drive the BMS equalization circuit according to the time-sharing equalization control command of the adjacent cells to complete the differential SOC consistency adjustment operation of each cell. The duration acquisition unit is used to track the balance adjustment execution process in real time, and to collect and record the real-time balance runtime data of each unit.

[0013] This application also provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps of the above-described method for repairing a lithium battery BMS equalization algorithm.

[0014] This application also provides a computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the steps of the above-described method for repairing a lithium battery BMS equalization algorithm.

[0015] The beneficial effects of this application are as follows: This invention combines historical cell operating conditions and module spatial arrangement to construct an OCV-SOC mapping model adapted to single-cell aging. It calculates the module SOC dispersion based on differential voltage characteristic peaks, couples SOC dispersion with thermal topology power boundary to achieve time-sharing differentiated equalization of adjacent cells, and iterates and corrects the mapping model with periodic static thermal steady-state retesting. Finally, it completes the dual-dimensional repair compliance judgment based on SOC range and normalized full module thermal stability coefficient, forming a complete closed-loop BMS equalization repair process for retired lithium iron phosphate batteries. This effectively solves the problems of large SOC estimation deviation, easy heat accumulation during synchronous equalization, easy rebound of consistency after repair, and single judgment standard in traditional solutions. While improving the SOC measurement accuracy of aging cells, the safety and efficiency of equalization operations, and the repair quality and safety level of retired battery modules, this invention improves the quality of retired battery module repair and the safety level of cascade utilization. Attached Figure Description

[0016] Figure 1 This is a schematic diagram of a method flow proposed in an embodiment of this application.

[0017] Figure 2 This is a schematic diagram of the system structure proposed in an embodiment of the present invention.

[0018] The realization of the purpose, functional features and advantages of this application will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0019] It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.

[0020] like Figure 1 As shown, this application provides a method for repairing lithium battery BMS equalization algorithm, including the following steps: S1. Obtain real-time temperature field data, historical cell operating condition data and module spatial arrangement parameters of the retired lithium iron phosphate battery module to be repaired to obtain the active lithium loss of each cell and the thermal conduction distance parameters between cells in the module. Based on the active lithium loss of each cell and the thermal conduction distance parameters between cells in the module, construct the initial aging adaptation OCV-SOC mapping model of each cell and calibrate the thermal constraint equalization power boundary. S2. Obtain the static terminal voltage data and total circuit current data of each cell in the retired lithium iron phosphate battery module to be repaired to obtain the differential voltage characteristic peak sequence of each cell, and obtain the module SOC dispersion of each cell according to the differential voltage characteristic peak sequence of each cell and the initial aging adaptation OCV-SOC mapping model. S3. Match the corresponding equalization power and running time according to the single-cell deviation amplitude in the SOC dispersion of the module, determine the time-sharing start-stop sequence of adjacent cells in combination with the spatial arrangement position of the deviation cells, generate the time-sharing equalization control command of adjacent cells within the limit of the thermal constraint equalization power boundary, drive the BMS equalization circuit to perform differentiated SOC consistency adjustment on each single cell and collect equalization running time data in real time. S4. Generate a balance pause trigger signal based on the balance running time data and the preset balance iteration interval parameter to control the stop of the balance operation until the cell voltage reaches thermal steady state. Then, re-acquire differential voltage characteristics to iteratively correct the initial aging adaptation OCV-SOC mapping model of each cell to obtain the real-time SOC value of each cell after correction. S5. Obtain the SOC range of each module unit and the thermal stability coefficient of the whole module based on the real-time SOC value of each unit and the real-time temperature field data. Then, based on the comparison results of the SOC range of each module unit with the preset repair threshold and the comparison results of the thermal stability coefficient of the whole module with the preset safety threshold, terminate the equalization repair and output the repair results.

[0021] As described in steps S1-S5 above, this invention obtains real-time temperature field data, historical cell operating condition data, and module spatial arrangement parameters of the retired lithium iron phosphate battery module to be repaired, quantifies the active lithium loss of each cell and the thermal conduction distance parameters between cells within the module, and constructs an initial aging adaptation OCV-SOC mapping model adapted to the aging characteristics of each cell based on the two types of core parameters.

[0022] The specific construction is as follows: First, obtain the initial OCV-SOC baseline curve of the same model of cell as the retired lithium iron phosphate battery module to be repaired; then, based on the active lithium loss of each cell, determine the capacity loss percentage of each cell relative to the initial state, and linearly scale the SOC horizontal axis of the initial OCV-SOC baseline curve according to the capacity loss percentage, so that the original 100% SOC scale corresponds to the actual full-charge usable capacity limit of the current cell, and obtain the preliminary aging OCV-SOC mapping model of each cell; finally, construct a thermal coupling weighting matrix according to the thermal conduction distance parameter between cells in the module, where the correction weight between any two cells is inversely proportional to the thermal conduction distance between them. Use this thermal coupling weighting matrix to perform neighborhood smoothing constraint processing on the preliminary aging OCV-SOC mapping model of each cell—cells with high thermal coupling degree and small operating temperature difference in physical adjacent locations are constrained to keep their SOC value deviation under the same voltage within a preset range, and complete the spatial consistency adaptation of the model to obtain the initial aging adapted OCV-SOC mapping model.

[0023] Simultaneously, by combining the module's thermal topology to calibrate the limited equalization power boundary, this effectively solves the problems of traditional BMS equalization repair schemes that uniformly adopt standard OCV-SOC curves, fail to match the individual aging differences of retired cells, and do not consider module thermal conduction constraints. This leads to large deviations in initial SOC estimation, unreasonable equalization power configuration, and easy occurrence of thermal accumulation or low equalization efficiency. After long-term cycle service, retired lithium iron phosphate batteries exhibit significant individual differences in the degree of active lithium loss and electrode aging state of individual cells. Standard OCV-SOC curves cannot adapt to the true electrochemical characteristics of aged cells. At the same time, different cell arrangements within the module result in differences in heat dissipation conditions and thermal conduction distances, making synchronous equalization prone to thermal superposition risks. This invention achieves personalized adaptation of individual cell models and equalization safety constraints from the source through targeted aging modeling and thermal power boundary calibration, providing fundamental support for equalization repair. Subsequently, it acquires the static terminal voltage data and total circuit current data of each cell in the module to be repaired. Differential voltage characteristic peak sequences of each cell are extracted through data differential processing. Based on the aging adaptation OCV-SOC mapping model, the SOC dispersion of each module cell is calculated. This solves the shortcomings of traditional lithium iron phosphate equalization detection, which relies on the original terminal voltage for judgment, suffers from low SOC identification accuracy due to the flatness of the voltage plateau, and cannot truly quantify the discrete differences in individual cell charge. The differential voltage characteristic peaks can anchor the SOC phase transition node of the cell, avoiding the voltage plateau detection blind zone and improving the accuracy of module SOC dispersion calculation. In this scheme, the use of thermal conduction constraints is not a simple threshold limitation, but rather a method that constructs a module thermal resistance network model. Based on the thermal conduction distance parameter between cells, it calculates the steady-state temperature difference prediction of any adjacent cells under a given equalization power. Then, based on the preset safe temperature difference threshold, it reverses to solve for the maximum allowable equalization power value of each cell, and introduces a method derived from the thermal resistance network. The thermoelectric coupling inversion correction factor establishes a correlation mapping between the maximum equalization power and the cell spacing, generating an equalization power attenuation coefficient inversely proportional to the heat conduction distance. This binds the physical thermal field depth to the electrical power boundary, forming a thermally constrained equalization power boundary. Simultaneously, the SOC dispersion is not obtained based on simple statistics of the original terminal voltage. Instead, the stationary terminal voltage data of each cell is first differentially processed to extract the characteristic peak positions of the corresponding lithium iron phosphate cathode lithium-ion deintercalation / intercalation phase transition stages. These characteristic peak positions reflect the intrinsic structural information of the electrochemical phase transition node within the cell, independent of the voltage amplitude. Then, the characteristic peak positions of each cell are mapped to their corresponding initial aging adaptation OCV-SOC mapping model to obtain the real-time SOC value of each cell. Finally, the statistical distribution width is calculated based on the real-time SOC values ​​of all cells to obtain the module SOC dispersion. This not only utilizes the intrinsic structure of the electrochemical phase transition data to avoid SOC identification errors caused by terminal voltage values ​​in flat voltage ranges, but also directly constrains the equalization power and timing configuration based on the deviation level and spatial distribution of the dispersion, achieving deep utilization of the intrinsic structure of the data. Then, based on the calculated module SOC dispersion and the thermal constraint equalization power boundary, a time-sharing equalization control command for adjacent cells in the module is generated. This drives the BMS equalization circuit to complete the differential SOC consistency adjustment of each cell and collect equalization runtime data in real time. Traditional equalization methods often adopt a full module synchronous, same power equalization mode, which cannot adapt to the repair needs of cells with different SOC deviations. It is easy to cause over-equalization or incomplete repair. Moreover, synchronous equalization of adjacent cells can easily cause heat accumulation, which aggravates the secondary aging of retired cells. This invention achieves on-demand equalization within the thermal safety boundary by combining differentiated parameter matching with a time-sharing equalization strategy for adjacent cells, avoiding the risk of thermal superposition and improving the efficiency and safety of equalization repair. At the same time, it records the equalization runtime in real time to provide data support for iterative correction. Then, based on the equalization runtime data and the preset equalization iteration interval parameters, an equalization pause trigger signal is generated to shut down the equalization operation in real time and keep the module in a static state.

[0024] As a preferred implementation, a dual judgment condition of temperature and polarization is added simultaneously during the resting stage. After the cell voltage and temperature have fully reached thermal steady state and the polarization effect has completely dissipated, the differential voltage characteristics of each individual cell are re-acquired, the initial aging adaptation OCV-SOC mapping model is iteratively corrected, and the real-time SOC value of each individual cell is solved. This resting correction mechanism makes up for the shortcomings of traditional technology's continuous balancing and lack of a resting buffer, which leads to the accumulation of polarization error and temperature drift error. It effectively solves the industry pain points of insufficient single-modeling accuracy and easy rebound of module SOC consistency after repair. Through multiple rounds of static iteration and correction, the aging model parameters are dynamically optimized to continuously match the real-time aging state of the cells, significantly improving the real-time SOC calculation accuracy and repair stability. Finally, by simultaneously calculating the real-time SOC values ​​of each individual cell and the real-time temperature field data of the module, the SOC range of each module cell and the thermal stability coefficient of the entire module are obtained. The SOC range is compared with the preset repair threshold, and the thermal stability coefficient is compared with the preset safety threshold in two dimensions to comprehensively determine the module's equalization repair compliance status, thereby terminating the equalization repair operation and outputting the repair results. This overcomes the limitations of traditional single voltage threshold judgment methods, which cannot accurately reflect the SOC values. The present invention addresses the shortcomings of OC consistency without considering thermal safety. It combines the dual indicators of charge consistency and module thermal stability for closed-loop judgment, ensuring the accuracy of SOC consistency repair of retired battery modules while completely avoiding the thermal safety hazards of the re-use of repaired modules. The overall solution abandons the traditional one-size-fits-all coarse balancing mode. Relying on the closed-loop logic of aging modeling, feature calculation, peak balancing, iterative correction, and two-dimensional judgment, it achieves intelligent balancing repair of retired lithium iron phosphate battery modules with high precision, high safety, and high stability, effectively improving the consistency and service life of retired batteries for re-use.

[0025] In one embodiment, the step of obtaining the resting terminal voltage data and total circuit current data of each cell in the retired lithium iron phosphate battery module to be repaired to obtain the differential voltage characteristic peak sequence of each cell, and obtaining the module SOC dispersion of each cell based on the differential voltage characteristic peak sequence of each cell and the initial aging adaptation OCV-SOC mapping model, includes: S6. Collect the static terminal voltage data of each cell of the retired lithium iron phosphate battery module to be repaired and the total current data of the module circuit to perform timing alignment and filtering and noise reduction processing to obtain the basic data of the original electrical signal of the module. S7. Perform differential operation processing on the original electrical signal basic data of the module to obtain the voltage differential data sequence of each unit, and extract peak feature parameters based on the voltage differential data sequence of each unit to construct the voltage differential feature peak sequence of each unit. S8. Align the peak positions of each individual differential voltage characteristic peak sequence with the reference characteristic peak in the initial aging adaptation OCV-SOC mapping model in ascending order of SOC, and lock the SOC reference anchor point corresponding to each characteristic peak.

[0026] S9. Between two adjacent SOC reference anchor points, linear interpolation is performed based on the current measured static terminal voltage to obtain the initial value of interpolated SOC. The ampere-hour integral correction of the total circuit current before static is introduced to correct the initial value of interpolated SOC, and the real-time SOC value of each individual cell is obtained. S10. Based on the real-time SOC values ​​of each individual cell, perform discrete calculations to obtain the SOC dispersion parameters of the battery module individual cells. As described in steps S6-S10 above, the front-end analog acquisition unit of the BMS system triggers acquisition after the module has been stationary for a preset polarization dissipation time. Simultaneously, the analog voltage values ​​of the positive and negative terminals of each individual cell are sampled through a high-precision voltage acquisition channel, and the real-time current value of the module's total circuit is acquired through a series current sampling sensor. During the acquisition process, the timing alignment of the multi-channel data is completed based on the unified clock of the main control unit. Then, a sliding average filter is used to remove sampling noise and electromagnetic interference. Finally, the original electrical signal basic data of the module with timing tags and individual cell number mapping relationships is output and cached in the system storage unit. The BMS main control unit retrieves the stored raw electrical signal data and performs a first-order forward differential operation on the voltage time sequence of each cell at rest, using equal SOC intervals as the calculation step size, to solve for the rate of change curve of voltage with SOC. During the operation, voltage jump anomalies are removed and local smoothing is performed to suppress baseline interference in the flat section of the lithium iron phosphate voltage plateau. The flat raw voltage curve is then converted into a differential numerical sequence with significant variation characteristics, ultimately generating a voltage differential data sequence corresponding to each cell. The system feature extraction module iterates through the differential voltage data sequence of each individual cell point by point, and uses a local extremum judgment algorithm to identify all local maxima where the differential value is higher than the adjacent points. Then, it performs dual screening using preset amplitude thresholds and peak width thresholds to filter out false feature peaks caused by sampling noise. For the retained valid feature peaks, four types of feature parameters are extracted in sequence: peak amplitude, peak corresponding SOC position, half peak width, and peak area. These parameters are then sorted and encapsulated in order of SOC value from low to high, finally constructing a structured sequence of differential voltage feature peaks for each individual cell. The SOC estimation module retrieves the characteristic peak sequence of the differential voltage of each individual cell and performs feature matching with the standard characteristic peak reference library in the initial aging adaptation OCV-SOC mapping model. A peak alignment algorithm is used to establish a one-to-one correspondence between the measured peak position and the model reference peak position, locking the SOC reference anchor point corresponding to the current individual cell voltage. Then, based on the current measured resting terminal voltage, linear interpolation is performed between two adjacent SOC anchor points, while simultaneously adding a current ampere-hour integral correction, ultimately obtaining the real-time SOC value for each individual cell. The dispersion calculation module extracts the real-time SOC values ​​of all individual units in the entire module, calculates the difference between the maximum and minimum SOC values ​​to obtain the module SOC range, and calculates the standard deviation and coefficient of variation of the SOC values ​​of the entire module. This invention employs a weighted fusion algorithm to couple the calculations of the three core indicators; the calculation formula is as follows: ;in, For the SOC dispersion parameter of a single battery module cell, The module's SOC is extremely poor. The standard deviation of the SOC value for the entire module. The coefficient of variation of the total module's SOC. These are the preset weighting coefficients for the corresponding indicators, and they satisfy... The weighting coefficients can be adaptively fine-tuned according to the aging level of the retired battery modules.

[0027] The preset weighting coefficient can be adaptively and finely adjusted according to the aging level of the retired battery module, and the adjustment rule is quantified and bound to the aging level of the battery cell: Under the new battery cell benchmark conditions, the default weight configuration is as follows: , , Prioritize characterizing the consistency differences caused by the overall range of the module; With each increase in the cell aging level, the range weight increases. Increase by 0.05, coefficient of variation weight Increase by 0.03, standard deviation weighting The corresponding adjustment is 0.08; the higher the degree of aging, the stronger the characterization weight of extreme deviation cells and the relative degree of dispersion, which is adapted to the characteristic that the individual differences of retired cells are amplified with aging.

[0028] The initial baseline value of the preset weighting coefficient was determined through offline calibration experiments. The calibration objective was to maximize the capacity utilization of the module after equalization repair. The calibration and verification process is as follows: Battery modules from the same batch but with different aging levels were selected as experimental samples, and multiple weighted combinations were set up to carry out a control-balanced repair experiment. The ratio of the actual discharge capacity to the rated capacity of the module under standard charge and discharge conditions after equalization repair (capacity utilization rate) is used as the evaluation index, and the weight combination with the highest capacity utilization rate is selected as the benchmark weight for the corresponding aging level. The verification benchmark is: the goodness of fit between the calculated SOC dispersion and the actual capacity loss rate of the module is not less than 0.9, ensuring that the dispersion parameter can truly reflect the impact of module consistency on capacity performance.

[0029] The calculated SOC dispersion parameters of individual battery modules are not only used for state assessment, but also serve as the core basis for equalization control, directly driving the generation of time-sharing equalization strategies for subsequent adjacent cells. Based on the dispersion value, the deviation is divided into three levels: severe, moderate and mild. Different levels correspond to different equalization power levels and equalization start-up priorities. Based on the positions of the highest and lowest SOC cells marked by dispersion, and combined with the time-sharing rules of adjacent cells, a balanced time slot is allocated, and the cell with the greater deviation has a higher start-up priority. When the overall SOC dispersion of the module exceeds the preset threshold, the phased equalization mode of the entire module is activated; when it is below the threshold, equalization is performed only on the high-deviation cells, taking into account both equalization repair efficiency and cell aging loss control.

[0030] After completing the multi-dimensional index fusion calculation through the above formula, the position numbers of the highest SOC cell and the lowest SOC cell in the module are retrieved and marked simultaneously. Finally, the battery module cell SOC dispersion parameter, which can comprehensively characterize the degree of inconsistency of cell charge in the module, is output and transmitted to the equalization control module in real time. This serves as the core basis for the subsequent formulation of differentiated adjacent cell time-sharing equalization strategy and power parameter configuration. It should be noted that the standard deviation and coefficient of variation calculated in this scheme are not based on the original terminal voltage of the battery cell or the conventional ampere-hour integral SOC estimate, but rather on a high-precision real-time SOC value after joint correction using a differential voltage characteristic peak sequence and an initial aging-adapted OCV-SOC mapping model. This SOC value eliminates the identification error caused by the flat voltage plateau region of lithium iron phosphate batteries and the systematic offset caused by differences in cell aging. Therefore, the standard deviation and coefficient of variation calculated based on this high-precision SOC value can truly reflect the degree of inconsistency in the charge capacity of individual cells within the module. Its technical effectiveness depends on the joint processing of the aforementioned steps in this scheme, rather than the mathematical properties of the statistical formula itself.

[0031] This invention ensures the accuracy and timing consistency of the original electrical signal data through systematic time-series synchronous acquisition and filtering preprocessing, providing a reliable data foundation for subsequent full-process calculations. It transforms the flat voltage platform of lithium iron phosphate batteries into a distinctive differential curve through differential operations, overcoming the industry bottleneck of low sensitivity in traditional voltage-based SOC identification. Through structured feature peak extraction and model matching calibration, it achieves high-precision estimation of the SOC of retired and aged cells, effectively avoiding estimation errors caused by aging offsets. Through multi-dimensional discrete quantification, it comprehensively characterizes the consistency status of individual modules, providing data support for subsequent differentiated equalization control. Overall, it forms a high-precision SOC discreteness measurement process that can be implemented and executed within a BMS system.

[0032] In one embodiment, the steps of matching the equalization power and runtime corresponding to the individual cell deviation amplitude in the module's SOC dispersion, determining the time-sharing start-stop sequence of adjacent cells based on the spatial arrangement of the deviation cells, generating time-sharing equalization control commands for adjacent cells within the limits of the thermally constrained equalization power boundary, driving the BMS equalization loop to perform differentiated SOC consistency adjustment on each individual cell and collecting equalization runtime data in real time; include: S11. Normalize and preprocess the module SOC dispersion parameters and battery thermal topology parameters to obtain the basic data for determining the equilibrium boundary. Based on the basic data for determining the equilibrium boundary, calibrate the single-cell equilibrium power constraint interval. After temperature-power mapping, obtain the thermal topology-constrained equilibrium power boundary parameters. S12. Obtain the time-sharing equalization configuration parameters of adjacent cells in a single unit based on the module SOC dispersion parameters and the thermal topology-constrained equalization power boundary parameters. S13. Generate corresponding adjacent cell time-sharing equalization control commands based on the time-sharing equalization configuration parameters of multiple adjacent cells, drive the BMS equalization circuit according to the adjacent cell time-sharing equalization control commands, complete the differential SOC consistency adjustment drive output, and synchronously collect the real-time equalization runtime data of each cell during the adjustment process.

[0033] As described in steps S11-S13 above, this invention constructs basic data for determining the equilibrium boundary by retrieving the module's SOC dispersion parameters and battery thermal topology parameters. Prior to this, multi-dimensional variables such as module charge dispersion parameters, cell spatial arrangement parameters, and thermal coupling parameters are normalized and preprocessed to eliminate dimensional differences between different physical variables. The aim is to ensure that all computational variables are within the same order of magnitude, avoiding computational deviations and model oscillations caused by differences in the numerical range of multi-dimensional parameters, and guaranteeing the stability and effectiveness of subsequent coupling calculations and parameter calibration. The basic data for determining the module equilibrium boundary integrates the characteristics of individual module charge dispersion differences and the characteristics of the module's internal thermal distribution structure. Unlike traditional single-data-dimensional determination modes, this data comprehensively covers the charge adjustment and thermal safety constraints of retired battery module equilibrium operations, providing complete and reliable basic data support for subsequent power boundary calibration. The specific method of temperature-power mapping is as follows: Based on the module thermal resistance network model, the thermal conduction distance parameter between cells is converted into thermal resistance value. Based on the preset safe temperature difference threshold, the maximum allowable balanced power of each cell is solved in reverse. That is, the greater the temperature rise, the smaller the allowable balanced power. In this way, the mapping relationship between temperature and power is established, and the differentiated power upper limit of each cell is obtained. In this embodiment, the temperature-power mapping is constructed based on a lumped-parameter thermal model, and is implemented as follows: Forward temperature rise calculation: mapping from equalization power to individual unit temperature rise; Single-cell equalization heat generation calculation: Joule heating during the operation of the equalization circuit is the core heat source for cell temperature rise. The equilibrium heat production power is calculated using the following formula:

[0034] In the formula, For battery cells The balanced heat production power; For battery cells Balanced operating power; To equalize the heat generation coefficient, which characterizes the proportion of equal power converted into heat, this coefficient can be pre-determined through a static charging and discharging temperature rise calibration experiment of the same type of battery cell.

[0035] Neighboring thermal coupling temperature rise superposition: The thermal conduction distance between battery cells directly determines the thermal coupling strength; the thermal resistance between two cells is proportional to the thermal conduction distance. The balanced heat generation of adjacent cells will be superimposed on the target cell through thermal conduction, collectively forming the total steady-state temperature rise of the individual cell. The steady-state total temperature rise is calculated using the following formula:

[0036] In the formula, For battery cells The steady-state total temperature rise; The equivalent thermal resistance of the battery cell itself is determined by the spatial position of the battery cell within the module (edge ​​or center); Adjacent cells The balanced heat production power; For battery cells With battery cells The thermal resistance between cells is calculated by considering the thermal conduction distance between cells, the equivalent thermal conductivity of the heat transfer medium between cells, and the adjacent heat exchange area of ​​the cells. The summation term iterates through all cells. Physically adjacent battery cells.

[0037] Inverse power calculation: The mapping from temperature rise constraint to power boundary is based on the module's preset maximum allowable temperature rise per unit. To establish safety constraints, and considering the start-stop timing rules for time-sharing equalization of adjacent cells, the thermal superposition effect of cells not operating simultaneously is deducted. The above temperature rise formula is then solved in reverse to obtain the maximum allowable equalization power of cell i under the condition of meeting thermal safety requirements, which is the thermal constraint equalization power boundary corresponding to this cell. The calculation formula is as follows:

[0038] In the formula, For battery cells Thermally constrained equilibrium power boundary; This is the preset maximum allowable temperature rise threshold for a single cell; the summation term only iterates over the cell. Simultaneously activate adjacent cells for balancing.

[0039] In practical engineering applications, the equivalent thermal conductivity, heat transfer area, equilibrium heat generation coefficient, and thermal resistance of the battery cells at each location involved in the above model can all be obtained as fixed parameters through temperature rise calibration experiments of the same batch of modules. The calculation process can be achieved through table lookup operations or simple floating-point operations of the BMS main control chip.

[0040] This invention relies on standardized equilibrium boundary determination data to calibrate the equilibrium power constraint range of individual cells, solves the thermal topology-constrained equilibrium power boundary parameters, and adaptively corrects and calibrates the equilibrium power range of each individual cell by combining the cell's thermal tolerance limit, the thermal coupling coefficient of adjacent cells, the equilibrium heat generation threshold, and the aging degradation characteristics of retired cells. Traditional BMS equilibrium technologies mostly use fixed and uniform equilibrium power parameters, failing to distinguish the spatial arrangement differences and thermal coupling degrees of different cells within the module, and ignoring the thermal tolerance degradation characteristics of retired cells after aging. Simultaneous equilibrium of multiple cells in high thermal coupling areas easily leads to heat superposition and localized heat accumulation, which can exacerbate secondary aging of cells or even cause thermal safety hazards. This invention, through refined partitioning of the module's thermal topology structure, calibrates the power constraint range and matches differentiated safety power boundaries for cells in different locations and with different thermal coupling degrees. This can fundamentally avoid the overheating risk during the equilibrium operation and ensure the operational safety of aging cell equilibrium repair. This invention performs a two-factor coupled calculation based on the module SOC dispersion parameter and the thermal topology-constrained equalization power boundary parameter to obtain refined time-sharing equalization configuration parameters for adjacent cells. The coupled calculation process uses the deviation level of the individual cell SOC from the module average SOC as the adjustment basis to dynamically match the equalization adjustment weight. At the same time, it relies on the thermal topology power boundary to trim the over-limit power parameters in real time, realizing a two-way adaptation between power regulation accuracy and thermal safety constraints.

[0041] Specifically, the implementation of coupled computation and time-series scheduling is as follows: Target equilibrium power coupling calculation: The target equilibrium power of each individual unit is proportional to its own SOC deviation amplitude and does not exceed the corresponding thermal constraint equilibrium power boundary. The calculation formula is as follows:

[0042] In the formula, For the first The target balanced power of energy-saving cells; For the first Real-time SOC value of the energy-saving cell; The average SOC value of the module; This represents the maximum SOC deviation amplitude of a single unit within the module; Let be the thermally constrained equalization power boundary of the i-th cell.

[0043] Estimated equalization duration calculation: Based on the capacity difference corresponding to the SOC deviation and the target equalization power, the estimated equalization runtime of each cell is calculated.

[0044] In the formula, For the first Estimated balancing time of the energy-saving cell; This refers to the rated capacity of a single unit; This refers to the nominal voltage of a single unit.

[0045] Adjacent cell time-sharing scheduling: The equalization time slot is allocated according to the rule of "high deviation priority and neighbor mutual exclusion time-sharing". The start priority is determined according to the SOC deviation amplitude from large to small, and the time slot is allocated in order of priority. If the physical adjacent cells of the current cell have occupied the same time slot, it is postponed to the next time slot to ensure that no adjacent cells in the same direction to be equalized work at the same time slot. Finally, the target power, estimated duration and the time slot are encapsulated into the time-sharing equalization configuration parameters of individual adjacent cells.

[0046] Traditional balancing methods rely solely on voltage or SOC differences for coarse synchronous balancing, failing to account for variations in charge dispersion and the limitations of module thermal constraints on balancing power boundaries. Cells with high dispersion exhibit incomplete balancing repair and low consistency correction efficiency, while low-dispersion cells are prone to over-balancing losses. Furthermore, synchronous start-stop balancing of adjacent cells easily leads to heat accumulation and aggregation. This invention, through dual-dimensional parameter linkage and coupling calculation, adaptively allocates the balancing start-up sequence, operating power, and duration for each individual cell, forming a refined configuration scheme for time-sharing and zone-based control of adjacent cells, effectively solving the technical drawbacks of traditional synchronous balancing methods.

[0047] This invention analyzes the time-sharing equalization configuration parameters of adjacent cells in a single unit, compiles and generates standardized time-sharing equalization control instructions for adjacent cells that can be recognized by the BMS hardware circuit, and completes hardware drive output to achieve differentiated SOC consistency adjustment of multiple units. By matching the staggered start-stop timing and differentiated power parameters of each unit, it breaks the traditional module-wide synchronous equalization operation mode, realizing a control method of staggered start-stop of adjacent cells and differentiated power replenishment of cells with high and low deviations. This ensures the SOC consistency repair effect of highly discrete units, and avoids the problem of thermal superposition of neighboring cells through staggered operation. While ensuring equalization repair efficiency, it minimizes the secondary aging loss of retired cells. This invention relies on a BMS hardware monitoring unit to track the equalization adjustment process in real time, dynamically capturing core operational information such as the start / stop status, power operation status, and operation continuity status of each individual equalization circuit. It collects and summarizes real-time equalization runtime data for each individual cell and stores it synchronously in the system iteration database. Traditional equalization technologies lack refined runtime monitoring and data retention mechanisms, relying solely on fixed durations or fixed voltage thresholds to terminate equalization operations. This fails to adapt to the dynamically changing equalization conditions of aging cells and makes it difficult to achieve iterative optimization and control of equalization operations. This invention, through real-time runtime acquisition throughout the entire process, provides effective timing data support for subsequent equalization iteration pause triggering and dynamic correction of the OCV-SOC model, constructing a closed-loop equalization iteration control system. This invention combines multi-dimensional data preprocessing, thermal topology power boundary calibration, dual-parameter coupling peak-shifting configuration, differentiated command driving, and full-process duration monitoring to form a systematic, intelligent, and safe BMS equalization and repair mechanism adapted to retired lithium iron phosphate battery modules. Compared to the traditional coarse-grained equalization scheme with fixed parameters and synchronous operation, this invention takes into account the individual aging differences of retired cells, the thermal topology coupling characteristics of modules, and the need for discrete power repair. It effectively solves the industry pain points of low equalization accuracy, high thermal safety risks, easy rebound of inconsistency after repair, and severe secondary aging of cells. It significantly improves the accuracy, safety, and long-term service stability of equalization and repair of retired battery modules, providing reliable technical support for the consistency repair and safety management of retired batteries for cascade utilization.

[0048] In one embodiment, the step of generating an equalization pause trigger signal based on the equalization runtime data and a preset equalization iteration interval parameter to control the cessation of equalization operation until the cell voltage reaches thermal steady state, and then re-acquiring differential voltage characteristics to iteratively correct the initial aging adaptation OCV-SOC mapping model of each cell to obtain the corrected real-time SOC value of each cell includes: S14. Collect real-time equalization runtime data and preset equalization iteration interval parameters, obtain equalization iteration judgment basic data after comparing the difference with the threshold, and perform duration comparison judgment based on the equalization iteration judgment basic data to generate equalization pause trigger signal. S15. The BMS equalization operation is shut down according to the equalization pause trigger signal, the battery module is kept in a static state, and the cell voltage fluctuation data during the static process is continuously monitored. S16. Determine and lock the voltage thermal steady state based on the cell voltage fluctuation data, collect the individual differential voltage characteristic data under static conditions, and iteratively correct the initial aging adaptation OCV-SOC mapping model based on the individual differential voltage characteristic data to obtain the real-time SOC value of each individual cell after correction.

[0049] As described in steps S14-S16 above, this invention constructs basic data for determining the equilibrium boundary by retrieving module SOC dispersion parameters and battery thermal topology parameters. Prior to this, multi-dimensional variables such as module charge dispersion parameters, cell spatial arrangement parameters, and thermal coupling parameters are normalized and preprocessed to eliminate dimensional differences between different physical variables. The aim is to ensure that all computational variables are within the same order of magnitude, avoiding computational deviations and model oscillations caused by differences in the numerical range of multi-dimensional parameters, and guaranteeing the stability and effectiveness of subsequent coupling calculations and parameter calibration. The basic data for determining the module equilibrium boundary integrates the characteristics of individual module charge dispersion differences and the characteristics of the module's internal thermal distribution structure. Unlike traditional single-data-dimensional determination modes, this data comprehensively covers the charge adjustment and thermal safety constraints of retired battery module equilibrium operations, providing complete and reliable basic data support for subsequent power boundary calibration. By combining the cell's thermal tolerance limit, the thermal coupling coefficient between adjacent cells, the equalization heat generation threshold, and the aging degradation characteristics of retired cells, the equalization power range of each individual cell is adaptively corrected and calibrated. Traditional BMS equalization technologies mostly use fixed and uniform equalization power parameters, failing to differentiate between the spatial arrangement differences and thermal coupling degrees of different cells within the module, and ignoring the thermal tolerance degradation characteristics of retired cells after aging. Simultaneous equalization of multiple cells in high thermal coupling areas easily leads to heat superposition and localized heat accumulation, which can exacerbate secondary aging of cells or even cause thermal safety hazards. This invention uses a refined partitioning and calibration of the module's thermal topology to define the power constraint range, matching differentiated safety power boundaries for cells in different locations and with different thermal coupling degrees. This can fundamentally avoid the overheating risk during the equalization process, ensuring the safety of equalization repair of aging cells. This invention performs a two-factor coupled calculation based on the module SOC dispersion parameter and the thermal topology-constrained equalization power boundary parameter to obtain refined time-sharing equalization configuration parameters for adjacent cells. The coupled calculation process uses the deviation level of the individual cell SOC from the module average SOC as the adjustment basis to dynamically match the equalization adjustment weight. At the same time, it relies on the thermal topology power boundary to trim the over-limit power parameters in real time, realizing a two-way adaptation between power regulation accuracy and thermal safety constraints.

[0050] Traditional balancing methods rely solely on voltage or SOC differences for coarse synchronous balancing, failing to account for variations in charge dispersion and the limitations of module thermal constraints on balancing power boundaries. Cells with high dispersion exhibit incomplete balancing repair and low consistency correction efficiency, while low-dispersion cells are prone to over-balancing losses. Furthermore, synchronous start-stop balancing of adjacent cells easily leads to heat accumulation and aggregation. This invention, through dual-dimensional parameter linkage and coupling calculation, adaptively allocates the balancing start-up sequence, operating power, and duration of each individual cell, forming a refined configuration scheme for time-sharing and zone-based control of adjacent cells, effectively solving the technical drawbacks of traditional synchronous balancing methods. By matching the staggered start-stop timing and differentiated power parameters of each individual cell, the traditional module's overall synchronous and balanced operation mode is broken, realizing the regulation method of staggered start-stop of adjacent cells and differentiated energy replenishment of cells with high and low deviations. This not only ensures the consistent SOC repair effect of highly discrete cells, but also avoids the problem of thermal superposition of neighboring cells through staggered operation. While ensuring the efficiency of balanced repair, it maximizes the reduction of secondary aging loss of retired cells. This invention relies on a BMS hardware monitoring unit to track the equalization adjustment process in real time, dynamically capturing core operational information such as the start / stop status, power operation status, and operation continuity status of each individual equalization circuit. It collects and summarizes real-time equalization runtime data for each individual cell and stores it synchronously in the system iteration database. Traditional equalization technologies lack refined runtime monitoring and data retention mechanisms, relying solely on fixed durations or fixed voltage thresholds to terminate equalization operations. This fails to adapt to the dynamically changing equalization conditions of aging cells and makes it difficult to achieve iterative optimization and control of equalization operations. This invention, through real-time runtime acquisition throughout the entire process, provides effective timing data support for subsequent equalization iteration pause triggering and dynamic correction of the OCV-SOC model, constructing a closed-loop equalization iteration control system. This invention combines multi-dimensional data preprocessing, thermal topology power boundary calibration, dual-parameter coupling peak-shifting configuration, differentiated command driving, and full-process duration monitoring to form a systematic, intelligent, and safe BMS equalization and repair mechanism adapted to retired lithium iron phosphate battery modules. Compared to the traditional coarse-grained equalization scheme with fixed parameters and synchronous operation, this invention takes into account the individual aging differences of retired cells, the thermal topology coupling characteristics of modules, and the need for discrete power repair. It effectively solves the industry pain points of low equalization accuracy, high thermal safety risks, easy rebound of inconsistency after repair, and severe secondary aging of cells. It significantly improves the accuracy, safety, and long-term service stability of equalization and repair of retired battery modules, providing reliable technical support for the consistency repair and safety management of retired batteries for cascade utilization.

[0051] In one embodiment, the step of obtaining the module's individual unit SOC range and the overall module thermal stability coefficient based on the real-time SOC values ​​of each individual unit after correction and real-time temperature field data, and terminating the equalization repair and outputting the repair results based on the comparison results of the module's individual unit SOC range with a preset repair threshold and the comparison results of the overall module thermal stability coefficient with a preset safety threshold, includes: S17. Collect the real-time SOC value of each individual unit after correction and the real-time temperature field data of the whole module, unify the time sequence and integrate multi-dimensional information to obtain the basic data of module SOC and thermal state. S18. Process the module's SOC and thermal state data respectively to obtain the module's individual unit SOC range parameter and the overall module's thermal stability coefficient. S19. Based on the SOC range parameter of the individual module and the thermal stability coefficient of the whole module, and by comparing the SOC range parameter of the individual module and the thermal stability coefficient of the whole module with the preset repair threshold and the preset safety threshold respectively, a two-dimensional safety compliance comparison result is obtained. S20. Based on the dual-dimensional safety compliance comparison results, it is comprehensively determined that the module balance repair meets the standards and the overall thermal state is safe, and the final repair judgment status is obtained. The BMS balance repair operation is terminated according to the final repair judgment status, and the module balance repair result instruction is output synchronously. As described in steps S17-S20 above, this invention constructs basic data on module SOC and thermal status by collecting real-time SOC values ​​of each individual unit after iterative correction and real-time temperature field data of the entire module. Beforehand, multi-dimensional variables such as individual unit SOC values, multi-point temperature data, and spatial arrangement parameters are normalized and preprocessed to eliminate dimensional differences between different physical variables. The purpose is to ensure that all judgment variables are on a unified computational scale, avoid the problem of unbalanced judgment weights caused by different parameter value spans, and ensure the accuracy and stability of subsequent index calculations and threshold comparisons. The basic data on module SOC and thermal status synchronously integrates two core types of information: module electrical consistency status and overall thermal safety status. Unlike traditional judgment modes that only collect voltage data or single-point temperature data, this approach comprehensively covers both the verification of repair effectiveness and operational safety verification, providing complete and multi-dimensional basic data support for subsequent repair compliance judgments. This invention utilizes module SOC and thermal state data for classification and computation, calculating the individual module SOC range parameter and the overall module thermal stability coefficient. The SOC range parameter is obtained by subtracting the maximum and minimum SOC values ​​of the individual modules, directly representing the dispersion of charge between individual modules. The overall module thermal stability coefficient is calculated by weighting and fusing three indicators: module temperature uniformity, maximum temperature rise, and local heat accumulation, comprehensively reflecting the overall thermal safety status of the module. Traditional equalization repair schemes mostly calculate the individual voltage difference as the sole criterion, failing to quantitatively assess the overall thermal stability of the module. This results in a failure to fully reflect the true service status of the repaired module, easily leading to issues such as voltage compliance but insufficient SOC consistency, or consistent charge but potential thermal safety hazards. This invention, through parallel computation of two indicators, simultaneously quantifies the charge repair effect and thermal safety level, providing a two-dimensional quantitative basis for subsequent compliance determination, ensuring the comprehensiveness and reliability of the repair assessment. This invention compares the calculated SOC range parameter of a single module with the thermal stability coefficient of the entire module, and performs item-by-item comparison and verification against preset repair thresholds and preset safety thresholds, respectively, to obtain a two-dimensional safety compliance comparison result. The SOC range parameter is used to determine whether the module's power consistency meets the repair requirements, and the thermal stability coefficient is used to determine whether the overall thermal state of the module is within the safe service range. The two indicators are compared independently, and compliance status is output separately for each. Traditional repair termination judgments mostly use a single threshold comparison mode, terminating repair only after verifying the power consistency indicator, without incorporating thermal safety status into the termination judgment system. This fails to identify potential thermal stability hazards in the repaired module, making it difficult to guarantee long-term operational safety in tiered utilization scenarios. This invention, through item-by-item comparison of two-dimensional thresholds, forms a dual verification mechanism for power consistency and thermal safety, avoiding the one-sidedness and omissions caused by single-indicator judgment, and providing a rigorous comparison basis for the final repair status judgment. This invention performs a comprehensive judgment calculation based on the results of a two-dimensional safety compliance comparison. Only when the SOC range meets a preset repair threshold and the thermal stability coefficient of the entire module simultaneously meets a preset safety threshold is the module's balanced repair deemed compliant and its overall thermal state deemed safe, resulting in a final repair pass determination. If any indicator fails to meet the corresponding threshold requirement, the repair is deemed non-compliant, and the process returns to the balanced repair process to continue adjustment. Traditional judgment mechanisms terminate repairs as soon as the power index meets the standard, ignoring thermal safety compliance, and easily classifying modules with thermal risks as qualified products, creating potential safety hazards for subsequent reuse. This invention, through a comprehensive judgment logic linked to two indicators, strictly controls both repair quality and safety standards, ensuring the service reliability of repaired retired battery modules from the terminal stage, effectively improving the pass rate and safety level of the repaired products. This invention issues a termination control command based on the final repair determination status, shutting down all BMS equalization repair operation loops. Simultaneously, it outputs a module equalization repair result command, which includes full repair information such as the module's final SOC dispersion, thermal stability coefficient, compliance status, and individual cell anomaly markers. This information is also simultaneously uploaded to the system data storage unit and the upper-level management platform. Traditional equalization repair systems only output a simple pass / fail status upon termination, lacking complete repair process and result data records, which is detrimental to subsequent status traceability and hierarchical management for the tiered utilization of modules. This invention, through standardized result command output, achieves closed-loop feedback of the repair status and full data retention, providing complete data support for the tiered utilization and operation and maintenance management of retired battery modules. This invention combines multi-dimensional basic data collection, dual-index quantitative calculation, dual-threshold item-by-item comparison, comprehensive state judgment, and standardized result output to form a dual-dimensional balanced repair closed-loop judgment mechanism adapted to retired lithium iron phosphate batteries. Compared with the traditional crude mode of single voltage threshold judgment, this invention takes into account both the module's power consistency repair effect and overall thermal safety service performance, effectively solving the technical problems of one-sided judgment standards, uncontrollable repair quality, and insufficient thermal safety hazard investigation in traditional methods. It not only ensures the accuracy and effect of balanced repair but also strengthens the safety defense line for the tiered utilization of retired batteries, providing complete terminal judgment technology support for the high-quality repair and graded application of retired lithium iron phosphate battery modules.

[0052] In one embodiment, the step of processing the module's SOC and thermal state baseline data to obtain the individual module SOC range parameter and the overall module thermal stability coefficient includes: S21. Extract the real-time temperature data and temperature point layout information of each unit from the module SOC and thermal state basic data, complete coordinate binding and anomaly filtering, and obtain the module temperature sampling basic dataset. S22. Analyze the basic dataset of module temperature sampling to obtain the maximum and minimum temperature difference amplitude of individual units and the standard deviation of global temperature, and construct the module temperature distribution characteristic parameters based on the standard deviation of global temperature. S23. Based on the module temperature distribution characteristic parameters, the module SOC and thermal state basic data are weighted and fused to obtain the module weighted thermal state index, and the module weighted thermal state index is normalized to obtain the full module thermal stability coefficient characterizing the overall safety state.

[0053] As described in steps S21-S23 above, this invention extracts real-time temperature data and temperature point layout information of each unit from the module's SOC and thermal state basic data, binds and matches the temperature data with the corresponding acquisition coordinates, and filters out abnormal temperature data such as acquisition distortion and over-range, thus generating a standardized basic dataset for module temperature sampling. Traditional temperature data acquisition and processing only retains the temperature value, discarding the spatial layout information of the points. The data lacks spatial dimension, making it difficult to accurately analyze the thermal field distribution pattern inside the module. This invention simultaneously binds the temperature value and point coordinate information, eliminates invalid and abnormal data, and forms a standardized dataset that combines temperature value and spatial location information, providing complete and reliable data support for subsequent temperature feature calculations and comprehensive thermal state assessment. This invention utilizes a basic dataset of module temperature sampling for quantitative calculations. First, it calculates the maximum and minimum temperature differences of all individual units as the temperature difference amplitude. Then, it calculates the global temperature standard deviation of all sampling points, integrating these two indicators to form the module's temperature distribution characteristic parameters. Traditional thermal analysis methods rely solely on extreme temperature differences to determine the module's thermal state, only reflecting the difference between hot and cold extremes, failing to reflect the overall discrete distribution of the module's temperature, resulting in a single evaluation dimension. This invention combines extreme temperature differences and global dispersion to fully reconstruct the overall distribution characteristics of the module's temperature field, providing complete characteristic parameters for calculating heat loss and hotspot intensity. Based on the characteristic parameters of module temperature distribution, this invention introduces two types of operating parameters: real-time operating power of the equalization circuit and heat conduction distance between cells. The heat loss per unit time and the degree of hot spot concentration in the high-temperature area are calculated separately under equalization operation. Then, the two indicators are integrated by weighted fusion to obtain the comprehensive evaluation parameters of module thermal state.

[0054] The heat loss during balanced operation is calculated based on Joule's law, and the formula for calculating the heat loss power per unit time is:

[0055] In the formula, To balance the heat loss power per unit time of the circuit; To balance the real-time operating current of the circuit; The equivalent total resistance of the equalization circuit consists of three parts: the DC internal resistance of the battery cell, the resistance of the equalization bus, and the on-resistance of the equalization switching devices. These are determined in advance through a circuit impedance calibration experiment.

[0056] The degree of hotspot clustering is quantitatively characterized by the local temperature gradient, and the calculation formula is as follows:

[0057] In the formula, This represents the degree of hotspot aggregation; a higher value indicates a higher risk of localized heat accumulation. This represents the difference between the highest temperature and the average temperature within the high-temperature region. The equivalent heat conduction distance in this high-temperature region is calculated based on the spatial arrangement coordinates of the battery cells.

[0058] After normalization, the two indicators are then integrated using a weighted fusion method to obtain the comprehensive evaluation parameters of the module's thermal state. The fusion calculation formula is as follows:

[0059] In the formula, These are parameters for comprehensive evaluation of the module's thermal state. These are the normalized heat loss and hotspot concentration indicators, respectively. The calibration weights for the corresponding indicators satisfy the following conditions: .

[0060] This parameter can reflect both the current static temperature distribution of the module and the dynamic heat accumulation trend brought about by balanced and continuous operation.

[0061] This parameter reflects both the current static temperature distribution of the module and the dynamic heat accumulation trend brought about by balanced and continuous operation. Traditional thermal assessments rely solely on instantaneous temperature for static judgments, failing to consider the heat generation development trend under balanced operating conditions, resulting in delayed thermal risk prediction. This invention achieves a three-dimensional assessment by linking temperature distribution with balanced heat generation conditions, simultaneously covering the current thermal state and subsequent heat accumulation risks, providing a comprehensive assessment basis for calculating the thermal stability coefficient. This invention imports the module's weighted thermal state indicators into a pre-defined normalized conversion model for standardized calculation. Relying on a benchmark threshold and a linear mapping algorithm, it uniformly converts multi-dimensional composite evaluation parameters to the 0-1 range, outputting a full-module thermal stability coefficient that can intuitively assess the module's safety level. The closer the coefficient is to 1, the more uniform the module's temperature distribution, the lower the risk of heat accumulation, and the better the thermal safety performance; conversely, the closer the coefficient is to 0, the higher the potential thermal safety hazard. Traditional thermal safety assessments lack unified quantitative standards, and the dimensions of various thermal parameters are inconsistent, making it impossible to directly integrate with the BMS's automatic threshold comparison logic, thus hindering the implementation of automated control. This invention transforms multi-dimensional complex thermal indicators into a single standardized value through normalized conversion, adapting to the system's automatic comparison and online safety assessment needs, and simplifying the BMS integrated control logic. This invention integrates the entire calculation process of dataset normalization, temperature distribution characteristic calculation, comprehensive thermal state assessment, and normalization coefficient conversion.

[0062] The normalization uses a linear mapping conversion model, and the calculation formula is as follows:

[0063] In the formula, The value is the thermal stability coefficient of the entire module, ranging from 0 to 1; This is the current real-time weighted thermal status indicator; This is the upper limit benchmark threshold for extreme operating conditions; This is the lower limit of the steady-state baseline threshold at room temperature; , The temperature rise was determined in advance through offline temperature rise calibration experiments of the module.

[0064] Compared to traditional, simplistic thermal analysis methods that rely solely on a single set of temperature differences for rough assessment, this invention comprehensively considers multiple thermal dimensions, including temperature spatial distribution, balanced heat loss, and local hot spot aggregation. It addresses the technical shortcomings of existing thermal assessment indicators, such as their singularity, low quantification, and inability to adapt to automated equalization control. This provides a unified and quantitative evaluation standard for the equalization repair of retired lithium iron phosphate battery modules, ensuring that the thermal state is controllable and the operation is safe throughout the equalization repair process.

[0065] like Figure 2 As shown, this invention also discloses a repair system using a lithium battery BMS equalization algorithm, comprising: The initial calibration module is used to acquire real-time temperature field data, historical cell operating condition data and module spatial arrangement parameters of the retired lithium iron phosphate battery module to be repaired, calculate the active lithium loss of the single cell and the thermal conduction distance between cells, construct the initial aging adaptation OCV-SOC mapping model of each cell based on the above parameters, and calibrate the thermal constraint equalization power boundary. The feature calculation module is used to collect the static terminal voltage data of each cell in the battery module and the total circuit current data, process and generate the differential voltage feature peak sequence of each cell, and calculate the SOC dispersion of the module cell by combining the initial aging adaptation OCV-SOC mapping model. The equalization control module is used to generate time-sharing equalization control commands for adjacent cells based on the module's SOC dispersion and thermal constraint equalization power boundary, drive the BMS equalization circuit to complete the individual cell differentiated SOC consistency adjustment, and collect equalization runtime data in real time. The iterative correction module is used to generate an equalization pause trigger signal based on the equalization runtime data and the preset equalization iteration interval parameter, shut down the equalization operation and place it in the cell thermal steady state, re-acquire the differential voltage characteristics iteratively correct the OCV-SOC mapping model, and obtain the real-time SOC value of each cell after correction. The result determination module is used to calculate the module SOC range and the overall module thermal stability coefficient based on the real-time SOC values ​​of each individual unit and the real-time temperature field data of the module. By comparing the repair threshold and the safety threshold in two dimensions, it determines and terminates the equalization repair operation and outputs the module repair result instruction.

[0066] In one embodiment, the equalization control module includes: The equalization calculation unit is used to obtain the module SOC dispersion parameters and thermal constraint equalization power boundary parameters to obtain the basic parameters of the module equalization working condition. The equalization configuration unit is used to perform coupling calculations based on the basic parameters of the module's equalization operating conditions to match and obtain the time-sharing equalization configuration parameters of each adjacent cell in a single unit. The instruction generation unit is used to generate corresponding adjacent cell time-sharing control instructions based on the time-sharing configuration parameters of each individual cell. The equalization execution unit is used to drive the BMS equalization circuit according to the time-sharing equalization control command of the adjacent cells to complete the differential SOC consistency adjustment operation of each cell. The duration acquisition unit is used to track the balance adjustment execution process in real time, and to collect and record the real-time balance runtime data of each unit.

[0067] This application also provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps of the above-described method for repairing a lithium battery BMS equalization algorithm.

[0068] This application also provides a computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the steps of the above-described method for repairing a lithium battery BMS equalization algorithm.

[0069] Those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the embodiments of the above methods. Any references to memory, storage, databases, or other media used in this application and in the embodiments can include non-volatile and / or volatile memory. 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 a variety of forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), dual-speed SDRAM (SSRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), RAMbus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).

[0070] It should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, apparatus, article, or method that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, apparatus, article, or method. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, apparatus, article, or method that includes that element.

[0071] The above description is merely a preferred embodiment of the present invention and does not limit the scope of this application. Any equivalent results or equivalent process transformations made based on the content of the present invention's specification and drawings, or direct or indirect applications in other related technical fields, are similarly included within the scope of protection of this application.

Claims

1. A method for repairing lithium batteries using a BMS equalization algorithm, characterized in that, Includes the following steps: Real-time temperature field data, historical cell operating condition data, and module spatial arrangement parameters of the retired lithium iron phosphate battery module to be repaired are obtained to obtain the active lithium loss of each cell and the thermal conduction distance parameters between cells in the module. Based on the active lithium loss of each cell and the thermal conduction distance parameters between cells in the module, an initial aging adaptation OCV-SOC mapping model for each cell is constructed, and the thermal constraint equalization power boundary is calibrated. The stationary terminal voltage data and total circuit current data of each cell in the retired lithium iron phosphate battery module to be repaired are obtained to obtain the differential voltage characteristic peak sequence of each cell, and the module SOC dispersion of each cell is obtained according to the differential voltage characteristic peak sequence of each cell and the initial aging adaptation OCV-SOC mapping model. Based on the single-cell deviation amplitude in the module SOC dispersion, the corresponding equalization power and running time are matched. Combined with the spatial arrangement of the deviation cells, the time-sharing start-stop sequence of adjacent cells is determined. Within the limit of the thermal constraint equalization power boundary, the time-sharing equalization control command of adjacent cells is generated, driving the BMS equalization circuit to perform differentiated SOC consistency adjustment on each single cell and collect equalization running time data in real time. Based on the equalization runtime data and the preset equalization iteration interval parameters, an equalization pause trigger signal is generated to control the cessation of equalization operation until the cell voltage reaches thermal steady state. Then, the differential voltage characteristics are re-acquired to iteratively correct the initial aging adaptation OCV-SOC mapping model of each cell to obtain the real-time SOC value of each cell after correction. The module's individual unit SOC range and overall module thermal stability coefficient are obtained based on the real-time SOC values ​​and real-time temperature field data after correction of each unit. The equalization repair is terminated and the repair results are output based on the comparison results of the individual unit SOC range with the preset repair threshold and the comparison results of the overall module thermal stability coefficient with the preset safety threshold.

2. The method for repairing lithium battery BMS equalization algorithm according to claim 1, characterized in that, The steps of obtaining the resting terminal voltage data and total circuit current data of each cell in the decommissioned lithium iron phosphate battery module to be repaired to obtain the differential voltage characteristic peak sequence of each cell, and obtaining the module SOC dispersion of each cell based on the differential voltage characteristic peak sequence of each cell and the initial aging adaptation OCV-SOC mapping model, include: Collect the static terminal voltage data of each cell of the retired lithium iron phosphate battery module to be repaired and the total current data of the module circuit to perform timing alignment and filtering and noise reduction processing to obtain the basic data of the module's original electrical signal. Differential operation processing is performed on the original electrical signal data of the module to obtain the voltage differential data sequence of each unit, and peak characteristic parameters are extracted from the voltage differential data sequence of each unit to construct the voltage characteristic peak sequence of each unit. The peak positions of each individual differential voltage characteristic peak sequence are aligned one by one with the reference characteristic peaks in the initial aging adaptation OCV-SOC mapping model in ascending order of SOC, and the SOC reference anchor point corresponding to each characteristic peak is locked. Between two adjacent SOC reference anchor points, linear interpolation is performed based on the current measured static terminal voltage to obtain the initial interpolated SOC value. The ampere-hour integral correction of the total circuit current before static is introduced to correct the initial interpolated SOC value, and the real-time SOC value of each individual cell is obtained. Based on the real-time SOC values ​​of each individual cell, discrete calculations are performed to obtain the SOC dispersion parameters of the battery module individual cells.

3. The method for repairing lithium battery BMS equalization algorithm according to claim 1, characterized in that, The steps of matching the equalization power and running time corresponding to the single-cell deviation amplitude in the module's SOC dispersion, determining the time-sharing start-stop sequence of adjacent cells based on the spatial arrangement of the deviation cells, generating time-sharing equalization control commands for adjacent cells within the limits of the thermal constraint equalization power boundary, driving the BMS equalization circuit to perform differentiated SOC consistency adjustment for each single cell, and collecting equalization running time data in real time include: The module SOC dispersion parameters and battery thermal topology parameters are normalized and preprocessed to obtain the basic data for determining the equilibrium boundary. Based on the basic data for determining the equilibrium boundary, the single-cell equilibrium power constraint interval is calibrated. After temperature-power mapping, the thermal topology-constrained equilibrium power boundary parameters are obtained. The time-sharing equalization configuration parameters of adjacent cells in a single unit are obtained based on the module SOC dispersion parameters and the thermal topology-constrained equalization power boundary parameters. Based on the time-sharing equalization configuration parameters of multiple adjacent cells, corresponding adjacent cell time-sharing equalization control commands are generated. The BMS equalization circuit is driven according to the adjacent cell time-sharing equalization control commands to complete the differential SOC consistency adjustment drive output, and real-time equalization runtime data of each cell are collected synchronously during the adjustment process.

4. The method for repairing lithium battery BMS equalization algorithm according to claim 1, characterized in that, The steps include: generating a balancing pause trigger signal based on the balancing runtime data and preset balancing iteration interval parameters to control the balancing operation to stop until the cell voltage reaches thermal steady state, then re-acquiring differential voltage characteristics to iteratively correct the initial aging adaptation OCV-SOC mapping model of each cell to obtain the corrected real-time SOC value of each cell; and so on. Collect real-time balancing runtime data and preset balancing iteration interval parameters, compare the difference with the threshold to obtain the balancing iteration judgment base data, and perform duration comparison judgment based on the balancing iteration judgment base data to generate a balancing pause trigger signal. The BMS equalization operation is shut down according to the equalization pause trigger signal to maintain the battery module in a static state and continuously monitor the cell voltage fluctuation data during the static process. Based on the cell voltage fluctuation data, the voltage thermal steady state is determined and locked. Individual differential voltage characteristic data under static conditions are collected, and the initial aging adaptation OCV-SOC mapping model is iteratively corrected based on the individual differential voltage characteristic data to obtain the real-time SOC value of each individual cell after correction.

5. The method for repairing lithium battery BMS equalization algorithm according to claim 1, characterized in that, The steps of obtaining the module's individual unit SOC range and overall module thermal stability coefficient based on the real-time SOC values ​​of each individual unit after correction and real-time temperature field data, and terminating the equalization repair and outputting the repair results based on the comparison results of the module's individual unit SOC range and the preset repair threshold, and the comparison results of the overall module thermal stability coefficient and the preset safety threshold, include: Collect the real-time SOC value of each individual unit after correction and the real-time temperature field data of the whole module, unify the time series and integrate multi-dimensional information to obtain the basic data of module SOC and thermal state; The module's SOC and thermal state data are processed separately to obtain the module's individual unit SOC range parameter and the overall module's thermal stability coefficient. Based on the SOC range parameter of the individual module and the thermal stability coefficient of the whole module, and by comparing the SOC range parameter of the individual module and the thermal stability coefficient of the whole module with the preset repair threshold and the preset safety threshold respectively, a two-dimensional safety compliance comparison result is obtained. Based on the dual-dimensional safety compliance comparison results, the module balance repair is comprehensively determined to be up to standard and the overall thermal state is safe, resulting in the final repair judgment status. The BMS balance repair operation is terminated based on the final repair judgment status, and the module balance repair result instruction is output synchronously.

6. The method for repairing lithium battery BMS equalization algorithm according to claim 5, characterized in that, The step of processing the module's SOC and thermal state data to obtain the individual module SOC range parameter and the overall module thermal stability coefficient includes: Extract real-time temperature data and temperature point layout information of each unit from the module SOC and thermal state basic data, complete coordinate binding and anomaly filtering, and obtain the module temperature sampling basic dataset. Based on the analysis of the module temperature sampling dataset, the maximum and minimum temperature difference amplitude of each unit and the global temperature standard deviation are obtained, and the module temperature distribution characteristic parameters are constructed based on the global temperature standard deviation. The module's temperature distribution characteristic parameters are combined with the module's SOC and thermal state basic data to perform weighted fusion, resulting in a module weighted thermal state index. The module's weighted thermal state index is then normalized to obtain the overall module thermal stability coefficient, which characterizes the overall safety status.

7. A repair system using a lithium battery BMS equalization algorithm, characterized in that, include: The initial calibration module is used to acquire real-time temperature field data, historical cell operating condition data and module spatial arrangement parameters of the retired lithium iron phosphate battery module to be repaired, calculate the loss of active lithium in a single cell and the thermal conduction distance between cells, construct an initial aging adaptation OCV-SOC mapping model based on the above parameters, and calibrate the thermal constraint equalization power boundary. The feature calculation module is used to collect the static terminal voltage data of each cell in the battery module and the total circuit current data, process and generate the differential voltage feature peak sequence of each cell, and calculate the SOC dispersion of the module cell by combining the initial aging adaptation OCV-SOC mapping model. The equalization control module is used to generate time-sharing equalization control commands for adjacent cells based on the module's SOC dispersion and thermal constraint equalization power boundary, drive the BMS equalization circuit to complete the individual cell differentiated SOC consistency adjustment, and collect equalization runtime data in real time. The iterative correction module is used to generate an equalization pause trigger signal based on the equalization runtime data and the preset equalization iteration interval parameter, shut down the equalization operation and place it in the cell thermal steady state, re-acquire the differential voltage characteristics iteratively correct the OCV-SOC mapping model, and obtain the real-time SOC value of each cell after correction. The result determination module is used to calculate the module SOC range and the overall module thermal stability coefficient based on the real-time SOC values ​​of each individual unit and the real-time temperature field data of the module. By comparing the repair threshold and the safety threshold in two dimensions, it determines and terminates the equalization repair operation and outputs the module repair result instruction.

8. The lithium battery BMS equalization algorithm repair system according to claim 7, characterized in that, The equalization control module includes: The equalization calculation unit is used to obtain the module SOC dispersion parameters and thermal constraint equalization power boundary parameters to obtain the basic parameters of the module equalization working condition. The equalization configuration unit is used to perform coupling calculations based on the basic parameters of the module's equalization operating conditions to match and obtain the time-sharing equalization configuration parameters of each adjacent cell in a single unit. The instruction generation unit is used to generate corresponding adjacent cell time-sharing control instructions based on the time-sharing configuration parameters of each individual cell. The equalization execution unit is used to drive the BMS equalization circuit according to the time-sharing equalization control command of the adjacent cells to complete the differential SOC consistency adjustment operation of each cell. The duration acquisition unit is used to track the balance adjustment execution process in real time, and to collect and record the real-time balance runtime data of each unit.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1 to 6.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 6.

Images