Hybrid battery module equalization method and system based on battery cell health state recognition

Abstract

The invention relates to the technical field of battery management, and discloses a hybrid battery module equalization method and system based on battery cell health state identification, and the method comprises the steps: carrying out the multi-dimensional state fusion evaluation of a battery cell based on the voltage, temperature and total loop current of the battery cell in a battery module, and obtaining the charge state and health state of the battery cell; based on a preset voltage difference threshold value, performing threshold value judgment on the voltage range of the battery cell to obtain a diagnosis trigger signal of the battery cell; carrying out capacity cross validation on the charge state and the health state to obtain a determination result of capacity inconsistency of the battery cell; carrying out abnormal fluctuation analysis on the voltage change characteristics of the battery cells to obtain an internal resistance inconsistency judgment result of the battery cells; carrying out equalization strategy decision on the battery cell to obtain an equalization mode instruction of the battery cell; performing equalization operation on the battery cell according to the equalization mode instruction; according to the invention, the equalization rate of the hybrid battery module based on cell health state identification can be improved.

Description

Technical Field

[0001] This invention relates to the field of battery management technology, and in particular to a method and system for equalizing hybrid battery modules based on cell health status identification. Background Technology

[0002] In existing technologies, the assessment of the cell status of battery modules is mostly limited to single-dimensional parameters, failing to integrate voltage, temperature and total circuit current for multi-dimensional analysis. This results in the inability to accurately obtain the cell's state of charge and health status, making it difficult to provide reliable status information for subsequent balancing operations. Consequently, it is difficult to address potential inconsistencies in the cells and thus affect the overall balancing effect of the battery module.

[0003] Meanwhile, existing equalization methods do not accurately distinguish the types of cell inconsistencies, and only perform equalization operations based on a single voltage difference. This cannot effectively identify the essential differences between capacity inconsistencies and internal resistance inconsistencies, nor can it match appropriate equalization strategies according to different failure modes. As a result, the equalization operation is not targeted enough and cannot fundamentally improve the cell inconsistency problem. In addition, improper equalization may accelerate the degradation of cell performance and reduce the operational stability and service life of the battery module. Summary of the Invention

[0004] This invention provides a method and system for balancing hybrid battery modules based on cell health status identification, in order to solve the problems mentioned in the background art.

[0005] To achieve the above objectives, the present invention provides a hybrid battery module equalization method based on cell health status identification, comprising: S1. Based on the voltage, temperature and total circuit current of the cells in the battery module, perform a multi-dimensional state fusion evaluation of the cells to obtain the state of charge and health status of the cells; S2. Based on a preset voltage difference threshold, perform threshold discrimination on the voltage range of the battery cell to obtain the diagnostic trigger signal of the battery cell; S3. Based on the diagnostic trigger signal, perform capacity cross-validation on the state of charge and the health state to obtain the capacity inconsistency determination result of the cell; S4. Based on the diagnostic trigger signal, perform abnormal fluctuation analysis on the voltage change characteristics of the battery cell to obtain the determination result of the inconsistency of the internal resistance of the battery cell; S5. Based on the capacity inconsistency determination result and the internal resistance inconsistency determination result, perform balancing strategy decision on the battery cell to obtain the balancing mode instruction of the battery cell. S6. Perform balancing operation on the battery cell according to the balancing mode instruction.

[0006] In a preferred embodiment, the multi-dimensional state fusion evaluation of the battery cell based on its voltage, temperature, and total circuit current in the battery module to obtain the cell's state of charge and health status includes: The total loop current of the cells in the battery module is accumulated over time to obtain the charge change of the cells; The voltage of the battery cell is extracted during a resting period to obtain the voltage reference parameters of the battery cell; Based on the safety operation specifications of the battery module, the historical deviation data between the voltage reference parameter and the voltage are statistically analyzed to obtain the voltage deviation threshold of the cell. Based on the voltage reference parameters and the voltage deviation threshold, the charge change is dynamically weighted to obtain the state of charge of the battery cell; By tracking the voltage, temperature, and total circuit current over a long period, the capacity decay trend and internal resistance change trend of the battery cell can be obtained. The health status of the battery cell is obtained by combining the capacity decay trend and the internal resistance change trend.

[0007] In a preferred embodiment, the charge change is dynamically weighted based on the voltage reference parameter and the voltage deviation threshold to obtain the state of charge of the battery cell. The formula for calculating the state of charge is as follows: ; In the formula, The state of charge, This refers to the initial state of charge of the battery cell. The change in charge is the amount of charge. The rated capacity parameter of the battery cell is... This is the estimated current voltage value of the battery cell. The voltage reference parameters are as follows. The voltage deviation threshold is defined as follows.

[0008] In a preferred embodiment, the step of threshold discrimination of the voltage difference of the battery cell based on a preset voltage difference threshold to obtain a diagnostic trigger signal for the battery cell includes: The voltage range of the battery cell is quantified to obtain the voltage difference degree of the battery cell; Based on a preset voltage difference threshold, the voltage difference degree is compared to obtain the difference comparison result; The results of the difference comparison are used to perform a diagnostic trigger judgment to obtain the diagnostic trigger signal of the battery cell.

[0009] In a preferred embodiment, the step of performing capacity cross-validation on the state of charge and the health state based on the diagnostic trigger signal to obtain the capacity inconsistency determination result of the cell includes: A continuous deviation trend analysis was performed on the state of charge to identify anomalies in the state of charge that were consistently higher than the group average. The health status of the abnormal state of charge set is traced by capacity degradation characteristics to obtain the capacity degradation identifier of the cell; The distribution characteristics of the capacity decay indicators are used to determine the capacity inconsistency, and the capacity inconsistency determination result of the battery cell is obtained.

[0010] In a preferred embodiment, the step of performing abnormal fluctuation analysis on the voltage change characteristics of the battery cell based on the diagnostic trigger signal to obtain the internal resistance inconsistency determination result of the battery cell includes: The voltage response of each cell is dynamically monitored to obtain the transient voltage response characteristics of the cell. The voltage surge or voltage drop characteristics are analyzed to identify amplitude anomalies, thereby obtaining a voltage anomaly identifier set for the battery cell. The internal resistance correlation of the voltage change patterns of the voltage anomaly identifier set is verified to obtain the internal resistance correlation index of the battery cell. Based on the internal resistance correlation index, internal resistance abnormality and internal resistance failure diagnosis are performed to obtain the internal resistance inconsistency judgment result of the battery cell.

[0011] In a preferred embodiment, the step of making a balancing strategy decision on the battery cell based on the capacity inconsistency determination result and the internal resistance inconsistency determination result to obtain the balancing mode instruction of the battery cell includes: The failure mode classification is performed on the capacity inconsistency determination results and the internal resistance inconsistency determination results to obtain the fundamental failure identifier and temporary inconsistency identifier of the cell. Active balancing strategy matching is performed on the fundamental failure identifier to obtain the active balancing scheduling scheme of the battery cell; The temporary inconsistency identifier is matched with a passive balancing strategy to obtain the passive balancing scheduling scheme of the battery cell. The active balancing scheduling scheme and the passive balancing scheduling scheme are integrated into a system to obtain the balancing mode command for the battery cell.

[0012] In a preferred embodiment, the step of balancing the battery cell according to the balancing mode command includes: By performing balanced parameter co-analysis on the fundamental failure identifier and the temporary inconsistency identifier, the transfer path planning and power dissipation level of the battery cell can be obtained; When the active balancing scheduling scheme exists, the switching matrix is ​​configured based on the cell's location data and the transfer path planning to obtain the cell's energy transfer path; Inductive energy storage is performed on the energy transfer path to achieve balanced operation of the battery cell; When the passive equalization scheduling scheme exists, the resistor path is activated based on the cell's location data and the power dissipation level to form an energy dissipation loop. Selective energy release is performed on the energy dissipation circuit to achieve balanced operation of the battery cell.

[0013] In a preferred embodiment, the step of performing balanced parameter co-analysis on the fundamental failure identifier and the temporary inconsistency identifier to obtain the cell's transfer path planning and power dissipation level includes: Energy distribution analysis is performed on the fundamental failure indicator to obtain the energy source of the battery cell; A comprehensive evaluation is performed on the diagnostic conclusion of the capacity consistency deviation and the diagnostic report of the internal resistance consistency deviation to obtain the energy receiving end of the battery cell; The relative positions of the energy source and the energy receiver are optimized to form the transfer path plan; A state-of-charge difference analysis is performed on the temporary inconsistency markers to obtain the degree of state-of-charge deviation of the battery cell; Based on the degree of state of charge deviation and the temperature parameters of the battery cell, power classification is performed to form the power dissipation level.

[0014] To address the aforementioned problems, the present invention also provides a hybrid battery module balancing system based on cell health status identification, the system comprising: The multi-dimensional state fusion evaluation module is used to perform multi-dimensional state fusion evaluation on the battery cell based on the voltage, temperature and total circuit current of the battery cell in the battery module, so as to obtain the state of charge and health status of the battery cell. The range threshold discrimination and diagnostic trigger module is used to perform threshold discrimination on the voltage range of the battery cell based on a preset voltage difference threshold, and obtain the diagnostic trigger signal of the battery cell. The cross-validation determination module is used to perform capacity cross-validation on the state of charge and the health state based on the diagnostic trigger signal to obtain the capacity inconsistency determination result of the cell. The voltage characteristic anomaly analysis and judgment module is used to perform abnormal fluctuation analysis on the voltage change characteristics of the battery cell based on the diagnostic trigger signal, and obtain the internal resistance inconsistency judgment result of the battery cell. The equalization strategy decision and mode instruction generation module is used to make an equalization strategy decision for the battery cell based on the capacity inconsistency determination result and the internal resistance inconsistency determination result, and obtain the equalization mode instruction of the battery cell. The balancing operation execution module is used to perform balancing operations on the battery cell according to the balancing mode instruction.

[0015] Compared with the prior art, the present invention has the following beneficial effects: 1. This invention conducts a multi-dimensional state fusion assessment based on cell voltage, temperature, and total circuit current, which can accurately obtain the state of charge and health status of the cells. Then, it generates a diagnostic trigger signal by combining voltage range threshold discrimination. Furthermore, through capacity cross-validation and abnormal fluctuation analysis of voltage change characteristics, it obtains accurate capacity inconsistency judgment results and internal resistance inconsistency judgment results, respectively. This provides a comprehensive and accurate basis for balancing strategy decision-making, effectively improves the accuracy of balancing decisions, ensures the reliability of the state assessment of each cell in the battery module, and helps subsequent balancing operations to better meet the actual state requirements of the cells.

[0016] 2. Based on the results of the inconsistency determination of capacity and internal resistance, this invention classifies the cell failure modes and matches them with corresponding active and passive balancing scheduling schemes. It also determines the transfer path planning or power dissipation level through collaborative analysis of balancing parameters, thereby realizing active balancing operation of inductive energy storage and passive balancing operation of resistive dissipation. This invention can specifically solve the inconsistency problem of different types of cells, effectively improve the effectiveness of balancing operation, enhance the stability of battery module operation, extend the overall service life of battery module, and ensure the rationality of cell energy utilization during the balancing process. Attached Figure Description

[0017] Figure 1 This is a flowchart illustrating a hybrid battery module balancing method based on cell health status identification, provided in an embodiment of the present invention. Figure 2 This is a functional block diagram of a hybrid battery module balancing system based on cell health status identification, provided in an embodiment of the present invention. The realization of the objective, functional features and advantages of the present invention will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0018] It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.

[0019] This application provides a hybrid battery module balancing method based on cell health status identification. The execution entity of this hybrid battery module balancing method includes, but is not limited to, at least one of the following electronic devices that can be configured to execute the method provided in this application: a server, a terminal, etc. In other words, the hybrid battery module balancing method based on cell health status identification can be executed by software or hardware installed on a terminal device or a server device. The server includes, but is not limited to, a single server, a server cluster, a cloud server, or a cloud server cluster. The server can be an independent server or a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, security services, content delivery networks (CDNs), and big data and artificial intelligence platforms.

[0020] Reference Figure 1 The diagram shown is a flowchart illustrating a hybrid battery module balancing method based on cell health status identification according to an embodiment of the present invention. In this embodiment, the hybrid battery module balancing method based on cell health status identification includes: S1. Based on the voltage, temperature and total circuit current of the cells in the battery module, perform a multi-dimensional state fusion evaluation of the cells to obtain the state of charge and health status of the cells. In this embodiment of the invention, the multi-dimensional state fusion evaluation of the battery cell based on the voltage, temperature, and total circuit current of the battery cell in the battery module to obtain the state of charge and health status of the battery cell includes: The total loop current of the cells in the battery module is accumulated over time to obtain the charge change of the cells; The voltage of the battery cell is extracted during a resting period to obtain the voltage reference parameters of the battery cell; Based on the safety operation specifications of the battery module, the historical deviation data between the voltage reference parameter and the voltage are statistically analyzed to obtain the voltage deviation threshold of the cell. Based on the voltage reference parameters and the voltage deviation threshold, the charge change is dynamically weighted to obtain the state of charge of the battery cell; By tracking the voltage, temperature, and total circuit current over a long period, the capacity decay trend and internal resistance change trend of the battery cell can be obtained. The health status of the battery cell is obtained by combining the capacity decay trend and the internal resistance change trend.

[0021] The state of charge of the battery cell is obtained by dynamically weighting the charge change based on the voltage reference parameter and the voltage deviation threshold. The formula for calculating the state of charge is as follows: ; In the formula, The state of charge, This refers to the initial state of charge of the battery cell. The change in charge is the amount of charge. The rated capacity parameter of the battery cell is... This is the estimated current voltage value of the battery cell. The voltage reference parameters are as follows. The voltage deviation threshold is defined as follows.

[0022] Specifically, when accumulating the total circuit current of the cells in the battery module over time, a real-time current acquisition mechanism needs to be established to continuously acquire total circuit current data and record the corresponding time period. Within each period, the current value is multiplied by the period duration to obtain the charge transfer amount for that period. Then, the transfer amounts of all consecutive periods are accumulated to finally obtain the charge change amount reflecting the total charge transfer amount during the monitoring period.

[0023] Furthermore, when extracting the resting period of the cell voltage, it is first determined whether the module is in a resting state where it is not charging or discharging and the voltage does not fluctuate significantly. After confirmation, this time period is determined as the voltage resting period. Voltage data is collected within the resting period, outliers caused by transient interference are removed, the remaining stable data is processed, and the voltage data with the longest duration and stable value is selected to obtain the voltage reference parameter representing the stable operating reference voltage level of the cell.

[0024] Furthermore, when processing historical deviation data based on the module's safe operation specifications, the differences between the actual cell voltage and the voltage reference parameter at different time points during normal operation are collected. According to the safe voltage fluctuation range in the specifications, the historical deviation data is categorized and statistically analyzed, recording the frequency and probability of different deviations. The maximum value within the safe range that covers the vast majority of normal deviations is then selected and determined as the cell's voltage deviation threshold.

[0025] Furthermore, when dynamically weighting the charge change based on the voltage reference parameter and the deviation threshold, the absolute value of the difference between the current actual cell voltage and the reference parameter is first calculated. The weight coefficient is then determined by comparing this difference with the deviation threshold; a smaller coefficient indicates a closer difference to the threshold, and a larger coefficient indicates a greater difference. The adjusted charge change is obtained by multiplying the charge change by the weight coefficient. Combined with the initial state of charge, the charge increment corresponding to the adjusted transfer amount is added to the initial state to obtain the state of charge reflecting the current charge storage status of the cell.

[0026] Furthermore, when tracking the long-term characteristics of voltage, temperature, and total circuit current, a long-term data monitoring system is established. This involves periodically collecting three types of data from the battery cells under charging, discharging, and resting conditions, and recording the time points and operating conditions. After accumulating sufficient historical data, the maximum charge storage capacity under the same charging and discharging conditions at different times is compared to summarize the reduction pattern and capacity decay trend. Voltage changes under the same current are analyzed; if voltage fluctuations increase or discharge voltage drops faster when the current is stable, the pattern is summarized to determine the internal resistance change trend.

[0027] Furthermore, when synthesizing the health status of capacity decay trend and internal resistance change trend, health impact weights are assigned to both based on module design standards and expected lifespan. A capacity health score is assigned based on the degree and rate of capacity decay, and an internal resistance health score is assigned based on the degree and rate of internal resistance increase. The two scores are multiplied by their respective weights to obtain a health contribution value, which is then summed to obtain the overall health score. The cell health level is determined based on this score, ultimately yielding a health status that reflects the cell's performance.

[0028] Specifically, the initial state of charge is obtained from the initial state record of the cell at the time of manufacture or the preset initial state calibration before the evaluation begins, before the multi-dimensional state fusion evaluation of the cell is carried out. This parameter is used as the basic starting value for calculating the state of charge.

[0029] Furthermore, the source of the charge change is the accumulation of the total circuit current of the battery cell in the battery module over time. The specific process is to continuously collect the current data in the total circuit of the battery cell, record the time period corresponding to each current collection, multiply the current value in each time period by the period duration to obtain the charge transfer amount in that period, and then add up the charge transfer amounts of all consecutive time periods in sequence to finally obtain the charge change.

[0030] Furthermore, the rated capacity parameter is a parameter preset by the manufacturer based on the design standards and performance indicators of the battery cell when it leaves the factory. This parameter represents the maximum total amount of charge that the battery cell can store under standard operating conditions and is the benchmark used to measure the charge ratio corresponding to the change in charge when calculating the state of charge.

[0031] Furthermore, the current voltage estimate is obtained by real-time monitoring and calculation of the voltage during the actual operation of the battery cell. The voltage data of the battery cell is continuously acquired through a voltage acquisition device. After eliminating abnormal voltage values ​​caused by transient interference, the effective voltage data is processed and calculated to obtain the current voltage estimate that reflects the current voltage status of the battery cell.

[0032] Furthermore, the voltage reference parameter is obtained by extracting the voltage of the battery cell during a resting period. The specific process is to determine whether the battery module is in a non-charging / discharging resting state. When it is confirmed that the battery cell voltage does not fluctuate significantly within a certain period of time, this period of time is determined as the resting period. Stable voltage data is collected within the resting period, and the voltage data with the longest duration and stable value is selected as the voltage reference parameter.

[0033] Furthermore, the voltage deviation threshold is derived from the safety operation specifications of the battery module by statistically analyzing the historical deviation data between the voltage reference parameter and the voltage. Specifically, the process involves collecting the difference between the actual voltage and the corresponding voltage reference parameter at different time points during the normal operation of the battery module, i.e., historical deviation data. Based on the safe range of voltage fluctuations specified in the safety operation specifications, the historical deviation data is classified and statistically analyzed, and the frequency and probability of different deviation values ​​are recorded. The maximum deviation value that can cover the vast majority of normal historical deviation data within the safe range is selected and determined as the voltage deviation threshold.

[0034] Furthermore, the significance of this formula lies in its ability to accurately calculate the state of charge (SOC) of a battery cell. By using the initial SOC as a basis and combining the ratio of charge change to rated capacity parameters to determine the theoretical charge change, and then using the ratio of the absolute value of the difference between the current voltage estimate and the voltage reference parameter to the voltage deviation threshold, the theoretical charge change is dynamically weighted and adjusted. This allows the calculated SOC to be corrected based on voltage deviation, more accurately reflecting the actual charge storage status of the battery cell and providing a reliable SOC basis for subsequent cell status assessment and balancing strategy development.

[0035] In summary, a time-accumulation operation is performed on the total circuit current of the cells in the battery module. By collecting the total circuit current data and recording the time period, the current value of each period is multiplied by the duration to obtain the periodic charge transfer amount. Then, all periodic transfer amounts are accumulated to finally obtain the charge change amount of the cell.

[0036] In summary, the process of extracting the voltage of the battery cell during a resting period involves first determining whether the module is in a resting state. Once it is confirmed that there is no significant voltage fluctuation, the resting period is determined. Stable voltage data within the period is collected, and outliers are removed. The longest and most stable voltage data is then selected to obtain the voltage reference parameters of the battery cell.

[0037] In summary, based on the safe operation specifications of battery modules, the historical deviation data between the voltage reference parameter and the voltage are statistically analyzed. The difference between the actual voltage and the reference parameter during normal operation of the module is collected. The frequency and probability of deviations are statistically analyzed according to the safe range. The maximum value that covers the vast majority of normal deviations within the safe range is selected, and finally the voltage deviation threshold of the battery cell is obtained.

[0038] In summary, based on the voltage reference parameters and voltage deviation threshold, a dynamic weighting operation is performed on the charge change. The absolute value of the difference between the current actual cell voltage and the reference parameter is calculated, and the weighting coefficient is determined by comparing it with the threshold. The charge change is multiplied by the coefficient to obtain the adjusted transfer amount, which is then added to the initial state of charge to finally obtain the cell's state of charge.

[0039] In summary, by performing long-term characteristic tracking operations on voltage, temperature, and total circuit current, a long-term data monitoring system is established. The three types of data under different operating conditions are collected periodically and the operating conditions are recorded. The maximum charge storage capacity under the same charging and discharging conditions at different times is compared to obtain the capacity decay trend, and the voltage change under the same current is analyzed to obtain the internal resistance change trend.

[0040] In summary, the health status is synthesized by combining the capacity decay trend and the internal resistance change trend. Based on the module design standards and expected lifespan, the health impact weights are set for the two trends. The capacity and internal resistance are scored separately according to the degree and rate of decay. The health contribution values ​​are obtained by multiplying by the weights and then summed to obtain the overall score. The health level is determined according to the score, and finally the health status of the cell is obtained.

[0041] S2. Based on a preset voltage difference threshold, the voltage range of the battery cell is threshold-determined to obtain a diagnostic trigger signal for the battery cell. In this embodiment of the invention, the step of threshold discrimination of the voltage range of the battery cell based on a preset voltage difference threshold to obtain the diagnostic trigger signal of the battery cell includes: The voltage range of the battery cell is quantified to obtain the voltage difference degree of the battery cell; Based on a preset voltage difference threshold, the voltage difference degree is compared to obtain the difference comparison result; The results of the difference comparison are used to perform a diagnostic trigger judgment to obtain the diagnostic trigger signal of the battery cell.

[0042] Specifically, when quantifying the voltage range of battery cells, real-time voltage data of all cells in the battery module are first acquired synchronously using a voltage acquisition device. During the acquisition process, abnormal voltage values ​​caused by interference in the acquisition circuit are removed to ensure that the voltage data of each cell accurately reflects its current state. From all the processed cell voltage data, the highest and lowest voltage values ​​are selected, and the difference between these two voltage values ​​is calculated. This difference is the voltage range of the battery cell.

[0043] Furthermore, referring to the allowable voltage fluctuation range of the battery cell determined during the battery module design, the calculated voltage range is compared with this allowable fluctuation range. Based on the comparison results, the proportion of the voltage range within the allowable fluctuation range is determined, and then the corresponding difference level is divided according to this proportion. In this way, the voltage range is transformed into an indicator that can clearly measure the degree of difference, and finally the voltage difference of the battery cell is obtained.

[0044] Furthermore, when comparing voltage differences based on a preset voltage difference threshold, this threshold is pre-set according to the battery module's safe operation standards, the cell's rated performance parameters, and reliability data accumulated during long-term operation. This threshold is used to determine whether the cell's voltage difference requires initiating subsequent diagnostic procedures. The previously obtained cell voltage difference is directly compared with this preset voltage difference threshold. The magnitude of the voltage difference is compared to the preset threshold value one by one, clearly recording whether the voltage difference is greater than, equal to, or less than the preset threshold. Through this explicit comparison, the final difference comparison result is obtained.

[0045] Furthermore, when triggering diagnostic judgments based on the difference comparison results, a clear judgment rule is first established. This rule stipulates that if the difference comparison result shows a voltage difference greater than a preset voltage difference threshold, it indicates that the voltage difference between the cells has exceeded the normal allowable range, potentially causing inconsistencies that could affect the battery module's performance. In this case, the subsequent cell inconsistency diagnostic process needs to be initiated, thus generating a signal to trigger the diagnosis. If the difference comparison result shows a voltage difference equal to or less than the preset voltage difference threshold, it indicates that the voltage difference between the cells is within the normal allowable range, and there are no inconsistencies requiring urgent diagnosis. In this case, the diagnostic process does not need to be initiated, thus generating a signal not to trigger the diagnosis. The difference comparison results are judged one by one according to this judgment rule, and the specific signal type is determined based on the judgment result, ultimately yielding the cell's diagnostic trigger signal.

[0046] In summary, the voltage range of the battery cells is quantified to obtain the voltage difference degree. Specifically, the real-time voltage of all battery cells in the module is acquired synchronously by a voltage acquisition device, abnormal values ​​caused by line interference are eliminated, the difference between the highest and lowest voltage values ​​is selected to calculate the voltage range, the range ratio is determined with reference to the allowable fluctuation range of battery cell voltage, the difference level is divided according to the ratio, and finally converted into a measurable voltage difference degree.

[0047] In summary, the voltage difference is compared with a preset voltage difference threshold to obtain the difference comparison result. The preset threshold is set according to the module safety standard, cell rated parameters and long-term reliability data. The voltage difference is directly compared with the threshold to determine whether it is greater than, equal to or less than the threshold, and finally the difference comparison result is obtained.

[0048] In summary, the diagnostic trigger signal is obtained by judging the difference comparison results. The specific rules are as follows: if the voltage difference is greater than the threshold, the diagnosis needs to be initiated; if it is equal to or less than the threshold, the diagnosis does not need to be initiated. After judging according to the rules, a signal is generated to trigger or not trigger the diagnosis, and finally the diagnostic trigger signal of the battery cell is obtained.

[0049] S3. Based on the diagnostic trigger signal, perform capacity cross-validation on the state of charge and the health state to obtain the capacity inconsistency determination result of the cell; In this embodiment of the invention, the step of performing capacity cross-validation on the state of charge and the health state based on the diagnostic trigger signal to obtain the capacity inconsistency determination result of the battery cell includes: A continuous deviation trend analysis was performed on the state of charge to identify anomalies in the state of charge that were consistently higher than the group average. The health status of the abnormal state of charge set is traced by capacity degradation characteristics to obtain the capacity degradation identifier of the cell; The distribution characteristics of the capacity decay indicators are used to determine the capacity inconsistency, and the capacity inconsistency determination result of the battery cell is obtained.

[0050] Specifically, the state of charge (SOC) data of all cells in the battery module is collected periodically and continuously. The collection cycle is set according to the operating conditions of the battery module to ensure that the data can reflect the dynamic changes in the SOC of the cells. The SOC values ​​of all cells in each collection cycle are added together and then divided by the total number of cells to obtain the average SOC level of the group in that cycle. The SOC of each cell in multiple consecutive collection cycles is compared with the average group level of the corresponding cycle. If the SOC of a certain cell is higher than the average group level in consecutive collection cycles, the SOC data of that cell is extracted separately. The SOC data of all cells that meet the condition of "SOC continuously higher than the average group level" are integrated to form an abnormal SOC set.

[0051] Furthermore, from the previous multi-dimensional state fusion assessment results, the health status data corresponding to each cell in the abnormal state of charge set is extracted. The focus is on the capacity decay trend information contained in the health status. The capacity decay trend of these cells is compared with the capacity decay trend of other non-abnormal collector cells in the module. It is observed whether the capacity of abnormal collector cells shows a faster rate of decline, or whether its current actual capacity is significantly lower than the average actual capacity of non-abnormal collector cells. At the same time, it is verified whether the phenomenon of the abnormal collector cells having a continuously high state of charge is directly related to capacity decay. That is, due to capacity decay, the calculated state of charge value of the cell is more likely to be higher under the same charge and discharge capacity. For each cell that is confirmed to have a "continuously high state of charge accompanied by obvious capacity decay" relationship, it is marked as having capacity degradation characteristics. All such marks are integrated to obtain the capacity degradation identifier of the cell.

[0052] Furthermore, the distribution of capacity degradation indicators within the battery module is statistically analyzed, including the number of cells with capacity degradation indicators, their location within the module, and the degree of capacity degradation for each cell with an indicator. The capacity degradation degree of cells with capacity degradation indicators is compared with that of cells without the indicator within the module. If the capacity degradation degree of a cell with a capacity degradation indicator significantly exceeds that of a cell without the indicator, and this difference in degradation has led to a significant gap in charge storage capacity between cells, then the cell can be determined to have capacity inconsistency. Information on all cells determined to have capacity inconsistency is compiled, the degree of capacity inconsistency for each cell and the relevant basis are clarified, and finally, the capacity inconsistency determination result of the cells is obtained.

[0053] In summary, continuous deviation trend analysis of the state of charge (SOC) of battery cells is conducted to identify SOC anomalies. Specifically, SOC data of all cells are collected simultaneously during each stage of battery module charging and discharging and during the resting stage. Abnormal values ​​with sudden increases or decreases are eliminated. The effective data of each stage are added together and divided by the total number of cells to obtain the average SOC group level. The SOC of each cell in continuous stages is compared with the corresponding group average level. Data of cells that are continuously higher than the average level of the same period are integrated to finally form an SOC anomaly set with complete traceability information.

[0054] In summary, capacity degradation characteristics are traced to identify the health status of cells with abnormal states of charge (SOC) to obtain capacity decay indicators. Specifically, capacity-related information is extracted from the healthy state of cells in the abnormal SOC and compared with the same type of information from cells in the non-abnormal SOC to check whether there are cases of greater capacity decay, lower remaining rate and stable differences. This verifies the correlation between high SOC and capacity decay. Cells that simultaneously meet the criteria of continuously high SOC and more significant capacity decay are marked with capacity degradation characteristics. After integrating the marking information, the capacity decay indicator is obtained.

[0055] In summary, the distribution characteristics of capacity decay indicators are analyzed to determine inconsistencies in capacity, resulting in a capacity inconsistency assessment. Specifically, the distribution of capacity decay indicators within the module is statistically analyzed, and the capacity decay levels of the marked cells and non-abnormal collector cells are compared to determine the general range. If the capacity decay level of the marked cells exceeds the upper limit of the range and affects the module's charging and discharging efficiency, capacity inconsistency is determined in conjunction with safety regulations. The inconsistent cell numbers, levels, and bases are compiled to ultimately form the capacity inconsistency assessment result.

[0056] S4. Based on the diagnostic trigger signal, perform abnormal fluctuation analysis on the voltage change characteristics of the battery cell to obtain the determination result of the inconsistency of the internal resistance of the battery cell; In this embodiment of the invention, the step of performing abnormal fluctuation analysis on the voltage change characteristics of the battery cell based on the diagnostic trigger signal to obtain the internal resistance inconsistency determination result of the battery cell includes: The voltage response of each cell is dynamically monitored to obtain the transient voltage response characteristics of the cell. The voltage surge or voltage drop characteristics are analyzed to identify amplitude anomalies, thereby obtaining a voltage anomaly identifier set for the battery cell. The internal resistance correlation of the voltage change patterns of the voltage anomaly identifier set is verified to obtain the internal resistance correlation index of the battery cell. Based on the internal resistance correlation index, internal resistance abnormality and internal resistance failure diagnosis are performed to obtain the internal resistance inconsistency judgment result of the battery cell.

[0057] Specifically, when dynamically monitoring the voltage response of each cell, it is necessary to cover the typical operating conditions of the battery module, including scenarios such as charging / discharging mode switching, sudden changes in load current, and transitions between idle and operating states. A high-precision voltage acquisition unit is used to collect the voltage of each cell in real time, maintaining a fixed acquisition interval to ensure data continuity. Simultaneously, the operating state corresponding to each voltage acquisition is recorded, such as whether the cell is currently charging, discharging, or idle, and whether the load current has changed. After acquiring continuous voltage data, the voltage change characteristics at the moment of each operating condition transition and during stable operation are extracted, including the voltage rise rate, voltage fall rate, time to reach a stable voltage value, and duration of voltage fluctuations. These characteristics are then associated and organized according to the cell number and corresponding operating conditions to ultimately obtain the transient voltage response characteristics that reflect the voltage change patterns of each cell under different operating conditions.

[0058] Furthermore, when identifying voltage surge or voltage drop anomalies, the normal range of voltage surge and voltage drop amplitudes under different operating conditions is first determined based on the battery module's design standards and the cell's rated performance parameters. For example, during charge / discharge switching, the normal amplitude of the voltage surge must match the cell's nominal voltage variation range, and the normal amplitude of the voltage drop must not exceed the module's allowable instantaneous voltage fluctuation limit. The voltage surge and voltage drop amplitudes included in the previously obtained voltage transient response characteristics are compared one by one with their corresponding normal amplitude ranges. If a cell's voltage surge amplitude exceeds the upper limit of the normal range, or its voltage drop amplitude exceeds the lower limit of the normal range, the cell is marked as having a voltage amplitude anomaly. Simultaneously, information such as the operating condition where the anomaly occurred, the anomaly amplitude value, and the anomaly duration are recorded. All information related to cells marked as having voltage amplitude anomalies is then compiled to form a voltage anomaly identifier set containing complete anomaly traceability information.

[0059] Furthermore, when verifying the correlation between internal resistance and voltage change patterns in the voltage anomaly identifier set, the relationship between cell internal resistance and voltage change is first clarified. Under stable total circuit current changes, the larger the cell internal resistance, the greater the voltage response to current changes; that is, for the same current change, cells with higher internal resistance experience larger voltage jumps or drops. Based on this logic, the total circuit current data at the time of each anomaly in the voltage anomaly identifier set is extracted to confirm that the change in total circuit current remains stable during that period, excluding cases where voltage anomalies are caused by drastic current fluctuations. Then, the voltage change amplitude of the cell during the anomaly event is compared with the voltage change amplitude of other normal cells in the module under the same current change. If the voltage change amplitude of the cell is significantly greater than the average voltage change amplitude of normal cells, and this difference cannot be explained by other factors such as cell nominal capacity differences or usage time differences, then it is determined that the voltage anomaly is related to internal resistance changes. Based on the degree of correlation, such as the proportion of voltage changes exceeding the normal range, the correlation level is divided. The correlation level of each abnormal cell is combined with the corresponding abnormal information to obtain an internal resistance correlation index that can quantify the degree of correlation between voltage abnormality and internal resistance.

[0060] Furthermore, when diagnosing internal resistance anomaly failures based on internal resistance correlation indicators, a judgment threshold for the internal resistance correlation indicators is first set. For example, a strong correlation level indicates a suspected internal resistance anomaly, while an extremely strong correlation level indicates a high probability of internal resistance failure. The internal resistance correlation indicators for each cell are compared with the judgment thresholds to identify cells that are suspected of internal resistance anomalies or highly likely to fail. Then, the internal resistance change trend data obtained from the multi-dimensional state fusion evaluation of these cells is retrieved to verify whether their internal resistance shows a continuously increasing trend, excluding single-event internal resistance anomalies caused by transient interference. Simultaneously, the current internal resistance level of these cells is compared with the average internal resistance level of other cells in the module. If their internal resistance level exceeds the reasonable range of the average internal resistance level, and the difference is confirmed to be continuously formed based on the internal resistance change trend, then the cell is determined to have internal resistance inconsistency. The system compiles the cell number, degree of internal resistance abnormality (e.g., slight increase in internal resistance, moderate increase in internal resistance, severe internal resistance failure), and the basis for judgment, including the level of internal resistance correlation index, the trend of internal resistance change, and the difference from the average internal resistance, ultimately forming a complete result for determining the internal resistance inconsistency of the cells.

[0061] In summary, dynamic response monitoring of the voltage response of each cell is carried out to obtain the transient voltage response characteristics. Specifically, it covers typical operating conditions such as module charging and discharging switching, load current sudden change, and static and running transition. High-precision units collect voltage data of each cell at fixed intervals, synchronously record the operating conditions, extract the voltage rise rate, stabilization time and other characteristics under each operating condition, and after associating and organizing them with cell number and operating condition, the transient voltage response characteristics are obtained.

[0062] In general, voltage rise or fall characteristics are identified to obtain a voltage anomaly identifier set. Specifically, the normal range of voltage changes under different operating conditions is determined based on the module design standards and cell rated parameters. The rise and fall amplitudes in the voltage transient response characteristics are compared with the normal range. If they exceed the range, the voltage amplitude is marked as abnormal. The abnormal information is recorded and summarized to form a voltage anomaly identifier set.

[0063] In summary, the internal resistance correlation of voltage change patterns in the voltage anomaly identifier set is verified to obtain an internal resistance correlation index. Specifically, the logic that the larger the internal resistance, the greater the voltage response amplitude when the total circuit current is stable is clarified. Current data of abnormal events is extracted to confirm stability. The voltage change amplitude of abnormal cells and normal cells is compared. After excluding other factors, it is determined to be related to internal resistance. The correlation is classified according to the degree of tightness. The internal resistance correlation index is obtained by combining the anomaly information.

[0064] In summary, internal resistance correlation indicators are used to diagnose internal resistance anomalies and obtain internal resistance inconsistency judgment results. Specifically, indicator judgment thresholds are set to screen suspected cells, and their internal resistance change trend data are retrieved to verify whether they are continuously increasing. The current internal resistance is compared with the module average internal resistance. If it exceeds the reasonable range and the difference is continuous, internal resistance inconsistency is judged. The cell number, degree of abnormality and basis are compiled to form the judgment result.

[0065] S5. Based on the capacity inconsistency determination result and the internal resistance inconsistency determination result, perform balancing strategy decision on the battery cell to obtain the balancing mode instruction of the battery cell. In this embodiment of the invention, the step of making a balancing strategy decision on the battery cell based on the capacity inconsistency determination result and the internal resistance inconsistency determination result to obtain the balancing mode instruction of the battery cell includes: The failure mode classification is performed on the capacity inconsistency determination results and the internal resistance inconsistency determination results to obtain the fundamental failure identifier and temporary inconsistency identifier of the cell. Active balancing strategy matching is performed on the fundamental failure identifier to obtain the active balancing scheduling scheme of the battery cell; The temporary inconsistency identifier is matched with a passive balancing strategy to obtain the passive balancing scheduling scheme of the battery cell. The active balancing scheduling scheme and the passive balancing scheduling scheme are integrated into a system to obtain the balancing mode command for the battery cell.

[0066] Specifically, when classifying failure modes based on the results of capacity inconsistency and internal resistance inconsistency assessments, the criteria for defining fundamental failure indicators and temporary inconsistency indicators are first clarified. Fundamental failure indicators correspond to long-term irreversible cell performance degradation, while temporary inconsistency indicators correspond to short-term reversible cell state fluctuations. The degree and duration of capacity inconsistency for each cell are extracted from the capacity inconsistency assessment results. If the cell capacity decay exceeds the long-term deviation range allowed by the module's safe operation specifications, and this inconsistency persists across multiple charge-discharge cycles, it is considered to meet the characteristics of fundamental failure. If the capacity inconsistency is minor and only occurs briefly under specific operating conditions, disappearing after the operating conditions recover, it is considered to meet the characteristics of temporary inconsistency. The trend and stability of internal resistance changes for each cell are extracted from the internal resistance inconsistency assessment results. If the cell's internal resistance shows a continuous increasing trend and the current internal resistance level exceeds the reasonable range of the average internal resistance of healthy cells in the module, and this difference cannot be eliminated through short-term adjustments, it is considered to meet the characteristics of fundamental failure. If the internal resistance only increases briefly due to a sudden change in load, returning to the normal range after the load stabilizes, it is considered to meet the characteristics of temporary inconsistency. Based on the above determination, each cell is marked with a fundamental failure identifier or a temporary inconsistency identifier, thus obtaining the fundamental failure identifier and temporary inconsistency identifier of the cell.

[0067] Furthermore, when matching active balancing strategies for cells with fundamental failure indicators, the failure type and specific degradation degree of the cells with fundamental failure indicators are first analyzed. Failure types include capacity-related fundamental failure and internal resistance-related fundamental failure. For cells with capacity-related fundamental failure, the difference between their current actual capacity and the average capacity of healthy cells in the module is assessed to determine the total amount of energy that needs to be supplemented. For cells with internal resistance-related fundamental failure, the impact of increased internal resistance on energy transfer is assessed to determine the upper limit of power to be avoided during energy transfer. Based on the failure type and degradation degree, corresponding schemes are matched from a preset active balancing strategy library. Capacity-related fundamental failure cells are matched with an energy-directed transfer strategy, which clearly defines the energy transfer from healthy cells with sufficient energy in the module to the failed cell, determines the healthy cell number involved in the energy transfer, and selects the intermediate components for energy transfer. Internal resistance-related fundamental failure cells are matched with a slow energy transfer strategy, which sets a low energy transfer rate and is combined with a real-time temperature monitoring mechanism. The matched strategies are refined into specific execution parameters, including the energy transfer start threshold, transfer rate, and termination threshold, ultimately yielding the active balancing scheduling scheme for the cells.

[0068] Furthermore, when matching passive balancing strategies for cells with temporary inconsistency flags, the reasons for inconsistency and their current status are first analyzed. If the inconsistency stems from a short-term high state of charge (SOC) of the cell, the excess SOC difference is assessed to determine the excess energy that needs to be dissipated. If the inconsistency stems from temporary voltage fluctuations, the amplitude and duration of the voltage fluctuations are assessed to determine the duration of energy dissipation. Based on the cause and status of the inconsistency, a corresponding scheme is matched from a pre-defined passive balancing strategy library. Cells with a short-term high SOC are matched with a tiered resistor dissipation strategy, selecting resistors of the corresponding power level based on the excess SOC difference. Cells with temporary voltage fluctuations are matched with a time-limited resistor dissipation strategy, setting a fixed dissipation duration and a voltage monitoring point. The matched strategy is then converted into specific execution parameters, including the activated resistor path number, dissipation power level, start conditions, and stop conditions, ultimately yielding the passive balancing scheduling scheme for the cell.

[0069] Furthermore, when integrating the active and passive balancing scheduling schemes into a system, a compatibility check is first performed to check if the same cell is included in both active and passive balancing scheduling. If so, active balancing is prioritized, and the execution time of passive balancing is adjusted or the passive balancing of that cell is cancelled. The energy transfer path of active balancing and the resistance path of passive balancing are checked for hardware conflicts. If so, the active balancing path is replanned or the resistance path of passive balancing is adjusted. Then, the balancing execution logic is unified, clarifying the balancing priority of each cell within the module. Cells with a fundamental failure flag have higher priority than cells with a temporary inconsistency flag. The order of balancing initiation is clarified: active balancing is initiated first, followed by passive balancing. The collaborative monitoring indicators during the balancing process are also defined. Finally, the verified active and passive balancing scheduling parameters and collaborative execution logic are integrated into structured instructions. These instructions clearly define the balancing mode, specific operation steps, monitoring feedback requirements, and emergency stop conditions for each cell, ultimately yielding the cell's balancing mode instructions.

[0070] In summary, the failure mode classification of the results of capacity and internal resistance inconsistency determination is used to obtain fundamental failure indicators and temporary inconsistency indicators. Specifically, the criteria for fundamental failure corresponding to long-term irreversible degradation and temporary inconsistency corresponding to short-term reversible fluctuations are clarified. Data such as the degree of capacity inconsistency and internal resistance change trend of the cells are extracted, judged and marked according to characteristics, and finally two types of indicators are obtained.

[0071] In summary, an active balancing strategy is used to match the fundamental failure identifier to obtain an active balancing scheduling scheme. The failure type and degree of degradation are analyzed in detail, and the corresponding scheme is matched from the strategy library. The parameters such as the starting threshold, rate, and termination threshold of energy transfer are refined to finally obtain the active balancing scheduling scheme.

[0072] In summary, a passive balancing strategy is used to match temporary inconsistency identifiers to obtain a passive balancing scheduling scheme. The causes and states of inconsistency are analyzed in detail, and corresponding schemes are matched from the strategy library. The schemes are then transformed into parameters such as resistor path number, power dissipation level, and start / stop conditions, ultimately resulting in a passive balancing scheduling scheme.

[0073] In summary, the two equalization scheduling schemes are integrated into a system to obtain equalization mode instructions. Specifically, the scheme compatibility is first verified and conflicts are adjusted. Then, the equalization priority, start-up order and monitoring indicators are unified. The parameters and execution logic are integrated into structured instructions, the cell equalization mode, steps and emergency stop conditions are clarified, and finally the equalization mode instructions are obtained.

[0074] S6. The battery cell is balanced according to the balancing mode instruction.

[0075] In this embodiment of the invention, the step of performing a balancing operation on the battery cell according to the balancing mode instruction includes: By performing balanced parameter co-analysis on the fundamental failure identifier and the temporary inconsistency identifier, the transfer path planning and power dissipation level of the battery cell can be obtained; When the active balancing scheduling scheme exists, the switching matrix is ​​configured based on the cell's location data and the transfer path planning to obtain the cell's energy transfer path; Inductive energy storage is performed on the energy transfer path to achieve balanced operation of the battery cell; When the passive equalization scheduling scheme exists, the resistor path is activated based on the cell's location data and the power dissipation level to form an energy dissipation loop. Selective energy release is performed on the energy dissipation circuit to achieve balanced operation of the battery cell.

[0076] The step of performing balanced parameter collaborative analysis on the fundamental failure identifier and the temporary inconsistency identifier to obtain the cell's transfer path planning and power dissipation level includes: Energy distribution analysis is performed on the fundamental failure indicator to obtain the energy source of the battery cell; A comprehensive evaluation is performed on the diagnostic conclusion of the capacity consistency deviation and the diagnostic report of the internal resistance consistency deviation to obtain the energy receiving end of the battery cell; The relative positions of the energy source and the energy receiver are optimized to form the transfer path plan; A state-of-charge difference analysis is performed on the temporary inconsistency markers to obtain the degree of state-of-charge deviation of the battery cell; Based on the degree of state of charge deviation and the temperature parameters of the battery cell, power classification is performed to form the power dissipation level.

[0077] Specifically, when performing balanced parameter collaborative analysis on fundamental failure markers and temporary inconsistency markers, the core information corresponding to the two types of markers is first extracted separately: For fundamental failure markers, the focus is on analyzing their failure type and degree of energy imbalance. By comparing the energy difference between the cell and healthy cells in the module, the total amount and direction of energy transfer required are determined, and the physical location information of the cell within the module is recorded. For temporary inconsistency markers, the focus is on analyzing the core reasons for the inconsistency and the current state of charge deviation value. Combined with the real-time temperature data of the cell, the scale of excess energy that needs to be dissipated is determined. Subsequently, the analysis results of the two types of markers are collaboratively integrated: If cells with both types of markers exist simultaneously in the same module, the energy transfer needs of cells with fundamental failure markers are prioritized, and the dissipation timing of cells with temporary inconsistency markers is adjusted to avoid energy resource conflicts. Based on the energy transfer needs, the relative positions of the energy source and the energy receiver are determined, and a transfer path plan is formed through a path planning algorithm. Based on the excess energy scale and cell temperature parameters, the dissipation power level is determined according to a preset power grading standard, and finally, the cell's transfer path plan and dissipation power level are obtained.

[0078] Furthermore, when an active balancing scheduling scheme exists, the cell's location data is first acquired. This data comes from the cell's physical location code preset at the module's factory. Combined with the energy transmission direction and intermediate elements specified in the transfer path planning, the switch matrix is ​​configured. The switch matrix consists of multiple independent electronic switches, each corresponding to the connection path between the cell and the intermediate element. According to the transfer path planning, the on / off state of the corresponding switch is controlled one by one: the switch is closed for paths that need to be connected, and the switch is opened for paths that do not need to be connected, ensuring that energy is transmitted only along the planned path and avoiding short circuits or excessive energy loss. Through this configuration process, the energy transfer path of the cell is finally obtained.

[0079] Furthermore, when performing inductive energy storage operation on the energy transfer path, first confirm that the inductor in the energy transfer path is in an initial unstored state, and then start the energy transfer process: First, control the switch between the energy source cell and the inductor to close, so that the energy source cell releases energy to the inductor. The inductor converts electrical energy into magnetic energy for storage through the principle of electromagnetic induction. During this process, the current change of the inductor is monitored in real time. When the energy stored in the inductor reaches the preset total amount of transferred energy, the switch between the energy source cell and the inductor is opened. Next, control the switch between the inductor and the energy receiving end to close, and the inductor converts the stored magnetic energy back into electrical energy and releases energy to the fundamentally failed cell. At the same time, the state of charge change of the fundamentally failed cell is monitored in real time. When the difference between the state of charge of the cell and the state of charge of the healthy cell is reduced to a preset safe range, the switch between the inductor and the energy receiving end is opened to stop the energy transfer. Through this energy storage-release process, the cell's balancing operation is achieved.

[0080] Furthermore, when a passive equalization scheduling scheme exists, the specific location of the battery cell with the temporary inconsistency marker is first found based on the battery cell's positioning data. The corresponding resistor path interface for that battery cell is then determined, and a matching resistor element is selected based on the power dissipation level. Subsequently, the resistor path activation process is initiated: by closing the electronic switch in the control path, the battery cell with the temporary inconsistency marker and the selected resistor element form a closed loop. This loop only connects the target battery cell and the corresponding resistor and does not affect the normal operation of other battery cells in the module. Through this activation process, an energy dissipation loop is formed.

[0081] Furthermore, when selectively releasing energy in the energy dissipation circuit, a real-time monitoring unit is first connected to the energy dissipation circuit to continuously monitor the state of charge (SOC) of the battery cell with the temporary inconsistency marker and the temperature data of the resistive element. During energy release, the battery cell converts excess electrical energy into heat energy through the resistive path. The monitoring unit compares the current SOC of the battery cell with the average SOC of the healthy battery cells in the module in real time: when the SOC of the battery cell drops to match the average SOC, the switch in the resistive path is immediately opened to stop energy release; if the temperature of the resistive element exceeds the preset safety range, the switch will also be opened in advance, and restarted after the temperature drops to the safety range. Through this on-demand controlled energy release method, excess energy of the battery cell is precisely dissipated, achieving balanced operation of the battery cell.

[0082] Specifically, when performing energy distribution analysis on fundamental failure identifiers, the core information corresponding to the identifiers is first extracted to clarify the failure type of the associated cell, i.e., fundamental capacity failure or fundamental internal resistance failure. Then, real-time state of charge (SOC) and state of health (SOH) data of all cells within the battery module are collected. SOC reflects the current energy storage capacity of a cell, while SOH reflects its long-term performance stability. By comparing the SOC of each cell, cells with consistently high SOC and good SOH, showing no signs of performance degradation, are selected. These cells possess the ability to stably output energy and can replenish the energy of fundamentally failed cells. These selected cells are then identified as energy suppliers, ultimately yielding the energy source for the cells.

[0083] Furthermore, when comprehensively evaluating the diagnostic conclusions of capacity consistency deviation and the diagnostic reports of internal resistance consistency deviation, the specific degree of capacity decay, duration of decay, and impact on energy storage of each cell in the capacity consistency deviation diagnostic conclusion are first obtained. Then, the magnitude of the increase in internal resistance of each cell and the degree to which abnormal internal resistance hinders energy transfer efficiency are extracted from the internal resistance consistency deviation diagnostic report. Combining the failure type corresponding to the fundamental failure identifier, if it is a capacity-related fundamental failure, the focus is on the cell with the most severe capacity decay and the weakest energy storage capacity in the capacity consistency deviation; if it is an internal resistance-related fundamental failure, the focus is on the cell with the most significant increase in internal resistance and the difficulty in energy acquisition in the internal resistance consistency deviation. The energy demand of these cells is assessed to confirm that they need external energy supplementation to improve performance. These cells requiring supplemental energy are identified as energy receiving targets, ultimately yielding the energy receiving end of the cells.

[0084] Furthermore, when optimizing the path for the relative positions of the energy source and the energy receiver, the physical locations of the energy source and the energy receiver within the battery module are first determined, including the row and column arrangement of the cells within the module, the layout of the connection lines between the cells, and the resistance characteristics of the lines. Possible energy transmission paths are analyzed, with each path covering the connection nodes and lines from the energy source to the energy receiver. The transmission distance and expected energy loss for each path are calculated. Simultaneously, the heat dissipation distribution within the module is considered, avoiding paths through areas with weak heat dissipation to prevent localized overheating due to heat generation during transmission. From all possible paths, the path with the shortest transmission distance, lowest energy loss, and avoidance of heat dissipation risk areas is selected. The specific route of this path is determined, including the cell numbers, connection nodes, and line order along the way, ultimately forming the transfer path plan.

[0085] Furthermore, when performing state-of-charge (POC) difference analysis on temporary inconsistency markers, the cell number corresponding to the temporary inconsistency marker is first extracted. Real-time POC data for that cell under the current operating conditions is collected, ensuring the continuity and accuracy of data acquisition and eliminating abnormal data caused by transient interference. Simultaneously, real-time POC data for all healthy cells within the battery module is collected, and the average POC of these healthy cells is calculated by summing the POC values ​​of all healthy cells and dividing by the total number of healthy cells. The difference between the real-time POC of the temporarily inconsistent cell and the average POC of the healthy cells is calculated. If the POC of the cell with the temporary inconsistency marker is higher than the average level, the difference represents a positive deviation; if it is lower than the average level, the difference represents a negative deviation. This calculation clarifies the magnitude of the deviation, ultimately yielding the degree of POC deviation for the cell.

[0086] Furthermore, when performing power grading based on the degree of state of charge deviation and the cell's temperature parameters, a power grading standard is first preset. This standard divides the state of charge deviation into three levels: low, medium, and high. Simultaneously, the dissipation power corresponding to each level is set in conjunction with the temperature parameters: low dissipation power corresponds to a low deviation level when the cell temperature is below the safe temperature threshold; medium dissipation power corresponds to a medium deviation level when the temperature is within the safe range; and high dissipation power corresponds to a high deviation level when the temperature does not exceed the safe upper limit. If the temperature is close to the safe upper limit, even if it is in the high deviation level, it is adjusted to medium or low dissipation power to avoid overheating. Real-time temperature parameters of cells with temporary inconsistencies are collected, combined with the obtained state of charge deviation level, and compared with the preset power grading standard to determine the corresponding dissipation power level for that cell. For example, if the state of charge deviation is high but the temperature is normal, it is determined to be high dissipation power, ultimately forming the dissipation power level.

[0087] In summary, balanced parameter co-analysis is performed on fundamental failure identifiers and temporary inconsistency identifiers to obtain transfer path planning and dissipation power levels. Specifically, the core information of the two types of identifiers is extracted, energy and dissipation requirements are co-integrated, the optimal transmission path and reasonable power standard are determined, and finally the transfer path planning and dissipation power levels are obtained.

[0088] In summary, when an active balancing scheduling scheme exists, the energy transfer path is obtained by configuring the switch matrix based on the cell location data and the transfer path planning. Specifically, the physical location of the cell is determined, the transmission path nodes are identified, and the switching on and off is controlled to construct the path along the planned path, thus obtaining the energy transfer path.

[0089] In summary, inductive energy storage is performed on the energy transfer path to achieve cell balancing. Specifically, the energy source releases energy to the inductor through the path and stores it as magnetic energy. After reaching the target, the inductor releases energy to the receiving end to replenish the energy of the fundamentally failed cell and achieve balancing operation.

[0090] In general, when a passive equalization scheduling scheme exists, the resistor path is activated based on the cell location data and the power dissipation level to form an energy dissipation loop. Specifically, the resistor path interface is located, a matching resistor is selected, and the switch is closed to make the cell and the resistor form a closed loop, ultimately forming an energy dissipation loop.

[0091] In summary, selective energy release in the energy dissipation circuit is used to achieve cell balancing. Specifically, the state of charge and resistance temperature of the temporarily inconsistent cells are monitored. Once the target is met, the switch is disconnected to accurately dissipate excess energy and achieve balanced operation.

[0092] In summary, energy distribution analysis is performed on the fundamental failure identifier to obtain the energy source, the specific associated cell failure type is identified, the charge and health status of the module cells are collected, and cells with high charge and good health are selected as the energy source, thus obtaining the energy source.

[0093] In summary, a comprehensive evaluation of the capacity and internal resistance consistency deviation report is conducted to obtain the energy receiving end. Specifically, the impact of capacity decay and internal resistance increase is extracted, and cells requiring energy replenishment are screened based on failure type to determine the energy receiving end, thus obtaining the final energy receiving end.

[0094] In summary, the relative positions of the energy source and receiver are optimized to form a transfer path plan. The specific location and line layout are determined, the path loss is analyzed and weak heat dissipation areas are avoided, the optimal path is selected and the nodes along the way are identified, and finally the transfer path plan is formed.

[0095] In summary, a state of charge (SOC) difference analysis is performed on temporary inconsistencies to determine the degree of deviation. Specifically, the SOC of the labeled cells and healthy cells is collected, the average SOC of the healthy cells is calculated, the difference is calculated to clarify the deviation, and finally the degree of SOC deviation is obtained.

[0096] In summary, power dissipation levels are formed by classifying power based on the degree of state of charge deviation and cell temperature. The specific deviation range and corresponding power standard are preset, the real-time temperature of the cell is collected, the power level is determined by comparing with the standard, and finally the power dissipation level is formed.

[0097] like Figure 2 The diagram shown is a functional block diagram of a hybrid battery module balancing system based on cell health status identification provided in an embodiment of the present invention.

[0098] The hybrid battery module balancing system 100 based on cell health status recognition described in this invention can be installed in an electronic device. Depending on the functions implemented, the hybrid battery module balancing system 100 may include a multi-dimensional state fusion evaluation module 101, a range threshold discrimination and diagnosis triggering module 102, a cross-validation judgment module 103, a voltage characteristic anomaly analysis and judgment module 104, a balancing strategy decision and mode instruction generation module 105, and a balancing operation execution module 106. The module described in this invention can also be called a unit, which refers to a series of computer program segments that can be executed by the processor of an electronic device and can perform a fixed function, stored in the memory of the electronic device.

[0099] In this embodiment, the functions of each module / unit are as follows: The multi-dimensional state fusion evaluation module 101 is used to perform multi-dimensional state fusion evaluation on the battery cell based on the voltage, temperature and total circuit current of the battery cell in the battery module, so as to obtain the state of charge and health status of the battery cell. The range threshold discrimination and diagnostic trigger module 102 is used to perform threshold discrimination on the voltage range of the battery cell based on a preset voltage difference threshold, and obtain the diagnostic trigger signal of the battery cell. The cross-validation determination module 103 is used to perform capacity cross-validation on the state of charge and the health state based on the diagnostic trigger signal to obtain the capacity inconsistency determination result of the cell. The voltage characteristic anomaly analysis and judgment module 104 is used to perform abnormal fluctuation analysis on the voltage change characteristics of the battery cell based on the diagnostic trigger signal, and obtain the internal resistance inconsistency judgment result of the battery cell. The equalization strategy decision and mode instruction generation module 105 is used to make an equalization strategy decision for the battery cell based on the capacity inconsistency determination result and the internal resistance inconsistency determination result, and obtain the equalization mode instruction of the battery cell. The equalization operation execution module 106 is used to perform equalization operation on the battery cell according to the equalization mode instruction.

[0100] In the several embodiments provided by this invention, it should be understood that the disclosed methods and systems can be implemented in other ways. For example, the system embodiments described above are merely illustrative; for instance, the division of modules is only a logical functional division, and other division methods may be used in actual implementation.

[0101] The modules described as separate components may or may not be physically separate. The components shown as modules may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs.

[0102] Furthermore, the functional modules in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or in the form of hardware plus software functional modules.

[0103] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the present invention can be implemented in other specific forms without departing from the spirit or essential characteristics of the present invention.

[0104] This application embodiment can acquire and process relevant data based on artificial intelligence technology. Artificial intelligence is the theory, method, technology, and application system that uses digital computers or machines controlled by digital computers to simulate, extend, and expand human intelligence, perceive the environment, acquire knowledge, and use that knowledge to obtain optimal results.

[0105] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention.

Claims

1. A method for balancing hybrid battery modules based on cell health status identification, characterized in that, The method includes: S1. Based on the voltage, temperature and total circuit current of the cells in the battery module, perform a multi-dimensional state fusion evaluation of the cells to obtain the state of charge and health status of the cells; S2. Based on a preset voltage difference threshold, perform threshold discrimination on the voltage range of the battery cell to obtain the diagnostic trigger signal of the battery cell; S3. Based on the diagnostic trigger signal, perform capacity cross-validation on the state of charge and the health state to obtain the capacity inconsistency determination result of the cell; S4. Based on the diagnostic trigger signal, perform abnormal fluctuation analysis on the voltage change characteristics of the battery cell to obtain the determination result of the inconsistency of the internal resistance of the battery cell; S5. Based on the capacity inconsistency determination result and the internal resistance inconsistency determination result, perform balancing strategy decision on the battery cell to obtain the balancing mode instruction of the battery cell. S6. Perform balancing operation on the battery cell according to the balancing mode instruction.

2. The hybrid battery module equalization method based on cell health status identification as described in claim 1, characterized in that, The method involves performing a multi-dimensional state fusion evaluation of the battery cell based on its voltage, temperature, and total circuit current within the battery module, to obtain the cell's state of charge and health status, including: The total loop current of the cells in the battery module is accumulated over time to obtain the charge change of the cells; The voltage of the battery cell is extracted during a resting period to obtain the voltage reference parameters of the battery cell; Based on the safety operation specifications of the battery module, the historical deviation data between the voltage reference parameter and the voltage are statistically analyzed to obtain the voltage deviation threshold of the cell. Based on the voltage reference parameters and the voltage deviation threshold, the charge change is dynamically weighted to obtain the state of charge of the battery cell; By tracking the voltage, temperature, and total circuit current over a long period, the capacity decay trend and internal resistance change trend of the battery cell can be obtained. The health status of the battery cell is obtained by combining the capacity decay trend and the internal resistance change trend.

3. The hybrid battery module equalization method based on cell health status identification as described in claim 2, characterized in that, The state of charge of the battery cell is obtained by dynamically weighting the charge change based on the voltage reference parameter and the voltage deviation threshold. The formula for calculating the state of charge is as follows: ； In the formula, The state of charge, This refers to the initial state of charge of the battery cell. The change in charge is the amount of charge. The rated capacity parameter of the battery cell is... This is the estimated current voltage value of the battery cell. The voltage reference parameters are as follows. The voltage deviation threshold is defined as follows.

4. The hybrid battery module equalization method based on cell health status identification as described in claim 1, characterized in that, The step of threshold discrimination based on a preset voltage difference threshold to obtain a diagnostic trigger signal for the battery cell includes: The voltage range of the battery cell is quantified to obtain the voltage difference degree of the battery cell; Based on a preset voltage difference threshold, the voltage difference degree is compared to obtain the difference comparison result; The results of the difference comparison are used to perform a diagnostic trigger judgment to obtain the diagnostic trigger signal of the battery cell.

5. The hybrid battery module equalization method based on cell health status identification as described in claim 1, characterized in that, The step of performing capacity cross-validation on the state of charge and the health state based on the diagnostic trigger signal to obtain the capacity inconsistency determination result of the cell includes: A continuous deviation trend analysis was performed on the state of charge to identify anomalies in the state of charge that were consistently higher than the group average. The health status of the abnormal state of charge set is traced by capacity degradation characteristics to obtain the capacity degradation identifier of the cell; The distribution characteristics of the capacity decay indicators are used to determine the capacity inconsistency, and the capacity inconsistency determination result of the battery cell is obtained.

6. The hybrid battery module equalization method based on cell health status identification as described in claim 1, characterized in that, The step of performing abnormal fluctuation analysis on the voltage change characteristics of the battery cell based on the diagnostic trigger signal to obtain the determination result of the inconsistency of the internal resistance of the battery cell includes: The voltage response of each cell is dynamically monitored to obtain the transient voltage response characteristics of the cell. The voltage surge or voltage drop characteristics are analyzed to identify amplitude anomalies, thereby obtaining a voltage anomaly identifier set for the battery cell. The internal resistance correlation of the voltage change patterns of the voltage anomaly identifier set is verified to obtain the internal resistance correlation index of the battery cell. Based on the internal resistance correlation index, internal resistance abnormality and internal resistance failure diagnosis are performed to obtain the internal resistance inconsistency judgment result of the battery cell.

7. The hybrid battery module equalization method based on cell health status identification as described in claim 1, characterized in that, The step of making a balancing strategy decision for the battery cell based on the capacity inconsistency determination result and the internal resistance inconsistency determination result, and obtaining the balancing mode instruction for the battery cell, includes: The failure mode classification is performed on the capacity inconsistency determination results and the internal resistance inconsistency determination results to obtain the fundamental failure identifier and temporary inconsistency identifier of the cell. Active balancing strategy matching is performed on the fundamental failure identifier to obtain the active balancing scheduling scheme of the battery cell; The temporary inconsistency identifier is matched with a passive balancing strategy to obtain the passive balancing scheduling scheme of the battery cell. The active balancing scheduling scheme and the passive balancing scheduling scheme are integrated into a system to obtain the balancing mode command for the battery cell.

8. The hybrid battery module equalization method based on cell health status identification as described in claim 1, characterized in that, The step of balancing the battery cell according to the balancing mode command includes: By performing balanced parameter co-analysis on the fundamental failure identifier and the temporary inconsistency identifier, the transfer path planning and power dissipation level of the battery cell can be obtained; When the active balancing scheduling scheme exists, the switching matrix is ​​configured based on the cell's location data and the transfer path planning to obtain the cell's energy transfer path; Inductive energy storage is performed on the energy transfer path to achieve balanced operation of the battery cell; When the passive equalization scheduling scheme exists, the resistor path is activated based on the cell's location data and the power dissipation level to form an energy dissipation loop. Selective energy release is performed on the energy dissipation circuit to achieve balanced operation of the battery cell.

9. The hybrid battery module equalization method based on cell health status identification as described in claim 8, characterized in that, The step of performing balanced parameter collaborative analysis on the fundamental failure identifier and the temporary inconsistency identifier to obtain the cell's transfer path planning and power dissipation level includes: Energy distribution analysis is performed on the fundamental failure indicator to obtain the energy source of the battery cell; A comprehensive evaluation is performed on the diagnostic conclusion of the capacity consistency deviation and the diagnostic report of the internal resistance consistency deviation to obtain the energy receiving end of the battery cell; The relative positions of the energy source and the energy receiver are optimized to form the transfer path plan; A state-of-charge difference analysis is performed on the temporary inconsistency markers to obtain the degree of state-of-charge deviation of the battery cell; Based on the degree of state of charge deviation and the temperature parameters of the battery cell, power classification is performed to form the power dissipation level.

10. A hybrid battery module balancing system based on cell health status recognition, characterized in that, The system includes: The multi-dimensional state fusion evaluation module is used to perform multi-dimensional state fusion evaluation on the battery cell based on the voltage, temperature and total circuit current of the battery cell in the battery module, so as to obtain the state of charge and health status of the battery cell. The range threshold discrimination and diagnostic trigger module is used to perform threshold discrimination on the voltage range of the battery cell based on a preset voltage difference threshold, and obtain the diagnostic trigger signal of the battery cell. The cross-validation determination module is used to perform capacity cross-validation on the state of charge and the health state based on the diagnostic trigger signal to obtain the capacity inconsistency determination result of the cell. The voltage characteristic anomaly analysis and judgment module is used to perform abnormal fluctuation analysis on the voltage change characteristics of the battery cell based on the diagnostic trigger signal, and obtain the internal resistance inconsistency judgment result of the battery cell. The equalization strategy decision and mode instruction generation module is used to make an equalization strategy decision for the battery cell based on the capacity inconsistency determination result and the internal resistance inconsistency determination result, and obtain the equalization mode instruction of the battery cell. The balancing operation execution module is used to perform balancing operations on the battery cell according to the balancing mode instruction.

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