A method for analyzing an electric vehicle battery after sale

Abstract

The present application relates to the technical field of after-sales management, in particular to a method for analyzing electric vehicle battery after-sales, comprising the following steps: analyzing the temperature peak value of the electric vehicle battery monomer electric core and the synchronous charging and discharging current fluctuation, judging whether the temperature and current mutation are synchronous, screening the abnormal period, calculating the node offset proportion after normalizing the voltage and current curve, extracting the working condition characteristic amplitude, hierarchically screening the master control parameter group, and outputting the periodic processing suggestion information. The present application combines the node difference of periodic voltage and current trajectory to carry out distribution quantitative analysis, realizes the collection and deviation extraction of multiple working condition characteristics, screens the master control factor after parameter layering, outputs the targeted suggestion content for the periodic state adaptation, can improve the initiative of abnormal working condition identification and the comprehensiveness of periodic change trend analysis, realizes the detailed tracking of the fault evolution path and working condition distribution, forms the suggestion set suitable for the periodic working condition difference, and enhances the timeliness of maintenance response and the matching degree of suggestions.

Description

Technical Field

[0001] This invention relates to the field of after-sales management technology, and in particular to a method for after-sales analysis of electric vehicle batteries. Background Technology

[0002] After-sales management encompasses the entire product lifecycle after sale, including systematic management and services related to user feedback, product maintenance, fault handling, parts replacement, performance testing, data collection and analysis, and maintenance recommendations. This includes establishing a product traceability system, improving customer service response mechanisms, developing maintenance standards, enhancing problem response efficiency, optimizing service quality, and improving user experience. Traditional electric vehicle battery after-sales analysis methods involve analyzing the battery's actual operating conditions and health status during the electric vehicle's lifecycle, addressing issues such as performance degradation, malfunctions, and recycling testing. This analysis is achieved through manual inspection, battery voltage testing, current testing, charge / discharge performance testing, fault record registration, user feedback collection, and after-sales testing report completion. The analysis then provides recommendations for battery repair, replacement, or recycling.

[0003] Existing technologies mainly involve manual inspection, battery single-parameter testing, and operating condition registration. The inspection process is mostly static sampling and out-of-cycle recording, which makes it difficult to capture changes in operating conditions during dynamic evolution. Changes in key parameters are only noticed when anomalies increase. It is difficult to accurately characterize the fluctuations in operating conditions and the period of abnormal response within the cycle, resulting in hidden fault manifestations or unclear cycle trends. In scenarios where battery state boundaries are unclear, data collection is sparse, and abnormal intervals are not sensitive to identification, problems such as missed detection and delayed response are likely to occur, which in turn affect the rationality of collaborative diagnosis between parameters and reduce the pertinence and timeliness of maintenance recommendations. Summary of the Invention

[0004] The purpose of this invention is to address the shortcomings of existing technologies by proposing a method for after-sales analysis of electric vehicle batteries.

[0005] To achieve the above objectives, the present invention adopts the following technical solution: a method for after-sales analysis of electric vehicle batteries, comprising the following steps:

[0006] S1: Based on electric vehicle batteries, analyze the peak temperature of a single cell and the fluctuation range of the charging and discharging current during the same period, determine the relationship between temperature and current mutation at the time of data collection, identify the synchronous mutation period of parameters, and obtain the coordinated response period interval;

[0007] S2: Based on the coordinated response time interval, determine the operating data within the cycle, normalize the voltage curve and current trajectory, compare the node differences, count the continuous deviation nodes, and analyze them according to the distribution ratio to obtain the cycle trajectory offset ratio.

[0008] S3: Based on the periodic trajectory offset ratio, filter the periodic operating conditions, analyze the cell voltage fluctuation in the interval, calculate the maximum deviation, determine the SOC fluctuation, identify the maximum discharge segment, and obtain the characteristic amplitude of the operating condition difference.

[0009] S4: Based on the magnitude of the difference characteristics of the working conditions, compare the impact of each working condition parameter on the maintenance recommendations, sort and filter the parameter groups with the highest impact, determine the first-level parameters in layers, count the number of first-level parameters, and obtain the number of main control parameter groups.

[0010] S5: Based on the number of main control parameter groups, retrieve the corresponding maintenance suggestion actions, analyze the relationship between the periodic operating conditions and the suggested actions, combine the actual operating condition parameters to construct the periodic processing content, and obtain periodic processing suggestion information.

[0011] The present invention is improved in that the coordinated response time period includes a start time, an end time, and an anomaly identifier; the periodic trajectory offset ratio includes the offset node ratio, periodic coverage, and anomaly distribution characteristics; the working condition difference characteristic amplitude includes amplitude distribution type, maximum deviation location, and anomaly frequency; the number of main control parameter groups includes parameter name, hierarchical level, and priority order; and the periodic processing suggestion information includes suggestion type, operation points, and applicable conditions.

[0012] The present invention is improved in that the step of obtaining the coordinated response time interval is specifically as follows:

[0013] S111: Based on electric vehicle batteries, analyze the temperature and current fluctuation data of individual cells at each collection point. For the temperature peak and charging / discharging current fluctuation amplitude at each moment, determine whether their sudden changes are consistent. By comparing the synchronous change trends of temperature and current fluctuations, obtain the joint sequence of temperature and current fluctuations.

[0014] S112: Based on the joint sequence of temperature and current fluctuations, determine whether the change direction of the temperature peak and the current fluctuation amplitude at the acquisition time point is consistent. If there is consistency, filter out the synchronous mutation time period that meets the conditions to obtain the mutation consistency time period interval.

[0015] S113: Based on the mutation consistency time interval, combined with complete collected data, analyze the start and end times of each synchronous mutation time interval, and label them to obtain the collaborative response time interval.

[0016] The present invention is improved in that the step of obtaining the periodic trajectory offset ratio is specifically as follows:

[0017] S211: Based on the coordinated response time interval, extract the data nodes of the voltage curve within the period, collect each sampling node of the current trajectory, synchronize the original sequence data from each source to the same period dimension, divide and normalize the nodes according to a uniform step size, unify the number of nodes and reassemble the time series data to obtain a normalized node sequence.

[0018] S212: Based on the normalized node sequence, identify the voltage and current data of each normalized node in sequence, compare the offset state of the node with each node of the standard baseline curve point by point, record the continuous changes of adjacent nodes in the voltage and current dimensions in a consistent manner, and obtain the node offset dataset.

[0019] S213: Based on the node offset dataset, analyze the distribution density of continuous offset segments on the periodic normalized timeline, aggregate the proportion of segments on the timeline with the node coverage, record the continuous coverage range and distribution location of each segment, and obtain the periodic trajectory offset proportion.

[0020] The present invention is improved in that the step of obtaining the characteristic amplitude of the working condition difference is specifically as follows:

[0021] S311: Based on the periodic trajectory offset ratio, calculate the voltage difference of a single cell in the operating cycle, compare the voltage differences of all nodes and determine the node with the largest difference, and then combine the trajectory offset ratio to complete the induction and obtain the maximum voltage deviation.

[0022] S312: Based on the maximum voltage deviation, determine the fluctuation amplitude of the SOC curve, combine the discharge interval node sequence of the operating cycle, screen continuous discharge segments, and perform correlation comparison based on the maximum deviation to obtain the number of discharge duration segments.

[0023] S313: Based on the number of discharge duration segments, calculate the voltage amplitude mapped by the SOC curve fluctuation and the voltage offset mapped by the temperature active range to obtain the characteristic amplitude of the operating condition difference.

[0024] The present invention is improved in that the step of obtaining the number of master control parameter groups is specifically as follows:

[0025] S411: Based on the amplitude of the difference characteristics of the working conditions, analyze the amplitude distribution type, the location of the maximum deviation and the frequency of abnormality, identify the synchronous changes of parameter items within the cycle, determine which parameters have relatively concentrated synchronous fluctuations in the key working condition segment, screen the parameters with correlation, and obtain the working condition parameter coverage.

[0026] S412: Based on the coverage of the operating condition parameters, compare the coverage segments of each parameter in the operating condition trajectory with the synchronously occurring segments, calculate the coverage frequency of each parameter in the different operating condition segments, determine the set of parameters whose coverage frequency is greater than the preset coverage threshold, and obtain the number of period priority parameter groups.

[0027] S413: Based on the number of the period priority parameter groups, filter the parameter sets that are ranked first, analyze the synchronous fluctuation performance of each parameter in the abnormal segment, optimize the parameter stratification with continuous synchronous performance and change amplitude exceeding the preset fluctuation threshold, and count the number of parameter items at the top of the stratification to obtain the number of master control parameter groups.

[0028] The present invention is improved in that the step of obtaining the periodic processing suggestion information is specifically as follows:

[0029] S511: Based on the number of main control parameter groups, classify the parameter names, hierarchical levels and priority order, retrieve the corresponding maintenance suggestion actions for each parameter group, filter the mapping relationship between parameters and suggestion actions, determine the combination structure of suggestion actions, and obtain suggestion action matching information.

[0030] S512: Based on the suggested action matching information, analyze the applicable range of the actual working condition parameters and suggested actions within the cycle, compare the degree of fit between the parameter status and the suggested action adaptation conditions, adjust the suggested action screening strategy, and determine the applicability of each suggested action in the current cycle to obtain suggested action adaptation data.

[0031] S513: Based on the suggested action adaptation data, analyze the archiving relationship between suggested actions and actual working condition parameters, optimize the collection method of suggested actions, filter the operation points and suggestion types of suggested actions with an adaptation score higher than the adaptation threshold, and obtain periodic processing suggestion information.

[0032] The present invention is improved in that the temperature peak refers to the highest temperature reading reached by a single cell at each collection time within a certain cycle, the operating condition data within the cycle refers to the full collection data of various operating parameters of the battery within a defined cycle, and the interval analysis refers to the statistical comparison of the voltage change range of a single cell to identify its fluctuation range within a cycle.

[0033] Compared with the prior art, the advantages and positive effects of the present invention are as follows:

[0034] In this invention, by dynamically detecting the synchronous changes of key operating parameters, abnormal operating condition sections are determined by utilizing the abrupt change relationship between cell temperature and current signals. Distribution quantification analysis is performed by combining the node differences of periodic voltage and current trajectories, realizing the aggregation and deviation extraction of multiple sets of operating condition characteristics. After stratifying the parameters, the main control factors are screened, and targeted suggestions are output for periodic state adaptation. This can improve the initiative of abnormal operating condition identification and the comprehensiveness of periodic change trend analysis, realize detailed tracking of fault evolution paths and operating condition distribution, form a suggestion set suitable for periodic operating condition differences, enhance the timeliness of maintenance response and the matching degree of suggestions, and construct a data-driven operating condition feature classification and suggestion generation logic. Attached Figure Description

[0035] Figure 1 This is a flowchart of the main steps of the present invention;

[0036] Figure 2 This is a flowchart of the process for obtaining the collaborative response time interval in this invention;

[0037] Figure 3 This is a flowchart illustrating the process of obtaining the periodic trajectory offset ratio in this invention.

[0038] Figure 4 This is a flowchart illustrating the acquisition of the amplitude of the working condition difference characteristics in this invention.

[0039] Figure 5 This is a flowchart illustrating the process of obtaining the number of main control parameter groups in this invention.

[0040] Figure 6 This is a flowchart illustrating the process of obtaining periodic processing suggestion information in this invention. Detailed Implementation

[0041] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0042] In the description of this invention, it should be understood that the terms "length," "width," "upper," "lower," "front," "rear," "left," "right," "vertical," "horizontal," "top," "bottom," "inner," and "outer," etc., indicating orientation or positional relationships, are based on the orientation or positional relationships shown in the accompanying drawings and are only for the convenience of describing the invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation, and therefore should not be construed as a limitation of the invention. Furthermore, in the description of this invention, "a plurality of" means two or more, unless otherwise explicitly specified.

[0043] Example

[0044] Please see Figure 1 This invention provides a technical solution: a method for after-sales analysis of electric vehicle batteries, comprising the following steps:

[0045] S1: Based on electric vehicle batteries, analyze the temperature peak of a single cell and the fluctuation range of the charging and discharging current of the cell during the same period. Determine whether the change of the temperature peak at each collection point is consistent with the sudden change of the current fluctuation. By simultaneously checking the temperature data and current data, filter the time segments of the two parameters that change simultaneously to obtain the coordinated response time interval.

[0046] S2: Based on the coordinated response time interval, judge the operating data within the cycle, unify the voltage curve and current trajectory in the cycle according to the normalized node length, calculate the data point difference between each group of nodes and the standard baseline curve, count the total number of nodes that continuously deviate, and perform a weight analysis through the node distribution ratio to obtain the cycle trajectory offset ratio.

[0047] S3: Based on the proportion of periodic trajectory offset, filter periodic operating conditions, perform interval analysis on the voltage fluctuation of individual cells, calculate the maximum voltage deviation between cells, determine the fluctuation amplitude of the SOC curve within the same cycle, and determine the number of charge and discharge cycles and the temperature active range within the cycle by identifying the number of continuous segments of the maximum discharge range, thereby obtaining the characteristic amplitude of operating condition differences.

[0048] S4: Based on the magnitude of the difference in operating conditions, compare the impact of each operating condition parameter on the maintenance recommendations. By sorting the degree of influence of each parameter, filter the parameter groups with the highest influence. Determine the parameters belonging to the first layer according to the hierarchical structure of influence. Then count the actual number of parameters in the first layer to obtain the number of main control parameter groups.

[0049] S5: Based on the number of main control parameter groups, retrieve the maintenance suggestion actions for the corresponding parameters, analyze the correspondence between the periodic operating conditions and the suggested actions, combine each suggested action with the actual operating condition parameters, construct the processing content for the corresponding period, and obtain the periodic processing suggestion information.

[0050] The coordinated response time interval includes the start time, end time, and anomaly identifier; the periodic trajectory offset percentage includes the offset node ratio, periodic coverage, and anomaly distribution characteristics; the operating condition difference characteristic amplitude includes the amplitude distribution type, maximum deviation location, and anomaly frequency; the number of main control parameter groups includes the parameter name, hierarchical level, and priority order; and the periodic processing suggestion information includes the suggestion type, operation points, and applicable conditions.

[0051] In S1, peak temperature refers to the highest temperature reading reached by a single cell at each sampling time within a certain cycle, reflecting the thermal limit performance of the cell under specific operating conditions; charge / discharge current fluctuation amplitude refers to the absolute magnitude of current change per unit time during charging and discharging, reflecting the dynamic change level of the cell's load and energy release; sampling point refers to the specific time point recorded when the data acquisition system monitors the cell status in real time, usually each recording time under the sampling frequency setting; consistent sudden action means that at the same sampling point, both the peak temperature and current fluctuation amplitude show significant increases or decreases, and the direction and timing of their changes are consistent; synchronous verification refers to comparing and cross-validating the change curves of the peak temperature and the charge / discharge current fluctuation amplitude at the same sampling point to find cases where both parameters fluctuate abnormally at the same time; synchronous sudden change time segment refers to the data time segment in which both temperature and current parameters show synchronous and sudden changes, and the segment is extracted for subsequent anomaly analysis.

[0052] In S2, the operating condition data within a cycle refers to the full collection of data on various battery operating parameters within a defined cycle (such as a single complete charge-discharge cycle), including voltage, current, temperature, and SOC. Normalized node length refers to the standardization of all curves according to a uniform time interval and number of data points to ensure fair comparisons between different cycles or data groups. The standard baseline curve refers to the ideal voltage-current change curve under battery health conditions constructed based on empirical samples, used as a reference for comparing curve offsets across cycles. Data point difference calculation refers to the calculation of the difference (such as absolute difference or offset) between the measured voltage / current value at each node (at the same normalized acquisition point) and the standard baseline value. Nodes with continuous deviations refer to nodes in a data sequence where multiple adjacent nodes show a significant offset in the same direction from the baseline curve; these nodes are statistically considered as continuously deviating nodes. Node distribution ratio refers to the ratio of the number of continuously deviating nodes to the total number of data nodes in the current cycle, used to quantify the severity of cycle anomalies. Weighting analysis refers to the analysis of statistical results such as node distribution ratios to distinguish the impact range and intensity of offset anomalies in different cycles.

[0053] In S3, interval analysis refers to the statistical comparison of the voltage variation range of individual cells to identify their fluctuation range (maximum and minimum values, distribution characteristics, etc.) within a cycle; maximum deviation refers to the maximum voltage difference between all individual cells at the same moment, reflecting the consistency problem between cells and is an important indicator for judging the balance of battery performance; fluctuation amplitude within the same cycle refers to the maximum change from the beginning to the end of the cycle in the SOC (state of charge) or other operating condition parameter curves, used to measure the battery energy utilization and degradation trend; the number of continuous segments of the maximum discharge interval refers to the number of time periods during which an individual cell is continuously in a high discharge state during the discharge process, used to reveal the cell's load capacity and stress concentration; the temperature active interval refers to the time interval within the cycle where the cell temperature is at a drastic fluctuation or a high level, reflecting the thermal management pressure and abnormal distribution characteristics.

[0054] In S4, each operating condition parameter refers to a single parameter collected by the battery management system, such as cell voltage, SOC, maximum discharge current, cycle count, and temperature range. Maintenance recommendations refer to battery maintenance or upkeep suggestions corresponding to abnormal states of different parameters, such as replacement, repair, in-depth testing, or usage restriction warnings. The parameter group with the highest impact refers to the group of operating condition parameters that ranks first and has the greatest impact after ranking the correlation between each parameter and the maintenance recommendations. The hierarchical structure of impact refers to the hierarchical structure divided according to the importance of each parameter to the decision, with the first level representing the set of parameters that contributes the most to the final recommendation. The first-level parameters refer to the parameters that belong to the first level of the hierarchical structure of impact in the ranking results and are the most valuable for reference in maintenance decisions.

[0055] In S5, maintenance recommendation actions refer to the operational actions proposed in response to specific parameter anomalies detected, such as adjusting charging and discharging strategies or recommending battery replacement; correspondence refers to the logical or rule-based mapping between various operating parameters within a cycle and maintenance recommendation actions, with each parameter anomaly corresponding to a specific maintenance recommendation; actual operating parameters refer to the actual operating data parameters monitored within the current cycle of the battery, serving as the direct basis for generating maintenance recommendations.

[0056] Please see Figure 2 The specific steps for obtaining the coordinated response time interval are as follows:

[0057] S111: Based on electric vehicle batteries, analyze the temperature and current fluctuation data of individual cells at each acquisition time point, determine whether the sudden changes in temperature peak and charging / discharging current fluctuation amplitude at each moment are consistent, and obtain the joint sequence of temperature and current fluctuations by comparing the synchronous change trends of temperature and current fluctuations.

[0058] First, the battery cell temperature and current fluctuation data at each time point are extracted from the vehicle data acquisition system. For a sampling frequency of one second or five seconds, the current cell temperature reading at the current moment and the current difference between the current moment and the previous moment are read from the historical records to identify temperature peaks and current fluctuation amplitudes. Within a set 60-second window, the current temperature value is compared with all data within the window to determine if it is the highest value within that window, thus confirming the temperature peak. Simultaneously, the current value at the current moment and the current value at the previous moment are extracted, and their absolute difference is calculated to determine the degree of current fluctuation. This difference is compared with a sudden change threshold set based on sample statistical results. If the difference exceeds the threshold, it indicates a sudden change in current fluctuation. Based on this, it is further determined whether the current temperature value is in the same direction as the temperature change at the previous moment. The direction of change is consistent with that of the current, i.e., whether the temperature and current rise or fall simultaneously at the same moment. For example, at the 120th second, the temperature rises from 44.7 degrees to 45.2 degrees, and the current rises from 26.2 amps to 30.0 amps. Both of these changes are in the positive direction, satisfying the condition of consistent trend. At the same time, the current fluctuation is 3.8 amps, which exceeds the threshold of 3.5 amps obtained from previous sample statistics. Therefore, it can be determined that this point constitutes an effective synchronous change in temperature and current. This result is recorded as an event point. The same operation is continued for subsequent time points to construct a series of time point sequences that simultaneously satisfy the conditions of change threshold and consistency of change direction. The time of occurrence, cell temperature, current difference, and other attributes of each event are recorded to form a joint sequence of temperature and current fluctuations.

[0059] S112: Based on the joint sequence of temperature and current fluctuations, determine whether the change direction of the temperature peak and the current fluctuation amplitude at the acquisition time point is consistent. If there is consistency, screen out the synchronous mutation time period that meets the conditions to obtain the mutation consistency time period interval.

[0060] After obtaining the complete joint sequence of temperature and current fluctuations, it is necessary to further identify time periods with consistent directions of change. This is done by extracting the temperature and current change directions at each event point from the joint sequence and determining whether they are consistent (i.e., all changes at consecutive time points are either positive or negative), and the current change amplitude at each event point exceeds the abrupt change threshold set in the sample statistics. For continuous time periods that meet these conditions, their start and end times are extracted as preliminary synchronous abrupt change periods. To prevent the actual abrupt change periods from being fragmented due to slight interruptions in data sampling, adjacent two... The time interval between mutation periods is judged. If the interval between two segments is less than the maximum allowable interval of 30 seconds, they are regarded as the same continuous mutation area and are merged. For example, if the first segment is from 100 seconds to 105 seconds and the second segment is from 120 seconds to 124 seconds, the interval between the two segments is 15 seconds, which is less than the set maximum interval of 30 seconds. Therefore, the two segments are merged into a complete synchronous mutation period from 100 seconds to 124 seconds. This process is repeated to filter out all time period segments that meet the requirements. Each segment meets the preset rules in terms of time continuity, directional consistency, and mutation amplitude consistency. All identified mutation consistency time period intervals are summarized.

[0061] S113: Based on the mutation consistency time interval, combined with complete collected data, analyze the start and end times of each synchronous mutation time interval, and label them to obtain the collaborative response time interval;

[0062] After identifying all mutation consistency periods, alignment and labeling are required using the original full-volume collected data. This is done by locating the start and end times of each mutation consistency interval from the original data records, extracting all original sampling point data within that time interval, adding an anomaly flag field to each data record, and explicitly marking the time points as points where synchronous mutations occur. Simultaneously, a flag bit is added to each original record to indicate whether it is in an abnormal state; if it is in a synchronous mutation interval, it is set to 1, and if not, it is set to 0, thus achieving a structured data labeling process. For example, if a mutation consistency interval from 120 seconds to 130 seconds has been identified, a flag bit of 1 is added to each record in these 11 sampling points, and a field is added to record the mutation type. This ensures that each record of the entire collected data segment has a structured attribute indicating whether it belongs to an abnormal mutation. This process is repeated for multiple time periods to complete the precise alignment and complete labeling of the original data and mutation periods, forming a well-structured and complete collaborative response time interval.

[0063] Please see Figure 3 The specific steps for obtaining the percentage of periodic trajectory offset are as follows:

[0064] S211: Based on the coordinated response time interval, extract the data nodes of the voltage curve within the cycle, collect each sampling node of the current trajectory, synchronize the original sequence data from each source to the same cycle dimension, divide and normalize the nodes according to a uniform step size, unify the number of nodes and reassemble the time series data to obtain a normalized node sequence.

[0065] First, the start and end sampling times within the cycle are located, and all voltage and current sampling data for the corresponding time period are extracted from the original data records. The voltage data processing involves sequentially assigning the voltage readings at each time point to the voltage curve nodes of the cycle, ensuring uniform time intervals consistent with the sampling frequency. For example, if the cycle length is 600 seconds and the sampling frequency is 1Hz, then it should contain 600 voltage nodes. The current trajectory needs to be processed synchronously; the current values ​​for the same cycle are extracted from the complete current sampling sequence. A unified dimension synchronization operation is performed on these two sets of original sampling node data, that is, mapping the voltage and current sampling data to the same time line within the same cycle. Next, a unified node step size is determined, for example, set to the cycle length divided by a fixed number of nodes. If the data is normalized to 100 nodes, each node represents a 6-second interval. Within this interval, a data aggregation operation is performed, aggregating multiple sampling points within each 6-second interval into a representative value. This node value can be set by taking the interval average or median. The same segmentation and aggregation operations are then performed on the voltage and current data respectively, ensuring that the total number of nodes for both is equal and the time is aligned. Then, the normalized voltage and current sequences are recombined and arranged in ascending order of time to form a complete normalized node sequence. In a specific example, if 1200 points are obtained from periodic sampling, they can be compressed into 100 normalized nodes. Each node's voltage and current are aggregation results, and are accompanied by a corresponding time period label, resulting in a normalized node sequence with a uniform step size, consistent number of nodes, and time-series attributes.

[0066] S212: Based on the normalized node sequence, the voltage and current data of each normalized node are identified sequentially. The offset state of the node is compared with each node of the standard baseline curve point by point. The continuous changes of adjacent nodes in the voltage and current dimensions are recorded to obtain the node offset dataset.

[0067] The voltage and current values ​​at each node are read one by one and compared point by point with the data at the same node position in the standard baseline. In actual execution, the standard baseline is generated and normalized by healthy batteries under the same operating conditions and serves as a comparison reference. The comparison operation uses point-to-point absolute difference judgment to extract the voltage and current offset of each node and determine whether the offset exceeds the maximum allowable offset range set by the baseline. The range can be set to a fixed value through the fluctuation statistics of historical health data. For example, the voltage offset tolerance is set to 0.05 volts and the current offset tolerance is set to 1.5 amperes. If the offset of a node exceeds the tolerance, the node is marked as an offset node, and the offset trend is further judged to see if it is in the adjacent nodes. To maintain a consistent trend, i.e., both voltage and current continuously increase or continuously decrease, after identifying two or more nodes that satisfy this trend, it is confirmed that there is a continuous change segment. Based on this, the position, length, and offset direction of the continuous offset segment are recorded and included as part of the node offset data. For example, in nodes 10 to 18, if the voltage slowly rises from 3.68V to 3.74V and the current rises from 22.1A to 26.3A, and the offset of each node exceeds the tolerance and fluctuates in the same direction, then these 9 nodes are confirmed to constitute a continuous offset segment with a consistent trend and are included in the node offset dataset. This process is repeated until all node positions are traversed to form a complete node offset dataset.

[0068] S213: Based on the node offset dataset, analyze the distribution density of continuous offset segments on the periodic normalized timeline, aggregate the proportion of segments on the timeline with the node coverage, record the continuous coverage range and distribution location of each segment, and obtain the periodic trajectory offset proportion.

[0069] A continuous offset segment refers to a series of intervals formed by connecting nodes that maintain a consistent offset trend in the node offset dataset by judging the offset trend (direction, continuity of change) of adjacent nodes. In other words, it is to merge adjacent nodes with the same offset trend in the node offset dataset into segments.

[0070] Based on the continuous offset segments in the node offset dataset, their start and end positions on the normalized timeline are recorded to determine their distribution density. This operation first extracts the index positions of the start and end points of each offset segment in the normalized sequence, calculating the proportion of the segment length to the total number of normalized nodes. For example, if the normalized sequence has 100 nodes, and an offset segment covers nodes 30 to 45, its time coverage rate is 16%. The coverage rates of all offset segments are then summed to obtain the overall time percentage. Finally, the distribution of all offset segments on the timeline is statistically analyzed. This can be achieved by dividing the sequence into multiple time blocks, such as five equal segments of 20 nodes each. The proportion of offset segments in each segment is statistically analyzed to reflect the distribution trend of the offset. If the offset segments are mainly concentrated in the early and late stages of the cycle, it indicates that the distribution is skewed. If they are evenly distributed in all segments, the distribution density is balanced. In addition, it is necessary to record whether there is overlap, discontinuity or clustering between offset segments, and describe the specific behavior pattern of the offset on the time line accordingly. For example, if a total of 4 offset segments are identified, located at nodes 10-18, 25-30, 58-65 and 80-92 respectively, with coverage ratios of 9%, 6%, 8% and 13% respectively, and a cumulative coverage ratio of 36%, distributed in four time segments, then complete information on the proportion of periodic trajectory offset can be constructed.

[0071] Please see Figure 4 The specific steps for obtaining the amplitude of the operating condition difference characteristics are as follows:

[0072] S311: Based on the proportion of periodic trajectory offset, calculate the voltage difference of a single cell in the operating cycle, compare the voltage differences of all nodes and determine the node with the largest difference, and then combine the proportion of trajectory offset to complete the induction and obtain the maximum voltage deviation.

[0073] The voltage values ​​of all individual cells at each sampling node within the period are extracted, and a voltage matrix arranged in time series is constructed. Each row in the voltage matrix represents a time point, and each column represents a cell. For each time point, the voltage values ​​of all cells in that row are retrieved, and the maximum difference between individual cells at that time is calculated. This difference is the voltage difference at that time point. Subsequently, the voltage differences at all time points are traversed throughout the entire period, and the values ​​at each time point are compared. The time node with the largest voltage difference is found and its position is recorded as the region with the most concentrated voltage difference. For example, at the 240th second of the period, the voltage of cell 1 is 3.68V, cell 2 is 3.71V, cell 3 is 3.73V, and the voltages of the remaining cells are between 3.69V and 3.73V. If the voltage is between 0.70V and 0.70V, the maximum voltage difference at that point is 3.73V minus 3.68V, which equals 0.05V. This value is compared with the voltage differences of other nodes in the cycle. If it is the maximum value, then that node is the target node. Then, while analyzing the maximum voltage deviation, the period of the cycle trajectory offset calculated in the previous step is combined with the period of the maximum voltage difference. The time period with the larger offset is compared with the time period of the maximum voltage difference. If the two occur in similar time periods, it is determined that the maximum voltage deviation and the trajectory offset highly overlap. The overlap relationship is further summarized and organized, and the time point of the maximum voltage deviation, cell number, corresponding voltage value, and the number and percentage of covered nodes in the overlapping offset interval are recorded to form a complete record, thus obtaining the maximum voltage deviation.

[0074] S312: Based on the maximum voltage deviation, determine the fluctuation amplitude of the SOC curve, combine the discharge interval node sequence of the operating cycle, screen continuous discharge segments, and perform correlation comparison based on the maximum deviation to obtain the number of discharge duration segments.

[0075] Read the SOC curve data adjacent to the occurrence node and calculate the fluctuation amplitude of SOC within the cycle. During the operation, first extract the start and end values ​​of the SOC curve and calculate the difference between them. Then analyze the overall trajectory of the curve within the cycle to determine if there are multiple fluctuation intervals. Based on the curvature of the SOC change, determine whether the discharge process is continuous. If the curve continuously decreases within a certain length range and the slope value is within a stable range, then this time period is identified as a discharge segment. Based on this, read the node sequence of the discharge interval within the cycle, extract each node segment marked as a discharge state, and filter out segments whose continuous decrease time exceeds a set threshold. For example, if the continuous decrease duration is set to exceed 30 seconds, then if a certain discharge state... If the voltage lasts for 40 seconds, it is included in the valid segment; otherwise, if it only lasts for 15 seconds, it is not counted in the statistics. Then, the obtained maximum voltage deviation value is called, and the voltage difference is extracted from the middle or tail nodes of each continuous discharge segment. The value is compared with the maximum deviation value to determine whether the deviation shows a synchronous fluctuation trend in the current discharge segment. If the voltage difference value in a certain discharge segment is continuously close to or exceeds 80% of the reference value of the maximum deviation value, it is determined that the discharge segment is significantly correlated with the deviation value and is included in the number of valid discharge duration segments. For example, if a total of six discharge segments are identified in the cycle, and the voltage deviation value of four of them exceeds 80% of the maximum deviation value, it is confirmed that there are four valid discharge duration segments in the cycle. Combining the node sequence and deviation data, the number of discharge duration segments is obtained.

[0076] S313: Based on the number of discharge duration segments, calculate the voltage amplitude mapped from the SOC curve fluctuations and the voltage offset mapped from the temperature active range, using the following formula:

[0077] ;

[0078] Obtain the characteristic amplitude of working condition differences , This represents the total number of nodes within the period. Indicates the first The maximum deviation of node voltage is the voltage difference of that node relative to other individual nodes. Indicates the first The voltage amplitude offset at a node, obtained by mapping the fluctuations of the SOC curve, reflects the voltage difference corresponding to changes in the state of charge. Indicates the first The number of continuous discharge segments indicates the number of consecutive discharge segments formed within that node period. Indicates the first The voltage offset of a node, obtained by mapping the active temperature range, reflects the impact of temperature changes on voltage distribution.

[0079] The characteristic amplitude of operating condition difference is the "overall difference" formed by the voltage difference, SOC fluctuation and temperature effect of individual battery cells in a charge and discharge cycle, which reflects the degree of abnormality of battery operating conditions in that cycle.

[0080] The SOC curve fluctuations and temperature activity ranges are structured, and different participating terms are uniformly normalized to the voltage offset dimension. First, the maximum voltage deviation of each node within the period is extracted and normalized according to the voltage reference range, while retaining its original physical meaning. For example, the original maximum voltage deviation of node 1 is 12.5mV, which is expressed as follows after normalization: The maximum deviation of the original voltage at node 2 is 9.8mV, which, after normalization, is... The maximum deviation of the original voltage at node 3 is 11.3mV, which, after normalization, is... Subsequently, the fluctuation amplitude of the SOC curve was extracted. By fitting and transforming the SOC difference between nodes with their voltage response, the SOC voltage mapping offset of each node was obtained. After mapping, node 1 was 4.7mV, which was normalized to... Node 2 has a voltage of 3.9 mV, which is normalized to... Node 3 has a voltage of 5.1 mV, which is normalized to... Then, using the number of discharge segments as the intensity parameter, the response of the active temperature range is quantified, the nodes with significant changes in the temperature curve are extracted, and the voltage offset is represented by linear mapping of their disturbance amplitude.

[0081] The effect of temperature fluctuation at node 1 is 2.6 mV, normalized to... ;

[0082] Node 2 has a voltage of 3.1 mV, which is normalized to... ;

[0083] Node 3 has a voltage of 2.8 mV, which is normalized to... ;

[0084] The number of discharge duration segments corresponding to each node are as follows: , , The total number of nodes is set to The calculation is performed using the following formula:

[0085] ;

[0086] Substitute the normalized parameter values:

[0087] , , , , ;

[0088] , , , , ;

[0089] , , , , ;

[0090] Calculate each item in turn:

[0091] First item: ;

[0092] Second item: ;

[0093] Third item: ;

[0094] After substituting into the formula, perform the summation and square root operations:

[0095] ;

[0096] Based on the normalization analysis of historical data distribution characteristics during implementation, the amplitude of operating condition differences is... The judgment criteria are divided into the following three continuous intervals:

[0097] when When the time is determined to be "normal range", it means that there is no coordinated amplification behavior between voltage deviation, SOC fluctuation and temperature disturbance within the cycle, and the battery is in a stable operating state;

[0098] when When the period is defined as a "transitional interval", it indicates that there are slight abnormal correlation fluctuations within the period. Conditional monitoring is required, but direct maintenance operations will not be performed for the time being.

[0099] when When the interval is determined to be "abnormal interval", it indicates that there is a significant parameter coordination offset within the cycle, and the cycle should be marked as abnormal and the subsequent master control parameter identification process should be triggered.

[0100] This result indicates that the characteristic amplitude of the operating condition differences obtained in the current cycle is significant. The fact that the cycle has entered the "abnormal range" indicates that there is a significant synergistic amplification relationship between the voltage difference, SOC voltage mapping offset, and temperature disturbance voltage response within the cycle. The current cycle behavior belongs to an abnormal characteristic trajectory. The formula maps the voltage difference, SOC disturbance, and temperature response to the same physical quantity dimension and integrates the dynamic discharge behavior in a normalized weighted manner, thereby establishing a correlation between different types of anomalies, which helps to identify the abnormal characteristic amplitude in the operating cycle.

[0101] Please see Figure 5 The specific steps for obtaining the number of main control parameter groups are as follows:

[0102] S411: Based on the amplitude of the difference in operating conditions, analyze the amplitude distribution type, the location of the maximum deviation and the frequency of abnormality, identify the synchronous changes of parameter items within the cycle, determine which parameters have relatively concentrated synchronous fluctuations in the key operating condition section, screen the parameters with correlation, and obtain the operating condition parameter coverage.

[0103] First, time-series data of all operating parameters are extracted from the identified abnormal segments within the cycle. Segments where each parameter deviates within the cycle are labeled. For each labeled abnormal parameter, its fluctuation amplitude at different times throughout the entire cycle is statistically analyzed. The frequency distribution of the fluctuation amplitude in all fluctuation data is calculated, and the distribution intervals are divided according to the amplitude value. For example, amplitudes below 0.01 can be divided into a stable interval, between 0.01 and 0.03 into a moderate fluctuation interval, and above 0.03 into a sudden change interval, thus determining the amplitude distribution type of the parameter. Then, the fluctuation data of each parameter is traversed to locate the position of the most drastic value change in its time series, identifying the position of the parameter's maximum deviation within the cycle. For example, if the cell temperature rises from 35.4°C to 38.8°C in 300 seconds, this is the position of the maximum change within the cycle. Finally, the number of times the parameter enters the abnormal interval within the cycle is counted, which indicates an anomaly. For example, if the voltage of a single unit fluctuates by more than 0.04 volts five times within a cycle, the abnormal frequency is 5 times. After completing the above statistics for each parameter, a record item containing three attributes is formed, including amplitude distribution type, maximum deviation location, and abnormal frequency. Then, based on the time tag, it is determined whether multiple parameters fluctuate in the same period. If the current, voltage, SOC, and temperature all change beyond their respective set mutation thresholds in a certain operating condition, and the time difference between the occurrences does not exceed 2 seconds, it is considered that the period has synchronous parameter change performance. The parameter items involved in the synchronous change are recorded, and it is determined whether this type of synchronous fluctuation behavior is concentrated in certain key operating condition periods, such as the peak discharge period or the active temperature period. If the number of synchronous parameter fluctuations accounts for more than 60% in the operating condition period, it is regarded as the concentrated segment of synchronous mutation. The list of parameters appearing in the segment is regarded as the related parameter group. Then, the number of parameters in each segment is counted to obtain the operating condition parameter coverage.

[0104] S412: Based on the coverage of operating condition parameters, compare the coverage segments of each parameter in the operating condition trajectory with the synchronously occurring segments, calculate the coverage frequency of each parameter in the differential operating condition segments, determine the set of parameters whose coverage frequency is greater than the preset coverage threshold, and obtain the number of period priority parameter groups.

[0105] Extract the fluctuation time segment from each parameter participating in synchronous mutation and compare it with the various differential operating condition segments already divided within the cycle. First, determine the start and end times of the abnormal segments for each parameter in the cycle. Then, call the corresponding differential segment list in the operating condition trajectory and compare whether there is overlap between the parameter fluctuation time and the differential operating condition time. If the overlap interval length exceeds 10 seconds, it is considered that the parameter covers the operating condition segment. Accumulate such coverage behaviors and calculate the coverage frequency of the parameter in all differential segments. The frequency value is equal to the number of covered segments divided by the total number of differential segments. For example, if 12 differential segments are identified in the cycle, and the SOC parameter covers 9 of them, the coverage frequency of SOC is 75%. Perform the same operation for each parameter and sort the coverage frequencies to determine which parameters have a prominent coverage frequency. In the numerical setting, a coverage frequency greater than or equal to 70% can be used as the screening criterion for parameters with outstanding coverage performance. All parameter items with a frequency value not lower than this threshold can be included in the cycle priority parameter set. Then, accumulate the number of parameters in this set and record the total number of parameters that participate in coverage and have outstanding performance as the number of priority parameter groups for this cycle.

[0106] S413: Based on the number of period-priority parameter groups, filter the parameter sets that are ranked first, analyze the synchronous fluctuation performance of each parameter in the abnormal segment, optimize the parameter stratification with continuous synchronous performance and change amplitude exceeding the preset fluctuation threshold, and count the number of parameter items at the top of the stratification to obtain the number of master control parameter groups.

[0107] The parameters that rank highest after statistical analysis of coverage frequency are summarized. Within this set, the specific performance of each parameter in abnormal segments within the cycle is further analyzed. The fluctuation value sequence within each abnormal segment is extracted to determine whether the fluctuation changes are continuous and concentrated. In this judgment, if the number of consecutive fluctuation nodes accounts for more than 60% of the total number of nodes in the abnormal segment, and the fluctuation direction is continuous and the change amplitude remains within an upward or downward trend, then the parameter is considered to have good synchronization performance. The parameters with this characteristic are then statistically analyzed and classified into levels according to the degree of fluctuation concentration and the duration of synchronization. For example, parameters with continuous synchronization fluctuation time exceeding 30 seconds and fluctuation amplitude standard deviation less than 0.02 are set as the first level, and the rest are classified as the second level and below. The actual number of parameters in the first level is summarized as the number of the cycle's main control parameter groups. For example, if the priority parameter group contains 10 parameters, and 4 parameters show synchronous continuous fluctuation, concentrated change, and meet the first level standard in multiple abnormal segments, then the number of the cycle's main control parameter groups is output as 4.

[0108] Please see Figure 6 The specific steps for obtaining periodic processing suggestion information are as follows:

[0109] S511: Based on the number of main control parameter groups, classify the parameter names, hierarchical levels and priority order, retrieve the corresponding maintenance suggestion actions for each parameter group, filter the mapping relationship between parameters and suggestion actions, determine the combination structure of suggestion actions, and obtain suggestion action matching information;

[0110] Extract the parameter names, corresponding hierarchical levels, and priority order of all main control parameters, and sort them in ascending order of priority to construct a structured parameter list. Each parameter item in the list must be accompanied by a complete attribute value. For example, the parameter "individual voltage" belongs to the first level and has a priority of 1, the parameter "SOC" belongs to the second level and has a priority of 2, and so on. Next, perform a maintenance suggestion action retrieval process for each parameter item. Through a preset maintenance suggestion database, keyword indexing or attribute matching is used to locate the corresponding maintenance action for the parameter in historical cases or operation and maintenance rules. For example, for voltage anomalies, the search results are "limit maximum charging current" or "recommend module replacement". Associate these suggested actions with the corresponding parameters one by one to form a preliminary mapping relationship table. In this table, each parameter corresponds to one or more suggested actions. Then, an aggregation and classification operation is performed on the mapping relationship to analyze whether the same type of parameter corresponds to multiple suggested actions and to count their coverage frequency. If a parameter is associated with three suggested actions and the other parameters are associated with only one suggestion, then the parameter has a multi-suggestion structure. Then, the overall suggested action combination structure of the parameter group is judged to check whether there are duplicate actions, conflicting actions, or dependent action relationships. If multiple parameters are associated with the same maintenance suggested action, they are marked as duplicate relationships. If two parameter suggested actions are logically mutually exclusive, such as "suggest replacing the module" and "continue running and observing", they are marked as conflicting relationships. All relationships are summarized and the corresponding parameter combination structure is recorded to obtain suggested action matching information.

[0111] S512: Based on the suggested action matching information, analyze the applicable scope of the actual working condition parameters and suggested actions within the cycle, compare the degree of fit between the parameter status and the suggested action adaptation conditions, adjust the suggested action screening strategy, and determine the applicability of each suggested action in the current cycle to obtain suggested action adaptation data.

[0112] The matching degree between the actual operating parameters collected in the current cycle and the corresponding suggested actions within the set applicable range is analyzed item by item. For each suggested action, its applicable condition field is extracted, such as "voltage fluctuation exceeds 0.08 volts" or "SOC drop rate continuously exceeds 2% per minute". Then, the actual operating data of the current cycle is searched for time segments that meet the above conditions. If the operating parameters reach the activation threshold set by the suggested action within any time period, it is recorded as a successful fit; otherwise, it is recorded as an unsuitable fit. Subsequently, a one-to-one comparison is made between the suggested actions and parameters. If the suggested action conditions are met at most sampling points in the cycle... If the current fluctuation is high, the suitability is considered high. If the condition is met only in a few edge periods, the suitability is considered low. Based on this, the suggested actions are re-screened and sorted, with actions with high suitability placed in the priority list. For actions with unclear suitability or edge suitability, the judgment rules are further adjusted. For example, a 5% buffer is set for the boundary value. That is, if the suggested action requires the current fluctuation to exceed 10 amps and the actual fluctuation is 9.6 amps, it is considered to approximately meet the condition and is included in the matching reference range. The applicability of all suggested actions is summarized and statistically analyzed. The state matching status, start ratio and suitability status of each suggested action in the current cycle are recorded to obtain the suggested action suitability data.

[0113] S513: Based on the suggested action adaptation data, analyze the archiving relationship between suggested actions and actual working condition parameters, optimize the collection method of suggested actions, filter the operation points and suggestion types of suggested actions with an adaptation score higher than the adaptation threshold, and obtain periodic processing suggestion information;

[0114] First, an archiving relationship is established between all successfully adapted suggested actions and their corresponding parameter value ranges. During the archiving process, the applicable conditions, corresponding actual time periods, and related operating parameters of each suggested action are bound into a unified record item. The data structure must include the following fields: suggested action, trigger parameter, parameter value range, actual start time period, and matching degree level. Next, the archived suggested actions are classified and grouped according to similar trigger conditions. For example, if multiple suggested actions are triggered by voltage mutations or multiple actions are triggered by high temperature ranges, they are grouped into the same group category. Then, it is determined whether there are suggested action groups with higher matching strength in each group category. The start times and average matching degree levels of suggested actions in each group are counted, and suggested actions with more than two start times and a matching degree of "high" are selected and marked as key actions. The key operation points in the actions are extracted, such as "current limiting," "deep detection," or "replace the entire package" as recommended operation directions. At the same time, the suggestion type is filtered and classified, such as belonging to "conservative monitoring," "moderate intervention," or "immediate maintenance." The key points of the selected suggested actions and their suggestion types are aggregated and output to obtain the periodic processing suggestion information.

[0115] The above are merely preferred embodiments of the present invention and are not intended to limit the present invention in any other way. Any person skilled in the art may make changes or modifications to the above-disclosed technical content to create equivalent embodiments that can be applied to other fields. However, any simple modifications, equivalent changes, and modifications made to the above embodiments based on the technical essence of the present invention without departing from the scope of the present invention shall still fall within the protection scope of the present invention.

Claims

1. A method for after-sales analysis of electric vehicle batteries, characterized in that, Includes the following steps: S1: Based on electric vehicle batteries, analyze the peak temperature of a single cell and the fluctuation range of the charging and discharging current during the same period, determine the relationship between temperature and current mutation at the time of data collection, identify the synchronous mutation period of parameters, and obtain the coordinated response period interval; S2: Based on the coordinated response time interval, determine the operating data within the cycle, normalize the voltage curve and current trajectory, compare the node differences, count the continuous deviation nodes, and analyze them according to the distribution ratio to obtain the cycle trajectory offset ratio. S3: Based on the periodic trajectory offset ratio, filter the periodic operating conditions, analyze the cell voltage fluctuation in the interval, calculate the maximum deviation, determine the SOC fluctuation, identify the maximum discharge segment, and obtain the characteristic amplitude of the operating condition difference. The specific steps for obtaining the amplitude of the operating condition difference characteristics are as follows: S311: Based on the periodic trajectory offset ratio, calculate the voltage difference of a single cell in the operating cycle, compare the voltage differences of all nodes and determine the node with the largest difference, and then combine the trajectory offset ratio to complete the induction and obtain the maximum voltage deviation. S312: Based on the maximum voltage deviation, determine the fluctuation amplitude of the SOC curve, combine the discharge interval node sequence of the operating cycle, screen continuous discharge segments, and perform correlation comparison based on the maximum deviation to obtain the number of discharge duration segments. S313: Based on the number of discharge duration segments, calculate the voltage amplitude mapped from the SOC curve fluctuations and the voltage offset mapped from the temperature active range, using the formula: ； Obtain the characteristic amplitude of working condition differences , This represents the total number of nodes within the period. Indicates the first The maximum deviation of node voltage is the voltage difference of that node relative to other individual nodes. Indicates the first The voltage amplitude offset at a node, mapped from the SOC curve fluctuations, reflects the voltage difference corresponding to changes in the state of charge. Indicates the first The number of continuous discharge segments indicates the number of consecutive discharge segments formed within that node period. Indicates the first The voltage offset of a node, obtained by mapping the temperature active range, reflects the impact of temperature changes on voltage distribution; S4: Based on the magnitude of the difference characteristics of the working conditions, compare the impact of each working condition parameter on the maintenance recommendations, sort and filter the parameter groups with the highest impact, determine the first-level parameters in layers, count the number of first-level parameters, and obtain the number of main control parameter groups. S5: Based on the number of main control parameter groups, retrieve the corresponding maintenance suggestion actions, analyze the relationship between the periodic operating conditions and the suggested actions, combine the actual operating condition parameters to construct the periodic processing content, and obtain periodic processing suggestion information.

2. The method for after-sales analysis of electric vehicle batteries according to claim 1, characterized in that, The coordinated response time interval includes the start time, end time, and anomaly identifier; the periodic trajectory offset ratio includes the offset node ratio, periodic coverage, and anomaly distribution characteristics; the operating condition difference characteristic amplitude includes the amplitude distribution type, maximum deviation location, and anomaly frequency; the number of main control parameter groups includes parameter name, hierarchical level, and priority order; and the periodic processing suggestion information includes suggestion type, operation points, and applicable conditions.

3. The method for after-sales analysis of electric vehicle batteries according to claim 1, characterized in that, The specific steps for obtaining the coordinated response time interval are as follows: S111: Based on electric vehicle batteries, analyze the temperature and current fluctuation data of individual cells at each collection point. For the temperature peak and charging / discharging current fluctuation amplitude at each moment, determine whether their sudden changes are consistent. By comparing the synchronous change trends of temperature and current fluctuations, obtain the joint sequence of temperature and current fluctuations. S112: Based on the joint sequence of temperature and current fluctuations, determine whether the change direction of the temperature peak and the current fluctuation amplitude at the acquisition time point is consistent. If there is consistency, filter out the synchronous mutation time period that meets the conditions to obtain the mutation consistency time period interval. S113: Based on the mutation consistency time interval, combined with complete collected data, analyze the start and end times of each synchronous mutation time interval, and label them to obtain the collaborative response time interval.

4. The method for after-sales analysis of electric vehicle batteries according to claim 1, characterized in that, The specific steps for obtaining the periodic trajectory offset ratio are as follows: S211: Based on the coordinated response time interval, extract the data nodes of the voltage curve within the period, collect each sampling node of the current trajectory, synchronize the original sequence data from each source to the same period dimension, divide and normalize the nodes according to a uniform step size, unify the number of nodes and reassemble the time series data to obtain a normalized node sequence. S212: Based on the normalized node sequence, identify the voltage and current data of each normalized node in sequence, compare the offset state of the node with each node of the standard baseline curve point by point, record the continuous changes of adjacent nodes in the voltage and current dimensions in a consistent manner, and obtain the node offset dataset. S213: Based on the node offset dataset, analyze the distribution density of continuous offset segments on the periodic normalized timeline, aggregate the proportion of continuous offset segments on the timeline with the node coverage, record the continuous coverage range and distribution location of each continuous offset segment, and obtain the periodic trajectory offset proportion.

5. The method for after-sales analysis of electric vehicle batteries according to claim 1, characterized in that, The specific steps for obtaining the number of master control parameter groups are as follows: S411: Based on the amplitude of the difference characteristics of the working conditions, analyze the amplitude distribution type, the location of the maximum deviation and the frequency of abnormality, identify the synchronous changes of parameter items within the cycle, determine which parameters have relatively concentrated synchronous fluctuations in the key working condition segment, screen the parameters with correlation, and obtain the working condition parameter coverage. S412: Based on the coverage of the operating condition parameters, compare the coverage segments of each parameter in the operating condition trajectory with the synchronously occurring differential operating condition segments, calculate the coverage frequency of each parameter in the differential operating condition segments, determine the set of parameters whose coverage frequency is greater than the preset coverage threshold, and obtain the number of period priority parameter groups. S413: Based on the number of the period priority parameter groups, filter the parameter sets that are ranked first, analyze the synchronous fluctuation performance of each parameter in the abnormal segment, optimize the parameter stratification with continuous synchronous performance and change amplitude exceeding the preset fluctuation threshold, and count the number of parameter items at the top of the stratification to obtain the number of master control parameter groups.

6. The method for after-sales analysis of electric vehicle batteries according to claim 1, characterized in that, The specific steps for obtaining the periodic processing suggestion information are as follows: S511: Based on the number of main control parameter groups, classify the parameter names, hierarchical levels and priority order, retrieve the corresponding maintenance suggestion actions for each parameter group, filter the mapping relationship between parameters and suggestion actions, determine the combination structure of suggestion actions, and obtain suggestion action matching information. S512: Based on the suggested action matching information, analyze the applicable range of the actual working condition parameters and suggested actions within the cycle, compare the degree of fit between the parameter status and the suggested action adaptation conditions, adjust the suggested action screening strategy, and determine the applicability of each suggested action in the current cycle to obtain suggested action adaptation data. S513: Based on the suggested action adaptation data, analyze the archiving relationship between suggested actions and actual working condition parameters, optimize the collection method of suggested actions, filter the operation points and suggestion types of suggested actions with an adaptation score higher than the adaptation threshold, and obtain periodic processing suggestion information.

7. The method for after-sales analysis of electric vehicle batteries according to claim 1, characterized in that, The peak temperature refers to the highest temperature reading reached by a single cell at each sampling time within a certain cycle. The operating condition data within the cycle refers to the full collection of data on various operating parameters of the battery within a defined cycle. The interval analysis refers to the statistical comparison of the voltage variation range of a single cell to identify its fluctuation range within a cycle.

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