An intelligent online capacity verification management system for a battery pack

Abstract

The present invention relates to the technical field of battery state monitoring, and specifically to an intelligent online capacity verification management system for a battery pack. The system includes a jump perception module, a hysteresis correlation module, a starting point confirmation module, a state diagnosis module, and a correction output module. In the present invention, by locating the difference of current mutation points and tracking the fluctuation convergence interval, a dynamic mutation buffer structure is constructed to enhance the capture ability of key change behaviors. Combining the change trend of voltage derivative to extract the response time point, the accurate identification of voltage hysteresis effect is realized. Based on the voltage difference and current sampling, the state of charge is jointly bound to improve the real-time performance and pertinence of capacity evaluation. The mutation period is identified by the difference of state of charge, and a synchronization mark is established through voltage stability analysis. According to the state difference, a correction mapping is carried out to form a capacity correction path with coherence, traceability and dynamic adaptability, effectively improving the accuracy and stability of the capacity management of the battery pack.

Description

Technical Field

[0001] The present invention relates to the technical field of battery state monitoring, and particularly to an intelligent on-line capacity verification management system for a battery pack. Background Art

[0002] The technical field of battery state monitoring includes the real-time acquisition, analysis, and management of the operating state, electrical performance parameters, and health indicators of batteries. The core content includes continuously measuring and recording key parameters such as voltage, current, temperature, capacity, and internal resistance of a storage battery or battery pack during use, and realizing the judgment of the state of charge, remaining capacity, and health status of the battery through data processing, covering the entire process from battery data acquisition, state evaluation, life prediction to fault warning, and is widely used in industries such as power, communication, transportation, and energy storage. The purpose is to improve the safety, reliability, and maintenance efficiency of battery use, and provide a basic support for energy management.

[0003] Among them, the intelligent on-line capacity verification management system for a battery pack refers to a system device applied to the operation scenario of a battery pack for implementing on-line capacity verification and state management. It mainly aims at the problems of capacity attenuation, inaccurate power evaluation, and manual maintenance dependence existing in the long-term operation of the battery pack, and proposes a method for capacity verification through voltage, current, and time data. Combining periodic voltage acquisition and discharge process recording, it calculates the actual available capacity of the battery pack, and makes trend judgments based on multi-cycle operation data to assist management. Its means include capacity calculation based on chronological current integration, comparison basis for judging capacity changes with static voltage attenuation, capacity calibration method with data collected under constant discharge conditions as a reference, etc., and completes the capacity evaluation task through a clear electrical parameter data acquisition path and an electrochemical performance judgment model.

[0004] In the existing monitoring of the battery capacity of a battery pack, most are centered around periodic integration and fixed feature point acquisition, and cannot cope with the complex disturbances generated by the current signal in a mutation state, resulting in insufficient coverage of the capacity recognition for non-linear responses. The processing of voltage changes relies on interval averaging or static analysis, lacking refined recognition of the mutation response time points, leading to an expansion of the matching error between state evolution and parameter response. During the operation of the battery, in the face of complex scenarios such as sudden increase in load and sudden drop in voltage, conventional methods are difficult to locate the true influence nodes on the time axis, thus affecting the timeliness and accuracy of capacity trend judgment. The horizontal linkage and trend pairing between data have not formed a closed-loop mechanism, and the state judgment and capacity correction links are disjointed, and it is impossible to track the capacity change path based on the operation trajectory. The cumulative capacity error formed during long-term operation often cannot be effectively alleviated through single-point correction methods, thereby affecting the formulation of capacity prediction and maintenance strategies and increasing the uncertainty of battery management. Summary of the Invention

[0005] The object of the present invention is to solve the drawbacks existing in the prior art, and to propose an intelligent online capacity verification management system for a battery pack.

[0006] To achieve the above object, the present invention adopts the following technical solution: An intelligent online capacity verification management system for a battery pack includes:

[0007] The jump perception module obtains the current sampling sequence during the operation cycle of the battery pack, selects the starting point of current mutation, tracks the convergence interval of the difference value in each cycle after the starting point of mutation towards zero, and generates a data set of mutation buffer segments;

[0008] The hysteresis correlation module extracts the continuous voltage data after the jump end time point based on the data set of the mutation buffer segments, selects the starting point of response to statistically calculate the duration of the negative trend, and obtains the jump response time deviation data by comparing the response time limit;

[0009] The starting point confirmation module extracts the voltage value corresponding to the starting point of response based on the jump response time deviation data, determines whether the difference from the voltage value of the previous cycle exceeds the capacity change trigger threshold, statistically calculates the current and voltage values corresponding to the response point, and obtains the capacity sampling data packet;

[0010] The state diagnosis module calculates the difference in the state of charge per cycle based on the capacity sampling data packet, locates the key cycle of state mutation, analyzes whether the subsequent voltage difference set is less than the stable threshold value, and if satisfied, generates a state voltage synchronization mark cycle;

[0011] The correction output module incorporates the corresponding state of charge value into the capacity verification correction record sequence according to the state voltage synchronization mark cycle, calculates the capacity correction amplitude, and obtains the capacity adjustment record of the battery pack.

[0012] As a further solution of the present invention, the data set of the mutation buffer segments includes the current upper limit, the current lower limit, and the cycle length; the jump response time deviation data includes the starting point of response, the voltage change trend, and the duration of the negative trend; the capacity sampling data packet includes the current value, the voltage value, and the state of charge; the state voltage synchronization mark cycle includes the key cycle of state mutation, the voltage difference set, and the stability judgment flag; the capacity adjustment record includes the state of charge value, the source of capacity trend node correction, and the capacity correction amplitude.

[0013] As a further solution of the present invention, the jump perception module includes:

[0014] The difference extraction sub-module sequentially selects the current values of each sampling point and its adjacent points before and after based on the current sampling sequence during the operation cycle of the battery pack, calculates the difference between the current sampling point and the previous sampling point and the difference between the current sampling point and the next sampling point, obtains the difference set before and after each sampling point, and obtains the current difference sequence;

[0015] The mutation starting point recognition sub-module determines whether each difference exceeds the jump current threshold according to the current difference sequence, screens out the sequence segments where consecutive differences exceed the jump current threshold, locates the starting position of the sequence segment as the mutation starting point, and obtains the current mutation starting point index sequence;

[0016] The mutation buffer segment construction sub-module starts from each mutation starting point according to the current mutation starting point index sequence, combines the difference sequence in the subsequent period, determines whether the difference gradually converges to zero, records the end point of the convergence interval as the end point of the buffer segment, records the maximum current, minimum current and cycle length within the interval, and obtains the mutation buffer segment data set.

[0017] As a further solution of the present invention, the hysteresis correlation module includes:

[0018] The voltage sequence extraction sub-module extracts the continuous voltage data segment after the jump end time point from the original time sequence based on the jump end time point marked in the mutation buffer segment data set, and obtains the voltage change sequence;

[0019] The response starting point recognition sub-module performs differential processing on the voltage values at every two adjacent time points in the sequence based on the voltage change sequence, calculates the first-order derivative of the voltage and marks the derivative sign, screens out the time point segments where the derivative values are continuously negative, and uses the formula: ;

[0020] Determine the response starting time point , identify the interval length from the starting time point to before the trend reversal, and obtain the negative duration sequence, where, is the voltage value at the kth moment, is the sampling time interval, is the sign function of the voltage derivative, n is the minimum number of consecutive negative periods of the voltage derivative, is the kth sampling time point, is the negative trend threshold of the derivative; The deviation data acquisition sub-module calculates the time interval between the response starting time point sequence and the jump end time point data in the mutation buffer segment data set, and counts the difference between the time interval and the preset response time limit as the offset to obtain the jump response time deviation data.

[0021] As a further solution of the present invention, the starting point confirmation module includes:

[0022] The voltage deviation judgment sub-module extracts the voltage value corresponding to each response starting point based on the jump response time deviation data, obtains the voltage value of the previous period, calculates the difference, and compares the difference with the capacity change trigger threshold to determine whether it exceeds the capacity change trigger threshold, and obtains the trigger valid index sequence;

[0023] The response point binding sub-module combines with the trigger valid index sequence, extracts the voltage value, current value and state of charge information at the corresponding time point, performs synchronous recording and joint marking, and obtains the state of charge mapping data group;

[0024] The sampling data generation sub-module binds and arranges the combined voltage value, current value and state of charge information according to the state of charge mapping data group, constructs a standard data structure and unifies the recording format, and obtains the capacity sampling data packet.

[0025] As a further solution of the present invention, the state diagnosis module includes:

[0026] The rate judgment sub-module calculates the difference in the state of charge between consecutive sampling time points for each cycle based on the state of charge sequence in the recording time period of the capacity sampling data packet, determines whether the difference exceeds the state of charge rate mutation recognition threshold, screens the cycle index positions that meet the conditions, and obtains the state of charge rate mutation cycle sequence;

[0027] The key cycle extraction sub-module obtains the voltage value sequence at the corresponding cycle position according to the state of charge rate mutation cycle sequence, performs a sliding window process with a fixed cycle width, constructs a set of differences between adjacent cycle voltage values within each window, and uses the formula: ;

[0028] Calculate the voltage fluctuation stability of the th cycle , screen the stability, extract the cycle positions less than the stability threshold value, and obtain the state mutation key cycle sequence, where is the voltage value of the th cycle, N is the number of cycles in the sliding window, is the absolute difference between adjacent voltage values; the stable window marking sub-module performs a marking operation at each cycle position according to the state mutation key cycle sequence, synchronously binds the state mutation cycle with the stable voltage cycle within the subsequent sliding window, establishes a cycle synchronization information structure, and obtains the state voltage synchronization marking cycle.

[0029] As a further solution of the present invention, the correction output module includes:

[0030] The correction record inclusion sub-module extracts the state of charge value at the corresponding cycle based on the state voltage synchronization marking cycle, incorporates it into the correction sequence structure in sequence, and attaches the current capacity trend node position to each correction value to establish a mapping index between the correction and the trend point, and obtains the capacity correction reference sequence;

[0031] The capacity difference calculation sub-module calculates the numerical difference based on the correction nodes in the capacity correction reference sequence and their corresponding initial capacity starting values, and combines the voltage fluctuation stability and the voltage response fluctuations in the previous and subsequent cycles, using the formula: ;

[0032] Calculate the capacity correction amplitude of the battery pack in the j-th cycle , establish the relationship between the correction amplitude and the difference and change rate between nodes, and obtain the capacity correction amplitude sequence, where is the state of charge value in the j-th cycle, is the starting value of the capacity, and are the voltage values in the j-th cycle and the (j - 1)-th cycle respectively, is the voltage fluctuation stability in the j-th cycle, and m is the number of periods of trend continuation; The adjustment record generation sub-module combines each capacity correction amplitude with the corresponding state of charge and trend identifier according to the capacity correction amplitude sequence and the node positioning information in the capacity correction reference sequence to obtain the capacity adjustment record of the battery pack.

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

[0034] In the present invention, by analyzing the difference to identify the starting point of current mutation and tracking the boundary of the fluctuation period converging to zero, a mutation buffer structure with dynamic representativeness can be constructed, expanding the recognition range of the current change process of the battery pack. Combining the trend change of the first derivative of the voltage after the jump ends, the continuous segment with a negative derivative is extracted as the response time point to avoid the misjudgment risk brought by fixed time delay judgment, and accurately track the voltage hysteresis characteristic. The voltage difference at the response point is combined with the current sampling value to bind the state of charge, enabling the capacity recognition to transition from static measurement to dynamic calibration, improving the real-time performance and context relevance of capacity recognition. By analyzing the continuous state of charge difference to identify the key period of state mutation and forming a synchronous mark in combination with the stable trend of voltage change, the voltage behavior corresponding to the capacity mutation is effectively isolated, enhancing the logical consistency of the capacity trend evolution path. Pairing the state data with the capacity trend nodes for difference matching, extracting the correction amplitude and incorporating it into the correction record, enabling the capacity adjustment to have the ability of continuous tracking, trend correction and multi-cycle feedback, and supporting the battery pack capacity assessment to maintain accuracy, continuity and traceability under variable working conditions. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] Figure 1 is the system flow chart of the present invention;

[0036] Figure 2 is the flow chart of the jump perception module of the present invention;

[0037] Figure 3 is the flow chart of the hysteresis correlation module of the present invention;

[0038] Figure 4 This is the flowchart of the starting point confirmation module of the present invention;

[0039] Figure 5 This is the flowchart of the state diagnosis module of the present invention;

[0040] Figure 6 This is the flowchart of the correction output module of the present invention. Detailed implementation manners

[0041] In order to make the objectives, technical solutions and advantages of the present invention more clear and understandable, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0042] In the description of the present invention, it should be understood that the orientation or positional relationship indicated by the terms "length", "width", "upper", "lower", "front", "rear", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", etc. is based on the orientation or positional relationship shown in the drawings, and is only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore should not be construed as a limitation of the present invention. In addition, in the description of the present invention, the meaning of "a plurality of" is two or more unless otherwise specifically defined.

[0043] Please refer to Figure 1 , an intelligent online capacity verification management system for a battery pack includes:

[0044] The jump perception module obtains the current sampling sequence within the operation period of the battery pack, calculates the difference between any sampling point and the front and rear points, selects the starting point of the continuous difference exceeding the jump current threshold as the starting point of the current mutation, tracks the convergence interval of the difference in each period after the starting point of the mutation to zero, records the upper limit, lower limit and period length of the current, and generates a data set of the mutation buffer segment;

[0045] The hysteresis correlation module extracts the continuous voltage data in the time series after the jump end time point marked in the data set of the mutation buffer segment, calculates the change trend of the first derivative, selects the initial time point with the continuous derivative value being negative as the starting point of the response, and statistically calculates the duration of the negative trend, compares it with the response time limit, and obtains the jump response time deviation data;

[0046] The starting point confirmation module extracts the voltage value corresponding to the starting point of the response based on the jump response time deviation data, determines whether the difference from the voltage value of the previous period exceeds the capacity change trigger threshold. If it is satisfied, the current and voltage values corresponding to the response point are statistically calculated, the current state of charge is recorded and bound, and a capacity sampling data packet is obtained;

[0047] Based on the state-of-charge sequence within the recorded time period in the capacity sampling data packet, the state diagnosis module calculates the difference in the state of charge between consecutive sampling periods, determines whether it exceeds the threshold for identifying sudden changes in the charging rate. If satisfied, it locates the critical period of the state mutation and obtains the corresponding voltage sampling data. It uses a sliding window method to analyze whether the subsequent voltage difference sets are all less than the stable threshold value. If satisfied, it generates a state voltage synchronization marker period.

[0048] The correction output module incorporates the corresponding state-of-charge value into the nuclear capacity correction record sequence according to the state-of-charge value corresponding to the state voltage synchronization marker period, marks the source of the correction for the current capacity trend node, calculates the capacity correction amplitude by combining the difference between the current capacity starting point and the corresponding node, and obtains the battery pack capacity adjustment record.

[0049] The data set of the mutation buffer segment includes the current upper limit, current lower limit, cycle length. The data of the jump response time deviation includes the response starting point, voltage change trend, and negative trend duration. The capacity sampling data packet includes the current value, voltage value, and state of charge. The state voltage synchronization marker period includes the critical period of the state mutation, the voltage difference set, and the stability judgment flag. The capacity adjustment record includes the state-of-charge value, the source of the correction for the capacity trend node, and the capacity correction amplitude.

[0050] Please refer to Figure 2 , the jump perception module includes:

[0051] The difference extraction sub-module, based on the current sampling sequence within the operation cycle of the battery pack, sequentially selects the current values of each sampling point and its adjacent points before and after, calculates the difference between the current sampling point and the previous sampling point and the difference between the current sampling point and the next sampling point, obtains the difference set before and after each sampling point, and gets the current difference sequence.

[0052] Obtain the current sampling sequence within the operation cycle of the battery pack, which is continuously collected by the current sensor deployed at the output end of the battery pack at a sampling interval of 1 second For example, the current values (unit: Ampere A) of 10 consecutive sampling points collected form a sequence: ; This sequence is recorded in the storage unit of the system. The difference extraction sub-module first retrieves this current sampling sequence from the storage unit , and then sequentially processes each sampling point in the sequence, but skips the first and last two points to ensure that each processed point has adjacent points before and after. For the sampling point with index k (where ) in the sequence , the module will simultaneously read the current value of its previous sampling point and the current value of the next sampling point , and then perform two subtraction operations. The first calculation is for the current sampling point The current difference from the previous sampling point is calculated and then, after the second calculation, the current difference between the subsequent sampling point and the current sampling point is calculated For example, when processing the sampling point at index k = 2 in the sequence , its previous point is , and its subsequent point is . The following are calculated: , and . These two differences constitute the elements in the set of the front and back differences of the sampling point with index 2. Subsequently, the module continues to process the sampling point at index k = 3 , its previous point , and its subsequent point . The following are calculated: , , which constitute the elements in the set of the front and back differences of the sampling point with index 3 . This process continues until all sampling points within the allowed range are processed , for the sampling point , its previous point , and its subsequent point . The following are calculated: , which constitute . After processing all sampling points within the allowed range, the module combines all the calculated values in chronological order. For example, after processing the sequence , the forward difference sequence obtained is:

[0053] = = = ,

[0054] This sequence is the required current difference sequence, which is then passed to the mutation starting point recognition sub-module to obtain the current difference sequence

[0055] Based on the current difference sequence, the mutation starting point recognition sub-module determines whether each difference exceeds the jump current threshold, filters out the sequence segments where consecutive differences all exceed the jump current threshold, locates the starting position of the sequence segment as the mutation starting point, and obtains the current mutation starting point index sequence

[0056] Based on the current difference sequence, for example , the mutation starting point recognition sub-module first needs to set a jump current threshold The threshold is set based on the statistical analysis of the current fluctuation range caused by internal chemical reaction fluctuations, sensor noise, and small load changes under normal working conditions of the battery pack of this model. The upper limit of the fluctuation of the 99% confidence interval in the historical data is usually selected as a reference. For example, if the analysis shows that the normal fluctuation is usually within If it is within After setting the threshold, the module checks Each difference in the sequence , perform judgment operation: compare Is it greater than , for the sequence and threshold , the judgment result sequence is [False, False, True, False, False, True, False, False] (the corresponding value is ), next, the module filters out the segments that are continuously True in the above judgment result sequence. In this example, there are only two separate Trues, at index 2 (corresponding to the difference of 4.5) and index 5 (corresponding to the difference of -3.3). There are no continuous True sequence segments. Suppose we have another difference sequence , using the same threshold , the judgment result is [False, False, True, True, True, False, False]. At this time, there is a continuous True sequence segment, which covers the original difference sequence indexes of 2, 3, and 4 (corresponding to difference values 2.5, 3.1, and 2.8). The module locates the starting position of this continuous True sequence segment, that is, index 2, and records this starting index. If there are multiple such continuous segments, the starting index of each continuous segment is recorded. For example, if the judgment result is [F, T, T, F, T, T, T, F], two starting indexes will be recorded, namely 1 and 4. Let's go back to our first example. And the judgment result [F, F, T, F, F, T, F, F], since there are no consecutive paragraphs exceeding the threshold, but the original description requires screening "sequence segments whose consecutive differences all exceed the threshold", this may imply that a single point exceeding the threshold is also counted as a "continuous segment" of length 1, or the threshold or processing logic needs to be adjusted. Assuming that the logic is to find any point exceeding the threshold as a potential starting point, in this example, index 2 and index 5 will be recorded to form a mutation starting point index list [2, 5]. (Note: If "continuity" is strictly required, the result of this example is an empty list. In order to enable the subsequent steps to proceed, we assume that a single point exceeding the threshold is also marked, or the threshold is adjusted to , the judgment result is [F, F, T, F, F, T, F, F], the absolute value of the difference is [0.1, 0.3, 4.5, 0.1, 0.2, 3.3, 0.2, 0.1], and the ones exceeding the threshold are indices 2 and 5, and there is still no continuous segment. Next, starting from indices 2 and 5, the indices of these records will be output to obtain the current mutation starting index sequence.

[0057] The mutation buffer segment construction sub-module, according to the current mutation starting index sequence, starting from each mutation starting point, combines the difference sequence in the subsequent cycles to judge whether the difference gradually converges to zero, records the end point of the convergence interval as the termination point of the buffer segment, and records the maximum current value, minimum current value and cycle length within the interval to obtain the mutation buffer segment data set;

[0058] According to the current mutation starting index sequence, such as [2, 5], and the original current difference sequence and the original current sequence:

[0059] ;

[0060] Process the starting index one by one. Process the starting index 2. Starting from this point (i.e., the difference ), combine the subsequent difference sequence to judge whether the difference gradually converges to zero. The judgment criterion for convergence is whether the absolute value of the difference is less than a preset convergence threshold , and this threshold is usually set to a small proportion of the jump current threshold , for example . If , then: , now check the differences after index 2: , since the first subsequent difference is already less than the convergence threshold, it means that the current change has quickly stabilized. Record the end index of the convergence interval as 3. The original difference sequence indices covered by the buffer segment of this mutation event are 2 to 3 (i.e., differences 4.5 and 0.1), and the corresponding original current sequence indices are 2, 3, 4 (because the difference ), so the involved current values are , and the module then searches for the maximum current value and minimum current value and records the cycle length corresponding to this buffer segment, that is, the number of sampling points involved, as 3 cycles (or time length seconds, depending on the definition), and then process the next mutation starting index 5. The corresponding difference is , and the subsequent difference sequence is , and check the subsequent differences: , which is already less than the convergence threshold ​ The end index of the convergence interval is recorded as 6. The original difference indices covered by this buffer segment are from 5 to 6 (differences -3.3 and -0.2), and the corresponding original current sequence has indices 5, 6, 7, and the current values involved are within this interval to find the maximum current and the minimum current The cycle length is recorded as 3 cycles (or 2 seconds). The data obtained from each processing, including the start index, end index, maximum current within the interval, minimum current, and cycle length, are stored as a data structure. For example, the record is formed as follows:

[0061] (start_idx = 2, end_idx = 3, I_max = 15.1, I_min = 10.5, length = 3);

[0062] (start_idx = 5, end_idx = 6, I_max = 15.3, I_min = 11.8, length = 3);

[0063] All these records together constitute the data set of the mutation buffer segment for use by subsequent modules to obtain the data set of the mutation buffer segment.

[0064] Table 1 Data Table of Mutation Buffer Segment

[0065] Starting index Ending index Maximum current (A) Minimum current (A) Cycle length 2 3 15.1 10.5 3 5 6 15.3 11.8 3

[0066] As shown in Table 1, this table lists the specific data of two buffer segments identified by the mutation buffer segment construction sub-module, including the start and end indices in the original difference sequence, the maximum and minimum current values within the corresponding original current sequence interval, and the number of sampling points included in the buffer segment.

[0067] Please refer to Figure 3 , the lag correlation module includes:

[0068] The voltage sequence extraction sub-module extracts the continuous voltage data segment after the jump end time point from the original time sequence based on the jump end time points marked in the data set of the mutation buffer segment to obtain the voltage change sequence;

[0069] Based on the aforementioned data set of the mutation buffer segment, such as the data shown in Table 1, this sub-module needs to access the original voltage time sequence V collected and stored synchronously with the current sequence Assume the corresponding voltage sequence is Volts (V), and this sequence is synchronized with the current sequence The timestamps are exactly corresponding. The module processes each record in the mutation buffer segment data set in sequence. Taking the first record (start_idx = 2, end_idx = 3,...) as an example, the end time point of the jump (the current change tends to be stable) marked by this record corresponds to the index 3 of the original difference sequence, which corresponds to the index 4 of the original current / voltage sequence (because is associated with , and the buffer segment ends when the difference is stable, that is is small relative to ), so the corresponding voltage time point is index 4, that is . The module starts from this end time point (index 4) and extracts a continuous voltage data segment. The length of the extracted data segment is a preset parameter, for example, set to sampling points. Then, it extracts voltage values starting from index 4 + 1 = 5, that is . The corresponding voltage values are . This sequence constitutes the voltage change sequence associated with the first mutation event. Then, it processes the second record (start_idx = 5, end_idx = 6,...). Its end time point corresponds to the original difference sequence index 6, that is, the original voltage sequence index 7, corresponding to voltage . It starts extracting points from the next point (index 8), but since the original voltage sequence only goes to index 9, it can only extract , that is . If a fixed length is required, this event may be discarded or specially processed due to insufficient data. Assuming that we allow extraction to the end, the extracted sequence is . The voltage data segments successfully extracted each time are stored to form a set containing multiple voltage change sequences for the use of the next sub-module to obtain the voltage change sequences.

[0070] Based on the voltage change sequence, the response start point recognition sub-module performs differential processing on the voltage values at every two adjacent time points in the sequence, calculates the first-order derivative of the voltage and marks the derivative sign, and screens the time point segments where the derivative values are continuously negative. Using the formula:

[0071] ;

[0072] Determine the response start time point , identify the interval length from the start time point to before the trend reversal, and obtain the negative duration sequence. Among them, is the voltage value at the kth moment, is the sampling time interval, is the sign function of the voltage derivative, and n is the minimum number of consecutive negative periods of the voltage derivative. is the k-th sampling time point. is the derivative negative trend threshold; based on the extracted voltage change sequence, the first sequence and the second sequence , the response start point recognition sub-module processes each sequence to as an example, the original index corresponding to this sequence is , first, the voltage values at every two adjacent time points in the sequence are differentiated. Assuming the sampling time interval is seconds, the approximate value of the first-order derivative of the voltage is calculated . For , the derivative sequence obtained is , and the module then marks the sign of each derivative , obtaining the sign sequence [1, 1, -1, -1]. Then, the time point segments where the derivative values are continuously negative are filtered out. In this example, the continuously negative segment appears in the last two derivatives, corresponding to the derivatives at the original indices k = 7 and k = 8 (i.e., and ). Next, the formula is applied to determine the response start time point . The parameter description of the formula is as follows: : The voltage response start time point to be found. : The k-th sampling time point in the voltage change sequence. The voltage value at time point . : Sampling time interval (unit: second s). For example, : The voltage change rate at time point (approximate value of the first-order derivative, unit: volt per second V / s). : Sign function of the voltage change rate, which is 1 when the voltage rises, -1 when it drops, and 0 when it remains unchanged. : This term is actually equal to , that is, the voltage change rate itself. n: Window size for calculating the average voltage change rate, that is, how many consecutive periods of average change rate are required to meet the conditions. Set n = 2. This value is set based on the understanding of the battery voltage response characteristics, believing that at least two consecutive sampling points with a negative trend are considered valid response start points to avoid single-point noise interference. Derivative negative trend threshold (unit: V / s). Set This threshold is based on the evaluation of the voltage measurement noise level, and a value slightly larger than the normal fluctuation caused by noise is selected. For example, if the change rate caused by voltage noise is usually within , then choosing 0.005 V / s can effectively filter the noise. Summation symbol It represents the voltage change rate accumulated continuously for n time points starting from the current time point k. It represents calculating the arithmetic mean of these n change rates. It represents the requirement that this average voltage change rate is less than the negative threshold, that is, having a significant negative (downward) trend. It represents among all those that meet the conditions selecting the one with the earliest time (the smallest index) as the response starting point , formula logic: This formula finds the starting point where the voltage begins to continuously decline by calculating the moving average of the voltage change rate. The moving average can smooth the noise, n ensures the persistence of the trend, ensures the significance of the decline, and min ensures that the starting position of the downward trend is found. The benefit of the formula is that by combining the moving average and the negative threshold to judge the voltage response starting point, compared with the method that only relies on single-point derivative or simple threshold, it can more robustly identify the true starting point of the voltage decline caused by the current jump, effectively avoiding misjudgment caused by noise spikes or short-term fluctuations; example: Using the derivative sequence , the derivative corresponding to the original voltage sequence index , setting , when k = 5 (considering i = 5, 6), the average derivative: , does not meet , when k = 6 (considering i = 6, 7), the average derivative: , does not meet , when k = 7 (considering i = 7, 8), the average derivative: , meets , so the smallest k that meets the condition is 7, and the corresponding response starting time point is the time point with index 7 in the original voltage sequence , this result shows that at moment, the voltage begins to show a continuous and significant downward trend, and this time point is considered as the starting point of the voltage response after the current jump. Then, the module identifies the length of the interval where the voltage derivative is continuously negative starting from . The derivative sequence starting from index 7 is , and the length of being continuously negative is 2 periods. The module records this negative duration, for example, stores it as [2]. Repeat this process for all the extracted voltage sequences to obtain a series of response starting time points and the corresponding negative durations, and obtain the negative duration sequence.

[0073] The deviation data acquisition sub-module calculates the time interval between the response start time point sequence and the jump end time point data in the mutation buffer segment data set, counts the difference between the time interval and the preset response time limit as the offset, and obtains the jump response time deviation data;

[0074] According to the response start time point sequence, for example, for the first mutation event identified , and the mutation buffer segment data associated with this event recorded in the "mutation buffer segment construction sub-module" (see the first row of Table 1), especially the jump end time point therein (corresponding to the original sequence index 4, denoted as , the deviation data acquisition sub-module calculates the time interval between these two . In this example, , since the time point index represents the sampling serial number and the sampling interval is , the time interval is seconds. Next, the module compares the calculated time interval with a preset response time limit and calculates the difference as the offset . The preset response time limit is set based on the understanding of the electrochemical response speed of this type of battery and the requirements of the system application scenario. For example, for a system that requires a fast response, it may be required that the voltage starts to change significantly within 1 second after the current step is stable. Therefore, it can be set seconds. This value can be obtained by analyzing the data distribution under a large number of normal working conditions , and taking, for example, its 80% quantile, such as seconds, for calculation: seconds. This offset represents the delay of the actually observed voltage response start time relative to the expected response time limit. This calculation is performed for each identified and the corresponding . All calculated offsets are counted to form a list of offset data, such as [2.0,...] (if subsequent events are processed), and the jump response time deviation data is obtained.

[0075] Please refer to Figure 4 , the starting point confirmation module includes:

[0076] The voltage deviation judgment sub-module, based on the jump response time deviation data, extracts the voltage value corresponding to each response start point, obtains the voltage value of the previous cycle, calculates the difference, and compares the difference with the capacity change trigger threshold to determine whether it exceeds the capacity change trigger threshold, and obtains the trigger effective index sequence;

[0077] Based on the jump response time deviation data and the associated response start time point (e.g., ) and the original voltage sequence , the voltage deviation judgment sub-module first extracts each corresponding voltage value . For , extract . Then, the module also needs to obtain a voltage value of the "previous cycle" as a comparison reference. Here, the "previous cycle" refers to the steady-state voltage before the current jump that triggers this response. One way is to take the voltage at the point before the starting point of the mutation buffer segment data corresponding to this current jump (the first row of Table 1, start_idx = 2), that is . Take this voltage as , calculate the voltage difference . Then, take the absolute value of this difference and compare it with a preset capacity change trigger threshold . is set considering the voltage platform characteristics of the battery in a specific SOC range and the influence of capacity attenuation on the voltage response amplitude. For example, when the capacity drops significantly, the same current change may cause a larger voltage drop. This threshold can be determined through experiments or simulations to determine how much voltage response deviation usually indicates a capacity change that needs attention. Assume that for the current working condition, set , make a judgment . Compare whether 0.03 is greater than . The result is no . Therefore, this response event is not considered a valid trigger. If the calculated is greater than , for example, assume , then . . At this time, 0.06 > 0.05, the judgment is true, then record the time index of the starting point of this response into the trigger valid index sequence. The module repeats this process for all response events, filters out all indexes that meet the conditions, and obtains the trigger valid index sequence.

[0078] The response point binding sub-module combines the trigger valid index sequence, extracts the voltage value, current value, and state of charge information at the corresponding time points, performs synchronous recording and joint marking, and obtains the state of charge mapping data group;

[0079] Combining the trigger valid index sequence, assume that after screening, an effective index list is obtained, such as [t_{15}, t_{28}] (to avoid confusion with the previous example, a new index example is used), and the original synchronously collected voltage sequence V, current sequence and the state of charge (SOC) sequence S estimated by the battery management system (BMS), the response point binding sub-module processes each time point index in the valid index list in sequence. For the first valid index , the module extracts the data corresponding to this time point from the three original sequences: the voltage value , the current value , and the state of charge value , and queries to obtain: , synchronously records and jointly marks these three values together with the timestamp to form a data unit, such as (timestamp=t_{15},voltage=3.70,current=18.5,soc=55.8), and processes the next valid index , to obtain (timestamp=t_{28},voltage=3.68,current=19.0,soc=52.3). The data units extracted from all valid time points are assembled to form a data set, where each element contains the key state parameters at the same moment, and the state of charge mapping data group is obtained.

[0080] The sampling data generation sub-module binds and arranges the combined voltage value, current value, and state of charge information according to the state of charge mapping data group, constructs a standard data structure and unifies the recording format, and obtains the capacity sampling data packet;

[0081] According to the state of charge mapping data group, for example, it includes:

[0082] (timestamp=t_{15},voltage=3.70,current=18.5,soc=55.8);

[0083] (timestamp=t_{28},voltage=3.68,current=19.0,soc=52.3);

[0084] Based on the recorded data set, the sampling data generation sub-module binds and organizes these combined voltage values, current values, and state of charge information. This is mainly to ensure the unity and standardization of the data format for subsequent processing by analysis modules or storage systems. This may involve organizing the data into specific data structures, such as an array of JSON objects, a Pandas DataFrame, or database records. For example, a standard data structure can be defined, with each structure containing fields: timestamp (such as Unix timestamp or a specific format string), voltage (floating point number, unit V), current (floating point number, unit A, with positive and negative values possibly distinguished to represent charging and discharging), and state of charge (floating point number, unit %). The module maps each record in the data set into this standard structure. For example, the first record is converted to:

[0085] {'timestamp': 1678886415, 'voltage': 3.70, 'current': -18.5,'soc': 55.8} (assuming the corresponding timestamp, and a negative current indicates discharging);

[0086] The second record is converted to:

[0087] {'timestamp': 1678886428, 'voltage': 3.68, 'current': -19.0,'soc': 52.3};

[0088] Organize all the converted standard data structures together to form an ordered (sorted by timestamp) data packet. This data packet contains all the state sampling points that are considered valid and reflect the battery's state under specific dynamic responses, obtaining the capacity sampling data packet.

[0089] Please refer to Figure 5 , the state diagnosis module includes:

[0090] The rate judgment sub-module calculates the difference in the state of charge between consecutive sampling time points for each cycle based on the state of charge sequence in the capacity sampling data packet during the recorded time period, determines whether the difference exceeds the state of charge rate mutation identification threshold, filters the cycle index positions that meet the conditions, and obtains the state of charge rate mutation cycle sequence;

[0091] Based on the aforementioned capacity sampling data packet obtained, which contains records arranged in chronological order, each record containing a timestamp, voltage, current, and state of charge (SOC). For example, the SOC sequence part extracted from the data packet is: ;

[0092] The rate judgment sub-module calculates the difference in the state of charge between consecutive sampling time points for each cycle (i.e., for each sampling time point) , for example, calculate , , …, , assume , then , , next, the module takes the absolute value of each difference and compares it with a preset recognition threshold for sudden changes in the charging rate . The setting of this threshold needs to consider the normal SOC estimation update rate and the possible SOC jump amplitude caused by model calibration, Coulomb efficiency compensation adjustment, or specific events (such as OCV calibration after a long-term standstill). The goal is to identify abnormal SOC changes that far exceed the normal Coulomb integration change. For example, if the SOC changes by no more than 0.5% per second during normal operation, but occasional internal state refreshes of the BMS may cause jumps of up to 2%, then can be set for judgment: , and all cycle index positions k that satisfy are screened out. In this example, the index that satisfies the condition is k = 27. The module records these indexes and obtains the charging rate mutation cycle sequence

[27] .

[0093] According to the charging rate mutation cycle sequence, the key cycle extraction sub-module obtains the voltage value sequence corresponding to the corresponding cycle positions, performs a sliding window process with a fixed cycle width, constructs a set of differences between adjacent cycle voltage values within each window, and uses the formula:

[0094] ;

[0095] Calculate the voltage fluctuation stability of the th cycle, screen the stability, extract the cycle positions less than the stability threshold value, and obtain the key cycle sequence of state mutations. Among them, is the voltage value of the th cycle, N is the number of cycles in the sliding window, is the absolute difference between adjacent voltage values;

[0096] According to the charging rate mutation cycle sequence output by the previous sub-module, such as

[27] , and the voltage sequence V in the capacity sampling data packet, the key cycle extraction sub-module obtains the voltage value sequence near the position corresponding to the mutation cycle k = 27. For example, it extracts the voltage value sequence that expands several points before and after with k = 27 as the center (for example, a total of 10 points are extracted, from k = 22 to k = 31) , then perform a sliding window process on this voltage segment with a fixed cycle width N. Set the sliding window width N = 5 cycles. The selection of this width N aims to balance noise smoothing and sensitivity to voltage dynamic changes, usually selecting a time length corresponding to several seconds, such as 5 seconds (if the sampling interval is 1 second). For each window, construct a set of differences between adjacent cycle voltage values within the window and , where is the starting index of the window, and then apply the formula to calculate the voltage fluctuation stability of the th cycle . Explanation of formula parameters: : Represents the voltage fluctuation stability index within a window of length N starting from the th cycle. The smaller the value, the more stable the voltage : The voltage value of the th cycle (here U is consistent with V in the previous text, representing voltage), N: The number of cycles of the sliding window (window size), for example, N = 5 : The absolute difference between the voltage value of the th cycle within the window and the voltage value of its previous cycle : Represents the sum of all terms from index to within the window : Calculate the average value of the absolute differences of the voltages within the window, measuring the average fluctuation amplitude : The square of the voltage difference within the window : Calculate the square root of the sum of the square roots of the root mean squares of the voltage differences within the window (similar to the standard deviation, but not divided by N - 1), measuring the overall energy or dispersion of the fluctuations, and being more sensitive to large jumps. Formula logic: This formula evaluates the voltage stability by combining the average absolute deviation of the voltage changes within the window and the L2 norm (not normalized) of the voltage changes. The first term focuses on continuous small fluctuations, and the second term focuses on large instantaneous fluctuations. The sum of the two gives a comprehensive stability index. The advantage of the formula is that it simultaneously considers the average amplitude and peak amplitude of the voltage fluctuations (through squaring and taking the square root), and can describe the stable state of the voltage more comprehensively than a single index (such as only using the average absolute difference or standard deviation), especially being able to distinguish the different effects of continuous small noise and short-term large perturbations on stability; Numerical example: Assume that the voltage sequence segment extracted near k = 27 is:

[0097] ;

[0098] (index corresponding to ), calculate the stability starting from the window (the window contains , and it is necessary to value), assuming and calculating the difference within the calculation window to : for i = 27: ; sum of absolute differences: ; average absolute difference: ; sum of squared differences: ; square root of the sum of squares: ; calculating module filters all values calculated by the sliding window and extracts those periodic positions less than the preset stability threshold ; the stability threshold is set based on the statistical analysis of the voltage fluctuation stability of the battery under stable operating conditions (such as standing or constant current charge / discharge at a low rate). For example, if is usually less than 0.02 under stable conditions, can be set with a certain margin for judgment: ; if the condition is met, the index is recorded. If calculated for other windows is also less than the starting index is also recorded. This result indicates that within the window starting from cycle k = 27 where the charging rate changes abruptly, the voltage shows high stability ( is less than the threshold), which may mean that the jump in SOC is not caused by a drastic change in external operating conditions but by an internal state adjustment. This cycle is considered a critical cycle, and a critical cycle sequence of state mutations is obtained. ; if the condition is met, the index is recorded. If calculated for other windows is also less than the starting index is also recorded. This result indicates that within the window starting from cycle k = 27 where the charging rate changes abruptly, the voltage shows high stability ( is less than the threshold), which may mean that the jump in SOC is not caused by a drastic change in external operating conditions but by an internal state adjustment. This cycle is considered a critical cycle, and a critical cycle sequence of state mutations is obtained. is less than the threshold), which may mean that the jump in SOC is not caused by a drastic change in external operating conditions but by an internal state adjustment. This cycle is considered a critical cycle, and a critical cycle sequence of state mutations is obtained. is less than the threshold), which may mean that the jump in SOC is not caused by a drastic change in external operating conditions but by an internal state adjustment. This cycle is considered a critical cycle, and a critical cycle sequence of state mutations is obtained.

[0099] The stable window marking sub-module performs a marking operation at each periodic position according to the critical cycle sequence of state mutations, synchronously binds the state mutation cycle with the stable voltage cycle within the subsequent sliding window, establishes a periodic synchronization information structure, and obtains the state voltage synchronization marking period;

[0100] According to the critical cycle sequence of state mutations, such as including indices [27, 45], etc., the stable window marking sub-module performs a marking operation on each critical cycle index. Taking the critical cycle index as an example, the module marks it as a critical moment of state mutation. At the same time, it binds it to the All the cycles within the sliding window that are used and determined to be stable are synchronously bound. In the example of the previous module, the calculation used the voltage values from to (window width N = 5), and the calculated was less than the stability threshold , indicating that this window is a set of stable voltage cycles. The module associates the key cycle with this stable window to create an information structure, for example:

[0101] (key_period=t_{27},stable_window=[t_{27},t_{28},t_{29},t_{30},t_{31}]);

[0102] The same operation is performed for all other indices in the key cycle sequence (such as ). If is also less than , then the corresponding record is created:

[0103] (key_period=t_{45},stable_window=[t_{45},...,t_{49}]);

[0104] These information structures together constitute the cycle synchronization information, which links the key time points of the SOC rate mutation with a period of voltage stability immediately following (or containing it) to obtain the status voltage synchronization marker cycle.

[0105] Please refer to Figure 6 , the correction output module includes:

[0106] The correction record incorporation sub-module extracts the state of charge values corresponding to the periods based on the status voltage synchronization marker cycle, incorporates them into the correction sequence structure in sequence, and attaches the current capacity trend node position to each correction value to establish a mapping index between the correction and the trend points to obtain the capacity correction reference sequence;

[0107] Based on the status voltage synchronization marker cycle information, such as including:

[0108] (key_period=t_{27},stable_window=[...]);

[0109] (key_period=t_{45},stable_window=[...]);

[0110] A set of such records. The correction record incorporation sub-module processes each record in sequence. For the record (key_period=t_{27},...), extract the key period The corresponding state of charge (SOC) value, which is obtained from the original capacity sampling data packet, for example (Note: Here, the value After the SOC jump occurs, that is, the value in the example of the previous module Or Its own value, depending on the specific definition. Here, it is assumed to be the value after the jump , or just use The value of the record. Assume it is the jump value of 52.3% recorded in . Take this SOC value of 52.3% as a correction candidate value and incorporate it into a correction sequence structure. At the same time, the module needs to attach an identifier of the current capacity trend node position to this correction value. This identifier indicates the stage at which this correction point is located in the overall battery capacity decay or change trend (e.g., "initial acceleration decay", "mid-term stable", "late-stage dive", etc.). The determination of this identifier may depend on more long-term historical capacity data and decay models. Assume that according to historical analysis, the current battery is in the mid-term stable stage of capacity decay, then attach the identifier "Mid-Stable", and thus form a correction reference record:

[0111] (correction_soc=52.3,trend_pos='Mid-Stable',time=t_{27});

[0112] Perform the same operation on all other records in the set (such as the records corresponding to ), extract (e.g., 48.1%), judge its trend node position (e.g., still 'Mid-Stable'), and form a record:

[0113] (correction_soc=48.1,trend_pos='Mid-Stable',time=t_{45});

[0114] Combine all these records to establish a mapping index between the correction candidate SOC values and their positions in the capacity decay trend, and obtain the capacity correction reference sequence.

[0115] The capacity difference calculation sub-module calculates the numerical difference based on the correction nodes in the capacity correction reference sequence and their corresponding initial capacity starting values, and combines the voltage fluctuation stability and the voltage response fluctuations of the front and back cycles, using the formula:

[0116] ;

[0117] Calculate the correction amplitude of the battery pack capacity in the j-th cycle , establish the relationship between the correction amplitude and the difference and change rate between nodes, and obtain the capacity correction amplitude sequence, where is the state of charge value in the j-th cycle, is the starting value of the capacity, and are the voltage values in the j-th cycle and the (j - 1)-th cycle respectively, is the voltage fluctuation stability in the j-th cycle, and m is the number of periods for trend continuation;

[0118] Based on the obtained capacity correction reference sequence, for example, it includes:

[0119] (correction_soc = 52.3, trend_pos = 'Mid-Stable', time = t_{27});

[0120] (correction_soc = 48.1, trend_pos = 'Mid-Stable', time = t_{45});

[0121] and other recorded sequences, and an initial capacity starting value is required , this represents the reference SOC value of the current capacity evaluation cycle, or a certain reference point SOC theoretically without attenuation, depending on the specific correction strategy. Assume is the estimated SOC value at the start of this round of evaluation, for example . The capacity difference calculation sub-module processes each record in the correction reference sequence. Taking the record with j = 27 (time ) as an example, its corrected node SOC value is . The module applies the formula to calculate the correction amplitude of the battery pack capacity in the j-th cycle . Explanation of formula parameters: : The correction amplitude calculated in the j-th cycle, dimensionless, is a comprehensive indicator, : The state of charge value in the j-th cycle (obtained from the correction reference sequence), expressed in decimal form, for example , : The starting value of the capacity (reference SOC value), expressed in decimal form, for example , : The voltage value in the j-th cycle, obtained from the original data, for example , : The voltage value in the (j - 1)-th cycle, for example , : Voltage fluctuation stability in the j-th period (calculated by the key period extraction sub-module), for example , required and subsequent values, m: number of periods for trend continuation, used to examine the change trend of voltage stability within a period of time after the correction point. Set m = 3. The setting of this value is based on how long the stability change after the correction point is hoped to be observed to assist in judging the reliability of the correction. For example, observe for 3 seconds. : Absolute difference between the SOC at the correction point and the reference SOC, reflecting the deviation degree of SOC. : Absolute voltage change amount at the moment of the correction point, reflecting the voltage stability at that time. : Calculate the sum of the absolute changes in voltage fluctuation stability for each period within m periods starting from the correction point j. : Calculate the average change rate of stability S within m periods after the correction point, reflecting whether the voltage stability tends to be stable or is still changing. Formula logic: This formula calculates the correction amplitude by summing three parts: the absolute deviation of SOC, the voltage change at the correction moment, and the change trend of voltage stability within a short period of time after the correction moment. A larger value means a large SOC deviation, or a voltage jump at the correction moment, or the voltage stability is still changing after the correction (which may indicate that the system is not fully stable). Considering these factors comprehensively to judge the necessity and amplitude of the correction. The benefit of the formula is that it not only considers the static deviation of SOC ( ), but also incorporates dynamic information (voltage change ) and quasi-static information (stability change trend ), enabling the calculation of the correction amplitude to take into account the stability of the system state at the measurement point itself and for a period of time after it, improving the robustness of the correction decision; Example calculation: Calculate , known , , , , required , , (because m = 3, k = 27, 28, 29 are required, involving , , ), assuming that through calculation, is obtained. First part: . Second part: . Third part: Calculate , k = 27: , k = 28: , k = 29: ; Summation: ; Averaging: ; Calculate The module calculates the corresponding values for all records in the corrected reference sequence, forming a correction amplitude sequence, such as [0.06050,...]. This result is a quantization index with relatively small values, mainly contributed by the SOC deviation, indicating that although there is an SOC deviation, the voltage at the correction point is stable and the subsequent stability change is small. It is possible to determine the actual correction weight or whether to perform correction based on the magnitude of the value. For example, a threshold is set. Because , it is considered that correction is required, and a capacity correction amplitude sequence is obtained.

[0122] The adjustment record generation sub-module combines each capacity correction amplitude with the corresponding state of charge and trend identifier according to the capacity correction amplitude sequence and the node positioning information in the capacity correction reference sequence to obtain the battery pack capacity adjustment record;

[0123] According to the calculated capacity correction amplitude sequence, such as containing , etc., and the capacity correction reference sequence established in the "correction record inclusion sub-module", which contains the state of charge (SOC) value and trend identifier information corresponding to each correction point, such as:

[0124] (correction_soc=52.3,trend_pos='Mid-Stable',time=t_{27});

[0125] The adjustment record generation sub-module combines these two parts of information. For the time point , the module combines the calculated correction amplitude with the corresponding state of charge and the trend identifier 'Mid-Stable' to form a complete adjustment record, which clarifies at which time point, based on what correction driving force (amplitude), what the current SOC reading is, and which stage of capacity change it is in. For example, the record:

[0126] (time=t_{27},adjustment_magnitude=0.06050,soc_at_point=52.3,trend_phase='Mid-Stable');

[0127] This operation is performed on each value in the capacity correction amplitude sequence and its corresponding reference sequence information, and all the generated records are pooled. This set is the final output, which can be used as input by the capacity estimation algorithm in the BMS to determine how to adjust the current battery capacity (SOH) estimation or SOC estimation model parameters, and obtain the battery pack capacity adjustment record.

[0128] The above are only the preferred embodiments of the present invention, and are not intended to limit the present invention in other forms. Any person skilled in the art may use the technical content disclosed above to make changes or modifications into equivalent embodiments with equivalent changes and apply them to other fields. However, as long as the technical solution content of the present invention is not departed from, any simple modification, equivalent change and modification made to the above embodiments based on the technical essence of the present invention still fall within the protection scope of the technical solution of the present invention.

Claims

1. An intelligent online capacity verification management system for a battery pack, characterized in that, The system includes: The jump perception module obtains the current sampling sequence within the operation period of the battery pack, selects the starting point of current mutation, tracks the convergence interval of the difference value of each subsequent period towards zero after the starting point of mutation, and generates a set of data for the mutation buffer section; The hysteresis correlation module, based on the set of data for the mutation buffer section, extracts the continuous voltage data after the jump end time point, selects the starting point of response to count the duration of the negative trend, and obtains the jump response time deviation data by comparing the response time limit; The starting point confirmation module, based on the jump response time deviation data, extracts the voltage value corresponding to the starting point of response, determines whether the difference from the voltage value of the previous period exceeds the capacity change trigger threshold, counts the current and voltage values corresponding to the response point, and obtains the capacity sampling data packet; The state diagnosis module, based on the capacity sampling data packet, calculates the difference in the state of charge per period, locates the key period of state mutation, analyzes whether all subsequent voltage difference sets are less than the stability threshold value. If satisfied, it generates a state voltage synchronization marked period; The correction output module, according to the state voltage synchronization marked period, incorporates the corresponding state of charge value into the nuclear capacity correction record sequence, calculates the capacity correction amplitude, and obtains the battery pack capacity adjustment record.

2. The intelligent online capacity verification management system for battery packs according to claim 1, wherein, The set of data for the mutation buffer section includes the current upper limit, current lower limit, and period length. The jump response time deviation data includes the starting point of response, voltage change trend, and duration of the negative trend. The capacity sampling data packet includes the current value, voltage value, and state of charge. The state voltage synchronization marked period includes the key period of state mutation, voltage difference set, and stability judgment flag. The capacity adjustment record includes the state of charge value, source of capacity trend node correction, and capacity correction amplitude.

3. The intelligent online capacity verification management system for battery packs according to claim 1, characterized in that, The jump perception module includes: The difference extraction sub-module, based on the current sampling sequence within the operation period of the battery pack, sequentially selects the current values of each sampling point and its adjacent points before and after, calculates the difference between the current sampling point and the previous sampling point and the difference between the current sampling point and the next sampling point, obtains the set of differences before and after each sampling point, and obtains the current difference sequence; The mutation starting point identification sub-module, according to the current difference sequence, determines whether each difference exceeds the jump current threshold, filters out the sequence segments where consecutive differences exceed the jump current threshold, locates the starting position of the sequence segment as the mutation starting point, and obtains the current mutation starting point index sequence; The mutation buffer section construction sub-module, according to the current mutation starting point index sequence, starting from each mutation starting point, combines the difference sequence in the subsequent period, determines whether the difference gradually converges towards zero, records the end point of the convergence interval as the termination point of the buffer section, records the maximum current value, minimum current value, and period length within the interval, and obtains the set of data for the mutation buffer section.

4. The intelligent online capacity verification management system for a battery pack according to claim 1, wherein, The hysteresis correlation module includes: The voltage sequence extraction sub-module, based on the jump end time point marked in the set of data for the mutation buffer section, extracts the continuous voltage data segment after the jump end time point from the original time sequence, and obtains the voltage change sequence; Based on the voltage change sequence, the response start point recognition sub-module performs differential processing on the voltage values at every two adjacent time points in the sequence, calculates the first-order derivative of the voltage and marks the derivative sign, screens out the time point segments where the derivative values are continuously negative, and uses the formula: ; Determine the response start time point , identify the interval length from the start time point to before the trend reversal, and obtain a negative duration sequence, where is the voltage value at the k-th moment, is the sampling time interval, is the sign function of the voltage derivative, n is the minimum number of consecutive negative periods of the voltage derivative, is the k-th sampling time point, is the derivative negative trend threshold; The deviation data acquisition sub-module calculates the time interval between the response start time point sequence and the jump end time point data in the mutation buffer segment data set, counts the difference between the time interval and the preset response time limit as the offset, and obtains the jump response time deviation data.

5. The intelligent online capacity verification management system for a battery pack according to claim 1, wherein The start point confirmation module includes: The voltage deviation judgment sub-module extracts the voltage value corresponding to each response start point based on the jump response time deviation data, obtains the voltage value of the previous cycle, calculates the difference, and compares the difference with the capacity change trigger threshold to determine whether it exceeds the capacity change trigger threshold, and obtains the trigger effective index sequence; The response point binding sub-module combines the trigger effective index sequence, extracts the voltage value, current value, and state of charge information at the corresponding time points, performs synchronous recording and joint marking, and obtains the state of charge mapping data group; The sampling data generation sub-module binds and arranges the combined voltage value, current value, and state of charge information according to the state of charge mapping data group, constructs a standard data structure and unifies the recording format, and obtains the capacity sampling data packet.

6. The intelligent online capacity verification management system for a battery pack according to claim 1, wherein The state diagnosis module includes: The rate judgment sub-module calculates the difference in the state of charge between consecutive sampling time points cycle by cycle based on the state of charge sequence in the recording time period of the capacity sampling data packet, determines whether the difference exceeds the state of charge rate mutation recognition threshold, filters the cycle index positions that meet the conditions, and obtains the state of charge rate mutation cycle sequence; The key cycle extraction sub-module obtains a voltage value sequence corresponding to the cycle positions according to the charging rate mutation cycle sequence, performs a sliding window process with a fixed cycle width, constructs a difference set of the voltage values of adjacent cycles within each window, and uses the formula: ; Calculate the voltage fluctuation stability of the th cycle, screen the stability, extract the cycle positions less than the stability threshold value, and obtain the key cycle sequence of state mutation. Among them, is the voltage value of the th cycle, N is the number of cycles of the sliding window, is the absolute difference between adjacent voltage values; The stable window marking sub-module performs a marking operation at each cycle position according to the state mutation key cycle sequence, synchronously binds the state mutation cycle with the stable voltage cycle in the subsequent sliding window, establishes a cycle synchronization information structure, and obtains the state voltage synchronization marking cycle.

7. The intelligent online capacity verification management system for battery packs according to claim 1, characterized in that The correction output module includes: The correction record incorporation sub-module extracts the state of charge value corresponding to the cycle based on the state voltage synchronization marking cycle, incorporates it into the correction sequence structure in turn, and attaches the current capacity trend node position to each correction value to establish a mapping index between the correction and the trend point, and obtains the capacity correction reference sequence; The capacity difference calculation sub-module calculates the numerical difference based on the correction nodes in the capacity correction reference sequence and their corresponding initial capacity starting values, and combines the voltage fluctuation stability and the voltage response fluctuations in the previous and subsequent cycles, using the formula: ; Calculate the correction amplitude of the battery pack capacity in the j-th cycle , establish the relationship between the correction amplitude and the difference and change rate between nodes, and obtain the capacity correction amplitude sequence, where is the state of charge value in the j-th cycle, is the starting value of the capacity, and are the voltage values in the j-th cycle and the (j - 1)-th cycle respectively, is the voltage fluctuation stability in the j-th cycle, and m is the number of cycles for the trend to continue; The adjustment record generation sub-module combines each capacity correction amplitude with the corresponding state of charge and trend identifier according to the node positioning information in the capacity correction amplitude sequence and the capacity correction reference sequence, and obtains the battery pack capacity adjustment record.

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