Lead-acid battery health assessment method and system based on simulated working condition

Abstract

The invention belongs to the technical field of battery state detection, and discloses a lead-acid battery health assessment method and system based on simulated working conditions. Comprising the steps of collecting lead-acid battery simulation operation data and performing data cleaning to obtain high-quality battery data; screening recovery effect data and removing the recovery effect data from the high-quality battery data to obtain disturbance-removed battery data; performing polarization voltage compensation to generate voltage compensation battery data; abnormal impact identification is executed, abnormal data filtering is carried out on the voltage compensation battery data based on an identification result, and reasonable battery data is output; identifying long-period data and performing risk judgment to obtain corrected battery data and cover corresponding data in the reasonable battery data to obtain differentiated battery data; performing health state evaluation on the differentiated battery data, and outputting health indexes of the lead-acid battery; and the evaluation accuracy of the real health level of the battery under the simulated working condition is remarkably improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of battery state detection, and more particularly, to a lead-acid battery health evaluation method and system based on simulated working conditions. BACKGROUND

[0002] Currently, lead-acid batteries are widely used in many key industries due to their low cost, high reliability, and easy maintenance. In these application scenarios, the stability and health status of the battery as the core of energy storage have a significant impact on the overall safety of the system. Therefore, a high-precision battery health evaluation method is gradually becoming an important direction in optimizing the battery management system. However, traditional health evaluation methods gradually fail to meet the robustness and accuracy requirements in actual operation, so there is an urgent need for a health evaluation method with high accuracy and strong generality. Traditional health evaluation methods generally do not consider the recovery effect of the battery after deep discharge, which refers to the short-term rebound of voltage due to ion diffusion redistribution. This recovery effect can easily cause deviations in subsequent health state evaluation. For example, in the deep discharge simulation scenario data collection of a certain type of battery pack, the recovery effect occurs after discharge, and the voltage in the rebound section is inconsistent with the actual situation, resulting in evaluation errors in subsequent health state evaluation. On the other hand, traditional health evaluation methods often use ideal step current loading as the estimation trigger condition, but in reality, the current of devices such as UPS, IDC machine room, and elevator during startup is often ramp loading or nonlinear increment. In this loading process, the traditional health evaluation method does not consider the response delay and amplitude distortion of the polarization voltage caused by the change of the load curve slope, further causing evaluation errors. At the same time, in actual operation, sudden short-time reverse current or transient oscillation impact signals are also a major blind spot of traditional health evaluation methods. For example, a millisecond-level backflow current occurred during the switching process of a certain communication base station, causing a spike error in the voltage data. If this kind of jump data is not removed, it may directly lead to misjudgment of the health state. In addition, for a large number of batteries in the floating state, the sulfurization degradation of the plates may occur due to long-term static state, and traditional health evaluation methods often lack consideration of sulfurization degradation, easily regarding extreme stability as a normal phenomenon, thereby ignoring the possible degradation of the basic function of the battery, leading to significant deviations in the final health state evaluation.

[0003] In view of the above problems, the present application provides a lead-acid battery health evaluation method and system based on simulated working conditions. SUMMARY

[0004] To overcome the above-mentioned defects of the prior art, in order to achieve the above-mentioned purposes, the present application provides the following technical solutions: a lead-acid battery health evaluation method based on simulated working conditions, comprising: S1. Collecting lead-acid battery simulation operation data and performing data cleaning to obtain high-quality battery data; S2. Performing recovery effect analysis on the high-quality battery data to obtain recovery effect data and remove the same from the high-quality battery data to obtain disturbance-removed battery data; S3. Compensating the polarization voltage of the disturbance-removed battery data to generate voltage-compensated battery data; S4. Identifying abnormal impacts on the voltage-compensated battery data, and filtering abnormal data from the voltage-compensated battery data based on the identification result to output reasonable battery data; S5. Identifying long-period data in the reasonable battery data, performing risk discrimination on the long-period data to obtain modified battery data and cover corresponding data in the reasonable battery data to obtain differentiated battery data; S6. Performing health state evaluation on the differentiated battery data to output a lead-acid battery health index and send the same to a preset battery management terminal.

[0005] Further, the recovery effect analysis includes: Extracting voltage data and current data from the high-quality battery data, and constructing voltage value sequences and current value sequences, respectively; calculating the absolute value of the current difference between any two sampling points in the current value sequence, and identifying time segments with absolute values of the current difference less than a preset current amplitude threshold as non-load segments; calculating the change rate of each sampling point in the voltage value sequence and constructing a voltage change rate sequence, and identifying non-load voltage time segments corresponding to the non-load segments in the voltage change rate sequence; Constructing a dynamic sliding window to traverse the non-load voltage time segments, and if there is a sub-segment with a slope greater than a preset change rate slope threshold in the dynamic sliding window and the time length of the sub-segment is higher than a preset time length threshold, the time segment corresponding to the dynamic sliding window is determined as a candidate recovery effect segment; calculating the standard deviation of the voltage change rate of the candidate recovery effect segment, and if the standard deviation is less than a preset stable standard deviation threshold, the corresponding candidate recovery effect segment is determined as a recovery effect segment; indexing and marking the recovery effect segment, and filtering corresponding data from the high-quality battery data based on the marking to obtain recovery effect data.

[0006] Further, the indexing and marking includes: respectively, until the minimum voltage change rate slope in the extended recovery effect section is not lower than the preset continuous slope threshold or the section length is smaller than the preset minimum section length; marking the sample points corresponding to the extended start boundary and the extended end boundary of all the adjusted extended recovery effect sections, identifying the time section corresponding to each adjusted extended recovery effect section as a recovery effect time section based on the marking.

[0007] Further, the manner of performing polarization voltage compensation includes: Identifying a time section in which the absolute value of the current difference value in the current value sequence is greater than the preset current amplitude threshold as an original load application section; matching the original load application section with the disturbance-removed battery data, if there is no time section corresponding to the original load application section in the disturbance-removed battery data, no processing is performed, if there is a complete time section or a partial time section corresponding to the original load application section in the disturbance-removed battery data, the original load application section is determined as a load application section; Calculating the current change rate of the load application section and constructing a current change rate sequence; identifying a section in which the current change rate continuously increases in the current change rate sequence as a current increase section; extracting the disturbance-removed voltage value sequence corresponding to the current increase section, calculating the maximum change slope of the disturbance-removed voltage value sequence; extracting the voltage change rate sequence corresponding to the section after the current increase section reaches the standard current steady state, obtaining a steady state voltage sequence; calculating the average value of the change rate of the steady state voltage sequence to obtain a reference voltage response rate, calculating the difference between the maximum change slope of the steady state sequence and the reference voltage response rate and taking the absolute value to obtain a polarization voltage response deviation; Based on the polarization voltage response deviation, a voltage compensation factor is constructed, the disturbance-removed voltage value sequence is weighted using the voltage compensation factor, and the corresponding data in the disturbance-removed battery data is covered using the weighted disturbance-removed voltage value sequence, to obtain voltage compensation battery data.

[0008] Further, the manner of constructing the voltage compensation factor includes: The polarization voltage response deviation is normalized to obtain a normalized slope deviation; the standard deviation of the slope in the undisturbed voltage value sequence is calculated to obtain a stability fluctuation degree; the ratio of the stability fluctuation degree to the reference voltage response rate is calculated to obtain a response stability ratio; the slope average of the current increasing section is calculated, and the ratio of the slope average to the maximum slope in the current increasing section is calculated to obtain a dynamic adjustment ratio; the product of the normalized slope deviation, the response stability ratio, and the dynamic adjustment ratio is calculated to obtain an initial compensation factor; the time interval from the last sampling point of the current increasing section to the standard current steady state is measured, and a time adjustment factor is constructed based on the time interval; and the product of the initial compensation factor and the time adjustment factor is calculated to obtain a voltage compensation factor.

[0009] Further, the manner of performing abnormal impact identification includes: The voltage compensation battery data is windowed based on a preset fixed identification time length window to obtain identification window data; a local voltage value sequence and a local current value sequence belonging to the window are constructed based on the identification window data; the local fluctuation amplitude of the maximum local voltage value and the minimum local voltage value in the local voltage value sequence is calculated, and if the local fluctuation amplitude is higher than a preset sharp peak voltage threshold, the identification window data corresponding to the local voltage value sequence is determined as a voltage mutation risk window; The current change direction in each half window before and after the local current value sequence is judged, and the local current difference value of the maximum local current value and the minimum local current value in any half window is calculated; if the current change directions in each half window before and after the local current value sequence are opposite and the local current difference value is higher than a preset reverse current determination value, the identification window data corresponding to the local current value sequence is determined as a current direction mutation window; The trend disturbance window is identified based on the voltage mutation risk window for trend fitting of all sampling points; if any two types of windows among the voltage mutation risk window, the current direction mutation window, and the trend disturbance window belong to the same window, the window is determined as an abnormal impact window.

[0010] Further, the manner of performing trend fitting includes: A linear fitting trend curve is plotted based on the local voltage value sequence corresponding to the voltage mutation risk window; the voltage deviation value of the sampling points of the corresponding window relative to the linear fitting trend curve is calculated, and a fitting deviation sequence is constructed based on the voltage deviation value; the deviation variance of the fitting deviation sequence is calculated, and if the deviation variance is greater than a preset trend disturbance determination threshold, the window corresponding to the fitting deviation sequence is determined as a suspected trend disturbance window; a section in which the voltage deviation values of the continuous sampling points in the suspected trend disturbance window are higher than twice the average voltage deviation value is identified, and if the number of the continuous sampling points in the section is higher than a sharp peak time lower limit, the corresponding suspected trend disturbance window is determined as a trend disturbance window.

[0011] Further, the manner of performing risk discrimination includes: traverse the time index of all sampling points in the reasonable battery data, extract a data section with a continuous time length higher than a preset stationary time limit, and take the data section as long-period data; if the current value of the long-period data is in a preset current floating interval, and the absolute value of the difference between the voltage value of the long-period data and a preset floating voltage threshold is not higher than a preset difference value, the corresponding long-period data is determined as floating state data; the floating state data is divided into unequal periods based on the time stamp of the sampling points in the floating state data, and local stationary period floating data is obtained; calculate the voltage standard deviation of any one local stationary period floating data, if the duration of the local stationary period floating data is higher than a preset stationary period threshold and the voltage standard deviation is less than a preset fluctuation threshold, the data section corresponding to the local stationary period floating data is determined as a potential sulfuration risk section; calculate the floating voltage amplitude of the potential sulfuration risk section, and calculate the ratio of the floating voltage amplitude to a preset standard stable voltage to obtain a floating voltage stability ratio; calculate the output current change amplitude and average output current value of the previous data section of the potential sulfuration risk section; linearly combine the floating voltage stability ratio, the output current change amplitude and the average output current value of the previous data section according to a preset proportion to obtain a health weight coefficient; the specific data of the potential sulfuration risk section is weighted by using the health weight coefficient to obtain the corrected battery data.

[0012] Further, the health state evaluation method comprises: extracting parameters corresponding to dimensions in the differentiated battery data according to health state evaluation requirements, and constructing an evaluation input data set; matching the historical evaluation template with the evaluation input data set to output the health score of each dimension parameter; matching the health score with a preset health score interval to output the health level of the corresponding dimension parameter, and integrating the health levels of all dimension parameters to obtain the lead-acid battery health index.

[0013] A lead-acid battery health evaluation system based on simulated working conditions is used to implement a lead-acid battery health evaluation method based on simulated working conditions, and is characterized by comprising: a data acquisition module for acquiring lead-acid battery simulation operation data and performing data cleaning to obtain high-quality battery data; an effect analysis module for performing recovery effect analysis on the high-quality battery data to obtain recovery effect data and eliminate it from the high-quality battery data to obtain disturbance-removed battery data; a voltage compensation module for performing polarization voltage compensation on the disturbance-removed battery data to generate voltage-compensated battery data; An abnormality identification module is configured to perform abnormal impact identification on the voltage compensation battery data and perform abnormal data filtering on the voltage compensation battery data based on the identification result to output reasonable battery data; A risk identification module is configured to identify long-period data in the reasonable battery data, perform risk identification on the long-period data, obtain modified battery data, and cover corresponding data in the reasonable battery data to obtain differentiated battery data. A health assessment module is configured to perform health state assessment on the differentiated battery data, output a lead-acid battery health index, and send the lead-acid battery health index to a preset battery management terminal. The modules are connected through wired and / or wireless modes.

[0014] The technical effects and advantages of the lead-acid battery health assessment method and system based on simulated working conditions are as follows: The collected lead-acid battery simulation operation data is processed in multiple stages, and the health state is assessed based on the processed data, thereby realizing a more accurate lead-acid battery health assessment method based on simulated working conditions. Compared with existing experience, the voltage recovery phenomenon existing in the original data is screened out for analysis through a recovery effect identification and elimination mechanism, the data with a recovery effect is accurately identified and directly eliminated, and the recovery effect is avoided from being mistakenly considered as a normal discharge state. The accurate identification of the load is introduced, the interference strength of the polarization interference section is determined and compensated, the adaptability of the subsequent health state assessment to the true load response is ensured, and the evaluation accuracy is indirectly improved. The voltage mutation window, the current direction mutation window, and the trend disturbance window are constructed to realize real-time identification and cleaning of typical abnormal impact behaviors, and the influence of abnormal working condition interference on the evaluation accuracy is avoided. The potential sulfuration risk section is identified and the health weight coefficient is constructed to mark and weight correct the potential sulfuration risk section, effectively avoiding the static data from being mistakenly judged as a high health value in the subsequent health state assessment process, and improving the perception ability of the evaluation process to the battery aging risk. Therefore, the above-mentioned health state assessment method significantly improves the evaluation accuracy of the real health level of the battery under simulated working conditions. BRIEF DESCRIPTION OF DRAWINGS

[0015] Figure 1 A schematic diagram of a lead-acid battery health assessment method based on simulated working conditions according to the present application; Figure 2 A schematic diagram of a lead-acid battery health assessment system based on simulated working conditions according to the present application. DETAILED DESCRIPTION

[0016] With reference to the accompanying drawings, the technical solutions in the embodiments of the present application will be described clearly and completely. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative efforts belong to the scope of the present application.

[0017] Embodiment 1 Please refer to Figure 1 The lead-acid battery health assessment method based on simulation working conditions described in this embodiment includes: S1. Collecting lead-acid battery simulation running data and performing data cleaning to obtain high-quality battery data; S2. Performing recovery effect analysis on the high-quality battery data to obtain recovery effect data and remove the recovery effect data from the high-quality battery data to obtain disturbance-removed battery data; S3. Compensating the polarization voltage of the disturbance-removed battery data to generate voltage-compensated battery data; S4. Performing abnormal impact identification on the voltage-compensated battery data, and filtering abnormal data from the voltage-compensated battery data based on the identification result to output reasonable battery data; S5. Identifying long-period data in the reasonable battery data, performing risk discrimination on the long-period data, obtaining modified battery data, and covering the corresponding data in the reasonable battery data to obtain differentiated battery data; S6. Performing health state assessment on the differentiated battery data to output lead-acid battery health indicators and send them to a preset battery management terminal.

[0018] The lead-acid battery simulation running data includes voltage, current, and corresponding time stamp data that can represent the running state of the battery in the simulation scenario. Through missing value completion and data filtering on the lead-acid battery simulation running data, high-quality battery data with higher quality is obtained.

[0019] The recovery effect analysis includes: Extracting voltage data and current data from the high-quality battery data, and constructing voltage value sequences and current value sequences, respectively, wherein the voltage value sequences and the current value sequences are sorted according to the time stamp order of the corresponding sampling points to obtain continuous and ordered data sets, thereby ensuring time consistency and point-to-point correspondence.

[0020] An absolute value of a current difference between any two adjacent sampling points in the current value sequence is calculated, and a time section in which the absolute value of the current difference is less than a preset current amplitude threshold is identified as a non-load section, wherein the preset current amplitude threshold is set based on historical simulation experience, and if the absolute value of the current difference is less than the preset current amplitude threshold, it indicates that the lead-acid battery in the time section corresponding to the continuous sampling points is not in a significant loading or unloading process, and therefore the corresponding time section is determined as a non-load section; wherein “continuous” in this embodiment refers to at least 5 sampling points or more to form a section.

[0021] A rate of change of each sampling point in the voltage value sequence is calculated to construct a voltage rate of change sequence, and a non-load voltage time section corresponding to a non-load section in the voltage rate of change sequence is identified, wherein the rate of change refers to a slope between any two adjacent sampling points in the voltage value sequence, and the slope is the ratio of the difference between the voltage values of adjacent sampling points to the time length. The slope of each sampling point is sorted in the order of the corresponding time stamp to obtain the voltage rate of change sequence, and the non-load voltage time section corresponding to the position of the non-load section in the voltage rate of change sequence is identified.

[0022] A dynamic sliding window is constructed to traverse the non-load voltage time section, and if there is a sub-section in the dynamic sliding window in which the slopes of the continuous sampling points are greater than a preset rate of change slope threshold and the time length of the sub-section is higher than a preset time length threshold, the time section corresponding to the dynamic sliding window is determined as a candidate recovery effect section; it should be noted that the calculation method of the slope and the rate of change is the same, and only the slope of the continuous data in the mathematical aspect is used to represent the slope, and the rate of change is not named as the slope in order to reflect that the value is used to represent the change of the voltage value.

[0023] The preset rate of change slope threshold and the preset time length threshold are set based on historical simulation experience, and if there are 5 or more continuous sampling points in the dynamic sliding window whose slopes are higher than the preset rate of change slope threshold, it indicates that the rate of change in the sub-section formed by the corresponding continuous sampling points is relatively fast, and the time length of the sub-section is higher than the preset time length threshold, and the above two conditions are met simultaneously, which can determine that the time section in which the dynamic sliding window is located belongs to a candidate section in which the voltage presents a recovery rise, and therefore it is determined as a candidate recovery effect section; in this embodiment, the window size of the dynamic sliding window can be dynamically adjusted based on historical simulation experience or specific simulation conditions.

[0024] The voltage rate of change standard deviation of the candidate recovery effect section is calculated, and if the voltage rate of change standard deviation is less than a preset stable standard deviation threshold, the corresponding candidate recovery effect section is determined as a recovery effect section. The voltage rate of change standard deviation is used to determine the recovery effect section. When the voltage rate of change standard deviation is less than the preset stable standard deviation threshold set based on historical simulation experience, it indicates that the voltage rising trend is stable at this time, there is no high-frequency disturbance, and therefore the section where the recovery effect occurs can be formally confirmed. Conversely, if the voltage rate of change standard deviation is large, it may be a false recognition caused by invalid voltage fluctuation, which improves the screening credibility of the recovery effect section.

[0025] The recovery effect section is indexed and labeled, and the corresponding data in the high-quality battery data is screened based on the label to obtain recovery effect data. The data corresponding to the time position of the recovery effect time section is determined as the recovery effect data. The specific parameters and time stamps corresponding to the recovery effect data are removed from the high-quality battery data. The adjusted high-quality battery data is used as disturbance-removed battery data.

[0026] The indexing and labeling method includes: A preset number of sampling points are extended to the starting boundary direction and the ending boundary direction of each recovery effect section to obtain an extended recovery effect section. In this embodiment, a preset number of sampling points are extended to the starting boundary direction and the ending boundary direction of the recovery effect section to cover the early lifting and lagging falling phenomena that may occur during battery operation. Since the recovery process of electrochemistry is not instantaneous, there may be part of the recovery characteristics that are outside the determination range. By extending the preset number of sampling points based on historical simulation experience, the identification accuracy of the recovery effect is improved.

[0027] The minimum voltage rate of change slope in the extended recovery effect section is extracted. If the minimum voltage rate of change slope is lower than a preset continuous slope threshold, the extended starting boundary and the extended ending boundary of the extended recovery effect section are gradually contracted in the opposite direction until the minimum voltage rate of change slope in the extended recovery effect section is not lower than the preset continuous slope threshold or the section length is less than a preset minimum section length.

[0028] The minimum voltage rate of change slope refers to the slope of the minimum voltage rate of change in the extended recovery effect section. In order to ensure the continuity and stability of the voltage rise in the extended recovery effect section, a preset continuous slope threshold is set based on historical simulation experience. If the minimum voltage rate of change slope is less than the preset continuous slope threshold, it indicates that there is voltage suppression or non-recovery disturbance in the section, which may lead to discontinuity and cannot completely determine whether the recovery effect occurs normally. Therefore, the corresponding extended recovery effect section needs to be adjusted.

[0029] In the embodiment, the extension start boundary is contracted in the direction of the extension end boundary, and the extension end boundary is contracted in the direction of the extension start boundary, until the minimum voltage change rate slope is not lower than a preset continuous slope threshold value or the segment length is smaller than a preset minimum segment length set based on historical simulation experience, so as to avoid the invalid data for judging the recovery effect into the data to be processed. It should be noted that the extension recovery effect segment with the minimum voltage change rate slope greater than or equal to the preset continuous slope threshold value is not processed, and can be directly marked.

[0030] The sampling points corresponding to the extension start boundary and the extension end boundary of all the adjusted extension recovery effect segments are marked, and the time segment corresponding to each adjusted extension recovery effect segment is identified based on the marking as a recovery effect time segment. In this way, the sampling points corresponding to the extension start boundary and the extension end boundary of the adjusted extension recovery effect segment are marked, the time segment corresponding to the extension recovery effect segment is detected based on the marking, and the time segment is taken as the recovery effect time segment.

[0031] The polarization voltage compensation manner includes: The time segment in which the current difference absolute value is greater than a preset current amplitude threshold value in the current value sequence is identified as an original load application segment. In this way, the current difference absolute value of the adjacent two sampling points in the current value sequence is calculated point by point, and the time segment in which the current difference absolute value is continuous and greater than a preset current amplitude threshold value set based on historical simulation experience is screened. Since the current of the time segment presents a continuous rapid rising phenomenon, it can be determined as the original load application segment.

[0032] The original load application segment is matched with the disturbance-removed battery data. If the time segment corresponding to the original load application segment does not exist in the disturbance-removed battery data, no processing is performed. If the complete time segment or the partial time segment corresponding to the original load application segment exists, the original load application segment is determined as a load application segment.

[0033] In this way, whether the original load application segment exists in the disturbance-removed battery data is determined by matching the original load application segment with the time index of the disturbance-removed battery data. Since the data in step S2 is removed from the high-quality battery data, and the current value sequence is constructed based on the high-quality battery data, the time segment corresponding to the original load application segment may not exist in the disturbance-removed battery data. If the complete time segment or the partial time segment corresponding to the original load application segment exists, the original load application segment is determined as a load application segment. The specific time length threshold of the partial time segment is set based on historical records. If the time length is lower than the specific time length threshold of the partial time segment, no processing is performed.

[0034] The current rate of change of the load application section is calculated and a current rate of change sequence is constructed, wherein due to the fact that the load application section may only have partial data, the corresponding parameters are temporarily filled from the current value sequence of the high-quality battery data in the polarization voltage compensation process; the current rate of change is sorted according to the corresponding sampling point timestamp to obtain the current rate of change sequence.

[0035] A section in which the current rate of change continuously increases in the current rate of change sequence is identified as a current increase section, wherein if the current rate of change of consecutive sampling points in the current rate of change sequence continuously increases and exhibits positive growth, the corresponding section is taken as the current increase section.

[0036] A sequence of depolarization voltage values corresponding to the current increase section is extracted, and the maximum change slope of the sequence of depolarization voltage values is calculated, wherein the sequence of depolarization voltage values refers to a sequence of voltage values in the current increase section, and the maximum change slope refers to the maximum slope in the sequence of depolarization voltage values, which represents the degree of voltage change of the battery when facing rapid load changes.

[0037] A sequence of voltage change rates corresponding to the section after the current increase section reaches a standard current steady state is extracted to obtain a steady state voltage sequence, wherein the standard current steady state is a current stable target value set based on existing theoretical knowledge and historical experience. When the current value in the current increase section reaches the standard current steady state and begins to maintain continuously, the sampling point at which the standard current steady state is first reached is taken as the starting point, and the corresponding sampling points are extracted point by point until the current value is lower than the standard current steady state. Based on the section formed by these sampling points, a sequence of voltage change rates corresponding to this section is constructed, which is the steady state voltage sequence.

[0038] The average value of the change rate of the steady state voltage sequence is calculated to obtain a reference voltage response rate, and the difference between the maximum change slope of the steady state sequence and the reference voltage response rate is calculated and taken as an absolute value to obtain a polarization voltage response deviation, wherein the maximum change slope of the steady state sequence refers to the maximum slope in the steady state voltage sequence. The difference between the maximum change slope of the steady state sequence and the reference voltage response rate is calculated and taken as an absolute value to reflect the disturbance to the voltage during the load loading process.

[0039] A voltage compensation factor is constructed based on the polarization voltage response deviation, the weighted product of each specific value in the depolarization voltage value sequence is obtained using the voltage compensation factor, and the corresponding data in the disturbance-removed battery data is covered using the weighted depolarization voltage value sequence to obtain voltage-compensated battery data, wherein if the voltage value of a sampling point that has been excluded is covered, the voltage value is also excluded at this point.

[0040] The voltage compensation factor is constructed in the following ways: The polarization voltage response deviation is normalized to obtain a normalized slope deviation, wherein the normalized slope deviation is obtained by calculating the ratio of the polarization voltage response deviation to a reference deviation threshold set based on historical simulation experience, and converting the result to a value within a standardized range.

[0041] The standard deviation of the slope in the deturbulence voltage value sequence is calculated to obtain a stability fluctuation degree, wherein the stability fluctuation degree quantifies the jitter degree of the voltage response in the load stage, and the greater the value, the greater the influence of other factors on the polarization voltage.

[0042] The ratio of the stability fluctuation degree to the reference voltage response rate is calculated to obtain a response stability ratio, wherein the response stability ratio quantifies the actual instability degree in the current voltage polarization process, and the greater the value, the more the response state deviates from the ideal state.

[0043] The average slope of the current increase section is calculated, and the ratio of the average slope to the maximum slope in the current increase section is calculated to obtain a dynamic adjustment ratio, wherein the average slope is used to reflect the typical speed of the load, and the ratio of the average slope to the maximum slope in the current increase section reflects the mutation degree of the load climbing process, which is used to quantify the influence of the load loading strength.

[0044] The product of the normalized slope deviation, the response stability ratio, and the dynamic adjustment ratio is calculated to obtain an initial compensation factor, wherein the initial compensation factor reflects the influence strength of the three factors of deviation, unstable response, and rapid loading.

[0045] The time interval from the last sampling point of the current increase section to the standard current steady state is measured, and a time adjustment factor is constructed based on the time interval, wherein the calculation formula for constructing the time adjustment factor is: ; wherein, represents the time adjustment factor; represents the minimum value of the variable selected in the parentheses; represents the time interval from the last sampling point of the current increase section to the standard current steady state; represents the maximum allowed response time set based on historical simulation experience; wherein if the time interval from the last sampling point of the current increase section to the standard current steady state is greater than the maximum allowed response time, the constant 1 is directly selected as the value of the time adjustment factor to avoid excessive control; and the product of the initial compensation factor and the time adjustment factor is calculated to obtain a voltage compensation factor.

[0046] The manner of performing abnormal impact recognition includes: The voltage compensation battery data is divided into windows based on a preset fixed recognition time length window to obtain recognition window data. The window size of the preset fixed recognition time length window is set based on historical simulation experience and can be adjusted according to specific working conditions. Using this preset fixed recognition time length window, the voltage compensation battery data is divided into several data segments of equal length according to the time length, and the data in any one of these windows is the recognition window data.

[0047] Based on the identification window data, a local voltage value sequence and a local current value sequence belonging to the window are constructed. Specifically, the local voltage value sequence and the local current value sequence belonging to the window are constructed by extracting the current value, voltage value and corresponding timestamp from each identification window data, respectively, in order of time. This is used to more accurately identify data fluctuations within a local time period.

[0048] The local fluctuation amplitude of the maximum and minimum local voltage values ​​in the local voltage value sequence is calculated. If the local fluctuation amplitude is higher than the preset peak voltage threshold, the identification window data corresponding to the local voltage value sequence is determined to be a voltage surge risk window. The preset peak voltage threshold is set based on existing electrical theory knowledge. By calculating the local fluctuation amplitude and comparing it with the preset peak voltage threshold, if the amplitude is higher than the preset peak voltage threshold, it indicates that the window where the corresponding identification window data is located belongs to the voltage surge risk window, and there may be instantaneous voltage surge or sag.

[0049] The system determines the direction of current change within each half-window of the local current value sequence, and simultaneously calculates the local current difference between the maximum and minimum local current values ​​within any half-window. Each local current value sequence is divided into two sub-windows: the first half and the second half. The system identifies the number of times the current value increases and decreases within each sub-window; if the number of increases exceeds the number of decreases, the current change is considered positive; otherwise, it is considered negative. The local current difference is calculated to reflect the magnitude of current change over a short period.

[0050] If the current change direction is opposite in each half-window and the local current difference is higher than the preset reverse current judgment value, then the identification window data corresponding to the local current value sequence is judged as a current direction change window. In this embodiment, the preset reverse current judgment value is set based on existing electrical theory knowledge and historical simulation experience. If the current change direction is opposite in each half-window and the local current difference is higher than the preset reverse current judgment value, it indicates that a sudden change in load direction may occur at this time. The window to which the corresponding identification window data belongs is judged as a current direction change window.

[0051] Based on the voltage surge risk window, trend fitting is performed on all sampling points to identify trend disturbance windows. If any two of the voltage surge risk window, current direction surge window, and trend disturbance window belong to the same window, then the window is identified as an abnormal impact window. As long as any two of the above three conditions are met, the window that meets the conditions can be identified as an abnormal impact window, avoiding misjudgment that may be caused by a single condition. The data corresponding to the abnormal impact window is removed from the voltage compensation battery data to obtain reasonable battery data.

[0052] Methods for trend fitting include: A linear fitting trend curve is plotted based on the local voltage value sequence corresponding to the voltage mutation risk window. In this embodiment, the least squares linear fitting is used to plot all the sampling point data of the local voltage value sequence corresponding to the voltage mutation risk window as a linear fitting trend curve. This curve shows the overall trend of voltage in the above local voltage value sequence.

[0053] The voltage deviation of the sampling points in the corresponding window relative to the linear fitting trend curve is calculated. Based on the voltage deviation, a fitting deviation sequence is constructed. The deviation between the predicted voltage value in the linear fitting trend curve and the actual voltage value of the corresponding sampling point is calculated to obtain the voltage deviation value. The voltage deviation values ​​are sorted according to the timestamp of the corresponding sampling point to obtain the fitting deviation sequence, which quantifies the degree of fluctuation of the actual voltage around the linear fitting trend curve within the window.

[0054] The variance of the fitted deviation sequence is calculated. If the variance of ...

[0055] Identify segments within a suspected trend disturbance window where the voltage deviation of consecutive sampling points exceeds twice the average voltage deviation. If the number of consecutive sampling points in such a segment exceeds the lower limit of the peak time, the corresponding suspected trend disturbance window is determined as a trend disturbance window. Further iterate through all sampling points within the suspected trend disturbance window. If there are segments where the voltage deviation of consecutive sampling points exceeds twice the average voltage deviation, and the time length corresponding to the number of consecutive sampling points in such segments exceeds the lower limit of the peak time set based on historical simulation experience, then it can be determined that the disturbance level of the current suspected trend disturbance window has sufficient instability, and therefore, the suspected trend disturbance window is determined as a trend disturbance window.

[0056] Methods for risk assessment include: Traverse the time index of all sampling points in the reasonable battery data, extract the data segment whose continuous time length is higher than the preset resting time limit, and regard the data segment as long-cycle data. The continuous time length refers to the time length of the time segment composed of consecutive sampling points. The preset resting time limit is set based on historical simulation experience and specific load conditions. If the continuous time length is higher than the preset resting time limit, it means that the duration is too long, and the corresponding data segment is determined to be long-cycle data.

[0057] If the current values ​​of the long-cycle data are all within the preset current float charging range, and the absolute value of the difference between the voltage value of the long-cycle data and the preset float charging voltage threshold is not higher than the preset difference, then the corresponding long-cycle data is determined to be float charging state data. In this embodiment, the preset current float charging range and the preset float charging voltage threshold are set based on existing electrical theory knowledge, and the preset difference is set based on historical simulation experience. At the same time, it is determined whether the current value of the long-cycle data is within the preset current float charging range and whether the tolerance between the voltage value and the preset float charging voltage threshold is less than or equal to the preset difference. If both conditions are met, it means that in the state of the corresponding long-cycle data, the battery has not been charged or discharged for a long time and only maintains charge balance with extremely low current. Therefore, it is determined to be float charging state data.

[0058] Based on the timestamp records of the sampling points in the float charge state data, the float charge state data is divided into unequal periods to obtain local static period float charge data. The unequal period division means that, based on the differences in the timestamps in the float charge state data, the segments with time intervals that are significantly higher than the upper limit of the float charge time interval set based on historical simulation experience are independently divided into several sub-data segments, which are the local static period float charge data.

[0059] Calculate the voltage standard deviation of any local static period float charge data. If the duration of the local static period float charge data is higher than the preset static period threshold and the voltage standard deviation is lower than the preset fluctuation threshold, then the data segment corresponding to the local static period float charge data is identified as a potential sulfidation risk segment. The preset static period threshold and preset fluctuation threshold are set based on historical experience. Anomalies are further screened based on the local static period float charge data. If the local static period float charge data simultaneously meets the conditions that the corresponding duration is higher than the preset static period threshold and the voltage standard deviation is lower than the preset fluctuation threshold, then the electrochemical reaction in the data segment corresponding to the current local static period float charge data is considered to be slow, and therefore it is identified as a potential sulfidation risk segment.

[0060] The float charge voltage amplitude of the potential sulfidation risk zone is calculated, and the ratio of this float charge voltage amplitude to the preset standard stable voltage is calculated to obtain the float charge voltage stability ratio. The float charge voltage amplitude refers to the difference between the maximum and minimum voltage values ​​in the potential sulfidation risk zone. At the same time, a preset standard stable voltage is set based on existing electrical theory knowledge, and the ratio of the float charge voltage amplitude to the preset standard stable voltage is calculated. This value reflects the relative fluctuation intensity of the float charge state of the current potential sulfidation risk zone under the ideal standard float charge state.

[0061] Calculate the output current variation range and average output current value of the data segment preceding the potential sulfurization risk segment. The output current variation range refers to the difference between the maximum and minimum current values ​​in the data segment preceding the potential sulfurization risk segment, and the average output current value refers to the average value of all current values ​​in the aforementioned data segment.

[0062] The float charge voltage stability ratio, the output current variation amplitude of the previous data segment, and the average output current value are linearly combined according to a preset ratio to obtain the health weight coefficient. The health weight coefficient is obtained by weighting and summing the above three indicators according to a preset ratio based on historical simulation experience. This health weight coefficient is used to reflect the degree of potential risk.

[0063] By multiplying each specific data point in the potential sulfidation risk zone using a health weighting coefficient, corrected battery data is obtained. By weighting the specific data in the potential sulfidation risk zone using the health weighting coefficient, the contribution of this type of data in the health status assessment process is adjusted, indirectly reducing the misjudgment rate of health status assessment.

[0064] Methods for conducting health status assessments include: According to the health status assessment requirements, parameters of the corresponding dimensions in the differentiated battery data are extracted, and the assessment input dataset is constructed. The health status assessment requirements refer to the health status assessment standards set based on historical assessment records. The parameters of the corresponding dimensions required for health status assessment are extracted from the differentiated battery data, and these parameters are used to form the assessment input dataset.

[0065] By matching the historical evaluation template with the evaluation input dataset, a health score for each dimension parameter is output. The historical evaluation template refers to a parameter mapping template composed of a large number of simulated data of healthy batteries under different operating conditions in the historical record. In this embodiment, a regression model trained on the historical evaluation template is used to receive the evaluation input dataset, and the parameters of each dimension are mapped to the historical evaluation template to output a health score for each dimension parameter.

[0066] The health score is matched with a preset health score range to output the health level of the corresponding dimension parameter. The health levels of all dimension parameters are integrated to obtain the lead-acid battery health index. The preset health score range is set based on a large amount of historical simulation experience. The health score is mapped to the preset health score range to determine the health level of the corresponding dimension parameter. The health levels of all parameters used for health status assessment are integrated into the lead-acid battery health index.

[0067] This embodiment achieves a more accurate health assessment method for lead-acid batteries based on simulated operating conditions by performing multi-stage, layered processing on the collected simulated operating data and conducting a health status assessment based on the processed data. Compared with existing experience, it uses a recovery effect identification and elimination mechanism to screen out voltage rebound phenomena in the original data for analysis, accurately identifying and directly eliminating data with recovery effects, avoiding the misinterpretation of recovery effects as normal discharge states. Furthermore, it introduces precise load identification, determines the interference intensity of polarization interference sections, and compensates for it, ensuring that subsequent health status assessments accurately reflect the actual load conditions. The adaptability of the load response indirectly improves the assessment accuracy; by constructing voltage mutation windows, current direction mutation windows, and trend disturbance windows, real-time identification and cleaning of typical abnormal impact behaviors are achieved, avoiding the impact of abnormal operating condition interference on the assessment accuracy; by identifying potential sulfation risk sections and constructing health weight coefficients, potential sulfation risk sections are marked and weighted to effectively avoid static data being misjudged as high health values ​​in subsequent health status assessment processes, thus improving the assessment process's ability to perceive battery aging risks; therefore, the above-mentioned health status assessment method significantly improves the accuracy of assessing the true health level of the battery under simulated operating conditions.

[0068] Example 2 Please see Figure 2 As shown, parts not described in detail in this embodiment are described in Embodiment 1. A lead-acid battery health assessment system based on simulated operating conditions is provided, including: The data acquisition module is used to collect simulated operating data of lead-acid batteries and perform data cleaning to obtain high-quality battery data. The effect analysis module is used to perform recovery effect analysis on high-quality battery data, obtain recovery effect data, and remove it from the high-quality battery data to obtain perturbation-removed battery data. The voltage compensation module is used to perform polarization voltage compensation on the disturbance removal battery data to generate voltage-compensated battery data. The anomaly identification module is used to perform abnormal impact identification on the voltage-compensated battery data, and filter abnormal data based on the identification results to output reasonable battery data. The risk assessment module is used to identify long-cycle data in reasonable battery data, assess the risk of long-cycle data, obtain corrected battery data and cover the corresponding data in reasonable battery data to obtain differentiated battery data. The health assessment module is used to assess the health status of differentiated battery data, output lead-acid battery health indicators and send them to the preset battery management terminal; the modules are connected to each other via wired and / or wireless means.

[0069] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments or make equivalent substitutions for some of the technical features. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

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

[0071] In the description of this invention, it should be understood that the terms "first," "second," etc., are used only for distinguishing descriptions and should not be construed as indicating or implying relative importance.

[0072] In the description of this invention, unless otherwise stated, "a plurality of" means two or more.

[0073] In the description of this invention, "several" means one or more, and "a large number" means two or more.

[0074] In the description of this specification, references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of the invention. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

[0075] All formulas in this manual are dimensionless and calculated numerically. The formulas are derived from software simulations based on a large amount of collected data to obtain the most recent real-world results. The preset parameters and thresholds in the formulas are set by those skilled in the art according to the actual situation.

[0076] Although embodiments of the invention have been shown and described, those skilled in the art will understand that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the claims and their equivalents.

Claims

1. A health assessment method for lead-acid batteries based on simulated operating conditions, characterized in that, include: S1. Collect simulated operation data of lead-acid batteries and perform data cleaning to obtain high-quality battery data; S2. Perform recovery effect analysis on high-quality battery data to obtain recovery effect data and remove it from the high-quality battery data to obtain perturbation-removed battery data; S3. Perform polarization voltage compensation on the disturbance-removed battery data to generate voltage-compensated battery data; S4. Perform abnormal impact identification on the voltage-compensated battery data, and filter abnormal data based on the identification results to output reasonable battery data; S5. Identify long-cycle data in reasonable battery data, perform risk assessment on long-cycle data, obtain corrected battery data and cover the corresponding data in reasonable battery data to obtain differentiated battery data; S6. Perform a health status assessment on the differentiated battery data, output the lead-acid battery health indicators, and send them to the preset battery management terminal.

2. The method for health assessment of lead-acid batteries based on simulated operating conditions according to claim 1, characterized in that, The methods for performing recovery effect analysis include: Extract voltage and current data from high-quality battery data and construct voltage and current value sequences respectively; calculate the absolute value of the current difference between any two adjacent sampling points in the current value sequence, and identify the time segment where the absolute value of the current difference is less than a preset current amplitude threshold as the non-load segment; calculate the rate of change of each sampling point in the voltage value sequence and construct a voltage rate of change sequence, and identify the non-load voltage time segment corresponding to the non-load segment in the voltage rate of change sequence. A dynamic sliding window is constructed to traverse the non-load voltage time interval. If there is a sub-segment within the dynamic sliding window where the slope of consecutive sampling points is greater than a preset rate of change slope threshold and the time length of the sub-segment is greater than a preset time length threshold, then the time interval corresponding to the dynamic sliding window is determined as a candidate recovery effect segment. The standard deviation of the voltage change rate of the candidate recovery effect segment is calculated. If the standard deviation of the voltage change rate is less than a preset stability standard deviation threshold, then the corresponding candidate recovery effect segment is determined as a recovery effect segment. The recovery effect segments are indexed and marked, and corresponding data in high-quality battery data are filtered based on the markings to obtain recovery effect data.

3. The health assessment method for lead-acid batteries based on simulated operating conditions according to claim 2, characterized in that, The methods for performing index marking include: Extend a predetermined number of sampling points towards the starting and ending boundaries of each recovery effect segment to obtain an extended recovery effect segment. Extract the minimum voltage change rate slope in the extended recovery effect segment. If the minimum voltage change rate slope is lower than a preset continuous slope threshold, gradually shrink the extended starting and ending boundaries of the extended recovery effect segment in opposite directions until the minimum voltage change rate slope in the extended recovery effect segment is not lower than the preset continuous slope threshold or the segment length is less than a preset minimum segment length. Mark the sampling points corresponding to the extended starting and ending boundaries of all adjusted extended recovery effect segments. Based on the markings, identify the time segment corresponding to each adjusted extended recovery effect segment as the recovery effect time segment.

4. The health assessment method for lead-acid batteries based on simulated operating conditions according to claim 3, characterized in that, The methods for performing polarization voltage compensation include: The time segment in which the absolute value of the current difference in the current value sequence is greater than the preset current amplitude threshold is identified as the original load application segment; the original load application segment is matched with the disturbance removal battery data. If there is no time segment corresponding to the original load application segment in the disturbance removal battery data, no processing is performed. If there is a complete time segment or a partial time segment corresponding to the original load application segment, the original load application segment is determined as the load application segment. Calculate the rate of change of current in the load application section and construct a current rate of change sequence; identify the section in the current rate of change sequence where the rate of change of current increases continuously as the current increase section; extract the disturbance-free voltage value sequence corresponding to the current increase section and calculate the maximum slope of the disturbance-free voltage value sequence; extract the voltage rate of change sequence corresponding to the section that reaches the standard current steady state after the current increase section to obtain the steady-state voltage sequence; calculate the average rate of change of the steady-state voltage sequence to obtain the reference voltage response rate; calculate the difference between the maximum slope of the steady-state sequence and the reference voltage response rate and take the absolute value to obtain the polarization voltage response deviation; A voltage compensation factor is constructed based on the polarization voltage response deviation. The voltage compensation factor is used to weight the de-disturbed voltage value sequence, and the weighted de-disturbed voltage value sequence is used to cover the corresponding data in the de-disturbed battery data to obtain voltage-compensated battery data.

5. The health assessment method for lead-acid batteries based on simulated operating conditions according to claim 4, characterized in that, The methods for constructing the voltage compensation factor include: The polarization voltage response deviation is normalized to obtain the normalized slope deviation; the standard deviation of the slope in the undisturbed voltage value sequence is calculated to obtain the stability fluctuation degree; the ratio of the stability fluctuation degree to the reference voltage response rate is calculated to obtain the response stability ratio; the mean slope of the current increase segment is calculated, and the ratio of this mean slope to the maximum slope in the current increase segment is calculated to obtain the dynamic adjustment ratio; the product of the normalized slope deviation, the response stability ratio, and the dynamic adjustment ratio is calculated to obtain the initial compensation factor; the time interval from the last sampling point in the current increase segment to reaching the standard current steady state is measured, and the time adjustment factor is constructed based on this time interval; the product of the initial compensation factor and the time adjustment factor is calculated to obtain the voltage compensation factor.

6. The health assessment method for lead-acid batteries based on simulated operating conditions according to claim 5, characterized in that, The methods for performing abnormal impact identification include: The voltage-compensated battery data is divided into windows based on a preset fixed recognition time window to obtain recognition window data; a local voltage value sequence and a local current value sequence belonging to the window are constructed based on the recognition window data; the local fluctuation amplitude of the maximum local voltage value and the minimum local voltage value in the local voltage value sequence is calculated; if the local fluctuation amplitude is higher than the preset peak voltage threshold, the recognition window data corresponding to the local voltage value sequence is determined to be a voltage change risk window. Determine the direction of current change within each half-window before and after the local current value sequence, and simultaneously calculate the local current difference between the maximum and minimum local current values ​​within any half-window; if the direction of current change within each half-window is opposite and the local current difference is higher than the preset reverse current judgment value, then the identification window data corresponding to the local current value sequence is determined as a current direction change window. Based on the voltage surge risk window, trend fitting is performed on all sampling points to identify trend disturbance windows; if any two types of windows, including the voltage surge risk window, the current direction surge window, and the trend disturbance window, belong to the same window, then the window is determined to be an abnormal impact window.

7. The method for health assessment of lead-acid batteries based on simulated operating conditions according to claim 6, characterized in that, The methods for performing trend fitting include: A linear fitting trend curve is plotted based on the local voltage value sequence corresponding to the voltage mutation risk window; the voltage deviation of the sampling points of the corresponding window relative to the linear fitting trend curve is calculated, and a fitting deviation sequence is constructed based on the voltage deviation; the variance of the fitting deviation sequence is calculated, and if the variance of the variance is greater than the preset trend disturbance judgment threshold, the window corresponding to the fitting deviation sequence is judged as a suspected trend disturbance window; the segment where the voltage deviation of consecutive sampling points in the suspected trend disturbance window is higher than twice the average voltage deviation is identified, and if the number of consecutive sampling points in the segment is higher than the lower limit of the peak time, the corresponding suspected trend disturbance window is determined as a trend disturbance window.

8. The method for health assessment of lead-acid batteries based on simulated operating conditions according to claim 7, characterized in that, The methods for risk assessment include: Traverse the time index of all sampling points in the reasonable battery data, extract the data segment whose continuous time length is longer than the preset resting time limit, and take the data segment as long period data; if the current value of the long period data is within the preset current float charging range, and the absolute value of the difference between the voltage value of the long period data and the preset float charging voltage threshold is not higher than the preset difference, then the corresponding long period data is determined as float charging state data; based on the timestamp record of the sampling point in the float charging state data, divide the float charging state data into unequal periods to obtain local resting period float charging data; Calculate the voltage standard deviation of any local static period float charge data. If the duration of the local static period float charge data is higher than the preset static period threshold and the voltage standard deviation is lower than the preset fluctuation threshold, then the data segment corresponding to the local static period float charge data is identified as a potential sulfidation risk segment. Calculate the float charge voltage amplitude of the potential sulfation risk section and the ratio of this float charge voltage amplitude to the preset standard stable voltage to obtain the float charge voltage stability ratio; calculate the output current variation amplitude and average output current value of the previous data section of the potential sulfation risk section; linearly combine the float charge voltage stability ratio, the output current variation amplitude and average output current value of the previous data section according to a preset ratio to obtain the health weighting coefficient; use the health weighting coefficient to weight the specific data of the potential sulfation risk section to obtain the corrected battery data.

9. A method for health assessment of lead-acid batteries based on simulated operating conditions according to claim 8, characterized in that, The methods for conducting health status assessments include: According to the health status assessment requirements, parameters of the corresponding dimensions are extracted from the differentiated battery data, and an assessment input dataset is constructed. The historical assessment template is matched with the assessment input dataset to output the health score of each dimension parameter. The health score is matched with the preset health score range to output the health level of the corresponding dimension parameter. The health levels of all dimension parameters are integrated to obtain the lead-acid battery health index.

10. A lead-acid battery health assessment system based on simulated operating conditions, used to implement the lead-acid battery health assessment method based on simulated operating conditions according to any one of claims 1-9, characterized in that, include: The data acquisition module is used to collect simulated operating data of lead-acid batteries and perform data cleaning to obtain high-quality battery data. The effect analysis module is used to perform recovery effect analysis on high-quality battery data, obtain recovery effect data, and remove it from the high-quality battery data to obtain perturbation-removed battery data. The voltage compensation module is used to perform polarization voltage compensation on the disturbance removal battery data to generate voltage-compensated battery data. The anomaly identification module is used to perform abnormal impact identification on the voltage-compensated battery data, and filter abnormal data based on the identification results to output reasonable battery data. The risk assessment module is used to identify long-cycle data in reasonable battery data, assess the risk of long-cycle data, obtain corrected battery data and cover the corresponding data in reasonable battery data to obtain differentiated battery data. The health assessment module is used to assess the health status of differentiated battery data, output lead-acid battery health indicators and send them to the preset battery management terminal; the modules are connected to each other via wired and / or wireless means.

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