Power battery safety risk assessment method and system based on data driving

Abstract

The invention discloses a data driving-based power battery safety risk assessment method and system, particularly relates to the technical field of battery safety monitoring, and is used for solving the problems of false alarm and missing detection caused by confusion of electrochemical relaxation effect and real fault signal characteristics in the existing standing stage fault detection method. Through multi-source data acquisition based on voltage, temperature and state of charge in a standing stage, a current slope extreme point is identified through charge and discharge mode weighted correction, a time-delay mapping relation with a voltage curve geometrical morphology abrupt change point is established, abnormal inflection points are screened, and fractal dimension attenuation characteristics are quantified; according to the method, abnormal distribution density and material damage indexes are fused to generate a dynamic threshold value, composite criterion analysis is carried out by combining frequency domain energy offset characteristics extracted by time-frequency transformation and heat conduction time-lag phase difference, relaxation interference signals are effectively stripped, hidden danger characteristics are locked, full-dimension safety state evaluation from microdefect evolution to macroscopic parameter abnormity is realized, and the safety state evaluation accuracy is improved. And the reliability of power battery fault detection under complex working conditions is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of battery safety monitoring, and more specifically, to a data-driven power battery safety risk assessment method and system thereof. Background Art

[0002] The widespread application of power batteries in the field of electric vehicles has made their safety risk assessment a core concern of the industry; in the existing technology, safety monitoring of batteries in the static stage mainly relies on threshold judgment methods of parameters such as voltage and temperature, that is, by setting a fixed change rate threshold (such as voltage drop rate) to determine whether to trigger an abnormal alarm. This type of method assumes that parameter fluctuations in the static stage are only caused by self-discharge or environmental interference, and risk control can be achieved through smoothing filtering and static threshold rules; however, in complex environments and operating condition switching scenarios, the internal electrochemical relaxation effect of power batteries will dynamically evolve with factors such as temperature and historical load, resulting in a high similarity between the parameter change pattern in the static stage and the real fault signal, making it difficult to effectively distinguish the difference between the two.

[0003] The existing technology has not fully considered the dynamic offset characteristics of the electrochemical relaxation effect in complex environments and operating condition switching scenarios. Specifically, when the battery switches from high-load operation to a static state, its relaxation behaviors such as internal ion diffusion and interface charge redistribution are affected by the multiple coupling of temperature, charge state and aging degree, resulting in the evolution of relaxation signals and early fault signals (such as internal short circuit and lithium deposition) highly overlapping in the time and frequency domains. In scenarios such as high temperature and high charge state, normal relaxation can easily be misjudged as a fault, or real hidden dangers can be concealed, resulting in a safety blind spot where false alarms and missed detection risks coexist. Summary of the invention

[0004] In order to overcome the above-mentioned defects of the prior art, an embodiment of the present invention provides a data-driven power battery safety risk assessment method and system thereof to solve the problems raised in the above-mentioned background technology.

[0005] To achieve the above object, the present invention provides the following technical solutions: The data-driven power battery safety risk assessment method includes the following steps: S1. Collect the voltage, temperature and charge state of the power battery in the static stage, and simultaneously obtain the last charge and discharge current value and charge and discharge mode; S2, extracting the slope extreme value point of the last charge and discharge current value, weighting the slope extreme value point based on the charge and discharge mode, and identifying the geometric morphology mutation point timing of the voltage curve in the static stage; S3, establish the lag time mapping relationship between the slope extreme point and the geometric morphology mutation point, and screen the abnormal inflection point with excessive lag time; S4. According to the distribution density of abnormal inflection points and the fractal dimension attenuation characteristics of the voltage relaxation curve in the static stage, the cumulative damage of the microscopic defects of the electrode material caused by the historical working conditions is analyzed, and the dynamic threshold interval is generated in combination with the charge state; S5, performing time-frequency transformation on the voltage in the static stage, extracting the frequency domain energy center of gravity offset and the time domain fluctuation envelope, and extracting the time lag phase difference of the temperature in the static stage; S6. Based on the judgment result of whether the time domain fluctuation envelope is within the dynamic threshold range and whether the time lag phase difference is lower than the preset critical value, a safety risk judgment is performed.

[0006] In a preferred embodiment, collecting the voltage, temperature and charge state of the power battery in the static stage, and synchronously obtaining the last charge and discharge current value and charge and discharge mode, includes: S1-1. When the power battery enters the static stage, the static voltage data is continuously collected at a first preset sampling frequency, the static temperature data is continuously collected at a second preset sampling frequency, and the state of charge data is acquired in real time through the battery management system; S1-2. When the power battery finishes the last charge and discharge, the current value at the end of the charge and discharge is recorded as the last charge and discharge current value, and the charge and discharge mode classification identifier during the charge and discharge process is obtained, and the charge and discharge mode classification identifier includes at least one of constant current charging, pulse discharge and constant voltage charging; S1-3, aligning the timestamps of the static voltage data, static temperature data and state of charge data with the timestamps of the last charge and discharge current value and the charge and discharge mode classification identifier to generate a time-synchronized static phase data set.

[0007] In a preferred embodiment, the slope extreme value point of the last charge and discharge current value is extracted, the slope extreme value point is weighted based on the charge and discharge mode, and the geometric morphology mutation point timing of the voltage curve in the static stage is identified, including: S2-1. Based on the charge and discharge mode classification identification, the slope change characteristics of the last charge and discharge current curve are screened for mode adaptability, wherein the pulse discharge mode retains the slope extreme point in the charge and discharge switching stage, and the constant current charge mode retains the slope extreme point in the charge cut-off stage; S2-2, dynamically weight the selected slope extreme points, adjust the weight coefficient of the extreme points in the pulse discharge mode according to the nonlinear relationship of the corresponding charge and discharge switching rate, and adjust the weight coefficient of the extreme points in the constant current charging mode according to the charging cut-off current gradient; S2-3. Perform multi-scale curvature analysis on the geometric mutation points of the voltage curve in the static stage, extract the mutation points with abnormal curvature change direction and amplitude, and generate a time series of geometric mutation points.

[0008] In a preferred embodiment, a hysteresis time mapping relationship between the slope extreme point and the geometric morphology mutation point is established, and abnormal inflection points with excessive hysteresis are screened, including: S3-1, matching the time difference between the weighted corrected slope extreme value time series and the geometric morphology mutation point time series, calculating the time difference between each slope extreme value point and the first subsequent geometric morphology mutation point, and generating the original time-lag data set; S3-2, according to the charge and discharge mode classification identifier, the dynamic time lag matching window is set, the matching window in the pulse discharge mode is dynamically adjusted based on the inverse correlation between the charge and discharge switching rate and the relaxation rate, and the matching window in the constant current charging mode is dynamically adjusted based on the inverse correlation between the charge cut-off current gradient and the material stress release rate; S3-3, filtering the original time-delay data in the dynamic time-delay matching window, removing the time difference data outside the window, and retaining the time-delay data in the window as the effective time-delay set; S3-4. Based on the statistical distribution characteristics of the time-lag data of historical healthy batteries, the benchmark interval of the effective time-lag set is calculated, and the time-lag points that deviate from the benchmark interval are marked as abnormal inflection points.

[0009] In a preferred embodiment, according to the distribution density of abnormal inflection points and the fractal dimension attenuation characteristics of the voltage relaxation curve in the static stage, the cumulative damage of the microscopic defects of the electrode material caused by the historical working conditions is analyzed, and the dynamic threshold interval is generated in combination with the charge state, including: S4-1. Count the frequency of abnormal inflection points within a unit time, and calculate the distribution density of abnormal inflection points based on the degree of concentration of abnormal inflection points on the voltage curve; S4-2, calculating the fractal dimension decay rate of the voltage relaxation curve in the static stage, and extracting the average decrease rate of the fractal dimension over time as the fractal dimension decay characteristic; S4-3. Based on the weighted fusion results of the abnormal inflection point distribution density and the fractal dimension attenuation characteristics, quantify the cumulative damage degree of the micro defects of the electrode material caused by the historical working conditions; S4-4. Compensate and correct the accumulated damage degree according to the state of charge, and generate a dynamic threshold range that changes nonlinearly with the state of charge.

[0010] In a preferred embodiment, step S4-2 includes: S4-2a, performing fractal dimension sequence calculation on the voltage relaxation curve in the static stage, calculating the fractal dimension value by sliding multiple time windows, and generating a decay sequence of the fractal dimension changing with time; S4-2b, based on the correlation between stress release of electrode materials and ion diffusion path, the dynamic coupling characteristics of the fractal dimension decrease rate and the curvature change direction in the decay sequence are extracted, where the fractal dimension decrease rate and the curvature change direction are in the same direction as the rapid decay segment, and in the opposite direction as the slow decay segment; S4-2c. Count the cumulative duration of the fast decay segment and the number of curvature jumps in the slow decay segment to generate a quantitative index of the fractal dimension decay characteristic.

[0011] In a preferred embodiment, the voltage in the static stage is transformed into a time-frequency transform, the frequency domain energy center of gravity offset and the time domain fluctuation envelope are extracted, and the time lag phase difference is extracted for the temperature in the static stage, including: S5-1, perform wavelet packet transformation on the voltage data in the static stage, divide the preset frequency bands and calculate the energy distribution of each preset frequency band, and extract the frequency domain energy center of gravity offset as the difference between the center of gravity frequency at each moment and the reference frequency of the corresponding frequency band of the standard healthy battery; S5-2, filtering the interference component of the frequency domain energy center of gravity offset based on the dynamic threshold interval; S5-3, perform Hilbert transform on the voltage data time domain waveform, and extract the time domain fluctuation envelope as the average of the upper and lower envelopes of the voltage amplitude changing with time; S5-4. Perform cross-correlation analysis on the temperature data sequence in the static stage, and calculate the time-delay phase difference relative to the envelope of the voltage curve. The time-delay phase difference is the time offset corresponding to the peak of the cross-correlation function.

[0012] In a preferred embodiment, based on the judgment result of whether the time domain fluctuation envelope is within the dynamic threshold range and whether the time lag phase difference is lower than a preset critical value, a safety risk judgment is performed, including: If the time domain fluctuation envelope is within the dynamic threshold range and the time lag phase difference is lower than the preset critical value, it is judged as the superposition interference of the microscopic defects of the electrode material and the relaxation effect; otherwise, the frequency domain energy center of gravity offset is matched with the frequency domain fingerprint library of the preset fault to trigger a graded alarm.

[0013] In a preferred embodiment, the logic for constructing a frequency domain fingerprint library for preset faults is as follows: perform standardized charge and discharge tests on power batteries with known internal short circuit faults and lithium plating fault types, collect voltage data during the static stage and extract frequency domain energy center of gravity offset characteristics, wherein the internal short circuit fault presents a continuous positive offset characteristic of the energy center of gravity in the low frequency band, and the lithium plating fault presents a periodic fluctuation offset characteristic in the medium frequency band; associate and map the characteristics of different fault modes with the energy distribution laws of the corresponding frequency bands to form a frequency domain fingerprint library with the fault type as the index and the frequency domain energy center of gravity offset pattern as the characteristic, wherein the low frequency band corresponds to the positive offset pattern of the internal short circuit fault, and the medium frequency band corresponds to the periodic fluctuation pattern of the lithium plating fault.

[0014] On the other hand, the present invention provides a data-driven power battery safety risk assessment system, comprising: Static data acquisition module: collects the voltage, temperature and charge state of the power battery during the static stage, and simultaneously obtains the last charge and discharge current value and charge and discharge mode; Extreme value weight correction module: extracts the slope extreme value point of the last charge and discharge current value, performs weight correction on the slope extreme value point based on the charge and discharge mode, and identifies the timing of the geometric mutation point of the voltage curve in the static stage; Time lag screening analysis module: establish the lag time mapping relationship between the slope extreme point and the geometric morphology mutation point, and screen the abnormal inflection points with excessive time lag; Fractal dynamic threshold module: Based on the distribution density of abnormal inflection points and the fractal dimension attenuation characteristics of the voltage relaxation curve in the static stage, the module analyzes the cumulative damage of the microscopic defects of the electrode material caused by historical working conditions, and generates a dynamic threshold interval in combination with the charge state; Time-frequency feature extraction module: performs time-frequency transformation on the voltage in the static stage, extracts the frequency domain energy center of gravity offset and the time domain fluctuation envelope, and extracts the time lag phase difference of the temperature in the static stage; Safety risk determination module: Safety risk determination is performed based on the judgment results of whether the time domain fluctuation envelope is within the dynamic threshold range and whether the time lag phase difference is lower than the preset critical value.

[0015] Compared with the prior art, the present invention has the following beneficial effects: 1. Through multi-dimensional signal collaborative analysis and dynamic threshold generation mechanism, the problem of confusion between relaxation effect and real fault signal in the safety risk assessment of power batteries at rest stage is effectively solved. By introducing the cross-scale correlation model of the evolution characteristics of microscopic defects of electrode materials and electrochemical dynamic response, the cumulative damage to the internal structure of the battery caused by the historical charging and discharging conditions is incorporated into the real-time risk assessment system, so that the safety status judgment criteria can accurately reflect the actual health of the battery. Through the fusion of multiple features such as quantifying material damage through fractal dimension attenuation characteristics, capturing hidden faults through frequency domain energy center of gravity offset, and identifying thermodynamic anomalies through time lag phase difference, the sensitivity of fault signal identification under complex working conditions is significantly improved, and stable and reliable detection performance can still be maintained in extreme scenarios such as high temperature and high charge state; 2. Dynamically adjust the judgment boundary based on the quantification results of material damage and the real-time state of charge to achieve adaptive matching between the safety risk assessment model and the actual aging process of the battery. Through weighted correction of charging and discharging modes, timing analysis of geometric mutation points, and calculation of the distribution density of abnormal inflection points, a full-link monitoring logic from the evolution of microscopic defects to macroscopic parameter anomalies is constructed to ensure that early hidden dangers can be effectively identified before thermal runaway is triggered. The combination of time-frequency transformation and relaxation effect decoupling technology successfully removes the interference of the normal electrochemical recovery process on the monitoring signal, making the feature extraction of potential faults such as internal short circuit and lithium deposition highly specific, thereby enhancing the reliability of safety management and control of the power battery throughout its life cycle. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 This is a flow chart of a data-driven power battery safety risk assessment method of the present invention; Figure 2 It is a structural schematic diagram of the data-driven power battery safety risk assessment system of the present invention. DETAILED DESCRIPTION

[0017] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0018] Embodiment 1: Figure 1 The present invention provides a data-driven power battery safety risk assessment method, which includes the following steps: S1. Collect the voltage, temperature and charge state of the power battery in the static stage, and simultaneously obtain the last charge and discharge current value and charge and discharge mode; S2, extracting the slope extreme value point of the last charge and discharge current value, weighting the slope extreme value point based on the charge and discharge mode, and identifying the geometric morphology mutation point timing of the voltage curve in the static stage; S3, establish the lag time mapping relationship between the slope extreme point and the geometric morphology mutation point, and screen the abnormal inflection point with excessive lag time; S4. According to the distribution density of abnormal inflection points and the fractal dimension attenuation characteristics of the voltage relaxation curve in the static stage, the cumulative damage of the microscopic defects of the electrode material caused by the historical working conditions is analyzed, and the dynamic threshold interval is generated in combination with the charge state; S5, performing time-frequency transformation on the voltage in the static stage, extracting the frequency domain energy center of gravity offset and the time domain fluctuation envelope, and extracting the time lag phase difference of the temperature in the static stage; S6. Based on the judgment result of whether the time domain fluctuation envelope is within the dynamic threshold range and whether the time lag phase difference is lower than the preset critical value, a safety risk judgment is performed.

[0019] S1. Collect the voltage, temperature and state of charge of the power battery in the static stage, and simultaneously obtain the last charge and discharge current value and charge and discharge mode, including: S1-1. When the power battery enters the static stage, the static voltage data is continuously collected at a first preset sampling frequency through the voltage sensor, the static temperature data is continuously collected at a second preset sampling frequency through the temperature sensor, and the state of charge data is acquired in real time through the battery management system; S1-2. When the power battery finishes the last charge and discharge, the current value at the end of the charge and discharge is recorded as the last charge and discharge current value, and the charge and discharge mode classification identifier during the charge and discharge process is obtained, and the charge and discharge mode classification identifier includes at least one of constant current charging, pulse discharge and constant voltage charging; S1-3, aligning the timestamps of the static voltage data, static temperature data and state of charge data with the timestamps of the last charge and discharge current value and the charge and discharge mode classification identifier to generate a time-synchronized static phase data set.

[0020] When the power battery enters the static stage, the static voltage data is continuously collected at a first preset sampling frequency through the voltage sensor, wherein the first preset sampling frequency is set according to the battery type and the application scenario. For example, for a lithium-ion power battery, the first preset sampling frequency is set to collect data once per second; the static temperature data is continuously collected at a second preset sampling frequency through the temperature sensor, wherein the second preset sampling frequency is set according to the sensitivity of the temperature change. For example, for a high temperature environment monitoring scenario, the second preset sampling frequency is set to collect data once every 2 seconds; the state of charge data is acquired in real time through the battery management system, which calculates the state of charge through the voltage integration method or the open circuit voltage method at preset time intervals, and stores the state of charge data in association with the timestamps of the voltage data and the temperature data.

[0021] When the power battery finishes the last charge and discharge, the current value at the end of the charge and discharge is recorded as the last charge and discharge current value, specifically including: when the charge and discharge circuit is disconnected, the current sensor is triggered to record the current value at the moment of disconnection, and the current value is marked as the last charge and discharge current value; the charge and discharge mode classification identifier during the charge and discharge process is obtained, and the charge and discharge mode classification identifier is determined by parsing the charge and discharge control instructions of the battery management system. For example, when the continuous current fluctuation amplitude in the charge and discharge control instruction exceeds the preset fluctuation threshold, it is determined to be a pulse discharge mode, and when the current instruction is a constant value, it is determined to be a constant current charge or constant voltage charge mode, and the charge and discharge mode classification identifier is associated with the timestamp of the last charge and discharge current value and stored.

[0022] The timestamps of the static voltage data, static temperature data and state of charge data are aligned with the timestamps of the last charge and discharge current value and the charge and discharge mode classification mark to generate a time-synchronized static phase data set, specifically including: normalizing the timestamps of all data sources according to the system clock of the data acquisition device, and for data with different sampling frequencies, using a linear interpolation method to align the low-frequency sampled data with the high-frequency sampled data on the same time basis, for example, the temperature data sampled every 2 seconds is interpolated to generate equivalent data with a timestamp per second, so that it is consistent with the timestamp of the voltage data sampled every second; for the last charge and discharge current value and the charge and discharge mode classification mark, the corresponding data entries are inserted on the normalized time axis according to their recording time points, and finally a synchronized data set containing voltage, temperature, state of charge, last charge and discharge current value and charge and discharge mode classification mark is formed.

[0023] During the data set generation process in the static stage, the settings of the first preset sampling frequency and the second preset sampling frequency are dynamically adjusted according to the battery type and application scenario. For example, for high-energy-density power batteries, the first preset sampling frequency is increased to 2 times per second to ensure that transient voltage changes are fully captured; for the analysis of the charge and discharge mode classification identification, the setting of the preset fluctuation threshold in the charge and discharge control instruction is determined based on the percentage of the battery rated current. For example, when the current fluctuation amplitude exceeds 20% of the rated current and the duration is less than 1 second, it is determined to be a pulse discharge mode; during the timestamp alignment process, the linear interpolation method is used to adjust the time stamp. The specific implementation of the method includes: for the high-frequency timestamp between two adjacent low-frequency sampling points, the interpolation result is calculated according to the values ​​of the previous and next sampling points in time proportion. For example, the timestamps t1 and t3 of the temperature data correspond to the temperature values ​​T1 and T3 respectively, then the interpolation temperature T2 at time t2=(T1*(t3-t2)+T3*(t2-t1)) / (t3-t1); for the insertion of the last charge and discharge current value, if its recording time point does not completely match the existing timestamp, then the data entry is added at the closest high-frequency timestamp position and marked as the current value at the end of charge and discharge.

[0024] Through the above steps, each data item in the static stage data set is strictly aligned in the time dimension, ensuring that the temporal correlation analysis of features such as slope extreme points and geometric morphology mutation points in subsequent steps has a consistent data basis; the clear interpretation of the charging and discharging mode classification identifier provides a classification basis for the weight correction based on the charging and discharging mode in subsequent steps; the selection of the linear interpolation method in the time synchronization process ensures the compatibility of data with different sampling frequencies and avoids feature extraction errors caused by time deviations. For example, misaligned timestamps may cause deviations in the calculation of the lag time mapping relationship between the slope extreme points and the geometric morphology mutation points.

[0025] S2, extracting the slope extreme value point of the last charge and discharge current value, weighting the slope extreme value point based on the charge and discharge mode, and identifying the geometric morphology mutation point timing of the voltage curve in the static stage, including: S2-1. Based on the charge and discharge mode classification identification, the slope change characteristics of the last charge and discharge current curve are screened for mode adaptability, wherein the pulse discharge mode retains the slope extreme point in the charge and discharge switching stage, and the constant current charge mode retains the slope extreme point in the charge cut-off stage; S2-2, dynamically weight the selected slope extreme points, adjust the weight coefficient of the extreme points in the pulse discharge mode according to the nonlinear relationship of the corresponding charge and discharge switching rate, and adjust the weight coefficient of the extreme points in the constant current charging mode according to the charging cut-off current gradient; S2-3. Perform multi-scale curvature analysis on the geometric mutation points of the voltage curve in the static stage, extract the mutation points with abnormal curvature change direction and amplitude, and generate a time series of geometric mutation points.

[0026] In the mode adaptability screening, when the charge and discharge mode classification is identified as the pulse discharge mode, the slope extreme point where the current value in the charge and discharge switching stage changes at a rate of change exceeding twice the rated current change rate within the preset time window is extracted, wherein the preset time window is dynamically adjusted according to the rated charge and discharge rate of the battery. For example, for a battery with a rated charge and discharge rate of 1C, the preset time window is set to 100 milliseconds. If it is detected that the current value drops from 50A to 0A within the window and the rate of change exceeds 100A / s, then this moment is recorded as the slope extreme point; when the charge and discharge mode classification is identified as the constant current charging mode, the slope extreme point corresponding to the maximum rate of change of the current value in the process of dropping from a constant value to zero in the charging cut-off stage is extracted. Specifically, the current difference between adjacent time points is calculated at intervals of 10 milliseconds during the current drop process, and the time point with the largest current difference is selected as the extreme point.

[0027] When dynamically weighting the slope extreme points after screening, for the slope extreme points in the pulse discharge mode, the weight coefficient is calculated according to the length of time for the current to drop suddenly during the charge and discharge switching stage. The length of the sudden drop time is defined as the time required for the current to drop from 90% to 10% of the rated value. For example, when the sudden drop time is 50 milliseconds, the weight coefficient is set to 1.5, and when the sudden drop time is 200 milliseconds, the weight coefficient is set to 1.2; for the slope extreme points in the constant current charging mode, the weight coefficient is adjusted according to the absolute value of the current gradient in the charging cut-off stage. The absolute value of the current gradient is the maximum instantaneous rate of change during the current drop process. For example, when the maximum instantaneous rate of change is detected to be reduced by 5A per second, the weight coefficient is set to 1.2, and when it is reduced by 2A per second, it is set to 1.0.

[0028] When performing multi-scale curvature analysis on the geometric mutation points of the voltage curve in the static stage, sliding windows with different time spans are used to perform second-order difference calculations on the voltage data, where the time span is set according to the relaxation characteristics of the battery, for example, a 1-second time window is set for batteries with faster relaxation response to detect local curvature, and a 5-second time window is set for batteries with slower relaxation to detect macro curvature trends; at each scale, the reversal point of the curvature change direction and the event where the curvature value jump amplitude exceeds the preset curvature threshold of the scale are detected, where the curvature threshold is obtained based on historical healthy battery data statistics, for example, the curvature threshold is set to 0.1 in a 1-second time window and to 0.25 in a 5-second time window; when performing timestamp intersection screening on the mutation points detected at different scales, only mutation points that are identified at at least two adjacent scales are retained, for example, at a certain time point, a reversal of the curvature direction is detected in both the 1-second and 2-second windows and the amplitude exceeds the threshold, then it is determined to be a valid mutation point.

[0029] Through the above steps, the selected slope extreme points are corrected by the mode adaptability weight to form a weighted extreme point sequence. For example, the extreme point with a sudden drop time of 50 milliseconds in the pulse discharge mode has a weight of 1.5, and the curvature mutation point detected in the 1-second time window in the corresponding static stage will be given priority in subsequent analysis; the mutation point time series sequence generated by the multi-scale curvature analysis is strictly aligned with the weighted extreme point sequence in the time dimension. For example, when the extreme point with a weight of 1.0 in the constant current charging mode is associated with the macro curvature mutation point detected in the 5-second time window, the standard time difference threshold in this mode is used for hysteresis matching.

[0030] S3. Establish the lag time mapping relationship between the slope extreme point and the geometric morphology mutation point, and screen the abnormal inflection points with excessive lag time, including: S3-1, matching the time difference between the weighted corrected slope extreme value time series and the geometric morphology mutation point time series, calculating the time difference between each slope extreme value point and the first subsequent geometric morphology mutation point, and generating the original time-lag data set; S3-2, according to the charge and discharge mode classification identifier, the dynamic time lag matching window is set, the matching window in the pulse discharge mode is dynamically adjusted based on the inverse correlation between the charge and discharge switching rate and the relaxation rate, and the matching window in the constant current charging mode is dynamically adjusted based on the inverse correlation between the charge cut-off current gradient and the material stress release rate; S3-3, filtering the original time-delay data in the dynamic time-delay matching window, removing the time difference data outside the window, and retaining the time-delay data in the window as the effective time-delay set; S3-4. Based on the statistical distribution characteristics of the time-lag data of historical healthy batteries, the benchmark interval of the effective time-lag set is calculated, and the time-lag points that deviate from the benchmark interval are marked as abnormal inflection points.

[0031] The weighted corrected slope extreme point time series and the geometric morphology mutation point time series are matched with each other in time difference, specifically including: taking the weighted corrected slope extreme point timestamp as the benchmark, searching for the first mutation point after each extreme point in the geometric morphology mutation point time series, and calculating the time difference between the two. For example, an extreme point appears at 10:05:30, and the first mutation point appears at 10:06:05, then the time difference is 35 seconds. The time difference between all extreme points and the corresponding mutation points constitutes the original time-lag data set.

[0032] When setting the dynamic time-delay matching window according to the charge and discharge mode classification identifier, for the pulse discharge mode, the charge and discharge switching rate is defined as the rate at which the current drops from the rated value to zero, and the relaxation rate is defined as the average rate of change of the voltage curvature in the static stage. The time length of the dynamic time-delay matching window is inversely proportional to the charge and discharge switching rate and proportional to the relaxation rate. For example, when the charge and discharge switching rate is high, the window time is shortened, and when the relaxation rate is high, the window time is extended; for the constant current charging mode, the charge cut-off current gradient is defined as the maximum instantaneous rate of change of the current drop in the charging end stage, and the material stress release rate is defined as the average rate of change of the curvature of the voltage curve in the static stage. The time length of the dynamic time-delay matching window is inversely proportional to the charge cut-off current gradient and proportional to the material stress release rate. For example, when the charge cut-off current gradient is large, the window time is shortened, and when the material stress release rate is high, the window time is extended.

[0033] When filtering the original time-delay data within the dynamic time-delay matching window, for the time-delay data in the pulse discharge mode, only the data with a time difference less than or equal to the window time length is retained. For example, when the window time is set to 60 seconds, the data points with a time difference exceeding 60 seconds are eliminated. For the time-delay data in the constant current charging mode, the data points with a time difference within the window time range are retained. For example, when the window time is set to 120 seconds, only the data with a time difference between 0 and 120 seconds are retained.

[0034] When calculating the benchmark interval based on the statistical distribution characteristics of the time-delay data of historical healthy batteries, it specifically includes: collecting the time-delay data of multiple groups of healthy batteries under the same charge and discharge mode, and calculating the mean and standard deviation of their time difference. The benchmark interval is set as the mean plus or minus three times the standard deviation; for the pulse discharge mode, if the mean of the historical data is 50 seconds and the standard deviation is 10 seconds, the benchmark interval is 20 seconds to 80 seconds; for the constant current charging mode, if the mean is 100 seconds and the standard deviation is 20 seconds, the benchmark interval is 40 seconds to 160 seconds; compare the time difference data in the effective time-delay set with the benchmark interval of the corresponding mode, and if the time difference is lower than the lower limit of the interval or higher than the upper limit of the interval, it is marked as an abnormal inflection point. For example, if a certain time-delay data in the pulse discharge mode is 15 seconds, which is lower than the lower limit of the benchmark interval by 20 seconds, it is determined to be an abnormal inflection point; if a certain time-delay data in the constant current charging mode is 170 seconds, which is higher than the upper limit of the benchmark interval by 160 seconds, it is also determined to be an abnormal inflection point.

[0035] Through the above steps, the setting of the dynamic time-delay matching window fully combines the characteristics of charge and discharge behavior and the material response characteristics. For example, in the pulse discharge mode, a high switching rate corresponds to a short window time, reflecting the characteristics of rapid relaxation of the electrode after high-load switching. The statistical judgment of the reference interval avoids the subjective deviation of artificially set thresholds. For example, the interval is defined based on the standard deviation range of historical health data to improve the objectivity and accuracy of abnormality detection.

[0036] S4. According to the distribution density of abnormal inflection points and the fractal dimension attenuation characteristics of the voltage relaxation curve in the static stage, the cumulative damage of the microscopic defects of the electrode material caused by historical working conditions is analyzed, and the dynamic threshold interval is generated in combination with the charge state, including: S4-1. Count the frequency of abnormal inflection points within a unit time, and calculate the distribution density of abnormal inflection points based on the degree of concentration of abnormal inflection points on the voltage curve; S4-2, calculating the fractal dimension decay rate of the voltage relaxation curve in the static stage, and extracting the average decrease rate of the fractal dimension over time as the fractal dimension decay characteristic; Step S4-2 includes: S4-2a, performing fractal dimension sequence calculation on the voltage relaxation curve in the static stage, calculating the fractal dimension value by sliding multiple time windows, and generating a decay sequence of the fractal dimension changing with time; S4-2b, based on the correlation between stress release of electrode materials and ion diffusion path, the dynamic coupling characteristics of the fractal dimension decrease rate and the curvature change direction in the decay sequence are extracted, where the fractal dimension decrease rate and the curvature change direction are in the same direction as the rapid decay segment, and in the opposite direction as the slow decay segment; S4-2c. Count the cumulative duration of the fast decay segment and the number of curvature jumps in the slow decay segment to generate a quantitative index of the fractal dimension decay characteristic.

[0037] S4-3. Based on the weighted fusion results of the abnormal inflection point distribution density and the fractal dimension attenuation characteristics, quantify the cumulative damage degree of the micro defects of the electrode material caused by the historical working conditions; S4-4. Compensate and correct the accumulated damage degree according to the state of charge, and generate a dynamic threshold range that changes nonlinearly with the state of charge.

[0038] When calculating the fractal dimension decay rate of the voltage relaxation curve in the static stage, the specific implementation method of the fractal dimension sequence calculation is: using the fractal box counting method, the voltage curve is grid-covered and analyzed with different time windows, and the time window span is set according to the battery relaxation response time. For example, a 1-second time window is used in the early stage of relaxation to capture the rapid deformation characteristics, and a 5-second time window is used in the middle and late stages of relaxation to analyze the macroscopic decay trend. The grid division rule is: the initial grid size is one-tenth of the time window length, for example, a 1-second window corresponds to an initial grid size of 0.1 seconds, and the grid size is gradually reduced to 1% seconds. The minimum number of grids required to cover the curve at each size is counted, and the fractal dimension value is fitted by the least squares method to generate a sequence in which the fractal dimension decreases over time.

[0039] The calculation method of the fractal dimension decrease rate is: find the difference between the fractal dimension values ​​of two adjacent time windows and divide it by the window time interval. For example, in a 1-second time window interval, if the fractal dimension value at time t1 is 1.5 and at time t2 is 1.45, the decrease rate is (1.5-1.45) / 1=0.05 / s. The rule for determining the direction of curvature change is: calculate the second-order difference value of the voltage data at intervals of 0.1 seconds. The second-order difference is approximately the difference between three adjacent voltage points. If the mean of the second-order difference of three consecutive points is greater than zero, it is determined to be a positive change (curvature convex), and if it is less than zero, it is determined to be a negative change (curvature concave). When the fractal dimension decrease rate exceeds 0.05 per second and the curvature change direction is negative, it is marked as a rapid decay segment, indicating that the internal crack expansion of the electrode material causes ion diffusion to be blocked; when the fractal dimension decrease rate is less than 0.02 per second and the curvature change direction is positive, it is marked as a slow decay segment, indicating that local stress is released but no macro damage is caused.

[0040] When counting the cumulative duration of the fast decay segment, calculate the ratio of the total duration of all fast decay segments to the total duration of the static stage. For example, if the total duration of the static stage is 600 seconds and the cumulative duration of the fast decay segment is 120 seconds, the proportion is 20%. When counting the number of curvature jumps in the slow decay segment, detect the number of jump events in which the curvature value in the slow decay segment exceeds twice the average curvature value of the adjacent area. For example, if the average value of the curvature value in a slow decay segment in an adjacent 0.5 second window is 0.1, when the curvature value is detected to be 0.25, it is determined to be a jump event. If the cumulative number is 3, the number of jumps is recorded as 3.

[0041] The calculation method of abnormal inflection point distribution density is: count the number of abnormal inflection points per unit time and multiply it by the spatial clustering coefficient. The spatial clustering coefficient is obtained by calculating the ratio of the average time interval between adjacent points of abnormal inflection points on the voltage curve to the average time interval of a standard healthy battery under the same charge and discharge mode. For example, if the average interval of abnormal inflection points of a battery is 50 seconds and the average interval of a standard healthy battery is 100 seconds, the clustering coefficient is 50 / 100=0.5; if 10 abnormal inflection points are detected in a certain period of time, the distribution density is 10×0.5=5.

[0042] The generation rule of the quantitative index of the fractal dimension attenuation characteristic is as follows: the weight of the cumulative duration of the fast attenuation segment is set to 0.6, and the weight of the number of curvature jumps in the slow attenuation segment is set to 0.4. The weight ratio is determined based on the contribution of the two types of indicators to material damage in the historical data. Specifically, by analyzing 100 groups of aging battery data of the same model, it is concluded that the correlation coefficient between the duration of the fast attenuation segment and the capacity attenuation is 0.8, and the correlation coefficient between the number of jumps in the slow attenuation segment is 0.5. The weight is allocated according to the correlation coefficient ratio of 0.6:0.4. For example, if the cumulative duration accounts for 20% and the number of jumps is 3 times, the attenuation characteristic value is 0.2×0.6+3×0.4=1.32.

[0043] The weighted fusion method of the cumulative damage degree is: the distribution density and fractal dimension attenuation characteristic index are weighted and summed in a ratio of 7:3. This ratio is determined based on the influence weights of macro-abnormal events and micro-structural evolution in the formation mechanism of micro-defects in electrode materials. For example, the probability of the macro-abnormal inflection point corresponding to the growth of lithium dendrites is 70%, and the probability of the micro-fractal attenuation corresponding to the expansion of electrode cracks is 30%. The distribution density weight is 0.7 and the attenuation characteristic weight is 0.3. If the distribution density is 9.6 and the attenuation characteristic value is 1.32, the cumulative damage degree = 9.6×0.7+1.32×0.3=7.18.

[0044] The specific implementation method of the state of charge compensation correction is: calibrate the sensitivity curve of material damage under different states of charge through charge and discharge cycle experiments, and the experimental conditions are ambient temperature 25℃±2℃ and charge and discharge rate 1C. For example, the experiment shows that when the state of charge is higher than 80%, the sensitivity of the material to overcharge damage is reduced by 30%. Based on this, the compensation coefficient is set to 0.7, and the damage degree calculation result is multiplied by this coefficient to relax the lower limit of the threshold; at the same time, when the state of charge is higher than 80%, the risk of lithium plating increases, and the upper limit of the threshold is multiplied by a coefficient of 0.9 to shrink the interval. The generation logic of the dynamic threshold interval is: the threshold interval [50,100] of the new battery under standard working conditions is taken as the benchmark, which is obtained by statistically analyzing the static voltage fluctuation range of the new battery under 1C charge and discharge cycle; the normalization coefficient 10 is determined by historical data regression analysis, specifically fitting the linear relationship curve between the degree of damage and the threshold adjustment amount, and taking the inverse of the slope as the normalization coefficient.

[0045] For example, if the damage degree after compensation is 8, the dynamic threshold interval = reference interval × (damage degree / 10), that is, [50×0.8, 100×0.8] = [40, 80].

[0046] The quantitative indicators of the fractal dimension attenuation characteristics fully reflect the dynamic evolution process of the microscopic defects of the electrode material. For example, a high proportion of the cumulative duration of the rapid attenuation segment indicates that the material crack continues to expand, and a large number of curvature jumps in the slow attenuation segment reflects the frequent occurrence of local stress concentration. The weighted fusion of the abnormal inflection point distribution density and the fractal attenuation characteristics effectively couples the correlation between macroscopic abnormal events and microscopic material damage. The state of charge compensation correction dynamically adapts the threshold range based on the electrochemical characteristics of the battery. For example, the ion activity is enhanced under high charge state, and the lower limit is relaxed to avoid misjudging normal relaxation as a fault. At the same time, the upper limit is contracted to improve the detection sensitivity of early lithium precipitation.

[0047] S5. Perform time-frequency transformation on the voltage in the static stage, extract the frequency domain energy center of gravity offset and the time domain fluctuation envelope, and extract the time lag phase difference of the temperature in the static stage, including: S5-1, perform wavelet packet transformation on the voltage data in the static stage, divide the preset frequency bands and calculate the energy distribution of each preset frequency band, and extract the frequency domain energy center of gravity offset as the difference between the center of gravity frequency at each moment and the reference frequency of the corresponding frequency band of the standard healthy battery; S5-2, filtering the interference component of the frequency domain energy center of gravity offset based on the dynamic threshold interval; S5-3, perform Hilbert transform on the voltage data time domain waveform, and extract the time domain fluctuation envelope as the average of the upper and lower envelopes of the voltage amplitude changing with time; S5-4. Perform cross-correlation analysis on the temperature data sequence in the static stage, and calculate the time-delay phase difference relative to the envelope of the voltage curve. The time-delay phase difference is the time offset corresponding to the peak of the cross-correlation function.

[0048] When wavelet packet transform is performed on the voltage data in the static stage, the division of the preset frequency band is determined according to the battery fault sensitive frequency band. For example, for lithium-ion power batteries, the preset frequency band is divided into three bands: 0.1Hz-1Hz, 1Hz-10Hz and 10Hz-100Hz. The energy distribution of each frequency band is obtained by calculating the square sum of the signals of each node after wavelet packet decomposition. The method for extracting the frequency domain energy center of gravity offset is as follows: calculate the center of gravity frequency of the energy distribution of each frequency band, and the center of gravity frequency is obtained by the weighted average formula, and the weight is the proportion of the energy of each sub-band to the total energy. For example, the 0.1Hz-1Hz frequency band contains three sub-bands with energy proportions of 30%, 50%, and 20% respectively, and the corresponding center frequencies are 0.5Hz, 0.8Hz, and 0.3Hz, then the center of gravity frequency = 0.5×0.3+0.8×0.5+0.3×0.2=0.61Hz; compare the center of gravity frequency with the reference frequency of the corresponding frequency band of the standard healthy battery. The reference frequency is determined by collecting the average center of gravity frequency of 100 groups of healthy batteries under the same charging and discharging mode. For example, the reference frequency of the 0.1Hz-1Hz frequency band of the healthy battery is 0.6Hz. If the current center of gravity frequency is 0.61Hz, the offset is +0.01Hz.

[0049] When filtering the interference component of the frequency domain energy center of gravity offset based on the dynamic threshold interval, the upper and lower limits of the dynamic threshold interval are derived from the dynamic threshold interval generated in step S4. For example, for the 0.1Hz-1Hz frequency band, the dynamic threshold interval is [-0.05Hz, +0.05Hz]. If the current offset is +0.01Hz, it is determined to be an interference component and removed; if the offset is +0.06Hz, it is retained as a valid fault feature. During the filtering process, it is necessary to ensure that the frequency domain energy center of gravity offset is aligned with the timestamp of the dynamic threshold interval. For example, if the dynamic threshold interval is updated once per minute, the frequency domain offset needs to be matched and filtered according to the average value within each minute.

[0050] When performing Hilbert transform on the time domain waveform of voltage data, the specific steps are as follows: performing Hilbert transform on the voltage sampling sequence to generate an analytical signal, extracting the instantaneous amplitude of the analytical signal as the time domain fluctuation envelope, where the upper envelope is the local maximum sequence of the instantaneous amplitude, the lower envelope is the local minimum sequence, and the average of the upper and lower envelopes is the voltage fluctuation envelope.

[0051] For example, if the voltage sampling values ​​in a certain period of time are [3.2V, 3.3V, 3.1V, 3.4V], after Hilbert transform, the upper envelope is [3.25V, 3.35V, 3.25V, 3.45V], and the lower envelope is [3.15V, 3.25V, 3.05V, 3.35V], then the mean of the fluctuation envelope is [(3.25+3.15) / 2, (3.35+3.25) / 2, …] = [3.2V, 3.3V, …].

[0052] When performing cross-correlation analysis on the temperature data sequence in the static stage, first align the time stamps of the temperature data and the voltage fluctuation envelope data, and resample them in seconds; calculate the cross-correlation function of the temperature sequence and the envelope sequence, and search for the time offset corresponding to the maximum value of the cross-correlation function as the time-lag phase difference. For example, the temperature data starts to rise at the timestamp t=10s, the voltage envelope starts to rise at t=12s, and the peak of the cross-correlation function appears at a lag of 2 seconds, then the time-lag phase difference is 2 seconds. The physical meaning of the time-lag phase difference is the time measurement of the hysteresis effect of heat conduction. For example, the expansion of cracks in electrode materials leads to local heat generation, and there is a time delay in the transfer of heat to the temperature sensor.

[0053] Dynamic threshold filtering of the frequency domain energy center of gravity offset effectively distinguishes normal relaxation fluctuations from real fault signals. For example, the offset caused by normal relaxation is within ±0.05Hz, while the internal short circuit fault causes the offset to exceed +0.05Hz. The mean of the time domain fluctuation envelope reflects the overall voltage fluctuation trend and avoids instantaneous noise interference. The time lag phase difference quantifies the thermal conduction delay characteristics. For example, the time lag phase difference of a normal battery is less than 5 seconds, while the time lag phase difference under a lithium plating fault exceeds 10 seconds.

[0054] S6. Based on the judgment result of whether the time domain fluctuation envelope is within the dynamic threshold range and whether the time lag phase difference is lower than the preset critical value, a safety risk judgment is performed, including: If the time domain fluctuation envelope is within the dynamic threshold range and the time lag phase difference is lower than the preset critical value, it is judged as the superposition interference of the microscopic defects of the electrode material and the relaxation effect; otherwise, the frequency domain energy center of gravity offset is matched with the frequency domain fingerprint library of the preset fault to trigger a graded alarm.

[0055] The logic for constructing the frequency domain fingerprint library of preset faults is as follows: perform standardized charge and discharge tests on power batteries with known internal short circuit faults and lithium plating fault types, collect voltage data in the static stage and extract the frequency domain energy center of gravity offset characteristics, where the internal short circuit fault presents a continuous positive offset characteristic of the energy center of gravity in the low frequency band, and the lithium plating fault presents a periodic fluctuation offset characteristic in the medium frequency band; associate and map the characteristics of different fault modes with the energy distribution law of the corresponding frequency band to form a frequency domain fingerprint library with the fault type as the index and the frequency domain energy center of gravity offset pattern as the characteristic, the low frequency band corresponds to the positive offset mode of the internal short circuit fault, and the medium frequency band corresponds to the periodic fluctuation mode of the lithium plating fault.

[0056] When determining safety risks based on the judgment results of whether the time-domain fluctuation envelope is within the dynamic threshold range and whether the time-delay phase difference is lower than the preset critical value, the time-domain fluctuation envelope and the dynamic threshold range need to be preprocessed to ensure dimensional consistency. The preprocessing method is: normalize the voltage amplitude of the time-domain fluctuation envelope according to the rated voltage range of the standard healthy battery. For example, the voltage range of the standard healthy battery is 3.0V-4.2V, then the envelope voltage value 3.3V is normalized to (3.3-3.0) / (4.2-3.0)=0.25; the dynamic threshold range is also normalized according to this range, for example, the dynamic threshold range [0.2,0.3] represents the allowable normalized voltage fluctuation range.

[0057] The specific logic of safety risk determination is: if the normalized time domain fluctuation envelope is within the dynamic threshold range and the time lag phase difference is lower than the preset critical value, it is determined to be the superposition interference of the electrode material micro-defects and relaxation effects. For example, if the normalized envelope value is 0.25 and is in the interval [0.2, 0.3], and the time lag phase difference is 3 seconds (lower than the critical value of 5 seconds), it is determined to be superposition interference; if the envelope value is 0.35 and exceeds the interval or the time lag phase difference is 6 seconds (exceeding the critical value of 5 seconds), the subsequent fault matching process is triggered.

[0058] The preset critical value is set based on the maximum value of the time lag phase difference in the historical healthy battery data plus twice the standard deviation. For example, if the mean of the time lag phase difference of 100 groups of healthy batteries is 2 seconds and the standard deviation is 1 second, then the critical value = 2 + 2 × 1 = 4 seconds, rounded to 5 seconds.

[0059] The logic for building the frequency domain fingerprint library of the preset fault is: perform charge and discharge tests on batteries with known internal short circuit and lithium plating faults, collect voltage data in the static stage and extract the frequency domain energy center of gravity offset characteristics. For example, the energy center of gravity offset characteristic of the internal short circuit fault in the 0.1Hz-1Hz frequency band is a continuous positive offset of 0.1Hz-0.2Hz, and the characteristic of the lithium plating fault in the 1Hz-10Hz frequency band is a periodic fluctuation offset of ±0.05Hz; the fault characteristics are associated with the frequency band and stored as a frequency domain fingerprint library, for example, the internal short circuit corresponds to the positive offset mode of the frequency band 0.1Hz-1Hz, and the lithium plating corresponds to the periodic fluctuation mode of 1Hz-10Hz.

[0060] When matching the frequency domain energy center of gravity offset with the frequency domain fingerprint library, a similarity calculation method is used, such as calculating the cosine similarity between the current offset and the preset fault feature in the corresponding frequency band. If the similarity exceeds 0.8, it is determined to be a successful match. The hierarchical alarm triggering rule is: a level 1 alarm is triggered when the internal short circuit match is successful (immediate shutdown), and a level 2 alarm is triggered when the lithium plating match is successful (power-limited operation). For example, if the current offset has a similarity of 0.85 with the internal short circuit feature in the 0.1Hz-1Hz frequency band, a level 1 alarm is triggered; if the similarity with the lithium plating feature in the 1Hz-10Hz frequency band is 0.75, no alarm is triggered.

[0061] The specific implementation method of dimension unification in the preprocessing process is as follows: when normalizing the time domain fluctuation envelope, the rated voltage range is dynamically adjusted according to the battery model, for example, ternary lithium battery is 2.8V-4.25V, and lithium iron phosphate battery is 2.5V-3.65V; the dynamic threshold interval is synchronously normalized according to the same range, for example, the dynamic threshold interval [0.15,0.3] of ternary lithium corresponds to the actual voltage range of 2.8+0.15×(4.25-2.8)=3.01V to 2.8+0.3×(4.25-2.8)=3.36V. The normalized envelope value is directly compared with the interval without secondary conversion.

[0062] The experimental method for calibrating the preset critical value of the time lag phase difference is: at an ambient temperature of 25°C, perform standard charge and discharge cycle tests on the same batch of batteries, record the time lag phase difference data of healthy batteries, calculate their mean and standard deviation, and set the critical value to the mean plus three times the standard deviation. For example, in 100 cycle tests, the mean of the time lag phase difference is 3 seconds and the standard deviation is 0.5 seconds, then the critical value is 3+3×0.5=4.5 seconds, rounded to 5 seconds. The construction of the fault frequency domain fingerprint library needs to cover battery data with different degrees of aging. For example, collect the frequency domain energy center of gravity offset of 50 groups of short-circuited batteries at different cycle times (100-1000 times), and extract the common frequency offset features as fingerprints.

[0063] Through the above steps, the safety risk judgment logic realizes the coordinated analysis of multi-dimensional signals (time domain envelope, time lag phase difference, frequency domain offset). For example, the superposition interference judgment needs to satisfy the time domain fluctuation control (envelope within the threshold range) and normal heat conduction delay (low time lag phase difference), while the fault alarm depends on the precise matching of frequency domain features. Normalization preprocessing eliminates the interference of dimensional differences on the judgment logic, and the multi-fault mode coverage of the frequency domain fingerprint library improves the pertinence of the alarm.

[0064] It is worth noting that the superposition interference of microscopic defects and relaxation effects of electrode materials refers to the abnormal local electrochemical activity caused by defects (microscopic defects) such as microscopic cracks and lattice distortion caused by historical charge and discharge cycles of electrode materials during the static stage of the battery, which work together with normal relaxation behaviors (relaxation effects) such as internal ion redistribution and interface charge balance in the battery, resulting in a composite fluctuation phenomenon with similar morphological characteristics but different causes in the voltage curve. For example, microscopic cracks cause the lithium ion diffusion path to be blocked, causing a sudden drop in voltage, while the normal relaxation process also causes a slow drop in voltage due to the reconstruction of the double electric layer. After the superposition of the two, the voltage fluctuation amplitude and timing characteristics converge, making it impossible for the traditional threshold method to distinguish between material damage and normal relaxation.

[0065] Embodiment 2: Figure 2 A structural schematic diagram of a data-driven power battery safety risk assessment system of the present invention is provided. The data-driven power battery safety risk assessment system comprises: Static data acquisition module: collects the voltage, temperature and charge state of the power battery during the static stage, and simultaneously obtains the last charge and discharge current value and charge and discharge mode; Extreme value weight correction module: extracts the slope extreme value point of the last charge and discharge current value, performs weight correction on the slope extreme value point based on the charge and discharge mode, and identifies the timing of the geometric mutation point of the voltage curve in the static stage; Time lag screening analysis module: establish the lag time mapping relationship between the slope extreme point and the geometric morphology mutation point, and screen the abnormal inflection points with excessive time lag; Fractal dynamic threshold module: Based on the distribution density of abnormal inflection points and the fractal dimension attenuation characteristics of the voltage relaxation curve in the static stage, the module analyzes the cumulative damage of the microscopic defects of the electrode material caused by historical working conditions, and generates a dynamic threshold interval in combination with the charge state; Time-frequency feature extraction module: performs time-frequency transformation on the voltage in the static stage, extracts the frequency domain energy center of gravity offset and the time domain fluctuation envelope, and extracts the time lag phase difference of the temperature in the static stage; Safety risk determination module: Safety risk determination is performed based on the judgment results of whether the time domain fluctuation envelope is within the dynamic threshold range and whether the time lag phase difference is lower than the preset critical value.

[0066] The above formulas are all dimensionless and numerical calculations. The formula is a formula that is closest to the actual situation obtained by collecting a large amount of data and performing software simulation. The preset parameters and thresholds in the formula are set by technicians in this field according to actual conditions.

[0067] It should be noted that the present invention can be deployed on the device itself to realize embedded applications, and can also be run on a PC or other terminal with a user interface, so as to meet various hardware environments and usage requirements.

[0068] The above embodiments may be implemented in whole or in part by software, hardware, firmware or any other combination thereof. When implemented by software, the above embodiments may be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, the process or function described in the embodiment of the present application is generated in whole or in part. The computer may be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions may be stored in a computer-readable storage medium, or may be transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions may be transmitted from one website, computer, server or data center to another website, computer, server or data center by wired (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium may be any available medium that a computer can access or a data storage device such as a server or data center that contains one or more available media sets. The available medium may be a magnetic medium (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a DVD), or a semiconductor medium. The semiconductor medium may be a solid-state hard disk.

[0069] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and modules described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0070] In the several embodiments provided in the present application, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the modules is only a logical function division. There may be other division methods in actual implementation, such as multiple modules or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or modules, which can be electrical, mechanical or other forms.

[0071] The modules described as separate components may or may not be physically separated, and the components shown as modules may or may not be physical modules, and may be located in one place or distributed on multiple network modules. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0072] In addition, each functional module in each embodiment of the present application may be integrated into one processing module, or each module may exist physically separately, or two or more modules may be integrated into one module.

[0073] If the functions are implemented in the form of software function modules and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art or the part of the technical solution, can be embodied in the form of a software product, which is stored in a storage medium and includes several instructions for a computer device (which can be a personal computer, server or network device, etc.) to perform all or part of the steps of the methods described in each embodiment of the present application. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM), random access memory (RAM), disk or optical disk, and other media that can store program codes.

[0074] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

[0075] Finally: The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A data-driven power battery safety risk assessment method, characterized in that: The steps include: S1. Collect the voltage, temperature and charge state of the power battery in the static stage, and simultaneously obtain the last charge and discharge current value and charge and discharge mode; S2, extracting the slope extreme value point of the last charge and discharge current value, weighting the slope extreme value point based on the charge and discharge mode, and identifying the geometric morphology mutation point timing of the voltage curve in the static stage; S3, establish the lag time mapping relationship between the slope extreme point and the geometric morphology mutation point, and screen the abnormal inflection point with excessive lag time; S4. According to the distribution density of abnormal inflection points and the fractal dimension attenuation characteristics of the voltage relaxation curve in the static stage, the cumulative damage of the microscopic defects of the electrode material caused by the historical working conditions is analyzed, and the dynamic threshold interval is generated in combination with the charge state; S5, performing time-frequency transformation on the voltage in the static stage, extracting the frequency domain energy center of gravity offset and the time domain fluctuation envelope, and extracting the time lag phase difference of the temperature in the static stage; S6. Based on the judgment result of whether the time domain fluctuation envelope is within the dynamic threshold range and whether the time lag phase difference is lower than the preset critical value, a safety risk judgment is performed.

2. The data-driven power battery safety risk assessment method according to claim 1 is characterized in that: Collect the voltage, temperature and state of charge of the power battery in the static stage, and simultaneously obtain the last charge and discharge current value and charge and discharge mode, including: S1-1. When the power battery enters the static stage, the static voltage data is continuously collected at a first preset sampling frequency, the static temperature data is continuously collected at a second preset sampling frequency, and the state of charge data is acquired in real time through the battery management system; S1-2. When the power battery finishes the last charge and discharge, the current value at the end of the charge and discharge is recorded as the last charge and discharge current value, and the charge and discharge mode classification identifier during the charge and discharge process is obtained, and the charge and discharge mode classification identifier includes at least one of constant current charging, pulse discharge and constant voltage charging; S1-3, aligning the timestamps of the static voltage data, static temperature data and state of charge data with the timestamps of the last charge and discharge current value and the charge and discharge mode classification identifier to generate a time-synchronized static phase data set.

3. The data-driven power battery safety risk assessment method according to claim 1, characterized in that: Extract the slope extreme point of the last charge and discharge current value, perform weight correction on the slope extreme point based on the charge and discharge mode, and identify the geometric mutation point timing of the voltage curve in the static stage, including: S2-1. Based on the charge and discharge mode classification identification, the slope change characteristics of the last charge and discharge current curve are screened for mode adaptability, wherein the pulse discharge mode retains the slope extreme point in the charge and discharge switching stage, and the constant current charge mode retains the slope extreme point in the charge cut-off stage; S2-2, dynamically weight the selected slope extreme points, adjust the weight coefficient of the extreme points in the pulse discharge mode according to the nonlinear relationship of the corresponding charge and discharge switching rate, and adjust the weight coefficient of the extreme points in the constant current charging mode according to the charging cut-off current gradient; S2-3. Perform multi-scale curvature analysis on the geometric mutation points of the voltage curve in the static stage, extract the mutation points with abnormal curvature change direction and amplitude, and generate a time series of geometric mutation points.

4. The data-driven power battery safety risk assessment method according to claim 1, characterized in that: Establish the lag time mapping relationship between the slope extreme point and the geometric morphology mutation point, and screen the abnormal inflection points with excessive lag time, including: S3-1, matching the time difference between the weighted corrected slope extreme value time series and the geometric morphology mutation point time series, calculating the time difference between each slope extreme value point and the first subsequent geometric morphology mutation point, and generating the original time-lag data set; S3-2, according to the charge and discharge mode classification identifier, the dynamic time lag matching window is set, the matching window in the pulse discharge mode is dynamically adjusted based on the inverse correlation between the charge and discharge switching rate and the relaxation rate, and the matching window in the constant current charging mode is dynamically adjusted based on the inverse correlation between the charge cut-off current gradient and the material stress release rate; S3-3, filtering the original time-delay data in the dynamic time-delay matching window, removing the time difference data outside the window, and retaining the time-delay data in the window as the effective time-delay set; S3-4. Based on the statistical distribution characteristics of the time-lag data of historical healthy batteries, the benchmark interval of the effective time-lag set is calculated, and the time-lag points that deviate from the benchmark interval are marked as abnormal inflection points.

5. The data-driven power battery safety risk assessment method according to claim 1, characterized in that: According to the distribution density of abnormal inflection points and the fractal dimension attenuation characteristics of the voltage relaxation curve in the static stage, the cumulative damage of the microscopic defects of the electrode material caused by historical working conditions is analyzed, and the dynamic threshold interval is generated in combination with the charge state, including: S4-1. Count the frequency of abnormal inflection points within a unit time, and calculate the distribution density of abnormal inflection points based on the degree of aggregation of abnormal inflection points on the voltage curve; S4-2, calculating the fractal dimension decay rate of the voltage relaxation curve in the static stage, and extracting the average decrease rate of the fractal dimension over time as the fractal dimension decay characteristic; S4-3. Based on the weighted fusion results of the abnormal inflection point distribution density and the fractal dimension attenuation characteristics, quantify the cumulative damage degree of the micro defects of the electrode material caused by the historical working conditions; S4-4. Compensate and correct the accumulated damage degree according to the state of charge, and generate a dynamic threshold range that changes nonlinearly with the state of charge.

6. The data-driven power battery safety risk assessment method according to claim 5 is characterized in that: Step S4-2 includes: S4-2a, performing fractal dimension sequence calculation on the voltage relaxation curve in the static stage, calculating the fractal dimension value by sliding multiple time windows, and generating a decay sequence of the fractal dimension changing with time; S4-2b, based on the correlation between stress release of electrode materials and ion diffusion path, the dynamic coupling characteristics of the fractal dimension decrease rate and the curvature change direction in the decay sequence are extracted, where the fractal dimension decrease rate and the curvature change direction are in the same direction as the rapid decay segment, and in the opposite direction as the slow decay segment; S4-2c. Count the cumulative duration of the fast decay segment and the number of curvature jumps in the slow decay segment to generate a quantitative index of the fractal dimension decay characteristic.

7. The data-driven power battery safety risk assessment method according to claim 1, characterized in that: Perform time-frequency transformation on the voltage in the static stage, extract the frequency domain energy center of gravity offset and the time domain fluctuation envelope, and extract the time lag phase difference of the temperature in the static stage, including: S5-1, perform wavelet packet transformation on the voltage data in the static stage, divide the preset frequency bands and calculate the energy distribution of each preset frequency band, and extract the frequency domain energy center of gravity offset as the difference between the center of gravity frequency at each moment and the reference frequency of the corresponding frequency band of the standard healthy battery; S5-2, filtering the interference component of the frequency domain energy center of gravity offset based on the dynamic threshold interval; S5-3, perform Hilbert transform on the voltage data time domain waveform, and extract the time domain fluctuation envelope as the average of the upper and lower envelopes of the voltage amplitude changing with time; S5-4. Perform cross-correlation analysis on the temperature data sequence in the static stage, and calculate the time-delay phase difference relative to the envelope of the voltage curve. The time-delay phase difference is the time offset corresponding to the peak of the cross-correlation function.

8. The data-driven power battery safety risk assessment method according to claim 1, characterized in that: Based on the judgment results of whether the time domain fluctuation envelope is within the dynamic threshold range and whether the time lag phase difference is lower than the preset critical value, a safety risk judgment is performed, including: If the time domain fluctuation envelope is within the dynamic threshold range and the time lag phase difference is lower than the preset critical value, it is judged as the superposition interference of the microscopic defects of the electrode material and the relaxation effect; otherwise, the frequency domain energy center of gravity offset is matched with the frequency domain fingerprint library of the preset fault to trigger a graded alarm.

9. The data-driven power battery safety risk assessment method according to claim 8, characterized in that: The logic for constructing the frequency domain fingerprint library of preset faults is as follows: perform standardized charge and discharge tests on power batteries with known internal short circuit faults and lithium plating fault types, collect voltage data in the static stage and extract the frequency domain energy center of gravity offset characteristics, where the internal short circuit fault presents a continuous positive offset characteristic of the energy center of gravity in the low frequency band, and the lithium plating fault presents a periodic fluctuation offset characteristic in the medium frequency band; associate and map the characteristics of different fault modes with the energy distribution law of the corresponding frequency band to form a frequency domain fingerprint library with the fault type as the index and the frequency domain energy center of gravity offset pattern as the characteristic, the low frequency band corresponds to the positive offset mode of the internal short circuit fault, and the medium frequency band corresponds to the periodic fluctuation mode of the lithium plating fault.

10. A data-driven power battery safety risk assessment system, used to implement the data-driven power battery safety risk assessment method according to any one of claims 1 to 9, characterized in that: include: Static data acquisition module: collects the voltage, temperature and charge state of the power battery during the static stage, and simultaneously obtains the last charge and discharge current value and charge and discharge mode; Extreme value weight correction module: extracts the slope extreme value point of the last charge and discharge current value, performs weight correction on the slope extreme value point based on the charge and discharge mode, and identifies the timing of the geometric mutation point of the voltage curve in the static stage; Time lag screening analysis module: establish the lag time mapping relationship between the slope extreme point and the geometric morphology mutation point, and screen the abnormal inflection points with excessive time lag; Fractal dynamic threshold module: Based on the distribution density of abnormal inflection points and the fractal dimension attenuation characteristics of the voltage relaxation curve in the static stage, the module analyzes the cumulative damage of the microscopic defects of the electrode material caused by historical working conditions, and generates a dynamic threshold interval in combination with the charge state; Time-frequency feature extraction module: performs time-frequency transformation on the voltage in the static stage, extracts the frequency domain energy center of gravity offset and the time domain fluctuation envelope, and extracts the time lag phase difference of the temperature in the static stage; Safety risk determination module: Safety risk determination is performed based on the judgment results of whether the time domain fluctuation envelope is within the dynamic threshold range and whether the time lag phase difference is lower than the preset critical value.

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