A method for identifying accumulated damage of a relaxation voltage-based energy storage battery and related device

Abstract

The present application belongs to the technical field of energy storage battery safety monitoring, and discloses a method for identifying accumulated damage of an energy storage battery based on relaxation voltage and a related device. The method comprises: obtaining a health baseline feature vector of a target battery monomer and a micro-overcharge event relaxation feature vector; accumulating and fusing feature offsets corresponding to each micro-overcharge event of the target battery monomer to obtain an accumulated damage factor; determining an accumulated damage state of the target battery monomer according to the accumulated damage factor, and outputting safety warning information corresponding to the accumulated damage state and a charging safety intervention instruction. By accumulating and fusing feature offsets of the micro-overcharge event relaxation feature vector relative to the health baseline feature vector, the present application can convert weak relaxation abnormalities caused by single micro-overcharge and easily submerged by noise into a sustainable and trackable accumulated damage factor, thereby realizing online quantitative identification of micro-overcharge accumulated damage.

Description

Technical Field

[0001] This invention belongs to the field of energy storage battery safety monitoring technology, and specifically relates to a method and related device for identifying cumulative damage to energy storage batteries based on relaxation voltage. Background Technology

[0002] Lithium iron phosphate (LFP) batteries are widely used in power storage systems due to their high safety and long cycle life. The Battery Management System (BMS) implements overcharge protection based on the manufacturer-set upper limit charging voltage. When the voltage of any single cell exceeds this limit, the BMS typically cuts off the charging circuit. However, in actual operation, several factors can cause the battery to operate in a slightly overcharged state: for example, differences in cell consistency within the battery pack may cause individual aging cells to reach the upper limit first during the constant current charging phase, and if the BMS uses the overall pack state as a reference, it may not trigger immediate protection; limited voltage sampling accuracy of the BMS or response delays in the control logic may cause the voltage to fluctuate briefly in a range slightly above the upper limit; in addition, abnormal operating conditions such as charger malfunction or communication failure may also cause the battery to be continuously charged with a small amount of power. All of these situations cause the battery voltage to be slightly higher than the rated full charge voltage, but not yet reaching the hard threshold of traditional overcharge protection.

[0003] The amount of lithium plating on the negative electrode caused by a single micro-overcharge is extremely small, and the relaxation voltage curve shows no obvious distortion, making it impossible for traditional BMS to detect such events. However, long-term accumulation of micro-overcharge cycles leads to lithium plating buildup on the negative electrode surface, SEI film thickening, and continuous loss of active lithium, ultimately inducing internal short circuits or even thermal runaway. Existing relaxation voltage analysis techniques are mainly used to detect severe single-event lithium plating, determining whether lithium plating has occurred by identifying the voltage plateau during the relaxation phase. However, they are not sensitive to micro-overcharge events with low single-event lithium plating amounts and cannot quantify the cumulative effect. Some existing technologies have proposed lithium plating estimation models based on capacity decay or impedance spectroscopy, but these often rely on offline calibration or additional hardware, making them difficult to deploy at low cost in energy storage BMS.

[0004] Therefore, there is an urgent need for a method that can identify micro-overcharge cumulative damage, quantify the degree of damage, and dynamically adjust the safety boundary online. Summary of the Invention

[0005] The purpose of this invention is to provide a method and related device for identifying cumulative damage in energy storage batteries based on relaxation voltage, so as to solve the technical problem that the prior art cannot identify micro-overcharge cumulative damage online.

[0006] To achieve the above objectives, the present invention adopts the following technical solution: In a first aspect, the present invention provides a method for identifying cumulative damage in energy storage batteries based on relaxation voltage, comprising: Obtain the standard relaxation voltage curve of the target battery cell under a healthy baseline state, and extract relaxation features from the standard relaxation voltage curve to obtain the healthy baseline feature vector. Real-time acquisition of the charging state data and cell voltage data of the target battery cell; identification of micro-overcharge events based on the charging state data and cell voltage data; acquisition of the micro-overcharge event relaxation voltage curve during the resting phase after the micro-overcharge event; extraction of relaxation features from the micro-overcharge event relaxation voltage curve to obtain the micro-overcharge event relaxation feature vector. Based on the feature offset between the relaxation feature vector of the micro-overcharge event and the feature vector of the health baseline, the feature offsets corresponding to each micro-overcharge event of the target battery cell are accumulated and fused to obtain the cumulative damage factor. The cumulative damage state of the target battery cell is determined based on the cumulative damage factor, and safety warning information and charging safety intervention instructions corresponding to the cumulative damage state are output.

[0007] A further improvement of the present invention is that the health baseline feature vector and the micro-overcharge event relaxation feature vector are the same, both including relaxation slope feature, relaxation time feature, voltage change rate feature and voltage curvature feature; The relaxation slope feature is the linear fitting slope of the relaxation voltage curve within the same preset relaxation interval; the relaxation time feature is the time required for the relaxation voltage to drop from the relaxation initiation voltage to the midpoint between the relaxation initiation voltage and the resting end open circuit voltage; the voltage change rate feature is obtained based on the first derivative of the relaxation voltage curve; and the voltage curvature feature is obtained based on the second derivative of the relaxation voltage curve.

[0008] A further improvement of the present invention is that the step of obtaining the standard relaxation voltage curve of the target battery cell under a healthy baseline state specifically includes: In the early stage of the life of the target battery cell, the target battery cell is charged to the rated full charge voltage using a constant current and constant voltage charging method. Charging is stopped after the cutoff current reaches a preset low current threshold, allowing the target battery cell to enter a resting state. The cell voltage during the resting process is collected at a preset sampling frequency to obtain the standard relaxation voltage curve.

[0009] A further improvement of this invention is that the step of acquiring the charging state data and cell voltage data of the target battery cell in real time, and identifying micro-overcharge events based on the charging state data and cell voltage data, specifically includes: A micro-overcharge event is determined to have occurred when the target battery cell simultaneously meets the following conditions: it is in a charging state, the cell voltage is greater than the rated full charge voltage, and the duration for which the cell voltage is greater than the rated full charge voltage is greater than a preset duration.

[0010] A further improvement of the present invention is that: the feature offset includes relaxation slope offset, relaxation time offset, voltage change rate offset and voltage curvature offset, and each of the feature offsets is obtained based on the difference between the relaxation features of the same type of overcharge event and the corresponding healthy baseline features; The step of accumulating and fusing the feature offsets corresponding to each micro-overcharge event of the target battery cell to obtain the cumulative damage factor specifically includes: The mean values ​​of relaxation slope offset, relaxation time offset, voltage change rate offset, and voltage curvature offset up to the i-th micro-overcharge event are calculated respectively. The mean values ​​of each offset are then weighted and summed according to a preset weighting coefficient to obtain the cumulative damage factor corresponding to the i-th micro-overcharge event.

[0011] A further improvement of the present invention is that the preset weighting coefficient is calibrated by micro-overcharge cycle experiments of the same type of battery and quantitative analysis results of lithium plating.

[0012] A further improvement of the present invention is that: in the step of determining the cumulative damage state of the target battery cell according to the cumulative damage factor and outputting the safety warning information and charging safety intervention command corresponding to the cumulative damage state, the cumulative damage state of the target battery cell is determined by comparing the cumulative damage factor with a predetermined warning threshold and a danger threshold, and the safety warning information and charging safety intervention command corresponding to the cumulative damage state are output. The warning threshold and the danger threshold are determined by experimentally calibrating the mapping relationship between the cumulative damage factor and the lithium plating coverage of the negative electrode.

[0013] A further improvement of the present invention is as follows: in the step of determining the cumulative damage state of the target battery cell based on the cumulative damage factor and outputting safety warning information and charging safety intervention instructions corresponding to the cumulative damage state, when the cumulative damage factor is less than the warning threshold, the micro-overcharge event is recorded; when the cumulative damage factor is greater than or equal to the warning threshold and less than the danger threshold, a secondary safety warning message and a first charging safety intervention instruction are output, wherein the first charging safety intervention instruction is used to lower the maximum charging voltage of the target battery cell and perform lithium elimination maintenance; when the cumulative damage factor is greater than or equal to the danger threshold, a primary safety warning message and a second charging safety intervention instruction are output, wherein the second charging safety intervention instruction is used to physically isolate the target battery cell and replace it.

[0014] In a second aspect, the present invention provides a device for identifying cumulative damage to energy storage batteries based on relaxation voltage, comprising: The first feature extraction module is used to obtain the standard relaxation voltage curve of the target battery cell under a healthy baseline state, and to extract relaxation features from the standard relaxation voltage curve to obtain a healthy baseline feature vector. The second feature extraction module is used to acquire the charging state data and cell voltage data of the target battery cell in real time, identify micro-overcharge events based on the charging state data and cell voltage data, and collect the relaxation voltage curve of the micro-overcharge event during the resting stage after the micro-overcharge event; and extract relaxation features from the relaxation voltage curve of the micro-overcharge event to obtain the relaxation feature vector of the micro-overcharge event. The cumulative damage factor determination module is used to accumulate and fuse the feature offsets corresponding to each micro-overcharge event of the target battery cell based on the feature offsets of the relaxation feature vector of the micro-overcharge event and the health baseline feature vector to obtain the cumulative damage factor. The cumulative damage identification and early warning module is used to determine the cumulative damage state of the target battery cell based on the cumulative damage factor, and output safety early warning information and charging safety intervention instructions corresponding to the cumulative damage state.

[0015] Thirdly, the present invention provides an electronic device including a processor and a memory, wherein the processor is configured to execute a computer program stored in the memory to implement the aforementioned method for identifying cumulative damage in an energy storage battery based on relaxation voltage.

[0016] Fourthly, the present invention provides a computer-readable storage medium storing at least one instruction, which, when executed by a processor, implements the aforementioned method for identifying cumulative damage in an energy storage battery based on relaxation voltage.

[0017] Compared with the prior art, the present invention has the following beneficial effects: This invention provides a method for identifying cumulative damage in energy storage batteries based on relaxation voltage. By acquiring the standard relaxation voltage curve of the target battery cell under a healthy baseline state and extracting the health baseline feature vector, and then acquiring the relaxation voltage curve of the micro-overcharge event during the resting phase after a micro-overcharge event and extracting the corresponding feature vector, the method uses the health state of the same battery cell as a comparison benchmark, reducing the impact of manufacturing differences and initial consistency differences between different battery cells on the damage assessment results. Furthermore, by accumulating and fusing the feature offset of the micro-overcharge event relaxation feature vector relative to the health baseline feature vector over multiple events, the weak, noise-prone relaxation anomaly caused by a single micro-overcharge event can be transformed into a continuously trackable cumulative damage factor, thereby achieving online quantitative identification of micro-overcharge cumulative damage. By outputting safety warning information and charging safety intervention commands based on the cumulative damage factor, the identification results can directly affect subsequent charging safety control, realizing closed-loop control of damage identification, state judgment, and safety intervention, solving the problem that existing technologies struggle to timely identify micro-overcharge cumulative damage and dynamically adjust safety boundaries.

[0018] Furthermore, this invention unifies the healthy baseline feature vector and the micro-overcharge event relaxation feature vector into relaxation slope features, relaxation time features, voltage change rate features, and voltage curvature features. This allows for the characterization of the target battery cell's relaxation behavior changes from multiple dimensions, including the macroscopic decay trend of the relaxation voltage curve, polarization recovery time, instantaneous change rate, and local curvature anomalies. Specifically, the relaxation slope feature reflects the concentration polarization elimination rate, the relaxation time feature reflects the polarization recovery time constant, the voltage change rate feature reflects the polarization voltage decay rate, and the voltage curvature feature captures local curve distortions caused by trace lithium plating. Therefore, compared to methods using only a single voltage plateau or a single slope parameter, this invention improves the sensitivity to micro-overcharge cumulative damage and its resistance to random noise, avoiding the missed detection of trace lithium plating accumulation effects due to insignificant distortions in a single relaxation curve.

[0019] Furthermore, this invention establishes an individualized health baseline by charging the target battery cell to its rated full charge voltage using a constant current and constant voltage charging method during the early stages of the target battery cell's lifespan. Charging is stopped after the cutoff current reaches a preset low current threshold, allowing the cell to enter a resting state and a standard relaxation voltage curve is acquired. This approach avoids the compatibility issues caused by directly using a uniform empirical threshold as a judgment standard, ensuring that the relaxation characteristics of subsequent micro-overcharge events can be compared with the battery cell's own health state, thereby improving the accuracy and consistency of characteristic offset calculations. Simultaneously, baseline establishment can be completed using existing charging control and voltage sampling processes, eliminating the need for additional sensors or dedicated testing hardware, which facilitates low-cost deployment in energy storage battery management systems.

[0020] Furthermore, this invention, by limiting the identification conditions of micro-overcharge events to the simultaneous charging of the target battery cell, the cell voltage exceeding the rated full charge voltage for a duration exceeding a preset duration, can accurately distinguish between genuine micro-overcharge during the charging process and transient sampling spikes, static voltage rebounds, or abnormal voltage fluctuations in non-charging states. This judgment method avoids misjudgments caused by using only a momentary exceedance of the rated full charge voltage by the cell voltage as the trigger condition, and can promptly capture micro-overcharge events caused by differences in battery pack consistency, BMS control delays, or charging anomalies. This provides an accurate event trigger basis for subsequent relaxation curve acquisition and cumulative damage factor calculation, thereby improving the reliability of cumulative damage identification results.

[0021] Furthermore, this invention calculates the relaxation slope offset, relaxation time offset, voltage change rate offset, and voltage curvature offset separately, and then performs a weighted summation of the average values ​​of these offsets up to the i-th micro-overcharge event. This allows for the unified fusion of multidimensional relaxation feature drifts caused by each micro-overcharge event into a cumulative damage factor. This approach, on the one hand, utilizes the average offsets from multiple events to mitigate the impact of single-sampling noise, environmental disturbances, and occasional anomalies on the judgment results; on the other hand, it comprehensively reflects the combined effects of micro-overcharge cumulative lithium plating on polarization elimination rate, polarization recovery time, interface dynamics, and local curvature changes through weighted fusion of different feature dimensions. Therefore, this invention can transform the difficult-to-observe micro-lithium plating accumulation effect into a calculable, comparable, and continuously updated quantitative indicator, improving the stability and engineering usability of cumulative damage identification for energy storage batteries.

[0022] Furthermore, by limiting the preset weighting coefficients to be calibrated using micro-overcharge cycle experiments and quantitative analysis results of lithium plating from batteries of the same model, this invention enables the contribution of various relaxation characteristic shifts in the cumulative damage factor to match the actual lithium plating damage pattern of that battery model. Since the relaxation characteristic response sensitivity of batteries of different models, systems, or capacities after micro-overcharge may vary, calibrating the weighting coefficients through experiments with batteries of the same model avoids damage assessment biases caused by using fixed empirical weights. This invention can establish a quantitative correlation between relaxation characteristic drift and actual lithium plating damage, improving the accuracy of the cumulative damage factor in characterizing the degree of physical damage, thereby enhancing the reliability of subsequent safety warnings and charging interventions.

[0023] Furthermore, this invention determines the cumulative damage state of a target battery cell by comparing the cumulative damage factor with pre-determined warning and danger thresholds. The warning and danger thresholds are established based on experimental calibration of the mapping relationship between the cumulative damage factor and the lithium plating coverage of the negative electrode. This transforms the abstract relaxation characteristic drift quantification results into risk levels with practical safety implications. This invention not only determines whether the battery has experienced micro-overcharge cumulative damage, but also further distinguishes between general recording states, warning states requiring safety intervention, and dangerous states with high safety risks. This solves the problem of existing technologies struggling to dynamically define safety boundaries based on the degree of micro-overcharge accumulation. By determining the threshold based on the lithium plating coverage, the warning judgment can correspond to the actual degree of damage inside the battery, improving the objectivity and interpretability of the warning results.

[0024] Furthermore, this invention achieves a graded protection strategy that matches the degree of damage by implementing different levels of safety warnings and charging safety interventions based on the threshold range of the cumulative damage factor. When the cumulative damage factor is less than the warning threshold, only micro-overcharge events are recorded, avoiding excessive intervention for minor or early risks and maintaining the normal operation of the energy storage system. When the cumulative damage factor reaches the warning threshold but is less than the danger threshold, the maximum charging voltage of the target battery cell is reduced and lithium removal maintenance is performed, which can suppress further aggravation of subsequent micro-overcharging and promote reversible lithium plating mitigation, thereby delaying the development of cumulative damage. When the cumulative damage factor reaches the danger threshold, the target battery cell is physically isolated and a replacement prompt is given, which can reduce the risk of internal short circuits and thermal runaway. Thus, this invention directly transforms the cumulative damage identification results into graded control actions, realizing a complete safety closed loop from monitoring and warning to proactive intervention. Attached Figure Description

[0025] The accompanying drawings, which form part of this specification, are used to provide a further understanding of the invention. The illustrative embodiments of the invention and their descriptions are used to explain the invention and do not constitute an undue limitation of the invention. In the drawings: Figure 1 A schematic flowchart illustrating a method for identifying cumulative damage in an energy storage battery based on relaxation voltage, provided in an embodiment of the present invention. Figure 2 Voltage relaxation diagram during the first cycle of normal battery charging; Figure 3 This is a voltage relaxation diagram after a slight overcharge cycle of the battery. Figure 4 A flowchart illustrating a method for identifying cumulative damage in an energy storage battery based on relaxation voltage, as provided in another embodiment of the present invention; Figure 5 A schematic diagram of the structure of an energy storage battery cumulative damage identification device based on relaxation voltage provided in an embodiment of the present invention; Figure 6 This is a schematic diagram of the structure of an electronic device according to an embodiment of the present invention. Detailed Implementation

[0026] The present invention will now be described in detail with reference to the accompanying drawings and embodiments. It should be noted that, unless otherwise specified, the embodiments and features described herein can be combined with each other.

[0027] The following detailed description is exemplary and intended to provide further detailed explanation of the invention. Unless otherwise specified, all technical terms used in this invention have the same meaning as commonly understood by one of ordinary skill in the art. The terminology used in this invention is for describing particular embodiments only and is not intended to limit the scope of exemplary embodiments according to the invention.

[0028] Please see Figure 1 As shown, this embodiment of the invention provides a method for identifying cumulative damage in energy storage batteries based on relaxation voltage, including the following steps: S1. Modeling the relaxation fingerprint of a single battery cell; Early in the battery's lifespan, standard relaxation tests are performed to establish an individual health baseline for the battery: ① Charge the battery to the rated full charge voltage using the standard constant current and constant voltage charging mode, and set the cutoff current to a conventional small current value; in one specific embodiment, the conventional small current is set to 0.02C ~ 0.1C, for example: 0.02C, 0.05C or 0.1C; ② Cut off the current, let it stand for a sufficient time, and record the relaxation voltage curve V at a sampling frequency of not less than 1Hz. ref (t); In one specific embodiment, a sufficiently long time is greater than or equal to 30 min, for example: 30 min, 60 min or 120 min; ③Based on the relaxation voltage curve V ref (t) Extracting multidimensional feature vectors ; Where, k ref The slope of the relaxation phase is calculated by linearly fitting a fixed interval after the start of relaxation (e.g., the last third of the relaxation period, i.e., the interval where voltage changes are relatively gradual, and the rate of voltage change in this interval can be considered constant). This slope reflects the speed at which concentration polarization is eliminated. The relaxation slope of a healthy battery is stable within a specific range. ref When the relaxation period is halfway through, it is defined as the voltage decreasing from the initial relaxation value V0 to (V0 + V). ocv The time required is ) / 2, where V ocv The open-circuit voltage at the end of the settling period is the polarization time constant, which is directly related to the lithium-ion diffusion capability of the negative electrode; r refTo calculate the voltage change rate, the first derivative dV / dt at each point on the relaxation curve is calculated, and its mean or value at a specific moment (the inflection point value can be directly obtained through image analysis) is extracted. The voltage change rate directly reflects the decay rate of the polarization voltage and is sensitive to the interfacial kinetic changes caused by lithium plating; a ref Given the second derivative of the voltage, calculate the second derivative d of the relaxation curve. 2 V / dt 2 Extracting the maximum absolute value or inflection point value, the second derivative characterizes the curvature change of the relaxation curve. When lithium plating occurs, the relaxation curve will show local curvature anomalies, and the second derivative can effectively capture such subtle distortions.

[0029] Please see Figure 2 The figure shows a typical relaxation voltage curve after the battery is normally charged to 3.65V. Figure 2 In the middle (a), the voltage changes with time (V) curve. ref (t), where the arrow points to the relaxation half-space t. ref The magnified portion is the slope k of the middle segment of the relaxation voltage obtained by linear fitting of the relaxation smooth interval. ref ; Figure 2 In (b), the first derivative at each point on the relaxation curve is the voltage change rate r. ref And the second derivative of voltage a ref .from Figure 2 It can be seen that the relaxation curve of a healthy battery is smooth and monotonically decreasing, with a stable slope in the middle relaxation segment (-0.199 mV / s), a half-time of relaxation of about 211s, a gentle rate of voltage change, and a second derivative close to zero, reflecting a normal polarization elimination process.

[0030] S2. Online capture of micro-overcharge events and acquisition of relaxation data; The system monitors the individual cell voltage during the charging process in real time. A micro-overcharge event is determined to have occurred when the following conditions are met: ① The individual cell voltage is greater than the rated full charge voltage; ②Currently in charging state; ③The duration is not less than 1 second.

[0031] After the event ends, the event is marked, and the relaxation voltage curve V is automatically acquired during the first rest period thereafter. i (t), let stand for at least 30 minutes, and extract the same multidimensional feature vector as in step S1. It is then stored in association with timestamps and event sequence numbers.

[0032] Please see Figure 3 The figure shows the voltage relaxation diagram after the battery is slightly overcharged to 4.0V and cycled 300 times. Figure 3In the middle (a), the voltage change curve V1(t) is shown. The area within the box is the voltage anomaly region. Voltage fluctuations can be regarded as trace lithium plating signals. The arrow points to the relaxation half interval t1. The amplified part is the slope k1 of the relaxation voltage in the middle section obtained by linear fitting of the relaxation smooth interval. Figure 3 In equation (b), the first derivative at each point on the relaxation curve is the voltage rate of change r1, and the second derivative of the voltage is a1. From... Figure 3 As can be seen, the relaxation slope slows down significantly (-0.180mV / s), reflecting a decrease in the concentration polarization elimination rate. The relaxation curve shows slight fluctuations, corresponding to local anomalies in the voltage change rate and second derivative. These systematic drifts form the basis for calculating the cumulative damage factor.

[0033] S3, Construction of cumulative damage factor; To overcome the problems of minimal lithium deposition and relaxation distortion being submerged in noise during a single micro-overcharge event, this invention proposes a method for calculating the cumulative damage factor (CDV), using the characteristic drift after multiple micro-overcharge events as a quantification indicator of damage. ① For battery cells that have experienced micro-overcharge events, calculate the offset of each characteristic relative to the baseline. , , , ; ② Define the cumulative damage factor (CDV) for micro-overcharge events. i The weighted shift value of the offset: , , , , Let be the average offset of the previous i events. , , , The weighting coefficient is calibrated through micro-overcharge cycle experiments with the same type of battery, and its value ranges from 0 to 1 with a sum of 1. S4. Safety margin calibration and graded early warning; The mapping relationship between the cumulative damage factor CDV and the lithium plating coverage of the negative electrode was established through experimental calibration, and two key thresholds were set: ① Warning threshold CDV y The critical value corresponding to the irreversible lithium plating coverage but before dendrites have formed is 0.4. ② Danger threshold CDV r : This corresponds to the critical value at which the lithium plating coverage reaches a level that significantly increases the risk of internal short circuits, and is set to 1.

[0034] S5. Develop a tiered intervention strategy; ①If CDV i <CDVy It does not generate early warning information or charging safety intervention commands, but only records the number of micro-overcharge events and does not actively intervene; ②If CDV y ≤ CDV i <CDV r It generates a secondary warning message and a charging safety intervention instruction. The secondary warning message indicates a slight risk of overcharging. The charging safety intervention instruction limits the maximum charging voltage of the battery cell and actively performs low-current constant-voltage lithium elimination maintenance after charging is completed. ③If CDV i ≥ CDV r If the signal is received, a Level 1 warning message and a charging safety intervention command will be generated. The Level 1 warning message indicates a serious risk of micro-overcharging and the need to replace the battery cell. The charging safety intervention command is used to physically isolate the battery cell from the operating group.

[0035] This invention achieves online quantification of micro-overcharge cumulative damage for the first time: instead of relying on single relaxation curve distortion, it diagnoses the amount of cumulative lithium plating by statistical trends of multiple event feature drifts, improving the signal-to-noise ratio by an order of magnitude and solving the problem of traditional methods being unable to detect trace amounts of lithium plating.

[0036] This invention has zero hardware cost and high compatibility: it only requires reusing the existing voltage sampling channel of the battery management system, without the need for pressure sensors, three electrodes or additional circuits. The algorithm can be directly embedded into the battery management system firmware, and has extremely high engineering promotion value.

[0037] This invention reveals the dynamic evolution law of cumulative damage-safety margin: by establishing the mapping between cumulative damage factor and lithium plating coverage through experimental calibration, and pioneering the introduction of cumulative damage factor into the closed-loop control of charging voltage, enabling the battery management system to have the ability to remember battery history abuse and adaptively shrink the protection boundary.

[0038] This invention elucidates the complete causal chain of micro-overcharge → thermal runaway: Through long-term tracking of relaxation characteristics, this invention quantitatively correlates microscopic lithium accumulation with the decrease in macroscopic safety threshold, providing an in-situ diagnostic method for the study of thermal runaway mechanisms under extreme operating conditions of energy storage batteries.

[0039] This invention provides a method for identifying cumulative damage in energy storage batteries based on relaxation voltage. Using a 100Ah lithium iron phosphate energy storage battery from a certain brand as an example, the battery has a rated full-charge voltage of 3.65V, a nominal capacity of 100Ah, and is equipped with a BMS sampling frequency of 10Hz. A new battery is selected to establish an individual relaxation fingerprint: after charging to 3.65V using a constant current and constant voltage at 0.5C, it is left to stand for 30 minutes, and the relaxation curve is recorded. The slope k in the middle of the relaxation phase is extracted. ref Relaxation half-time t ref Voltage change rate r ref and the second derivative value a refAs a baseline for health.

[0040] The battery underwent 300 micro-overcharge cycles. The charging strategy was 1C constant current charging to 4.0V (micro-overcharge to 0.35V above the rated voltage), with a cutoff current of 0.05C. Relaxation data was collected after a 30-minute rest period, followed by 1C discharge to 2.5V. After each micro-overcharge event, relaxation features were extracted and compared with a baseline to calculate the offset. , , , The cumulative damage factor was calculated using the weighting coefficients determined in the previous calibration experiments. .

[0041] Experimental results showed that the CDV (Cellular Deposition Value) slowly increased to 0.21 in the first 50 cycles, reaching 0.45 after 100 cycles (exceeding the warning threshold). At this point, the BMS simulated lowering the charging upper limit to 3.64V and initiating lithium elimination maintenance. After 200 cycles, the CDV rose to 0.98, and after 300 cycles, the CDV reached 1.35 (exceeding the danger threshold). ICP analysis of the disassembled battery showed a measured lithium plating coverage of 15.8% on the negative electrode, highly consistent with the predicted CDV value of 15.6%. However, using the traditional relaxation platform detection method, no significant lithium plating events were detected in 300 cycles, making it impossible to quantify the damage.

[0042] This embodiment verifies that the method of the present invention can effectively quantify the cumulative lithium plating damage caused by micro-overcharging, can provide early warning of high-risk batteries through dynamic boundary adjustment, and fully reuses the existing voltage sampling of the BMS, thus possessing the advantages of low cost and high precision.

[0043] This invention presents a method for identifying cumulative damage in energy storage batteries based on relaxation voltage. It shifts from traditional single-event detection to cumulative trend diagnosis, relying not on significant distortion of the single relaxation curve, but rather quantifying cumulative lithium plating damage through the systematic drift of relaxation characteristics after multiple micro-overcharge events. The battery's historical micro-overcharge memory is incorporated into safety boundary control, enabling the BMS to adaptively reduce protection thresholds.

[0044] The core feature parameter system of this invention is as follows: the relaxation mid-slope (k) reflects the concentration polarization elimination rate, and cumulative lithium deposition leads to a decrease in the absolute value of the slope; the relaxation half-time (τ) characterizes the polarization time constant, and cumulative lithium deposition leads to an extension of the relaxation time; the voltage change rate (r): the first derivative characterizes the polarization decay rate and is sensitive to changes in interface dynamics; the voltage second derivative (a): captures local curvature anomalies in the relaxation curve and effectively identifies subtle distortions caused by trace amounts of lithium deposition. The fusion of these four features provides a higher signal-to-noise ratio and robustness compared to traditional single-parameter methods.

[0045] The proposed method for constructing the cumulative damage factor (CDV) is as follows: based on the average feature offset of multiple micro-overcharge events, a monotonically non-decreasing cumulative damage index is formed by weighted fusion; the weight coefficients are calibrated by combining micro-overcharge cycle experiments of the same type of battery with lithium plating quantitative analysis, and a quantitative correlation between feature drift and physical damage degree is established.

[0046] The dynamic safety margin closed-loop control proposed in this invention introduces CDV into the charging voltage closed-loop control. The more severe the damage and the deeper the aging, the greater the reduction in the charging threshold. This realizes the closed-loop logic of damage identification, boundary correction, and subsequent protection.

[0047] The proposed hierarchical early warning and intervention strategy is based on CDV threshold and divided into three levels: recording only, voltage limiting + lithium elimination maintenance, and physical isolation; active lithium elimination maintenance (low current constant voltage charging) promotes reversible lithium dissolution and delays damage accumulation.

[0048] Please see Figure 4 As shown, this embodiment of the invention provides a method for identifying cumulative damage in energy storage batteries based on relaxation voltage, including: S100. Obtain the standard relaxation voltage curve of the target battery cell under the health baseline state, and extract the relaxation features of the standard relaxation voltage curve to obtain the health baseline feature vector. In one specific implementation, it specifically includes: Early in the battery's lifespan, standard relaxation tests are performed to establish an individual health baseline for the battery: ① Charge the battery to the rated full charge voltage using the standard constant current and constant voltage charging mode, and set the cutoff current to a conventional small current value; in one specific embodiment, the conventional small current is set to 0.02C ~ 0.05C, for example: 0.02C, 0.04C or 0.05C; ② Cut off the current, let it stand for a sufficient time, and record the relaxation voltage curve V at a sampling frequency of not less than 1Hz. ref (t); In one specific embodiment, a sufficiently long time is greater than or equal to 30 minutes, for example: 30 minutes, 60 minutes or 360 minutes; ③Based on the relaxation voltage curve V ref (t) Extracting multidimensional feature vectors ; Where, k ref The slope of the relaxation phase is calculated by linearly fitting a fixed interval after the start of relaxation (e.g., the last third of the relaxation period, i.e., the interval where voltage changes are relatively gradual, and the rate of voltage change in this interval can be considered constant). This slope reflects the speed at which concentration polarization is eliminated. The relaxation slope of a healthy battery is stable within a specific range. refWhen the relaxation period is halfway through, it is defined as the voltage decreasing from the initial relaxation value V0 to (V0 + V). ocv The time required is ) / 2, where V ocv The open-circuit voltage at the end of the settling period is the polarization time constant, which is directly related to the lithium-ion diffusion capability of the negative electrode; r ref To calculate the voltage change rate, the first derivative dV / dt at each point on the relaxation curve is calculated, and its mean or value at a specific moment (the inflection point value can be directly obtained through image analysis) is extracted. The voltage change rate directly reflects the decay rate of the polarization voltage and is sensitive to the interfacial kinetic changes caused by lithium plating; a ref Given the second derivative of the voltage, calculate the second derivative d of the relaxation curve. 2 V / dt 2 Extracting the maximum absolute value or inflection point value, the second derivative characterizes the curvature change of the relaxation curve. When lithium plating occurs, the relaxation curve will show local curvature anomalies, and the second derivative can effectively capture such subtle distortions.

[0049] S200. Real-time acquisition of charging state data and cell voltage data of the target battery cell; identification of micro-overcharge events based on the charging state data and cell voltage data; acquisition of the micro-overcharge event relaxation voltage curve during the resting phase after the micro-overcharge event; extraction of relaxation features from the micro-overcharge event relaxation voltage curve to obtain the micro-overcharge event relaxation feature vector. In a specific implementation, the individual cell voltage is monitored in real time during the charging process. A micro-overcharge event is determined to have occurred when the following conditions are met: ① The individual cell voltage is greater than the rated full charge voltage; ②Currently in charging state; ③The duration is not less than 1 second.

[0050] After the event ends, the event is marked, and the relaxation voltage curve V is automatically acquired during the first rest period thereafter. i (t), let stand for at least 30 minutes, and extract the same multidimensional feature vector as in step S1. It is then stored in association with timestamps and event sequence numbers.

[0051] S300. Based on the feature offset of the relaxation feature vector of the micro-overcharge event and the feature vector of the health baseline, the feature offsets corresponding to each micro-overcharge event of the target battery cell are accumulated and fused to obtain the cumulative damage factor. In one specific embodiment, to overcome the problems of minimal lithium deposition and relaxation distortion being submerged in noise during a single micro-overcharge, this invention proposes a method for calculating the cumulative damage factor (CDV), using the characteristic drift after multiple micro-overcharge events as a damage quantification indicator: ① For battery cells that have experienced micro-overcharge events, calculate the offset of each characteristic relative to the baseline. , , , ; ② Define the cumulative damage factor (CDV) for micro-overcharge events. i The weighted shift value of the offset: , , , , Let be the average offset of the previous i events. , , , The weighting coefficient is calibrated through micro-overcharge cycle experiments with the same type of battery, and its value ranges from 0 to 1, with a sum of 1.

[0052] S400. Determine the cumulative damage state of the target battery cell based on the cumulative damage factor, and output safety warning information and charging safety intervention instructions corresponding to the cumulative damage state. In one specific embodiment, the cumulative damage state of the target battery cell is determined by comparing the cumulative damage factor with a predetermined warning threshold and a danger threshold, and safety warning information and charging safety intervention instructions corresponding to the cumulative damage state are output; wherein, the warning threshold and danger threshold are determined by experimentally calibrating the mapping relationship between the cumulative damage factor and the lithium plating coverage of the negative electrode.

[0053] When the cumulative damage factor is less than the warning threshold, the micro-overcharge event is recorded; when the cumulative damage factor is greater than or equal to the warning threshold and less than the danger threshold, a secondary safety warning message and a first charging safety intervention instruction are output, the first charging safety intervention instruction being used to lower the maximum charging voltage of the target battery cell and perform lithium removal maintenance; when the cumulative damage factor is greater than or equal to the danger threshold, a primary safety warning message and a second charging safety intervention instruction are output, the second charging safety intervention instruction being used to physically isolate the target battery cell and replace it.

[0054] In one specific implementation, the mapping relationship between the cumulative damage factor CDV and the lithium plating coverage of the negative electrode is established through experimental calibration, and two key thresholds are set: ① Warning threshold CDV y The critical value corresponding to the irreversible lithium plating coverage but before dendrites have formed is 0.4. ② Danger threshold CDV r : This corresponds to the critical value at which the lithium plating coverage reaches a level that significantly increases the risk of internal short circuits, and is set to 1.

[0055] In one specific implementation, the cumulative damage state of the target battery cell is determined based on a cumulative damage factor, and compared with a warning threshold and a danger threshold. A safety warning message and a charging safety intervention command corresponding to the cumulative damage state are then output. Specifically, this includes: ①If CDV i <CDV y It does not generate early warning information or charging safety intervention commands, but only records the number of micro-overcharge events and does not actively intervene; ②If CDV y ≤ CDV i <CDV r It generates a secondary warning message and a charging safety intervention instruction. The secondary warning message indicates a slight risk of overcharging. The charging safety intervention instruction limits the maximum charging voltage of the battery cell and actively performs low-current constant-voltage lithium elimination maintenance after charging is completed. ③If CDV i ≥ CDV r If the signal is received, a Level 1 warning message and a charging safety intervention command will be generated. The Level 1 warning message indicates a serious risk of micro-overcharging and the need to replace the battery cell. The charging safety intervention command is used to physically isolate the battery cell from the operating group.

[0056] Please see Figure 5 As shown, the present invention provides a cumulative damage identification device for energy storage batteries based on relaxation voltage, comprising: The first feature extraction module is used to obtain the standard relaxation voltage curve of the target battery cell under a healthy baseline state, and to extract relaxation features from the standard relaxation voltage curve to obtain a healthy baseline feature vector. The second feature extraction module is used to acquire the charging state data and cell voltage data of the target battery cell in real time, identify micro-overcharge events based on the charging state data and cell voltage data, and collect the relaxation voltage curve of the micro-overcharge event during the resting stage after the micro-overcharge event; and extract relaxation features from the relaxation voltage curve of the micro-overcharge event to obtain the relaxation feature vector of the micro-overcharge event. The cumulative damage factor determination module is used to accumulate and fuse the feature offsets corresponding to each micro-overcharge event of the target battery cell based on the feature offsets of the relaxation feature vector of the micro-overcharge event and the health baseline feature vector to obtain the cumulative damage factor. The cumulative damage identification and early warning module is used to determine the cumulative damage state of the target battery cell based on the cumulative damage factor, and output safety early warning information and charging safety intervention instructions corresponding to the cumulative damage state.

[0057] Please see Figure 6As shown, this embodiment of the invention provides an electronic device 100 for implementing a method for identifying cumulative damage of energy storage batteries based on relaxation voltage; the electronic device 100 includes a memory 101, at least one processor 102, a computer program 103 stored in the memory 101 and executable on the at least one processor 102, and at least one communication bus 104.

[0058] The memory 101 can be used to store the computer program 103. The processor 102 implements the steps of the energy storage battery cumulative damage identification method based on relaxation voltage described in the embodiment by running or executing the computer program stored in the memory 101 and calling the data stored in the memory 101. The memory 101 may mainly include a program storage area and a data storage area. The program storage area may store the operating system, at least one application program required for a function (such as sound playback function, image playback function, etc.), etc.; the data storage area may store data created according to the use of the electronic device 100 (such as audio data), etc. In addition, the memory 101 may include non-volatile memory, such as hard disk, memory, plug-in hard disk, smart media card (SMC), secure digital (SD) card, flash card, at least one disk storage device, flash memory device, or other non-volatile solid-state storage device.

[0059] The at least one processor 102 may be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The processor 102 may be a microprocessor or any conventional processor. The processor 102 is the control center of the electronic device 100, connecting various parts of the electronic device 100 via various interfaces and lines.

[0060] The memory 101 in the electronic device 100 stores multiple instructions to implement a method for identifying cumulative damage in an energy storage battery based on relaxation voltage. The processor 102 can execute the multiple instructions to achieve the following: Obtain the standard relaxation voltage curve of the target battery cell under a healthy baseline state, and extract relaxation features from the standard relaxation voltage curve to obtain the healthy baseline feature vector. Real-time acquisition of the charging state data and cell voltage data of the target battery cell; identification of micro-overcharge events based on the charging state data and cell voltage data; acquisition of the micro-overcharge event relaxation voltage curve during the resting phase after the micro-overcharge event; extraction of relaxation features from the micro-overcharge event relaxation voltage curve to obtain the micro-overcharge event relaxation feature vector. Based on the feature offset between the relaxation feature vector of the micro-overcharge event and the feature vector of the health baseline, the feature offsets corresponding to each micro-overcharge event of the target battery cell are accumulated and fused to obtain the cumulative damage factor. The cumulative damage state of the target battery cell is determined based on the cumulative damage factor, and safety warning information and charging safety intervention instructions corresponding to the cumulative damage state are output.

[0061] If the modules / units integrated in the electronic device 100 are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the methods of the above embodiments can also be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. The computer-readable medium can include: any entity or device capable of carrying the computer program code, a recording medium, a USB flash drive, a portable hard drive, a magnetic disk, an optical disk, a computer memory, and a read-only memory (ROM).

[0062] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0063] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0064] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0065] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0066] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the specific implementation of the present invention. Any modifications or equivalent substitutions that do not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. A method for identifying cumulative damage in energy storage batteries based on relaxation voltage, characterized in that, include: Obtain the standard relaxation voltage curve of the target battery cell under a healthy baseline state, and extract relaxation features from the standard relaxation voltage curve to obtain the healthy baseline feature vector. Real-time acquisition of the charging status data and cell voltage data of the target battery cell; identification of micro-overcharge events based on the charging status data and cell voltage data; and acquisition of the relaxation voltage curve of the micro-overcharge event during the resting phase after the micro-overcharge event. The relaxation feature vector of the micro-overcharge event is obtained by extracting relaxation features from the relaxation voltage curve of the micro-overcharge event. Based on the feature offset between the relaxation feature vector of the micro-overcharge event and the feature vector of the health baseline, the feature offsets corresponding to each micro-overcharge event of the target battery cell are accumulated and fused to obtain the cumulative damage factor. The cumulative damage state of the target battery cell is determined based on the cumulative damage factor, and safety warning information and charging safety intervention instructions corresponding to the cumulative damage state are output.

2. The method for identifying cumulative damage in energy storage batteries based on relaxation voltage according to claim 1, characterized in that, The health baseline feature vector and the micro-overcharge event relaxation feature vector are the same, both including relaxation slope feature, relaxation time feature, voltage change rate feature and voltage curvature feature; The relaxation slope feature is the linear fitting slope of the relaxation voltage curve within the same preset relaxation interval; the relaxation time feature is the time required for the relaxation voltage to drop from the relaxation initiation voltage to the midpoint between the relaxation initiation voltage and the resting end open circuit voltage; the voltage change rate feature is obtained based on the first derivative of the relaxation voltage curve; and the voltage curvature feature is obtained based on the second derivative of the relaxation voltage curve.

3. The method for identifying cumulative damage in energy storage batteries based on relaxation voltage according to claim 1, characterized in that, The step of obtaining the standard relaxation voltage curve of the target battery cell under a healthy baseline state specifically includes: In the early stage of the life of the target battery cell, the target battery cell is charged to the rated full charge voltage using a constant current and constant voltage charging method. Charging is stopped after the cutoff current reaches a preset low current threshold, allowing the target battery cell to enter a resting state. The cell voltage during the resting process is collected at a preset sampling frequency to obtain the standard relaxation voltage curve.

4. The method for identifying cumulative damage in energy storage batteries based on relaxation voltage according to claim 1, characterized in that, The step of acquiring the real-time charging status data and cell voltage data of the target battery cell, and identifying micro-overcharge events based on the charging status data and cell voltage data, specifically includes: A micro-overcharge event is determined to have occurred when the target battery cell simultaneously meets the following conditions: it is in a charging state, the cell voltage is greater than the rated full charge voltage, and the duration for which the cell voltage is greater than the rated full charge voltage is greater than a preset duration.

5. The method for identifying cumulative damage in energy storage batteries based on relaxation voltage according to claim 1, characterized in that, The feature offsets include relaxation slope offset, relaxation time offset, voltage change rate offset, and voltage curvature offset. Each feature offset is obtained based on the difference between the relaxation features of the same type of overcharge event and the corresponding healthy baseline features. The step of accumulating and fusing the feature offsets corresponding to each micro-overcharge event of the target battery cell to obtain the cumulative damage factor specifically includes: The mean values ​​of relaxation slope offset, relaxation time offset, voltage change rate offset, and voltage curvature offset up to the i-th micro-overcharge event are calculated respectively. The mean values ​​of each offset are then weighted and summed according to a preset weighting coefficient to obtain the cumulative damage factor corresponding to the i-th micro-overcharge event.

6. The method for identifying cumulative damage in an energy storage battery based on relaxation voltage according to claim 5, characterized in that, The preset weighting coefficients are calibrated using micro-overcharge cycle experiments of batteries of the same model and quantitative analysis results of lithium plating.

7. The method for identifying cumulative damage in energy storage batteries based on relaxation voltage according to claim 1, characterized in that, In the step of determining the cumulative damage state of the target battery cell based on the cumulative damage factor and outputting safety warning information and charging safety intervention instructions corresponding to the cumulative damage state, the cumulative damage state of the target battery cell is determined by comparing the cumulative damage factor with a predetermined warning threshold and a danger threshold, and the safety warning information and charging safety intervention instructions corresponding to the cumulative damage state are output. The warning threshold and the danger threshold are determined by experimentally calibrating the mapping relationship between the cumulative damage factor and the lithium plating coverage of the negative electrode.

8. The method for identifying cumulative damage in energy storage batteries based on relaxation voltage according to claim 1, characterized in that, In the step of determining the cumulative damage state of the target battery cell based on the cumulative damage factor and outputting safety warning information and charging safety intervention instructions corresponding to the cumulative damage state, when the cumulative damage factor is less than the warning threshold, the micro overcharge event is recorded. When the cumulative damage factor is greater than or equal to the warning threshold and less than the danger threshold, a secondary safety warning message and a first charging safety intervention instruction are output. The first charging safety intervention instruction is used to reduce the maximum charging voltage of the target battery cell and perform lithium elimination maintenance. When the cumulative damage factor is greater than or equal to the danger threshold, a first-level safety warning message and a second charging safety intervention instruction are output. The second charging safety intervention instruction is used to physically isolate the target battery cell and replace it.

9. A device for identifying cumulative damage to energy storage batteries based on relaxation voltage, characterized in that, include: The first feature extraction module is used to obtain the standard relaxation voltage curve of the target battery cell under a healthy baseline state, and to extract relaxation features from the standard relaxation voltage curve to obtain a healthy baseline feature vector. The second feature extraction module is used to acquire the charging state data and cell voltage data of the target battery cell in real time, identify micro-overcharge events based on the charging state data and cell voltage data, and collect the relaxation voltage curve of the micro-overcharge event during the resting stage after the micro-overcharge event. The relaxation feature vector of the micro-overcharge event is obtained by extracting relaxation features from the relaxation voltage curve of the micro-overcharge event. The cumulative damage factor determination module is used to accumulate and fuse the feature offsets corresponding to each micro-overcharge event of the target battery cell based on the feature offsets of the relaxation feature vector of the micro-overcharge event and the health baseline feature vector to obtain the cumulative damage factor. The cumulative damage identification and early warning module is used to determine the cumulative damage state of the target battery cell based on the cumulative damage factor, and output safety early warning information and charging safety intervention instructions corresponding to the cumulative damage state.

10. The energy storage battery cumulative damage identification device based on relaxation voltage according to claim 9, characterized in that, The health baseline feature vector and the micro-overcharge event relaxation feature vector are the same, both including relaxation slope feature, relaxation time feature, voltage change rate feature and voltage curvature feature; The relaxation slope feature is the linear fitting slope of the relaxation voltage curve within the same preset relaxation interval; the relaxation time feature is the time required for the relaxation voltage to drop from the relaxation initiation voltage to the midpoint between the relaxation initiation voltage and the resting end open circuit voltage; the voltage change rate feature is obtained based on the first derivative of the relaxation voltage curve; and the voltage curvature feature is obtained based on the second derivative of the relaxation voltage curve.

11. The energy storage battery cumulative damage identification device based on relaxation voltage according to claim 9, characterized in that, The step of obtaining the standard relaxation voltage curve of the target battery cell under a healthy baseline state specifically includes: In the early stage of the life of the target battery cell, the target battery cell is charged to the rated full charge voltage using a constant current and constant voltage charging method. Charging is stopped after the cutoff current reaches a preset low current threshold, allowing the target battery cell to enter a resting state. The cell voltage during the resting process is collected at a preset sampling frequency to obtain the standard relaxation voltage curve.

12. The energy storage battery cumulative damage identification device based on relaxation voltage according to claim 9, characterized in that, The step of acquiring the real-time charging status data and cell voltage data of the target battery cell, and identifying micro-overcharge events based on the charging status data and cell voltage data, specifically includes: A micro-overcharge event is determined to have occurred when the target battery cell simultaneously meets the following conditions: it is in a charging state, the cell voltage is greater than the rated full charge voltage, and the duration for which the cell voltage is greater than the rated full charge voltage is greater than a preset duration.

13. The energy storage battery cumulative damage identification device based on relaxation voltage according to claim 9, characterized in that, The feature offsets include relaxation slope offset, relaxation time offset, voltage change rate offset, and voltage curvature offset. Each feature offset is obtained based on the difference between the relaxation features of the same type of overcharge event and the corresponding healthy baseline features. The step of accumulating and fusing the feature offsets corresponding to each micro-overcharge event of the target battery cell to obtain the cumulative damage factor specifically includes: The mean values ​​of relaxation slope offset, relaxation time offset, voltage change rate offset, and voltage curvature offset up to the i-th micro-overcharge event are calculated respectively. The mean values ​​of each offset are then weighted and summed according to a preset weighting coefficient to obtain the cumulative damage factor corresponding to the i-th micro-overcharge event.

14. The energy storage battery cumulative damage identification device based on relaxation voltage according to claim 13, characterized in that, The preset weighting coefficients are calibrated using micro-overcharge cycle experiments of batteries of the same model and quantitative analysis results of lithium plating.

15. The energy storage battery cumulative damage identification device based on relaxation voltage according to claim 9, characterized in that, In the step of determining the cumulative damage state of the target battery cell based on the cumulative damage factor and outputting safety warning information and charging safety intervention instructions corresponding to the cumulative damage state, the cumulative damage state of the target battery cell is determined by comparing the cumulative damage factor with a predetermined warning threshold and a danger threshold, and the safety warning information and charging safety intervention instructions corresponding to the cumulative damage state are output. The warning threshold and the danger threshold are determined by experimentally calibrating the mapping relationship between the cumulative damage factor and the lithium plating coverage of the negative electrode.

16. The energy storage battery cumulative damage identification device based on relaxation voltage according to claim 9, characterized in that, In the step of determining the cumulative damage state of the target battery cell based on the cumulative damage factor and outputting safety warning information and charging safety intervention instructions corresponding to the cumulative damage state, when the cumulative damage factor is less than the warning threshold, the micro overcharge event is recorded. When the cumulative damage factor is greater than or equal to the warning threshold and less than the danger threshold, a secondary safety warning message and a first charging safety intervention instruction are output. The first charging safety intervention instruction is used to reduce the maximum charging voltage of the target battery cell and perform lithium elimination maintenance. When the cumulative damage factor is greater than or equal to the danger threshold, a first-level safety warning message and a second charging safety intervention instruction are output. The second charging safety intervention instruction is used to physically isolate the target battery cell and replace it.

17. An electronic device, characterized in that, It includes a processor and a memory, the processor being used to execute a computer program stored in the memory to implement a method for identifying cumulative damage in an energy storage battery based on relaxation voltage as described in any one of claims 1 to 8.

18. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores at least one instruction, which, when executed by a processor, implements a method for identifying cumulative damage in an energy storage battery based on relaxation voltage as described in any one of claims 1 to 8.

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