A method and system for scoring and screening overstated capacity in retired batteries

Abstract

This invention discloses a method and system for scoring and screening overcapacity in retired batteries, relating to the field of battery testing and evaluation technology. It aims to solve the problem of difficulty in identifying overcapacity in retired battery cells. The method includes the following steps: S1, performing a discharge operation on the battery and allowing it to stand still, monitoring the open-circuit voltage during the standing process in real time to obtain a voltage recovery curve; S2, extracting multimodal dynamic features from the voltage recovery curve, the multimodal dynamic features including a voltage recovery time constant and curve fitting residuals; S3, performing weighted fusion on the multimodal dynamic features and outputting a standardized score value to obtain a health score; S4, comparing the health score with a preset threshold to output the overcapacity risk level screening result.

Description

Technical Field

[0001] This invention relates to the field of battery testing and evaluation technology, specifically to a method and system for scoring and screening retired batteries with inflated capacity. Background Art

[0002] With the rapid popularization of new energy vehicles, a large number of power batteries are entering their retirement period. Retired power cells typically retain a certain amount of remaining capacity. If their true condition can be accurately assessed and they are sorted and recombined for use in low-rate scenarios such as energy storage, their lifespan can be significantly extended, achieving resource recycling. However, how to quickly and accurately identify the true capacity of retired cells is a core technical challenge that urgently needs to be solved in the current cascade utilization process. Currently, commonly used cell screening methods mainly rely on single-point parameters such as static voltage, voltage plateau, internal resistance, and residual capacity. While these methods are simple to implement, they cannot comprehensively reflect the true health level of the cells, especially exhibiting serious blind spots in identifying characteristics such as recovery after resting. In practical applications, it is frequently observed that some retired cells show a voltage rebound after being left to rest for several hours to several days, and their surface state parameters (such as open-circuit voltage and internal resistance) tend to be within the normal range, leading to them being mistakenly identified as "healthy cells" and included in the usable group. However, these cells often have problems such as severe polarization, slow response, and reduced reversible capacity; their voltage recovery is not accompanied by an actual capacity increase, constituting the so-called "artificially high capacity" phenomenon. When such cells are used in cascaded energy storage systems, they can easily cause SOC estimation errors, performance imbalances within clusters, premature cell exit from the cycle, and even thermal management risks, seriously affecting the stability and economy of the system.

[0003] Some studies and patents have attempted to assess the health status of retired battery cells from the perspectives of voltage recovery behavior, electrochemical impedance spectroscopy, and curve feature extraction. For example, patent CN118746769A uses the voltage recovery amplitude after resting as a screening criterion and assesses the degree of aging by the ratio of internal resistance before and after resting. Patent CN111398833A uses clustering identification based on the morphology of the recovery curve. These technologies have improved the accuracy of battery cell sorting to some extent, but they mostly focus on a single parameter or feature dimension and are not yet sufficient to comprehensively reflect the true electrochemical performance status of the battery cells.

[0004] Existing methods have the following limitations: First, they lack a coupling analysis mechanism between voltage and internal resistance, making it difficult to identify latently degraded cells where "voltage recovers but capacity does not." Second, they ignore the inconsistency between recovery amplitude and rate, failing to introduce reaction kinetic indicators such as recovery time constant. Third, they lack a scalable and universal scoring system, making it difficult to adapt to engineering-based online screening scenarios. Fourth, they lack quantitative indicators for the "falsely high capacity" phenomenon, leading to a high misjudgment rate in screening results. In summary, a multimodal analysis system integrating voltage recovery, internal resistance change, and recovery dynamic behavior has not yet been formed. There is an urgent need to propose quantifiable, scoreable, and deployable identification methods to improve the accuracy and consistency of screening retired cells in tiered utilization. Summary of the Invention

[0005] To address the difficulty in identifying overcapacity in retired battery cells, this invention proposes a scoring and screening method and system for overcapacity in retired batteries. By quantifying the kinetic rate of voltage recovery during the resting period, it identifies batteries with normal surface capacity but hidden aging, thereby improving the accuracy of the initial screening of retired batteries.

[0006] A further objective of this invention is to improve the robustness of identifying inflated risk through dynamic weight fusion.

[0007] To achieve the above objectives, the present invention adopts the following technical solution: a method for scoring and screening overstated capacity in retired batteries, characterized in that it includes:

[0008] S1, perform a discharge operation on the battery and let it stand still, monitor the open circuit voltage in real time during the standing process, and obtain the voltage recovery curve;

[0009] S2, extract multimodal dynamic features from the voltage recovery curve, the multimodal dynamic features including voltage recovery time constant and curve fitting residual;

[0010] S3, perform weighted fusion on the multimodal dynamic features and output standardized score values ​​to obtain a health score;

[0011] S4. Based on the comparison between the health score and the preset threshold, output the screening result of the risk level of the falsely high capacity.

[0012] In this technical solution, by fitting the open-circuit voltage-time curve during the resting process, the voltage recovery time constant is obtained to quantify the voltage recovery speed and relaxation process, which can significantly improve the identification accuracy and initial screening consistency.

[0013] Preferably, the method for extracting the curve fitting residual features in step S2 is as follows: calculate the overall deviation value between the measured voltage data and the fitted voltage curve during the static process, or extract the dynamic morphological parameters of the voltage recovery curve. The overall deviation value quantifies the degree of deviation between the measured data and the fitted model, and the dynamic morphological parameters describe the changing trend characteristics of the recovery curve.

[0014] Preferably, step S3 includes: weighted fusion of voltage recovery amplitude, internal resistance change rate, voltage recovery time constant and curve fitting residual; the contribution weight of voltage recovery amplitude to health score is positively correlated, and the contribution weight of voltage recovery time constant, internal resistance change rate and curve fitting residual features to health score is negatively correlated, and the weight coefficients are set by preset by expert experience or dynamically optimized by machine learning model.

[0015] Preferably, in step S2, the voltage recovery time constant is inversely proportional to the open-circuit voltage recovery rate, and the larger the value, the more severe the battery polarization phenomenon.

[0016] Preferably, step S2 includes: fitting a voltage recovery curve using an exponential decay model, wherein the voltage recovery time constant is solved by an optimization algorithm, the optimization algorithm aiming to minimize the residual between the measured voltage data and the fitted voltage data.

[0017] Preferably, the optimization objective of the machine learning model is to minimize the error between the score and the actual degree of battery capacity degradation.

[0018] Preferably, the weighted fusion calculation outputs a standardized score value, which is mapped to a range of 0 to 1, where 0 represents the highest risk of inflated capacity and 1 represents the highest reliability of actual capacity.

[0019] Preferably, the preset threshold includes a health threshold and a disposal threshold, and the grading decision in step S4 includes at least three levels of judgment: if the score is higher than the health threshold, it is judged as "usable" and enters the high-priority tiered utilization group; if the score is between the health threshold and the disposal threshold, it is judged as "warning" and assigned to the low-priority tiered utilization group; if the score is lower than the disposal threshold, it is judged as "disposal" and is scrapped or dismantled and recycled.

[0020] Preferably, the health threshold and the retirement threshold are set according to the initial capacity of the retired battery: when the initial capacity is ≥80% of the rated capacity, the health threshold is 0.8 and the retirement threshold is 0.6; when the initial capacity is <80% of the rated capacity, the health threshold is 0.7 and the retirement threshold is 0.5.

[0021] This invention also employs the following technical solution: a system for identifying falsely high capacity in retired power battery cells, comprising:

[0022] The battery cell testing device performs a discharge operation on the battery cell under test to bring it to a preset low charge state or cutoff voltage.

[0023] The data acquisition module collects the open-circuit voltage and AC internal resistance of the battery cell in real time during the resting phase after discharge.

[0024] The feature extraction module extracts multimodal dynamic features from the collected data;

[0025] The scoring module performs weighted fusion of the multimodal dynamic features and outputs a standardized score value;

[0026] The judgment module compares the health score with a preset threshold and outputs a sorting instruction for the risk level of excessive capacity.

[0027] The beneficial effects of the present invention are:

[0028] 1) Efficiently identify cells with inflated capacity, improve the consistency of initial cell screening and sorting, and reduce misjudgments and human intervention;

[0029] 2) Improved safety and performance stability of the cascade system, reducing the risk of thermal runaway;

[0030] 3) The method is highly versatile and applicable to different types and aging levels of battery cells;

[0031] 4) Improved resource utilization enhances the economic value of retired battery cells. Attached Figure Description

[0032] Figure 1 This is a flowchart of a method for identifying falsely high capacity in retired power battery cells according to the present invention.

[0033] Figure 2 This is a flowchart of a method for identifying falsely high capacity in retired power battery cells according to Embodiment 1 of the present invention. Detailed Implementation

[0034] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only one preferred embodiment of this invention and are only used to explain this invention. They do not limit the scope of protection of this invention. All other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this invention.

[0035] Example 1

[0036] This embodiment provides a method for scoring and screening retired batteries with inflated capacity. It uses retired lithium iron phosphate cells from a certain model of electric vehicle as the test sample. However, this method is also applicable to screening other types of lithium-ion or sodium-ion batteries. It includes several steps, which are described below. Figure 1 The flowchart is explained step by step.

[0037] Step S1, discharge pretreatment, and the settling and data acquisition stage.

[0038] The decommissioned battery cell under test is placed in a constant temperature test environment and subjected to pre-discharge treatment. Its polarization state is activated by constant current discharge mode, and its state of charge (SOC) is reduced to the preset cutoff condition.

[0039] Specifically, a discharge current of 0.5C to 1C (C is the rated capacity of the cell) is used to continuously discharge until the preset cutoff condition is reached.

[0040] In this embodiment, the test environment is 25±2℃. The preset cutoff condition can be a preset low SOC point, such as 20% SOC or a lower cutoff voltage. Specifically, it can be set according to the cell type; for example, it can be set to 2.5V for lithium iron phosphate cells and 3.0V for ternary material cells.

[0041] The core purpose of this step is to fully expose potential degradation defects inside the battery cell, laying the foundation for subsequent recovery characteristic analysis.

[0042] Immediately after the discharge is completed, a resting process is initiated. During this time, the cell status is monitored in real time using a high-precision data acquisition device, which can be a battery testing system or a dedicated data logger.

[0043] It is important to emphasize that the sampling frequency should be set between 10 and 100 times per second to ensure the accuracy of dynamic behavior capture.

[0044] The core parameters recorded include: the initial value of the open-circuit voltage (labeled V0) and the initial value of the AC internal resistance (labeled R0) at the start of the resting period, and the real-time open-circuit voltage V that changes over time. t and real-time AC internal resistance R t .

[0045] The settling time is usually set to 2 to 4 hours, or until the voltage change tends to stabilize, for example, the fluctuation is less than 5mV for 30 consecutive minutes.

[0046] Understandably, temperature stability needs to be ensured during this phase. If the ambient temperature fluctuates by more than ±3°C, it may be necessary to retest or enable the temperature compensation algorithm.

[0047] At this stage, batteries that are clearly aging can be screened out based on the real-time open-circuit voltage and real-time AC internal resistance during the resting process and classified into the "discard" level.

[0048] Step S2, Multimodal Dynamic Feature Extraction Stage.

[0049] Based on the time-series data collected in step S1, the following four key features are calculated by the data processing unit.

[0050] First, the voltage recovery amplitude characteristics are extracted.

[0051] The calculation method is the difference between the open-circuit voltage at the end of the resting period and the initial voltage. This parameter characterizes the apparent recovery capability of the cell; a larger value generally indicates a more significant voltage recovery.

[0052] Secondly, the characteristic of the rate of change of internal resistance is extracted.

[0053] The initial AC internal resistance value is obtained at the beginning of the resting stage, and the final AC internal resistance value is obtained at the end of the resting stage. The internal resistance recovery characteristic is characterized by calculating the relative change between the final AC internal resistance value and the initial AC internal resistance value. That is, it is obtained by calculating the relative change rate of the AC internal resistance at the end of the resting stage compared with the initial internal resistance. The larger the change value, the more severe the internal degradation of the battery.

[0054] Specifically, it is expressed as the ratio of the difference between the AC internal resistance at the end of the settling period and the initial internal resistance, divided by the initial internal resistance. This indicator reflects the recovery efficiency of internal resistance during polarization elimination; abnormal changes often point to latent degradation.

[0055] It is worth noting that the voltage recovery time constant feature is also extracted in this invention.

[0056] The curve of open-circuit voltage changing over time is fitted with a function, preferably using an exponential decay model, such as a single-exponential or double-exponential form. The fitting parameters are then solved using an optimization algorithm to minimize the deviation between the measured data and the fitted curve.

[0057] The obtained time constant parameter characterizes the speed of voltage recovery; the larger the value, the slower the polarization elimination.

[0058] It should be noted that this parameter is the core indicator for identifying slow-recovery inflated capacity.

[0059] In some other implementations, computational efficiency can be improved by piecewise exponential approximation. For example, fitting with a single exponential model in the initial resting period and then switching to a double exponential model in the later period can significantly improve computational efficiency.

[0060] Specifically, in this embodiment, the resting process is divided into an instantaneous recovery period (0-30 seconds), a main recovery period (30 seconds-30 minutes), and a long-term relaxation period (>30 minutes) according to the polarization type. A single exponential model is used to fit the instantaneous recovery period, while a double exponential model is used to fit the main recovery period and the long-term relaxation period, thereby improving processing efficiency while ensuring accuracy.

[0061] Finally, the morphological features of the recovery curve are extracted.

[0062] The morphological characteristics of the recovery curve include two sub-features: first, the fitting residual index, which quantifies the overall deviation strength between the measured voltage data and the fitted curve by calculating the root mean square error; and second, dynamic morphological parameters, such as the voltage rise slope or the inflection point position of the curve in the initial static stage. These parameters can help identify abnormal recovery patterns.

[0063] Specifically, the fitting residual index is calculated by measuring the difference between the measured voltage data points and the corresponding positions on the fitted curve, and the overall deviation intensity is quantified by the root mean square error.

[0064] For example, when the root mean square error exceeds 5 millivolts, it indicates that there is a systematic structural abnormality in the battery, such as an internal micro-short circuit or electrolyte drying out.

[0065] Dynamic morphological parameters, on the other hand, focus on capturing local anomalies in the curve.

[0066] For example, smooth exponential decay represents a healthy battery cell, dual-platform decay corresponds to lithium plating failure, and step-like decline characterizes the shedding of active material.

[0067] The peak value at the inflection point can be extracted by calculating the second derivative of the voltage curve in real time. This value reflects the steepness of the voltage recovery process. When the peak value at the inflection point exceeds 0.15 mV per square second, it indicates a high risk of lithium plating on the battery's negative electrode.

[0068] Another key parameter is the segmented slope change rate, which is obtained by dividing the curve into ten equal parts and calculating the total slope fluctuation of each segment. If this parameter exceeds 0.08 millivolts per square second, it indicates that active material has fallen off. Although such cells may have normal capacity in the short term, they are prone to a sudden drop in capacity under vibration.

[0069] These two types of features form a collaborative diagnostic mechanism. For example, when the inflection point peak exceeds the standard, the lithium plating repair process is triggered. When the slope change rate exceeds the standard, it is allocated to the low-speed electric vehicle power supply group. If both parameters exceed the standard at the same time, it is determined to be composite aging and directly disassembled.

[0070] This invention transforms microscopic aging characteristics into quantifiable indicators, which can solve the problem of missed detection of batteries with normal voltage recovery amplitude but internal degradation by traditional methods, and provides key technical support for the graded utilization of retired batteries.

[0071] However, in this embodiment, the curve morphology characteristics are taken into account in conjunction with indicators such as voltage recovery time constant, which further improves the robustness of the grading assessment. See step S3 for details.

[0072] Step S3, the stage of constructing the fusion scoring function.

[0073] The four types of features mentioned above are normalized to eliminate differences in dimensions, and then a health score S is generated through weighted fusion.

[0074] Specifically, the normalization process uses a linear scaling method to map each feature value to the interval between 0 and 1.

[0075] Weight allocation can be done in two ways: one is to preset weight coefficients based on the experience of battery experts, such as giving higher weight to the time constant feature to strengthen the influence of dynamic behavior; the other is to dynamically optimize the weights through machine learning models.

[0076] Specifically, by collecting a large number of retired battery cell samples with known true and false overcapacity (i.e., labeled training data), machine learning algorithms such as linear regression, support vector machine regression, neural networks, or decision trees are used to train the model. The model automatically learns the optimal weight coefficients, enabling the health score to effectively distinguish between cells with false overcapacity and healthy cells. For example, the weights can be optimized by minimizing the scoring error or maximizing the classification accuracy.

[0077] In this embodiment, the training data comes from historical battery cell samples with known inflated risk labels, and the optimization objective is to maximize the score differentiation between healthy battery cells and inflated capacity battery cells.

[0078] It should be noted that the design principle of the scoring function follows a specific directionality. The voltage recovery amplitude characteristic is positively correlated with the score, while the internal resistance change rate, time constant, and fitting residual are all negatively correlated with the score.

[0079] Step S4, Hierarchical Decision-Making Stage.

[0080] The calculated health score is compared with a preset threshold, and a three-level judgment mechanism is implemented.

[0081] When the score is higher than the health threshold, the cell is judged to be "usable", indicating that its actual capacity is reliable and there is no obvious risk of inflated capacity, and it can be directly used for secondary use.

[0082] When the score falls between the elimination threshold and the health threshold, it is classified as "warning" or "pending," indicating a potential risk of inflated capacity, requiring further capacity verification testing or restricted use.

[0083] When the score is below the elimination threshold, it is judged as "elimination" level, indicating that the cell polarization is serious or the capacity recovery is false, and it should be scrapped.

[0084] Understandably, the health threshold and the elimination threshold can be dynamically adjusted based on the initial capacity of the battery cell.

[0085] For example, cells with an initial capacity ≥ 80% of the rated capacity are subject to stricter thresholds, while low-capacity cells are subject to more lenient standards.

[0086] Preferably, when the initial capacity is ≥80% of the rated capacity, the health threshold is 0.8 and the elimination threshold is 0.6; when the initial capacity is <80% of the rated capacity, the health threshold is 0.7 and the elimination threshold is 0.5.

[0087] The method of the present invention can effectively avoid misjudging "voltage recovery type weak cells" with rising surface voltage but low actual capacity as healthy cells, thereby improving the accuracy of cell sorting.

[0088] In some other implementations, the data acquisition module can integrate a temperature sensor to synchronously record the cell surface temperature. If the temperature change exceeds a threshold, a data compensation algorithm is automatically triggered. This design enhances robustness under high and low temperature conditions.

[0089] It is important to emphasize that the function fitting in the time constant extraction process is not limited to exponential models. In actual deployment, other kinetic models, such as fractional derivative models, can be adapted according to the cell's chemical system, with the core objective being to quantify the recovery rate.

[0090] For machine learning weight optimization scenarios, the training dataset should cover multiple typical degradation modes (such as lithium deposition, SEI thickening, loss of active material, etc.). Interaction terms can be introduced during the feature engineering stage to improve the sensitivity of identifying falsely high capacity.

[0091] When this method is adapted to an online sorting system, the judgment module can directly link with the mechanical sorting mechanism. For example, when a "discard" command is output, the pneumatic pusher automatically moves the battery cell to the scrap recycling channel, achieving full-process automation.

[0092] This invention can serve as the core algorithm module of a smart sorting platform for retired power batteries and can be widely applied in scenarios such as energy storage, power peak shaving, and battery recycling.

[0093] Example 2

[0094] Based on the core technology framework of Example 1, this embodiment further introduces a deep quantification mechanism of polarization dynamics and multi-physics field collaborative analysis, which significantly improves the identification accuracy of "falsely high capacity" cells.

[0095] The following example illustrates the screening scenario of NCM ternary lithium batteries decommissioned from a certain energy storage power station, with the test environment being constant at 25±1℃.

[0096] Step S1, Polarization Activation and Multidimensional Data Acquisition Stage.

[0097] The cell under test is placed on a constant temperature test platform and discharged at a constant current rate of 1C until the cutoff voltage of 3.0V.

[0098] It is important to emphasize that this phase adds thickness deformation monitoring, which records the change in cell thickness in real time through a laser displacement sensor. The sampling frequency is set to 10 times per second, and the data is stored synchronously with voltage and internal resistance data.

[0099] Immediately after the discharge is completed, the system enters a resting phase, which lasts for a total of 3 hours.

[0100] Understandably, this embodiment pays particular attention to the rapid recovery behavior during the initial resting period. Therefore, a high-frequency sampling rate of 100 times per second is used for the first 5 minutes after the resting period begins, which is then reduced to 10 times per second. Simultaneously with the 100-times-per-second high-frequency sampling, an infrared thermal imager is activated to record the surface temperature distribution of the battery cell, focusing on capturing areas of localized temperature anomalies. This design can effectively identify micro-hot spots caused by lithium plating.

[0101] Step S2, multi-stage dynamic feature extraction stage.

[0102] Based on the collected time-series data, the voltage recovery process is divided into three characteristic time periods for differentiated processing.

[0103] First, for the instantaneous recovery period (0-30 seconds), the initial voltage rise rate is extracted, specifically calculated as the average slope of the first 10 voltage sampling points. Simultaneously, the thickness shrinkage ratio during this period is calculated to characterize the mechanical stress release characteristics. It should be noted that abnormal cells often exhibit a delayed thickness recovery during this stage.

[0104] Secondly, during the main recovery period (30 seconds to 30 minutes), a novel kinetic model was used to fit the voltage curve. This model describes the ion diffusion behavior through special mathematical functions, in which key parameters include the diffusion anomaly index and the principal time constant.

[0105] The diffusion anomaly index reflects the degree of obstruction to lithium-ion migration; a healthy cell has an index close to 1, while a degraded cell has an index below 0.6. The principal time constant quantifies the rate of charge transfer.

[0106] It is worth noting that the temperature uniformity index was extracted simultaneously in this stage. The calculation method is the difference between the highest and lowest surface temperatures. This parameter has indicative significance for local lithium deposition.

[0107] Finally, during the long relaxation period (>30 minutes), the final voltage recovery efficiency, a parameter characterizing the deep polarization elimination capability, was calculated. Simultaneously, the thickness stabilization time was recorded, defined as the moment when the thickness fluctuation decreased to below 0.1% of its initial value. Slow thickness stabilization is often accompanied by reversible capacity loss.

[0108] Step S3, the adaptive weighted scoring system construction phase.

[0109] The seven extracted core features were normalized. Positive indicators (such as voltage rise rate) were calculated as the ratio of the measured value to the healthy baseline; negative indicators (such as time constant) were calculated as the ratio of the healthy baseline to the measured value. The healthy baseline was derived from a database of new cells of the same model.

[0110] The scoring function employs a dynamic weighting mechanism. When significant temperature unevenness is detected, the weight of the kinetic parameters is automatically increased to 60%. If the thickness recovery is abnormal, a rejection rule is activated, and if the thickness stabilization time exceeds 1 hour, it is directly judged as "discarded". In other cases, the standard weighting configuration is applied.

[0111] In this embodiment, the criterion for determining significant temperature unevenness is a temperature difference exceeding 5 degrees Celsius, and the criterion for determining abnormal thickness recovery is a shrinkage rate of less than 0.3%.

[0112] Step S4, Cloud-Edge Collaborative Decision-Making and Sorting Linkage Stage.

[0113] The calculated health score is transmitted to a cloud-based analytics platform, which continuously aggregates data from multiple production lines and dynamically optimizes the thresholds.

[0114] The health threshold fluctuates between 0.75 and 0.85 and is calibrated in real time by the pass rate of the same batch of cells in the current month. The elimination threshold is fixed at 0.55.

[0115] The judgment result is transmitted to the sorting terminal in real time. When the "elimination" command is output, the pneumatic actuator moves the battery cell to the dismantling channel within 3 seconds, triggers the audible and visual alarm, and records the battery cell code and characteristic parameters to establish a degradation mode traceability file.

[0116] It should be noted that this embodiment, by introducing multi-stage recovery analysis and temperature-deformation coordinated monitoring, achieves for the first time accurate identification of concealed lithium plating-type false high capacity.

[0117] Compared to the basic scheme in Example 1, this embodiment can further reduce the failure rate of falsely high capacity cells and significantly improve the safety margin of battery packs used in tiered applications.

[0118] The above implementation methods demonstrate that this solution effectively addresses the technical challenges in sorting retired batteries through multiphysics coupling analysis and dynamic decision-making mechanisms, and possesses significant value for industrial application.

[0119] Example 3

[0120] This embodiment provides a system for identifying falsely high capacity in retired power battery cells, including: a cell testing device for performing the preset discharge process and providing a stable testing environment.

[0121] This device typically includes charging and discharging equipment, a temperature control module, and cell clamps. The charging and discharging equipment can be a high-precision battery tester, and the temperature control module ensures that the test is conducted at a constant temperature, avoiding the influence of temperature on internal resistance and voltage recovery.

[0122] The cell testing device can perform precise constant current or constant power discharge on the cell to achieve the preset low SOC point.

[0123] Data acquisition module: Used to acquire the open-circuit voltage (V) of the battery cell in real time and accurately during the cell's resting process. t ) and AC internal resistance (R) t The module collects and records the initial values ​​(V0, R0) at the start of the static period. It typically consists of a high-precision voltage acquisition unit (such as a digital multimeter or AD converter) and an AC internal resistance tester (such as a milliohm meter or impedance analyzer), ensuring the accuracy and synchronization of data acquisition.

[0124] Feature extraction module: Connected to the data acquisition module, it receives the acquired voltage, internal resistance, and time data, and automatically calculates the voltage recovery amplitude ΔV, the rate of change of internal resistance ΔR / R0, the voltage recovery time constant τ, and the fitting residual or morphological parameters of the recovery curve. This module can be implemented by a high-performance embedded processor, industrial control computer, or server, etc., with embedded corresponding data processing and fitting algorithms.

[0125] Scoring Module: Connected to the feature extraction module, it embeds the capacity accuracy coupled scoring function S. It receives various feature parameters calculated by the feature extraction module and calculates the cell's health score S based on preset weight coefficients or a trained machine learning model. This module can be a standalone processing unit or a software module integrated into an industrial control computer.

[0126] Judgment Module: Connected to the scoring module, this module compares the health score S output by the scoring module with a preset threshold and outputs a "usable / warning / rejected" rating for the battery cell. The judgment module can display the results on the user interface or send them to subsequent automated sorting equipment via a communication interface.

[0127] The system described in this invention can preferably be integrated into automated sorting equipment for the reuse of retired batteries. In such an integrated system, the output of the judgment module can directly drive an automated robotic arm or sorting mechanism to automatically classify the battery cells into different storage bins or subsequent processing lines, achieving online, high-throughput identification and rejection of battery cells with inflated capacity risks. For example, different sorting channels can be set up to correspond to three categories of battery cells: "usable," "warning," and "rejected."

[0128] Through the above methods and systems, the present invention can efficiently and accurately identify the problem of inflated capacity in retired battery cells, avoiding the limitations of single-parameter judgment in traditional methods, thereby significantly improving the overall efficiency and safety of the cascade utilization of retired battery cells.

Claims

1. A method for scoring and screening deregistered batteries with inflated capacity, characterized in that, The following steps are involved: S1, perform a discharge operation on the battery and let it stand still, monitor the open circuit voltage in real time during the standing process, and obtain the voltage recovery curve; S2, extract multimodal dynamic features from the voltage recovery curve, including voltage recovery amplitude, internal resistance change rate, voltage recovery time constant, and curve fitting residual; fit the voltage recovery curve, wherein the voltage recovery time constant is solved by an optimization algorithm, the optimization algorithm aims to minimize the residual between the measured voltage data and the fitted voltage data; the curve fitting residual is obtained by calculating the overall deviation between the measured voltage data and the fitted voltage curve during the resting process. S3. At the start of the settling phase, the initial AC internal resistance value is obtained, and at the end of the settling phase, the final AC internal resistance value is obtained. The ratio of the difference between the final AC internal resistance value and the initial AC internal resistance value to the initial AC internal resistance value is used to obtain the internal resistance change rate. The multimodal dynamic features are weighted and fused, and a standardized score value is output to obtain a health score. Among them, the contribution weight of voltage recovery amplitude to the health score is positively correlated, while the contribution weight of voltage recovery time constant, internal resistance change rate, and curve fitting residual features to the health score is negatively correlated. S4. Based on the comparison between the health score and the preset threshold, output the screening result of the risk level of the falsely high capacity.

2. The method for scoring and screening overstated capacity of retired batteries according to claim 1, characterized in that, The overall deviation value quantifies the degree of deviation between the measured data and the fitted model.

3. The method for scoring and screening overstated capacity of retired batteries according to claim 1, characterized in that, In step S3, the weighting coefficients are set by pre-setting based on expert experience or by dynamic optimization using a machine learning model.

4. A method for scoring and screening overstated capacity in retired batteries according to claim 1 or 3, characterized in that, In step S2, the voltage recovery time constant is inversely proportional to the open-circuit voltage recovery rate.

5. The method for scoring and screening overstated capacity of retired batteries according to claim 3, characterized in that, The optimization objective of the machine learning model is to minimize the error between the score and the actual degree of battery capacity degradation.

6. A method for scoring and screening overstated capacity in retired batteries according to claim 1 or 3, characterized in that, The weighted fusion calculation outputs a standardized score value, which is mapped to a range of 0 to 1, where 0 represents the highest risk of inflated capacity and 1 represents the highest reliability of real capacity.

7. The method for scoring and screening overstated capacity of retired batteries according to claim 1, characterized in that, The preset thresholds include a health threshold and an elimination threshold. The graded decision in step S4 includes at least three levels of judgment: if the score is higher than the health threshold, it is judged as "usable" and enters the high-priority tiered utilization group. If the score is between the health threshold and the elimination threshold, it is judged as "warning" level and assigned to a low-priority tiered utilization group; If the score is below the elimination threshold, it is judged as "elimination" level and will be scrapped or dismantled and recycled.

8. The method for scoring and screening overstated capacity of retired batteries according to claim 7, characterized in that, The health threshold and retirement threshold are set according to the initial capacity of the retired battery: when the initial capacity is ≥80% of the rated capacity, the health threshold is 0.8 and the retirement threshold is 0.6; when the initial capacity is <80% of the rated capacity, the health threshold is 0.7 and the retirement threshold is 0.

5.

9. A system for identifying inflated capacity in retired power battery cells, employing the scoring and screening method for inflated capacity in retired batteries as described in any one of claims 1-8, characterized in that, include: The battery cell testing device performs a discharge operation on the battery cell under test to bring it to a preset low charge state or cutoff voltage. The data acquisition module collects the open-circuit voltage and AC internal resistance of the battery cell in real time during the resting phase after discharge. The feature extraction module extracts multimodal dynamic features from the collected data; The scoring module performs weighted fusion of the multimodal dynamic features and outputs a standardized score value; The judgment module compares the health score with a preset threshold and outputs a sorting instruction for the risk level of excessive capacity.

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