A method for sorting retired batteries based on multi-magnification incremental capacity response characteristics
Patent Information
- Application Number
- CN202611272369.7
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2026-08-21
- Publication Date
- 2026-09-22
AI Technical Summary
本申请提供了一种基于多倍率增量容量响应特征的退役电池分选方法，通过构建多倍率放电测试，获取退役电池单体在不同电流负载下的动态放电数据，提取各倍率下的增量容量（Incremental Capacity，IC）曲线特征，并融合倍率响应信息、异常响应识别与概率精细聚类分选，可实现对电池倍率适应性、动态输出能力和一致性水平的综合评价，提高电池分选的可靠性。
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Figure CN122787212A_ABST
Abstract
Description
Technical Field
[0001] This application relates to the field of battery testing technology, and in particular to a method for sorting retired batteries based on multi-rate incremental capacity response characteristics. Background Technology
[0002] Against the backdrop of the global energy system's continuous shift towards low-carbon, electrification, and intelligentization, the new energy vehicle industry is rapidly advancing and has become a crucial support for energy conservation, emission reduction, and energy structure optimization in the transportation sector. Lithium-ion batteries, due to their high energy density, long cycle life, and low self-discharge rate, are widely used in the power systems of new energy vehicles. With increasing service time and cycle count, the capacity, power output, and safety performance of power batteries gradually decline, typically entering the retirement stage after a certain number of years of operation. Although retired power batteries can no longer fully meet the high-power, high-reliability application requirements of vehicles, they usually still retain a significant amount of remaining capacity, making them valuable for reuse. Standardized and comprehensive utilization of spent power batteries can not only extend the service life of battery materials and products and improve resource utilization efficiency, but also help alleviate the supply and demand pressure of key resources such as lithium, nickel, and cobalt, reducing dependence on primary mineral resources.
[0003] In the process of refactoring and reusing spent power batteries, the consistency between retired battery cells directly affects the operational performance and safety reliability of the refactored battery pack. If they are combined directly without effective sorting, differences in capacity levels, internal resistance, polarization, and rate response capabilities between different cells can lead to problems such as insufficient capacity utilization, increased voltage differentiation between cells, and increased risk of local overcharging and over-discharging during charging and discharging. Existing retired battery sorting methods typically use parameters such as capacity, voltage, and internal resistance under a single rate or fixed test conditions as the main criteria, which is insufficient to fully reveal the dynamic response characteristics of batteries at different discharge rates. Some batteries may exhibit similar capacity and voltage characteristics at low rates, but their capacity release capability, voltage plateau changes, and polarization response may show significant differences during higher rate discharges. Summary of the Invention
[0004] The purpose of this application is to provide a method for sorting retired batteries based on multi-rate incremental capacity response characteristics, which can achieve a comprehensive evaluation of battery rate adaptability, dynamic output capability and consistency level, thereby improving the reliability of battery sorting.
[0005] To achieve the above objectives, this application provides the following solution: In a first aspect, this application provides a method for sorting retired batteries based on multi-rate incremental capacity response characteristics, including: Performance testing is conducted on retired battery cells that have passed visual inspection. Perform multi-rate charge-discharge tests on retired battery cells that have passed performance testing, and record the test data at each discharge rate. Calculate the incremental capacity curve corresponding to each discharge rate based on the test data at each discharge rate; Feature extraction is performed on each of the incremental capacity curves to obtain the incremental capacity curve features corresponding to each of the incremental capacity curves; Based on the incremental capacity curve characteristics corresponding to each incremental capacity curve, a rate response difference feature is constructed to obtain a multi-rate feature vector; the multi-rate feature vectors of each retired battery cell are combined to obtain a multi-rate feature matrix. Based on the multi-rate feature matrix, an anomaly identification algorithm is used to screen abnormal retired battery cells among retired battery cells, remove abnormal retired battery cells, and obtain preliminary screening results. Clustering is performed on the multi-rate feature vectors of each retired battery cell in the preliminary screening results to obtain the sorting results; Determine the sample size and within-group dispersion for each category in the sorting results; All categories with a sample size greater than the preset minimum sample size and a group dispersion not greater than the preset threshold are selected as candidate retained cells. Consistency assessment is performed on each category of the candidate retained battery range based on the average baseline rate capacity, average rate response difference, and average group confidence of each category.
[0006] Optionally, performance testing is performed on retired battery cells that have passed visual inspection, specifically including: For retired battery cells that pass the appearance inspection, open circuit voltage, internal resistance, and static voltage retention capability tests are performed. If the open circuit voltage of the retired battery cell exceeds the corresponding factory voltage range, or the internal resistance exceeds 1.25 times the corresponding factory specified internal resistance, or the voltage drop value after being static for a set time under the set power condition exceeds the set voltage value, then the retired battery cell fails the performance test.
[0007] Optionally, retired battery cells that have passed performance testing are subjected to multi-rate charge-discharge tests, and test data at each discharge rate are recorded, specifically including: The retired battery cells that passed the performance test were subjected to charge-discharge tests at 0.2C, 0.5C, 1C and 2C respectively, and the test data at each discharge rate were recorded.
[0008] Optionally, the test data includes voltage-time data, voltage-capacity data, and actual discharge capacity at each discharge rate; the voltage-time data includes the voltage corresponding to each sampling time, and the voltage-capacity data includes the voltage and capacity corresponding to each sampling time during the discharge process.
[0009] Optionally, the incremental capacity curve corresponding to each discharge rate is calculated based on the test data at each discharge rate, specifically including: Outliers in the voltage-capacity data are removed to obtain preprocessed voltage-capacity data. The preprocessed voltage-capacity data is deduplicated. The deduplicated voltage-capacity data is interpolated to a uniform voltage sampling interval according to the voltage change sequence, and each voltage in the interpolated voltage-capacity data is within the set voltage range; The interpolated voltage-capacity data is then filtered and smoothed. The incremental capacity curves at each discharge rate are calculated based on the filtered and smoothed voltage-capacity data at each discharge rate.
[0010] Optionally, the incremental capacity curve features include average discharge capacity, main peak voltage, main peak height, inter-peak voltage difference, peak ratio, integral area of incremental capacity curve, and capacity normalized peak value; the capacity normalized peak value is the ratio of main peak height to the average actual discharge capacity of Z repeated tests at the corresponding discharge rate; Z is a positive integer greater than or equal to 3.
[0011] Optionally, the rate response difference characteristic is represented as: ; in, This represents the difference in response of the j-th incremental capacity curve characteristic of the i-th retired battery cell at a discharge rate r relative to the reference discharge rate, where the reference discharge rate is 0.2C. This represents the characteristic curve of the j-th incremental capacity of the i-th retired battery cell at the baseline discharge rate; This represents the characteristic curve of the j-th incremental capacity of the i-th retired battery cell at a discharge rate r; Indicates a preset positive number; The multiple-ratio eigenvector is represented as: ; ; ; ; in, Let represent the rate characteristic vector of the i-th retired battery cell. This represents the rate response feature vector of the i-th retired battery cell at a discharge rate r. , The average response change of the incremental capacity curve characteristic of the j-th retired battery cell in the i-th section. The rate stability evaluation index is used for the i-th retired battery cell.
[0012] Optionally, the multi-rate feature vectors of each retired battery cell in the preliminary screening results are clustered to obtain the sorting results, specifically including: A Gaussian mixture model was used to cluster the multi-rate feature vectors of each retired battery cell in the preliminary screening results to obtain the category label and group confidence of each retired battery cell. When the group confidence level of a retired battery cell is greater than or equal to the group confidence level threshold, the retired battery cell is assigned to the category group corresponding to the category label; otherwise, the retired battery cell is removed.
[0013] According to the specific embodiments provided in this application, the following technical effects are disclosed: This application provides a method for sorting retired batteries based on multi-rate incremental capacity response characteristics. By constructing a multi-rate discharge test, dynamic discharge data of retired battery cells under different current loads are obtained. The incremental capacity (IC) curve features at each rate are extracted, and rate response information, abnormal response identification, and probabilistic fine clustering sorting are integrated. This method can achieve a comprehensive evaluation of battery rate adaptability, dynamic output capability, and consistency level, thereby improving the reliability of battery sorting. Attached Figure Description
[0014] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.
[0015] Figure 1 This is a flowchart illustrating a method for sorting decommissioned batteries based on multi-rate incremental capacity response characteristics, provided as an embodiment of this application.
[0016] Figure 2 This is a detailed flowchart illustrating a method for sorting decommissioned batteries based on multi-rate incremental capacity response characteristics, provided as an embodiment of this application.
[0017] Figure 3 This is a schematic diagram of the IC curve of a single battery cell under multiple discharge rate conditions, provided in an embodiment of this application.
[0018] Figure 4 This is a schematic diagram of anomaly response identification using the Local Outlier Factor (LOF) algorithm, provided in an embodiment of this application.
[0019] Figure 5This is a schematic diagram of probabilistic fine clustering using a Gaussian Mixture Model (GMM) provided in an embodiment of this application. Detailed Implementation
[0020] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.
[0021] To make the above-mentioned objectives, features and advantages of this application more apparent and understandable, the application will be further described in detail below with reference to the accompanying drawings and specific embodiments.
[0022] To address the current issues that most retired lithium battery consistency sorting processes rely on fixed-rate test results, which fail to reflect dynamic response differences such as capacity decay, polarization enhancement, IC curve peak shift, and peak distortion as the rate increases, resulting in insufficient characterization of sorting features, inadequate differentiation between batteries with similar performance, high missorting rates of boundary batteries, and insufficient reliability of reprocessing, this application proposes a retired battery sorting method based on multi-rate incremental capacity (IC) response characteristics. This method combines rate factors with discharge testing and IC curve feature analysis, offering advantages such as accurate sorting, strong applicability, and safety and reliability. This approach is beneficial for further improving the consistency and reliability of retired lithium batteries during reprocessing.
[0023] In one exemplary embodiment, such as Figure 1 and Figure 2 As shown, a method for sorting retired batteries based on multi-rate incremental capacity response characteristics includes steps 101-110.
[0024] Step 101: Conduct performance testing on retired battery cells that have passed the visual inspection.
[0025] Step 102: Perform multi-rate charge-discharge tests on the retired battery cells that have passed the performance test, and record the test data at each discharge rate.
[0026] Step 103: Calculate the incremental capacity curve corresponding to each discharge rate based on the test data at each discharge rate.
[0027] Step 104: Extract features from each of the incremental capacity curves to obtain the incremental capacity curve features corresponding to each of the incremental capacity curves.
[0028] Step 105: Based on the incremental capacity curve characteristics corresponding to each incremental capacity curve, construct the rate response difference characteristics to obtain the multi-rate feature vector; combine the multi-rate feature vectors of each retired battery cell to obtain the multi-rate feature matrix.
[0029] Step 106: Based on the multi-rate feature matrix, the local outlier factor algorithm is used to screen abnormal retired battery cells among the retired battery cells, remove abnormal retired battery cells, and obtain preliminary screening results.
[0030] Step 107: Cluster the multi-rate feature vectors of each retired battery cell in the preliminary screening results to obtain the sorting results.
[0031] Step 108: Determine the number of samples and the intra-group dispersion for each category in the sorting results.
[0032] Step 109: Group the categories with a sample size greater than the preset minimum number of samples in each category and a group dispersion not greater than the preset threshold as candidate cells to be retained.
[0033] Step 110: Based on the average baseline rate capacity, average rate response difference, and average group confidence of each group within the candidate reserved battery range, perform a consistency assessment on each group within the candidate reserved battery range.
[0034] This application constructs a multi-rate discharge test to obtain dynamic discharge data of retired battery cells under different current loads, extracts the incremental capacity curve features at each rate, and integrates rate response information, abnormal response identification, and probabilistic clustering sorting. This enables a comprehensive evaluation of battery rate adaptability, dynamic output capability, and consistency level, thereby improving the reliability of battery sorting.
[0035] In an exemplary embodiment, the retired battery cell is specifically a lithium-ion battery. Existing retired lithium battery consistency sorting technology typically conducts charge-discharge tests based on a single rate or fixed operating condition, and mainly extracts characteristic parameters such as capacity, internal resistance, voltage plateau, or incremental capacity curve under a single operating condition for discrimination. Because lithium-ion batteries exhibit significant differences in polarization, voltage response, capacity release capability, and electrochemical reaction kinetics at different discharge rates, single-rate test results cannot fully reflect the dynamic performance differences of batteries under different load conditions. Especially under low-rate test conditions, battery polarization is weak, and some batteries with different aging levels may exhibit similar capacity and IC curve characteristics; however, under higher discharge rates, their voltage plateau decreases, release capacity decreases, and IC response changes may diverge significantly. This leads to inconsistent dynamic output capabilities in batteries sorted consistently at low rates during actual recombination and utilization, affecting the capacity utilization, power output stability, and operational reliability of the recombined battery pack. The retired battery sorting method based on multi-rate incremental capacity response characteristics proposed in this application can be applied to various types of lithium-ion batteries, such as lithium iron phosphate batteries, ternary batteries, and lithium cobalt oxide batteries. By incorporating the rate dimension into the sorting decision process, the feature system enhances its ability to characterize the performance differences of retired batteries, reduces the dependence on single rate test results and empirical thresholds, and improves the accuracy, stability, and engineering applicability of the sorting results.
[0036] In one exemplary embodiment, prior to step 101, the application further includes visual inspection of the retired battery cells and recording manufacturing information.
[0037] More specifically, the visual inspection includes: visually inspecting retired battery cells, recording factory information, and classifying them according to battery model. The inspection includes checking for bulging, deformation, damage, corrosion, rust, or leakage in the battery casing. Batteries with obvious mechanical damage or safety hazards are directly marked as abnormal and will not proceed to the subsequent multi-rate charge / discharge testing process.
[0038] In an exemplary embodiment, step 101 specifically includes: performing open-circuit voltage detection, internal resistance detection, and static voltage retention capability detection on the retired battery cells that have passed the appearance inspection. If the open-circuit voltage of the retired battery cell exceeds the corresponding factory voltage range, or the internal resistance exceeds 1.25 times the corresponding factory specified internal resistance, or the voltage drop value after being static for a set time under the set power condition exceeds the set voltage value, then the retired battery cell has not passed the performance test; otherwise, the retired battery cell has passed the performance test.
[0039] In an exemplary embodiment, step 102 specifically includes: under the same test conditions, performing charge and discharge tests at 0.2C, 0.5C, 1C and 2C on the retired battery cells that have passed the performance test, and recording the test data at each discharge rate, i.e., dynamic response data, thereby obtaining the dynamic discharge data of the retired battery cells under different current loads.
[0040] The test data includes voltage-time data, voltage-capacity data, and actual discharge capacity at each discharge rate, thereby obtaining dynamic response data of the battery under different current load conditions; the voltage-time data includes the voltage corresponding to each sampling time, and the voltage-capacity data includes the voltage and capacity corresponding to each sampling time during the discharge process.
[0041] More specifically, before each rate test, the retired battery cells were first charged to the same initial state and then allowed to rest. Subsequently, the retired battery cells were subjected to constant current discharge at the set discharge rate until the set lower cutoff voltage was reached. Three tests were performed for each rate. During the test, voltage-time, voltage-capacity data, and the actual discharge capacity at the corresponding rate were recorded. After completing one rate test, the batteries were charged and allowed to rest again before proceeding to the next rate test.
[0042] In an exemplary embodiment, this application preprocesses, interpolates, smooths, and differentiates the voltage-capacity data collected at different discharge rates to obtain IC curves at each rate, such as... Figure 3 As shown. Step 103 specifically includes steps 201-205.
[0043] Step 201: Remove outliers from the voltage-capacity data to obtain preprocessed voltage-capacity data, specifically including: removing data from the static stage, abnormal sampling points, and obvious abrupt changes.
[0044] Step 202: Deduplicatize the preprocessed voltage-capacity data.
[0045] Step 203: Interpolate the deduplicated voltage-capacity data to a uniform voltage sampling interval according to the voltage change sequence, and ensure that each voltage in the interpolated voltage-capacity data is within the set voltage range, so that the IC curves of different ratios have the same voltage coordinate range and sampling interval.
[0046] Step 204: Perform Savitzky-Golay filtering on the interpolated voltage-capacity data to smooth it, in order to reduce the impact of noise amplification during the differential calculation.
[0047] Step 205: Calculate the incremental capacity curve for each discharge rate based on the filtered and smoothed voltage-capacity data. Specifically, the IC curve is calculated using the following method: ; in, This is the IC curve expression, where r represents the discharge rate. This represents the cumulative discharge capacity as a function of voltage at the specified discharge rate (r), where V represents the voltage. Since the voltage decreases as the capacity increases during discharge, a sign correction is used to present the discharge IC curve as a positive peak, facilitating subsequent peak identification and curve characteristic comparison.
[0048] Existing studies on the sorting of retired lithium batteries are mostly based on single-rate or fixed test conditions, failing to fully reflect the battery performance response characteristics as the discharge rate changes. This results in conventional sorting results being unable to distinguish batteries with different rate adaptability. However, analyzing the IC curve characteristics of batteries under multiple rates and performing sorting accordingly can achieve more reliable and consistent sorting of retired lithium batteries.
[0049] In an exemplary embodiment, step 104 extracts key features (incremental capacity curve features) reflecting battery aging state, electrochemical reaction characteristics, and rate response differences from each rate IC curve. These incremental capacity curve features include average discharge capacity, peak voltage, peak height, inter-peak voltage difference, peak ratio, integral area of the incremental capacity curve, and capacity-normalized peak value. The capacity-normalized peak value is the ratio of the peak height to the average actual discharge capacity of Z repeated tests at the corresponding discharge rate, used to mitigate the impact of different battery capacity differences on the IC peak value; Z is a positive integer greater than or equal to 3.
[0050] The formula for calculating the normalized peak capacity is: ; in, This represents the normalized peak capacity at discharge rate r. This represents the average actual discharge capacity obtained from Z repeated tests of the battery at a discharge rate r.
[0051] This application no longer relies solely on capacity, internal resistance, or a single IC peak parameter at a single discharge rate for sorting retired lithium-ion batteries. Instead, it constructs IC curves under different discharge rate conditions and extracts characteristic parameters such as incremental capacity curve features. These features reflect the electrochemical response characteristics of the battery at different rates through peak position shift, peak height variation, peak shape differences, and overall curve response intensity, providing a multi-dimensional characteristic basis for judging the rate adaptability and aging differences of retired batteries.
[0052] In an exemplary embodiment, step 105 is a feature processing procedure, specifically including: fusing the IC curve features at each discharge rate to construct a multi-rate feature matrix and a rate stability evaluation index. Specifically, using the lower rate features as a benchmark (using the IC features at 0.2 C rate as a benchmark), rate response features are calculated, and the rate stability evaluation index is formed by combining the degree of feature change at each rate. Missing value processing, correlation screening, standardization, and fusion processing are performed on the above features to reduce redundant information and noise interference, resulting in a multi-rate feature matrix for clustering and sorting.
[0053] Let the set of IC feature parameters extracted from the i-th retired battery cell at rate r be: in, This represents the set of feature curves of the incremental capacity of the i-th retired battery cell at a discharge rate r; Indicates average discharge capacity; Indicates the main peak voltage; Indicates the height of the main peak; This represents the difference between the voltage positions corresponding to the characteristic peaks (peak-to-peak voltage difference). The ratio between the peak heights of the characteristic peaks (peak ratio); This represents the integral area of the incremental capacity curve; This represents the normalized peak value of the capacity.
[0054] Using the IC curve characteristics at a base magnification of 0.2C as a reference, for the above characteristic parameters... Construct normalized rate response difference features: ; in, This represents the difference in response of the j-th incremental capacity curve characteristic of the i-th retired battery cell at a discharge rate r relative to the reference discharge rate, where the reference discharge rate is 0.2C. This represents the characteristic curve of the j-th incremental capacity of the i-th retired battery cell at the baseline discharge rate; This represents the characteristic curve of the j-th incremental capacity of the i-th retired battery cell at a discharge rate r; This represents a preset positive number, specifically a very small positive number, used to avoid abnormal results when the baseline feature is zero or close to zero.
[0055] The rate response feature vector of the i-th battery at rate r can be expressed as: ; in, Let represent the rate response characteristic vector of the i-th retired battery cell at a discharge rate r, that is, the change in IC characteristic response relative to a 0.2C rate. ; , respectively, represent the response differences of the 1st, 2nd, and 7th incremental capacity curves of the i-th retired battery cell at discharge rate r relative to the baseline discharge rate, where j is a positive integer ranging from 1 to 7.
[0056] Based on the response differences of the retired battery cells in section i at all non-baseline rates, calculate the average response variation of each IC characteristic: ; Where m represents the number of discharge rates; The average response change is the characteristic of the j-th incremental capacity curve of the i-th retired battery cell.
[0057] Based on this, a rate stability evaluation index for the i-th retired battery cell is constructed: ; in, The rate stability evaluation index is used for the i-th retired battery cell. The larger the value, the smaller the change in IC characteristics of the battery at different discharge rates, and the better the rate stability.
[0058] Finally, by combining the rate response feature vectors and rate stability evaluation indices at each non-benchmark rate, the multi-rate fusion feature vector of the i-th retired battery cell is obtained: ; in, Let represent the multi-rate feature vector of the i-th retired battery cell.
[0059] For N retired battery cells to be sorted, the multi-rate fusion feature vectors of each retired battery cell are combined to obtain the multi-rate feature matrix: ; Where X represents the multiplier feature matrix; Let represent the multi-rate fusion feature vector of the i-th retired battery cell; N represents the number of retired battery cells to be sorted. The multi-rate feature matrix undergoes missing value processing, preliminary outlier correction, correlation screening, and standardization to obtain the input feature matrix used for anomaly response identification and probabilistic fine clustering, i.e. Figure 2 Comprehensive feature matrix.
[0060] In an exemplary embodiment, the anomaly identification algorithm in step 106 is used to identify abnormal responses. Based on the multi-rate feature matrix, it performs outlier evaluation and initial screening of retired battery cells, and identifies abnormal batteries or batteries to be reviewed whose rate response characteristics deviate significantly from the main distribution.
[0061] Step 106 specifically includes: based on the multi-rate feature matrix, using the local outlier factor algorithm to screen abnormal retired battery cells, removing abnormal retired battery cells, and obtaining preliminary screening results. The local outlier factor algorithm is used to measure the degree of deviation of a single battery cell from its neighboring cells in the multi-rate feature space, thereby identifying abnormal retired battery cells.
[0062] Let the multi-rate fusion feature vector of the i-th retired battery cell be... Its local outlier is .like A value close to 1 indicates that the characteristic distribution of the retired battery cell is relatively consistent with that of neighboring batteries, such as... Figure 4 As shown.
[0063] Let the anomaly detection threshold be... Then the set A of abnormally retired battery cells can be represented as: ; The set B of retired battery cells that enters the subsequent grouping can be represented as: ; Here, A represents the set of abnormally retired battery cells, B represents the set of retired battery cells that passed the initial screening for abnormalities, and i represents the i-th retired battery cell. Batteries in set A can be managed separately as abnormal batteries or batteries awaiting review; batteries in set B enter the subsequent fine-grained grouping process.
[0064] In an exemplary embodiment, step 107 uses a probabilistic clustering algorithm to perform fine-grained classification on the batteries retained after identifying abnormal responses, obtaining the probability of each battery belonging to different sorting categories. The confidence level of battery grouping is determined based on the maximum belonging probability, and boundary batteries located in the boundary regions of different categories are identified.
[0065] Step 107 specifically includes steps 301-302.
[0066] Step 301: Use a Gaussian mixture model to cluster the multi-rate feature vectors of each retired battery cell in the preliminary screening results to obtain the category label of each retired battery cell. Figure 5 The Gaussian mixture model represents the battery distribution in the multi-rate fusion feature space as a combination of several sub-distributions, each corresponding to a battery sorting category, such as (Class 1-5) and group confidence. Figure 5As shown. Specific classification results can include abnormal batteries, batteries awaiting review, and multiple valid category groups. The multi-category division in the classification results is used to distinguish performance differences in battery capacity levels, IC characteristics, and cross-rate response, ensuring high similarity among batteries within the same category and providing a basis for subsequent regrouping, performance ranking, and application condition matching.
[0067] The probability of the i-th retired battery cell belonging to the k-th category. It can be represented as: ; Where K is the number of category groups.
[0068] Category labels for retired battery cells in section i Determined by the maximum belonging probability: ; Grouping confidence of retired battery cells in section i Represented as: ; in, This represents the weight of the k-th battery category. This represents the weight of the j-th battery category; Indicates that the k-th Gaussian sub-distribution is in The probability density at that location, This indicates that the j-th Gaussian sub-distribution is in The probability density at that location; Subscripts are used for summation.
[0069] Step 302: Set the group confidence threshold as... When the group confidence level of a retired battery cell is greater than or equal to the group confidence level threshold, the retired battery cell is assigned to the category group corresponding to the category label. Otherwise, the retired battery cell is removed and classified as a boundary battery. Boundary batteries can be retested, downgraded for use, or archived separately.
[0070] In an exemplary embodiment, the method of this application further includes cluster quality evaluation: using indicators such as silhouette coefficient, intra-group dispersion and average group confidence to quantitatively evaluate the distinguishability, intra-group consistency and assignment stability of the sorted categories.
[0071] Overall profile coefficient for: ; in, This represents the profile coefficient of the i-th retired battery cell. This represents the number of retired battery cells in the sorting results. The larger the SC value, the more concentrated the batteries are within the same category, and the more significant the differences between different categories.
[0072] Within-group dispersion measures the degree of concentration of batteries within the same category. The within-group dispersion for the k-th category group... for: ; in, This represents the number of batteries in the k-th category. This represents the center vector of the category. The smaller the value, the closer the rate response of the batteries within that category is.
[0073] Average group confidence The degree of clarity in battery ownership can be expressed as: ; in, This represents the group confidence level of the retired battery cell in section i. The larger the value, the more stable the overall grouping results.
[0074] In an exemplary embodiment, in step 109, the minimum number of samples in the minimum category is set to 5, and the intra-group dispersion threshold is based on the intra-group dispersion of each category. The statistical distribution is determined and set to 1.0; categories that do not meet the requirements are listed as objects to be reviewed or reclassified.
[0075] In an exemplary embodiment, the retired battery sorting method based on multi-rate incremental capacity response characteristics further includes multi-rate comprehensive performance ranking: after fine grouping (obtaining the sorting results), the battery categories are comprehensively scored by combining capacity level, rate response difference, and group confidence, and different consistency levels are assigned. More specifically, for the k-th category group, the average baseline rate capacity, average rate response difference, and average group confidence are denoted as follows: , and Calculate the comprehensive performance score of the kth category group (kth class) in the sorting results.
[0076] in, This represents the average capacity of the k-th type of retired battery cell; This represents the average variation of the capacity characteristics and IC curve characteristics of the k-th type of retired battery cell under different discharge rates; This represents the average grouping confidence level of retired battery cells in category k, used to characterize the degree to which batteries in this category are stably classified into the current category.
[0077] and It is a positive indicator; the larger the value, the higher the capacity level or group confidence. This is a negative indicator; the larger the value, the more significant the characteristic changes of this type of battery at different rate.
[0078] The average rate response difference can be expressed as: ; ; in, This represents the number of retired battery cells of type k; The average response change of the incremental capacity curve characteristic of the j-th retired battery cell in the i-th section. This represents the rate response difference evaluation value of the i-th retired battery cell.
[0079] The average group confidence level can be expressed as: ; in, Let represent the maximum probability of the i-th retired battery cell belonging to the probabilistic fine clustering.
[0080] right , and After normalization, the following results were obtained: , and .in, and The value is a positive normalized value. This is the inverse normalized value of the difference in rate response.
[0081] Step 110 specifically includes: by comparing the comprehensive performance scores of each category group, further dividing the category groups that meet the intra-group consistency requirements into high-performance battery packs, general-performance battery packs, and low-performance battery packs. Comprehensive performance score The range of values is .
[0082] when When the corresponding category group (the kth category group) is grouped into a high-performance battery pack; when When, the corresponding categories are grouped into general performance battery packs; when At that time, the corresponding categories will be grouped into low-performance battery packs.
[0083] This step involves a consistency evaluation of the candidate retained cells, and, based on the target application's requirements for capacity level, rate performance, and other factors, selects the appropriate category for subsequent recombination and utilization.
[0084] The overall performance score for each category group is expressed as follows: ; in, The overall performance score for grouping into the k-th category. , and These represent the average baseline capacity, average rate response difference, and average group confidence level for the k-th category group after normalization.
[0085] In one exemplary embodiment, batteries retained after identifying abnormal responses are grouped into groups of batteries with similar performance using an unsupervised grouping method; when labeled data is available, a supervised classification model can also be used for grading. This is as long as the input still originates from multi-rate IC characteristics, rate response difference characteristics, or their fused characteristics, and is used to achieve consistent sorting and grading of retired lithium-ion batteries.
[0086] The sorting objective of this application is not only to select batteries with similar capacities, but also to further consider the battery's performance retention and response consistency under different discharge rates. By comparing the changes in IC characteristics under low, medium, and high discharge rates, a rate response difference evaluation index is constructed to characterize the differences in peak position shift, peak value decay, curve integral area change, and capacity normalized peak value change that occur when the battery discharge rate increases. This mechanism can avoid sorting errors caused by relying solely on low-rate test results, making the sorting results more consistent with the actual load requirements of retired battery reprocessing scenarios.
[0087] Grouping batteries based solely on single-rate test results can easily lead to batteries with different rate adaptability being classified into the same category, thus affecting the consistency maintenance capability, power output stability, and long-term operational reliability of subsequent reassembled battery packs. Therefore, this application constructs IC curves under different rate conditions and extracts and analyzes IC characteristics under multiple rates, enabling more reliable consistency sorting of retired lithium batteries.
[0088] Compared with the prior art, the beneficial effects of this application are as follows; Compared to sorting methods based solely on single-rate capacity, internal resistance, or single IC characteristics, this application can more comprehensively reflect the performance retention capability and rate adaptability of retired batteries under different load conditions. Therefore, it can be deduced that batteries sorted using this method are less prone to capacity differentiation, increased polarization differences, or inconsistent discharge performance due to changes in rate conditions during subsequent repackaging and utilization, thereby improving the engineering applicability of the sorting results and the consistency and stability of the repackaged battery packs.
[0089] This application constructs multi-rate IC characteristics and rate response difference characteristics, and combines them with comprehensive performance ranking or clustering sorting methods to uniformly incorporate curve structure changes, capacity retention capabilities, and response differences at different rates into the evaluation process. This avoids the missorting problems caused by traditional methods that rely solely on a single test condition or manual threshold judgment. Therefore, it can be deduced that this application can improve the automation level of sorting while more accurately distinguishing between batteries with similar performance, batteries with abnormal rate responses, and batteries with significant performance degradation. This improves the sorting accuracy of retired lithium-ion batteries and provides more reliable technical support for battery matching, safe operation, and efficient resource utilization in subsequent remanufacturing.
[0090] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.
[0091] This document uses specific examples to illustrate the principles and implementation methods of this application. The descriptions of the above embodiments are only for the purpose of helping to understand the methods and core ideas of this application. Furthermore, those skilled in the art will recognize that, based on the ideas of this application, there will be changes in the specific implementation methods and application scope. Therefore, the content of this specification should not be construed as a limitation of this application.
Claims
1. A method for sorting retired batteries based on multi-rate incremental capacity response characteristics, characterized in that, include: Performance testing is conducted on retired battery cells that have passed visual inspection. Perform multi-rate charge-discharge tests on retired battery cells that have passed performance testing, and record the test data at each discharge rate. Calculate the incremental capacity curve corresponding to each discharge rate based on the test data at each discharge rate; Feature extraction is performed on each of the incremental capacity curves to obtain the incremental capacity curve features corresponding to each of the incremental capacity curves; Based on the incremental capacity curve characteristics corresponding to each incremental capacity curve, a rate response difference feature is constructed to obtain a multi-rate feature vector; the multi-rate feature vectors of each retired battery cell are combined to obtain a multi-rate feature matrix. Based on the multi-rate feature matrix, an anomaly identification algorithm is used to screen abnormal retired battery cells among retired battery cells, remove abnormal retired battery cells, and obtain preliminary screening results. Clustering is performed on the multi-rate feature vectors of each retired battery cell in the preliminary screening results to obtain the sorting results; Determine the sample size and within-group dispersion for each category in the sorting results; All categories with a sample size greater than the preset minimum sample size and a group dispersion not greater than the preset threshold are selected as candidate retained cells. Consistency assessment is performed on each category of the candidate retained battery range based on the average baseline rate capacity, average rate response difference, and average group confidence of each category.
2. The method for sorting retired batteries based on multi-rate incremental capacity response characteristics according to claim 1, characterized in that, Performance testing is conducted on retired battery cells that have passed visual inspection, specifically including: For retired battery cells that pass the appearance inspection, open circuit voltage, internal resistance, and static voltage retention capability tests are performed. If the open circuit voltage of the retired battery cell exceeds the corresponding factory voltage range, or the internal resistance exceeds 1.25 times the corresponding factory specified internal resistance, or the voltage drop value after being static for a set time under the set power condition exceeds the set voltage value, then the retired battery cell fails the performance test.
3. The method for sorting retired batteries based on multi-rate incremental capacity response characteristics according to claim 1, characterized in that, Retired battery cells that have passed performance testing were subjected to multi-rate charge-discharge tests, and test data at each discharge rate were recorded, including: The retired battery cells that passed the performance test were subjected to charge-discharge tests at 0.2C, 0.5C, 1C and 2C respectively, and the test data at each discharge rate were recorded.
4. The method for sorting retired batteries based on multi-rate incremental capacity response characteristics according to claim 1, characterized in that, The test data includes voltage-time data, voltage-capacity data, and actual discharge capacity at each discharge rate. The voltage-time data includes the voltage corresponding to each sampling time, and the voltage-capacity data includes the voltage and capacity corresponding to each sampling time during the discharge process.
5. The method for sorting retired batteries based on multi-rate incremental capacity response characteristics according to claim 4, characterized in that, The incremental capacity curves for each discharge rate are calculated based on the test data at each discharge rate, specifically including: Outliers in the voltage-capacity data are removed to obtain preprocessed voltage-capacity data. The preprocessed voltage-capacity data is deduplicated. The deduplicated voltage-capacity data is interpolated to a uniform voltage sampling interval according to the voltage change sequence, and each voltage in the interpolated voltage-capacity data is within the set voltage range; The interpolated voltage-capacity data is then filtered and smoothed. The incremental capacity curves at each discharge rate are calculated based on the filtered and smoothed voltage-capacity data at each discharge rate.
6. The method for sorting retired batteries based on multi-rate incremental capacity response characteristics according to claim 4, characterized in that, The incremental capacity curve features include average discharge capacity, main peak voltage, main peak height, inter-peak voltage difference, peak ratio, integral area of incremental capacity curve, and capacity normalized peak value; the capacity normalized peak value is the ratio of main peak height to the average actual discharge capacity of Z repeated tests at the corresponding discharge rate; Z is a positive integer greater than or equal to 3.
7. The method for sorting retired batteries based on multi-rate incremental capacity response characteristics according to claim 6, characterized in that, The rate response difference characteristic is represented as follows: ； in, This represents the difference in response of the j-th incremental capacity curve characteristic of the i-th retired battery cell at a discharge rate r relative to the reference discharge rate, where the reference discharge rate is 0.2C. This represents the characteristic curve of the j-th incremental capacity of the i-th retired battery cell at the baseline discharge rate; This represents the characteristic curve of the j-th incremental capacity of the i-th retired battery cell at a discharge rate r; Indicates a preset positive number; The multiple-ratio eigenvector is represented as: ; ； ； ； in, Let represent the rate characteristic vector of the i-th retired battery cell. This represents the rate response feature vector of the i-th retired battery cell at a discharge rate r. , The average response change of the incremental capacity curve characteristic of the j-th retired battery cell in the i-th section. The rate stability evaluation index is used for the i-th retired battery cell.
8. The method for sorting retired batteries based on multi-rate incremental capacity response characteristics according to claim 1, characterized in that, The multi-rate feature vectors of each retired battery cell in the preliminary screening results are clustered to obtain the sorting results, specifically including: A Gaussian mixture model was used to cluster the multi-rate feature vectors of each retired battery cell in the preliminary screening results to obtain the category label and group confidence of each retired battery cell. When the group confidence level of a retired battery cell is greater than or equal to the group confidence level threshold, the retired battery cell is assigned to the category group corresponding to the category label; otherwise, the retired battery cell is removed.