Method and system for evaluating health state of retired battery

Abstract

The invention discloses an ex-service battery health state assessment method and system, and the method comprises the steps: applying an AC excitation signal in a preset frequency range to a to-be-tested ex-service battery, collecting the impedance response data of the ex-service battery at a plurality of frequency points, and forming an impedance spectrum original data set; the impedance spectrum original data set is processed, and multi-dimensional parameters including high-frequency-band electrolyte resistance characteristics, middle-frequency-band charge transfer impedance characteristics and low-frequency-band diffusion impedance characteristics are extracted; based on the multi-dimensional parameters, a target factor vector representing the health state of the retired battery is constructed, and the target factor vector comprises a capacity attenuation factor and an internal resistance growth factor; inputting the target factor vector into a health state mapping model obtained by training based on a decommissioned battery historical sample, and calculating to obtain a health index of the decommissioned battery to be tested; and according to the health indexes, the retired batteries are divided into different health levels. According to the method, evaluation efficiency and evaluation precision can be considered, and efficient and accurate grading of the retired batteries is realized.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of retired battery evaluation, in particular to a health state evaluation method and system of retired battery. BACKGROUND

[0002] With the rapid development of new energy vehicles and energy storage power stations, a large number of power batteries are gradually retired after serving for a certain period. If the retired batteries can be scientifically classified according to their actual health state, not only can the resource utilization rate be improved, but also the safety risk can be reduced, and the development of recycling can be promoted.

[0003] However, there are still many deficiencies in the evaluation of the health state of the retired batteries. The existing methods mainly include direct measurement method based on capacity detection and empirical estimation method based on voltage and current data. The direct measurement method usually needs to test the battery by charging and discharging cycle, which not only takes a long time and has low detection efficiency, but also is difficult to meet the needs of large-scale retired battery sorting; the health state determination method based on empirical estimation can shorten the detection time, but it is highly sensitive to environmental temperature, test conditions and historical working conditions, resulting in insufficient stability and precision of the evaluation results. In addition, some methods try to use a single feature parameter, such as direct current resistance or residual capacity, to determine the health, but this method cannot comprehensively reflect the comprehensive degradation of the retired battery in terms of electrolyte attenuation, polarization effect and diffusion resistance, etc., which is easy to cause classification error and affect the reliability of the decision of the cascade utilization and recycling.

[0004] Therefore, in order to solve the problems of low evaluation efficiency and insufficient evaluation precision in the prior art, a health state evaluation method and system of retired battery are needed, which can balance the evaluation efficiency and evaluation precision, and realize efficient and accurate grading of the retired battery. SUMMARY

[0005] Therefore, the purpose of the present application is to overcome the defects in the prior art, and to provide a health state evaluation method and system of retired battery, which can balance the evaluation efficiency and evaluation precision, and realize efficient and accurate grading of the retired battery.

[0006] The health state evaluation method of the retired battery of the present application comprises the following steps:

[0007] S1. An alternating excitation signal in a predetermined frequency range is applied to the retired battery to be tested, and impedance response data of the retired battery at multiple frequency points are collected to form an impedance spectrum original data set;

[0008] S2. The impedance spectrum original data set is processed to extract multi-dimensional parameters including high-frequency electrolyte resistance characteristics, medium-frequency charge transfer impedance characteristics and low-frequency diffusion impedance characteristics;

[0009] S3. Constructing a target factor vector representing the state of health of the retired battery based on the multi-dimensional parameters, the target factor vector comprising a capacity attenuation factor and an internal resistance growth factor;

[0010] S4. Inputting the target factor vector into a state of health mapping model trained based on historical samples of the retired battery to calculate a health index of the retired battery to be tested;

[0011] S5. Dividing the retired battery into different health grades according to the health index.

[0012] Further, the impedance spectrum original data set comprises frequency point data, real part impedance data and imaginary part impedance data at the corresponding frequency, impedance amplitude data and phase angle data calculated from the real part and the imaginary part, Nyquist curve data points formed by the real part and the imaginary part, and Bode graph data points composed of frequency and impedance amplitude, phase angle.

[0013] Further, the impedance spectrum original data set is processed, specifically including:

[0014] The impedance spectrum original data set is preprocessed; the preprocessing comprises denoising and amplitude calibration;

[0015] The preprocessed impedance spectrum original data is fitted with an equivalent circuit model to obtain an electrolyte resistance, a charge transfer impedance and a diffusion impedance;

[0016] The electrolyte resistance feature is extracted in the high frequency band, the charge transfer impedance feature is extracted in the medium frequency band, and the diffusion impedance feature is extracted in the low frequency band to form a multi-dimensional parameter vector.

[0017] Further, the capacity attenuation factor is obtained by comparing the ratio of the current capacity of the retired battery to the rated initial capacity thereof.

[0018] Further, the internal resistance growth factor is obtained by comparing the ratio of the current internal resistance of the retired battery to the rated initial internal resistance thereof.

[0019] Further, the capacity attenuation factor is determined according to the following formula :

[0020] ;

[0021] wherein, is the current available capacity of the retired battery; is a capacity reference value; represents truncating to the interval .

[0022] Further, the internal resistance growth factor is determined according to the following method :

[0023] I. Impedance parameters are temperature corrected to obtain temperature corrected impedance parameter values ; wherein, is the i-th impedance related parameter;

[0024] II. Calculate the relative growth rate of impedance parameters ; wherein, is the reference value of the impedance parameter in the reference state;

[0025] III. Synthesize the overall growth rate according to the weight ; wherein, is the weight coefficient of different impedance parameters in the combined calculation;

[0026] IV. Normalize the overall growth rate to obtain the internal resistance growth factor :

[0027] ;

[0028] wherein, is the maximum growth threshold for normalization; represents the truncation of to the interval .

[0029] Further, the health state mapping model is any one of a support vector machine model, a random forest model, a deep neural network model, or a gradient boosting tree model.

[0030] Further, according to the health index, the retired battery is divided into different health grades, specifically including:

[0031] If the health index is higher than a first threshold, the retired battery is determined to be a battery suitable for step-by-step utilization; if the health index is between the first threshold and a second threshold, the retired battery is determined to be a battery suitable for direct recycling; if the health index is lower than the second threshold, the retired battery is determined to be a battery that needs to be scrapped.

[0032] A health state evaluation system of a retired battery includes a data processing module, a health index calculation module, and a health rating module.

[0033] The data processing module is configured to apply an alternating excitation signal in a predetermined frequency range to a retired battery to be tested, collect impedance response data of the retired battery at multiple frequency points, and form an impedance spectrum original data set; process the impedance spectrum original data set to extract multi-dimensional parameters including high-frequency electrolyte resistance characteristics, medium-frequency charge transfer impedance characteristics, and low-frequency diffusion impedance characteristics.​

[0034] The health index calculation module is configured to construct a target factor vector representing the state of health of the retired battery based on the multi-dimensional parameters, wherein the target factor vector comprises a capacity attenuation factor and a resistance growth factor; and input the target factor vector into a state of health mapping model trained based on historical samples of the retired battery to calculate the health index of the retired battery to be measured.

[0035] The health rating module is configured to divide the retired battery into different health grades according to the health index.

[0036] The health state evaluation method and system for the retired battery disclosed by the application can realize more accurate evaluation by using impedance spectrum, characteristic parameters and a mapping model, and can realize automatic grading by constructing a health factor vector of a capacity attenuation factor and a resistance growth factor on the basis of multi-dimensional feature extraction in frequency band division and in combination with a training model. BRIEF DESCRIPTION OF DRAWINGS

[0037] The application will be further described below in combination with the drawings and embodiments:

[0038] Figure 1 FIG. 1 is a flowchart of a health state evaluation method for a retired battery according to the application;

[0039] Figure 2 FIG. 2 is a schematic structural diagram of a health state evaluation system for a retired battery according to the application. DETAILED DESCRIPTION

[0040] The application will be further described below in combination with the drawings and embodiments:

[0041] The embodiment discloses a health state evaluation method for a retired battery, which comprises the following steps:

[0042] S1. An alternating excitation signal in a preset frequency range is applied to a retired battery to be measured, and impedance response data of the retired battery at multiple frequency points are collected to form an impedance spectrum original data set;

[0043] S2. The impedance spectrum original data set is processed to extract multi-dimensional parameters including high-frequency electrolyte resistance characteristics, medium-frequency charge transfer impedance characteristics and low-frequency diffusion impedance characteristics;

[0044] S3. A target factor vector representing the state of health of the retired battery is constructed based on the multi-dimensional parameters, wherein the target factor vector comprises a capacity attenuation factor and a resistance growth factor;

[0045] S4. inputting the target factor vector into a health state mapping model trained based on historical samples of retired batteries, to calculate a health index of the retired battery to be measured;

[0046] S5. dividing the retired battery into different health grades according to the health index.

[0047] In this embodiment, in step S1, an alternating excitation signal in a preset frequency range (for example, 10 mHz-10 kHz) is applied to the positive and negative electrodes of the retired battery, and the collected impedance spectrum original data set includes at least frequency point data, real part impedance data and imaginary part impedance data corresponding to the frequency, impedance amplitude data and phase angle data calculated from the real part and the imaginary part, Nyquist curve data points formed by the real part and the imaginary part, and Bode graph data points composed of the frequency and the impedance amplitude and the phase angle;

[0048] The frequency point data is a numerical sequence of preset frequency points, used as a horizontal coordinate reference; the real part impedance data is the real part numerical value of the battery impedance at different frequencies, reflecting the electrolyte resistance and electrode contact resistance characteristics; the imaginary part impedance data is the imaginary part numerical value of the battery impedance at different frequencies, representing the charge transfer process and diffusion process; the impedance amplitude data is the impedance amplitude calculated from the real part and the imaginary part, reflecting the overall impedance intensity; the phase angle data reflects the phase angle change of the impedance with the frequency, used to judge the delay characteristics of the electrochemical process; the Nyquist curve data points are a two-dimensional data point set formed by the real part and the imaginary part; and the Bode graph data points are a two-dimensional or three-dimensional data set composed of the frequency and the impedance amplitude and the phase angle.

[0049] Through the above design, the impedance spectrum original data set is not only the "change of impedance value with frequency", but also a complete data set including frequency, real part, imaginary part, amplitude, phase angle and other dimensions. These data can be used as the input basis for subsequent feature extraction and health factor construction.

[0050] In step S2, the collected impedance spectrum original data is denoised, for example, by moving average filtering, Kalman filtering or wavelet denoising, to eliminate environmental interference and collection noise; and the measured impedance data is normalized or amplitude calibrated to ensure the comparability of data of different batches of batteries.

[0051] The impedance spectrum original data is fitted with an equivalent circuit model (such as a Randles circuit model or an extended transmission line model); model parameters are obtained through the fitting, including electrolyte resistance Rs, charge transfer impedance Rct, Warburg diffusion impedance Zw, etc.

[0052] Electrolyte resistance characteristics are extracted in the high-frequency range (e.g., 1kHz to 10kHz) to characterize the internal conductivity of the battery; charge transfer impedance parameters are extracted in the mid-frequency range (e.g., 1Hz to 1kHz) to reflect the electrochemical reaction rate at the electrode / electrolyte interface; and diffusion impedance parameters are extracted in the low-frequency range (e.g., 10mHz to 1Hz) to characterize the ion diffusion process and battery polarization.

[0053] By organizing the parameters of different frequency bands into multidimensional feature vectors, such as {Rs, Rct, Zw, phase angle change rate, Nyquist arc length}, and then using principal component analysis (PCA), linear discriminant analysis (LDA), or feature normalization methods, a target parameter set suitable for health assessment can be obtained.

[0054] The above processing ensures that the extracted parameters are directly related to the health status of the retired battery and facilitates subsequent mapping to the health index.

[0055] In this embodiment, in step S3, temperature and amplitude corrections are performed on the multidimensional parameters extracted in step S2 to ensure comparability; the parameters are normalized or standardized to reduce the influence of dimensions; and the parameters are mapped to two basic factors according to their physical meaning: capacity decay factor. With internal resistance growth factor Combine the two factors to form the target factor vector. .

[0056] The capacity decay factor quantifies the decrease in the actual usable capacity of a retired battery relative to its rated / reference capacity; the internal resistance growth factor quantifies the increase in the overall resistance (or key impedance parameter) of the battery relative to a reference value. The capacity decay factor is obtained by comparing the ratio of the current capacity of the retired battery to its rated initial capacity; the internal resistance growth factor is obtained by comparing the ratio of the current internal resistance of the retired battery to its rated initial internal resistance.

[0057] Specifically, the capacity attenuation factor is determined according to the following formula. :

[0058] ;

[0059] in, The current available capacity of the retired battery; For capacity reference values, you can take the factory rated capacity, the average value of new batteries of the same model, or the measurement value of the battery when it leaves the factory for the first time; Indicates will Cut off to interval .

[0060] The internal resistance growth factor is determined using the following method. :

[0061] I. For several impedance parameters Temperature correction is performed to obtain the temperature-corrected impedance parameter values. ;in, For the first Several impedance-related parameters, such as high-frequency electrolyte resistance, mid-frequency charge transfer impedance, Warburg diffusion impedance index, and equivalent DC internal resistance, etc.; in addition, existing linear approximation correction, Arrhenius type or exponential correction methods can be used, which will not be elaborated here.

[0062] II. Calculate the relative growth rate of impedance parameters ;in, This is a reference value for the impedance parameter under baseline conditions. This reference value can be the factory calibration value of a new battery, the average impedance value of a new battery of the same model, or the measured value of the battery in the initial stage (when the number of cycles is close to 0). The growth rate of a certain impedance parameter relative to the reference value is measured by setting a relative growth rate. For example, for charge transfer impedance, if its reference value under baseline conditions is 20mΩ and the temperature-corrected impedance parameter value is 30mΩ, then its relative growth rate is 0.5, which is a 50% increase.

[0063] III. Calculate the overall growth rate according to the weights. ;in, These are the weighting coefficients for different impedance parameters in the combined calculation. All weights sum to 1. Specific weight values ​​can be set based on mechanistic knowledge; for example, charge transfer impedance is most sensitive to performance degradation and can be given a larger weight. Setting the overall growth rate can comprehensively reflect the relative growth level of the battery's overall internal resistance.

[0064] IV. Normalize the overall growth rate to obtain the internal resistance growth factor. :

[0065] ;

[0066] in, The maximum growth threshold used for normalization can be based on experimental or industry standard experience. For example, if an internal resistance increase of more than 100% is defined as a complete decline, then take [the threshold value]. Or, the upper limit may be set according to the actual test limit for different battery types; Indicates will Cut off to interval .

[0067] With the above settings The value ranges from [0,1]. A larger value indicates a more severe increase in internal resistance. It is 0.6. If it is 1, then It is 0.6.

[0068] In this embodiment, in step S4, the health status mapping model is any one of the following: support vector machine model, random forest model, deep neural network model, or gradient boosting tree model.

[0069] The health status mapping model can be trained based on historical sample data of retired batteries (including measured capacity, internal resistance, and lifetime data). During the training phase, multiple sets of historical sample data from retired batteries are collected. ,in For the target factor vector of the sample battery, The corresponding true health index is used (e.g., obtained from measured capacity retention rate or cycle life); then a supervised learning method is used to minimize the error between the predicted value and the true health index, resulting in the final trained mapping model.

[0070] By inputting the target factor vector of the retired battery to be tested into a trained mapping model, the model outputs a predicted value, which is the health index of the retired battery. The output value is typically a value between 0% and 100%, with higher values ​​indicating better battery health.

[0071] If the health status mapping model is a neural network model, then the input vector After being mapped through a three-layer fully connected neural network, the output result, for example, can be obtained: health index. If the health status mapping model is a random forest model, then multiple decision trees are used to construct the mapping based on the training samples. Each tree provides a prediction result, and the average of the prediction results is finally calculated to obtain the health index. .

[0072] In this embodiment, step S5, classifying retired batteries into different health levels based on the health index, refers to comparing the health index result with a preset threshold range. Specifically, if the health index is greater than a first threshold, the retired battery is determined to be in good condition and can enter the cascade utilization stage, such as being used in energy storage power stations or low-speed vehicles; if the health index is between the first and second thresholds (including both thresholds), the retired battery is determined to be in a normal state and suitable for direct recycling, through disassembly and recovery of electrode materials; if the health index is lower than the second threshold, the retired battery is determined to be in a severely degraded state and should be scrapped to avoid safety risks. Through the above-mentioned classification, accurate classification of retired batteries and efficient utilization of resources can be achieved.

[0073] The first and second thresholds can be set according to actual working conditions. For example, the first threshold can be set to 80% and the second threshold to 50%. Above 80% can usually meet most high-value tiered utilization, such as household / commercial energy storage and some vehicle power needs; while below 50% in most cases, it has severely degraded and is only suitable for material recycling or scrapping.

[0074] The present invention also relates to a health status assessment system for retired batteries. The assessment system corresponds to the assessment method of the above embodiments and can be understood as an assessment system that implements the above assessment method. The assessment system includes a data processing module, a health index calculation module, and a health rating module.

[0075] The data processing module is used to apply an AC excitation signal within a preset frequency range to the decommissioned battery under test, collect impedance response data of the decommissioned battery at multiple frequency points, and form an impedance spectrum raw dataset; process the impedance spectrum raw dataset to extract multi-dimensional parameters including high-frequency electrolyte resistance characteristics, mid-frequency charge transfer impedance characteristics, and low-frequency diffusion impedance characteristics.

[0076] The health index calculation module is used to construct a target factor vector representing the health status of the retired battery based on the multidimensional parameters. The target factor vector includes a capacity decay factor and an internal resistance growth factor. The target factor vector is input into a health status mapping model trained based on historical samples of retired batteries to calculate the health index of the retired battery to be tested.

[0077] The health rating module is used to classify retired batteries into different health levels based on the health index.

[0078] This invention provides an objective and scientific method and system for assessing the health status of retired batteries. It enables the rapid construction of a multi-dimensional parameter system by collecting and processing the impedance characteristics of retired batteries across multiple frequency bands, without requiring prolonged charge-discharge testing. This system comprehensively reflects the degradation mechanisms of the battery, including electrolyte degradation, polarization effects, and diffusion resistance. Compared to traditional assessment methods that rely on a single capacity or internal resistance index, this method significantly improves the accuracy and robustness of health status assessment. Furthermore, by combining a target factor vector with a health status mapping model, it achieves rapid calculation and automated grading of the retired battery health index, ensuring both assessment efficiency and improved reliability and consistency of sorting results.

[0079] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A method for assessing the health status of retired batteries, characterized in that: Includes the following steps: S1. Apply an AC excitation signal within a preset frequency range to the decommissioned battery under test, collect impedance response data of the decommissioned battery at multiple frequency points, and form a raw impedance spectrum dataset; S2. Process the original impedance spectrum dataset to extract multi-dimensional parameters, including high-frequency electrolyte resistance characteristics, mid-frequency charge transfer impedance characteristics, and low-frequency diffusion impedance characteristics. S3. Based on the multidimensional parameters, construct a target factor vector characterizing the health status of the retired battery. The target factor vector includes a capacity decay factor and an internal resistance growth factor. S4. Input the target factor vector into the health status mapping model trained based on historical samples of retired batteries, and calculate the health index of the retired battery to be tested; S5. Based on the health index, the retired batteries are classified into different health levels.

2. The method for assessing the health status of retired batteries according to claim 1, characterized in that: The original impedance spectrum dataset includes frequency point data, real and imaginary impedance data at the corresponding frequencies, impedance amplitude and phase angle data calculated from the real and imaginary parts, Nyquist curve data points formed by the real and imaginary parts of the impedance, and Bode plot data points composed of frequency, impedance amplitude, and phase angle.

3. The method for assessing the health status of retired batteries according to claim 1, characterized in that: The raw impedance spectrum dataset is processed, specifically including: The original impedance spectrum dataset is preprocessed; the preprocessing includes denoising and amplitude calibration. The preprocessed impedance spectrum raw data is fitted with the equivalent circuit model to obtain the electrolyte resistance, charge transfer impedance and diffusion impedance. Electrolyte resistance characteristics are extracted in the high-frequency band, charge transfer impedance characteristics are extracted in the mid-frequency band, and diffusion impedance characteristics are extracted in the low-frequency band to form a multi-dimensional parameter vector.

4. The method for assessing the health status of retired batteries according to claim 1, characterized in that: The capacity decay factor is obtained by comparing the ratio of the current capacity of the retired battery to its rated initial capacity.

5. The method for assessing the health status of retired batteries according to claim 1, characterized in that: The internal resistance growth factor is obtained by comparing the ratio of the current internal resistance of the retired battery to its rated initial internal resistance.

6. The method for assessing the health status of retired batteries according to claim 4, characterized in that: The capacity decay factor is determined according to the following formula. : ； in, The current usable capacity of the retired battery; This is a capacity reference value; Indicates will Cut off to interval .

7. The method for assessing the health status of retired batteries according to claim 5, characterized in that: The internal resistance growth factor is determined using the following method. : I. For several impedance parameters Temperature correction is performed to obtain the temperature-corrected impedance parameter values. ;in, For the first One impedance-related parameter; II. Calculate the relative growth rate of impedance parameters ;in, This is a reference value for the impedance parameter under reference conditions; III. Calculate the overall growth rate according to the weights. ;in, These are the weighting coefficients for different impedance parameters in the combined calculation; IV. Normalize the overall growth rate to obtain the internal resistance growth factor. : ； in, The maximum growth threshold used for normalization; Indicates will Cut off to interval .

8. The method for assessing the health status of retired batteries according to claim 1, characterized in that: The health status mapping model is any one of the following: support vector machine model, random forest model, deep neural network model, or gradient boosting tree model.

9. The method for assessing the health status of retired batteries according to claim 1, characterized in that: Based on the aforementioned health index, retired batteries are classified into different health levels, specifically including: If the health index is higher than the first threshold, the retired battery is determined to be a battery suitable for secondary use; if the health index is between the first threshold and the second threshold, the retired battery is determined to be a battery suitable for direct recycling; if the health index is lower than the second threshold, the retired battery is determined to be a battery that needs to be scrapped.

10. A health status assessment system for retired batteries, characterized in that: It includes a data processing module, a health index calculation module, and a health rating module; The data processing module is used to apply an AC excitation signal within a preset frequency range to the decommissioned battery under test, collect impedance response data of the decommissioned battery at multiple frequency points, and form an impedance spectrum raw dataset; process the impedance spectrum raw dataset to extract multi-dimensional parameters including high-frequency electrolyte resistance characteristics, mid-frequency charge transfer impedance characteristics, and low-frequency diffusion impedance characteristics. The health index calculation module is used to construct a target factor vector characterizing the health status of retired batteries based on the multidimensional parameters. The target factor vector includes a capacity decay factor and an internal resistance growth factor. The target factor vector is input into the health status mapping model trained based on historical samples of retired batteries to calculate the health index of the retired battery to be tested. The health rating module is used to classify retired batteries into different health levels based on the health index.

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