A battery cell consistency evaluation method

Abstract

The application discloses a battery pack cell consistency evaluation method, and steps of the method comprise the following steps: 1. performing AC impedance test on cells in a battery pack; 2. performing feature extraction on obtained electrochemical impedance spectroscopy (EIS) curves of the cells in the battery pack; 3. taking the fluctuation degree of data as the selection basis of weights of various feature quantities, and taking the calculation result after the feature quantities are weighted as a cell evaluation factor; and 4. obtaining a consistency evaluation result of the cells in the battery pack by calculating the standard deviation of the cell evaluation factor. The application can improve the speed and accuracy of the cell consistency evaluation in the battery pack, thereby reducing the disassembly workload when the battery pack is recycled.

Description

Technical Field

[0001] This invention belongs to the field of retired battery cascade utilization, specifically a method for evaluating the consistency of battery pack cells. Background Technology

[0002] In practical applications, to meet power and energy requirements, battery cells are typically connected in series and parallel to form high-voltage battery packs. Due to differences in manufacturing processes and cell positions within the battery pack, the aging conditions of the internal cells vary, leading to differences in open-circuit voltage, internal resistance, temperature, capacity, and state of charge (SOC). This deteriorates the consistency between the internal cells, adversely affecting the energy utilization, safety, and overall health of the battery pack. Therefore, it is necessary to assess the consistency of the cells to ensure the safety and reliability of the battery pack.

[0003] There are three main existing methods for evaluating battery cell consistency: The first is to evaluate the consistency of cells within a battery pack using a single indicator such as capacity or internal resistance. This single-parameter evaluation method only considers differences in some battery indicators and cannot comprehensively assess battery consistency. The second method combines multiple performance indicators such as capacity and internal resistance to evaluate battery performance. Compared to the single-parameter method, this method can analyze battery performance more comprehensively, but measuring the performance parameters of multiple cells consumes a long testing time, and selecting appropriate weights for multiple parameters is a challenge. The third method uses battery test curves such as charge / discharge voltage-current curves and EIS curves for evaluation. This method can more comprehensively reflect battery characteristics, but battery test curves are usually high-dimensional data, which affects the evaluation speed.

[0004] Among the existing consistency evaluation methods, single-parameter evaluation methods only consider a single index, resulting in low evaluation accuracy. Multi-parameter evaluation methods measure multiple parameters, leading to long evaluation times. Voltage curve-based evaluation methods are slow due to long testing times and data redundancy issues. EIS curve-based battery evaluation methods may be affected by crosstalk issues in the cell's internal contact impedance. Summary of the Invention

[0005] To overcome the shortcomings of the prior art, this invention provides a method for evaluating the consistency of battery pack cells, aiming to improve the speed and accuracy of cell consistency evaluation within a battery pack, thereby reducing the disassembly workload during battery pack recycling.

[0006] The present invention adopts the following technical solution to solve the technical problem:

[0007] The present invention provides a method for evaluating the consistency of battery pack cells, characterized by the following steps:

[0008] Step 1: Perform AC impedance testing on each battery pack;

[0009] Apply current signals of a certain amplitude and multiple frequencies to the battery pack, and collect the voltage response signals of the cells in the battery pack to plot the EIS curves of all cells in the battery pack.

[0010] Step 2: Feature extraction;

[0011] Step 2.1: Denoise the test data on the EIS curves of all cells to obtain the effective EIS test dataset for all cells;

[0012] Step 2.2: Use the Distributed Relaxation Time (DRT) algorithm to extract multiple polarization resistances corresponding to the internal electrochemical reactions of each cell from the valid EIS test dataset. Then use the Principal Component Analysis (PCA) algorithm to extract the principal components corresponding to the mid-frequency data of each cell from the valid EIS test dataset. Combine the multiple polarization resistances corresponding to the internal electrochemical reactions of the cell and the principal components corresponding to the mid-frequency data of the cell to form the feature data for cell evaluation.

[0013] Step 3: Based on the fluctuation of the feature data, obtain the weight parameters of each feature and calculate the evaluation factor of the battery cell.

[0014] Step 3.1: Normalize the feature data to obtain the normalized feature matrix;

[0015] Step 3.2: Use the range of each column of the feature quantity in the feature quantity matrix as the first indicator to measure the degree of fluctuation of the feature quantity data. When the ranges are equal, use the variance of each column of the feature quantity in the feature quantity matrix as the second indicator to measure the degree of fluctuation of the feature quantity data.

[0016] Step 3.3: Calculate the weight w of the j-th characteristic of the i-th cell using equation (1). i,j ;

[0017]

[0018] In equation (1), s i,j represents the fluctuation level of the j-th column characteristic quantity of the i-th cell in the characteristic quantity matrix, and p represents the total number of characteristic quantities;

[0019] Step 3.4: Calculate the evaluation factor z of the i-th cell using equation (2). i :

[0020]

[0021] In equation (2), y i,j This represents the element in the i-th row and j-th column of the normalized eigenvalue matrix Y;

[0022] Step 4: Conduct a consistency assessment of the evaluation factors for all battery cells:

[0023] Based on the evaluation factors of each cell within the battery pack, the standard deviation of all cell evaluation factors is calculated and used as the basis for evaluating the consistency of cells within the battery pack. When the standard deviation of the evaluation factors reaches the set threshold, it indicates that the consistency of cells within the battery pack does not meet the requirements; otherwise, it indicates that the consistency of cells within the battery pack meets the requirements.

[0024] The present invention provides an electronic device, including a memory and a processor, wherein the memory is used to store a program that supports the processor in executing the battery pack cell consistency evaluation method, and the processor is configured to execute the program stored in the memory.

[0025] The present invention discloses a computer-readable storage medium on which a computer program is stored, wherein the computer program, when executed by a processor, performs the steps of the battery pack cell consistency evaluation method.

[0026] Compared with existing technologies, the beneficial effects of this invention are reflected in:

[0027] 1. The evaluation method of this invention uses only the EIS data of the battery cell for feature extraction, and mainly uses the frequency domain response information of the battery polarization internal resistance. This can reduce the interference of the cell contact impedance in the battery pack on the consistency evaluation, and eliminate the need to test each cell in the battery pack individually, thereby speeding up the evaluation process.

[0028] 2. The calculation basis of the feature weights in the evaluation method of this invention is based on the degree of data fluctuation, which can objectively reflect the degree of data fluctuation, making the consistency evaluation more reasonable, and the weight coefficients can be adaptively updated with the degree of data fluctuation.

[0029] 3. The evaluation method of the present invention quantifies the degree of consistency of the battery pack when conducting consistency evaluation, which helps to quantitatively evaluate the consistency of the battery pack. Attached Figure Description

[0030] Figure 1 This is a flowchart of the method described in this invention;

[0031] Figure 2 This is a flowchart of the feature extraction method in the embodiment;

[0032] Figure 3 This is a flowchart of the weight selection method in an embodiment;

[0033] Figure 4 This is a bar chart of the battery cell evaluation factors in an example. Detailed Implementation

[0034] In this embodiment, to improve the reliability and speed of the battery pack cell consistency evaluation method, a battery pack cell consistency evaluation method is proposed, such as... Figure 1 As shown, the cell consistency evaluation method specifically includes:

[0035] Step 1: Perform AC impedance testing on each battery pack;

[0036] Apply current signals of a certain amplitude and multiple frequencies to the battery pack, and collect the voltage response signals of the cells in the battery pack to plot the EIS curves of all cells in the battery pack.

[0037] (1) Apply a current signal with a certain amplitude to the battery pack. If the capacity of the cell is C, the excitation current is 1 / 10 to 1 / 20C to ensure that the injected current will not interfere with the original chemical reaction inside the cell. The excitation signal with a frequency of f is maintained for a cycles, and the voltage response signal of each cell within a cycle is collected synchronously.

[0038] (2) Calculate the average value of voltage and current amplitude within a cycle according to equation (3) to filter out noise;

[0039]

[0040] In equation (3), U n I is the average voltage signal of the cell at the nth injection frequency. n This represents the average voltage signal of the battery cell at the nth injection frequency.

[0041] (3) The cell impedance Z corresponding to frequency f is calculated according to equation (4). n The real part Re(Z) n ) and the imaginary part Im(Z) n );

[0042]

[0043] In equation (4), For the phase of the voltage, The phase of the current can be calculated using the Fast Fourier Transform (FFT) algorithm.

[0044] (4) Change the excitation signal frequency and repeat steps (1) to (2) to obtain EIS curves with the real part and the negative imaginary part of the impedance as coordinate axes at different frequency points.

[0045] Step 2: Feature extraction;

[0046] Figure 2 This is a flowchart of the feature extraction method in the embodiment, and the specific process is as follows:

[0047] Step 2.1: Denoise the test data on the EIS curves of all cells to obtain the effective EIS test dataset for all cells;

[0048] Step 2.2: Use the Distributed Relaxation Time (DRT) algorithm to extract multiple polarization resistances corresponding to the internal electrochemical reactions of each cell from the valid EIS test dataset. Then use the Principal Component Analysis (PCA) algorithm to extract the principal components corresponding to the mid-frequency data of each cell from the valid EIS test dataset. Combine the multiple polarization resistances corresponding to the internal electrochemical reactions of the cell and the principal components corresponding to the mid-frequency data of the cell to form the feature data for cell evaluation.

[0049] The EIS curve of a battery cell can reflect the AC impedance information of the cell. However, the ohmic impedance and inductive reactance in the high-frequency band are easily affected by the contact impedance, which can lead to deviations in the consistency results. Therefore, the impedance of the polarization part in the mid-frequency band is mainly used to evaluate the consistency of the battery cell.

[0050] Because there are multiple chemical reactions at different rates inside the battery cell, there are multiple polarization internal resistances on the equivalent circuit model of the battery cell. This will result in multiple arcs on the EIS curve of the battery cell. The DRT analysis method can be used to extract these multiple polarization internal resistances of the battery cell.

[0051] The mid-frequency data of the EIS curve of the battery cell not only reflects the polarization response of the cell, but also reflects the internal diffusion effect of the cell. Some information of the cell at low frequency can be extracted. Therefore, the PCA method is used to extract principal components of this part of the data to achieve dimensionality reduction, which helps to simplify the computation of subsequent operations.

[0052] Step 3: Based on the fluctuation of the feature data, obtain the weight parameters of each feature and calculate the evaluation factor of the battery cell.

[0053] Figure 3 This is a flowchart of the weight selection method in an embodiment, and the specific steps are as follows:

[0054] Step 3.1: Normalize the feature data to obtain a normalized feature matrix, so as to eliminate the influence of factors such as the dimensions of the feature data on the consistency assessment.

[0055] When there are m cells, the normalized formula is as shown in equation (5):

[0056]

[0057] In equation (5), x ij y represents the feature quantity in the i-th row and j-th column of the feature quantity data. ij This represents the element in the i-th row and j-th column of the normalized eigenvalue matrix Y;

[0058] Step 3.2: Use the range of each column of the feature quantity in the feature quantity matrix as the first indicator to measure the degree of fluctuation of the feature quantity data. When the ranges are equal, use the variance of each column of the feature quantity in the feature quantity matrix as the second indicator to measure the degree of fluctuation of the feature quantity data.

[0059] Step 3.3: The greater the data fluctuation, the greater the weight assigned. Use equation (6) to calculate the weight w of the j-th feature of the i-th cell. i,j :

[0060]

[0061] In equation (6), s i,j represents the fluctuation level of the j-th column characteristic quantity of the i-th cell in the characteristic quantity matrix, and p represents the total number of characteristic quantities;

[0062] Based on the calculated weights, when there are a total of p features, a weight matrix w is constructed. px1 ;

[0063] Step 3.4: Calculate the evaluation factor z of the i-th cell using equation (7). i :

[0064]

[0065] In equation (7), y i,j This represents the element in the i-th row and j-th column of the normalized eigenvalue matrix Y;

[0066] Step 4: Conduct a consistency assessment of the evaluation factors for all battery cells:

[0067] Based on the evaluation factors of each cell within the battery pack, the standard deviation of all cell evaluation factors is calculated and used as the basis for evaluating the consistency of cells within the battery pack. The smaller the standard deviation, the better the consistency, and vice versa. When the standard deviation of the evaluation factors reaches the set threshold, it indicates that the consistency of cells within the battery pack does not meet the requirements; otherwise, it indicates that the consistency of cells within the battery pack meets the requirements.

[0068] In this embodiment, an electronic device includes a memory and a processor. The memory stores a program that supports the processor in executing the above-described method, and the processor is configured to execute the program stored in the memory.

[0069] In this embodiment, a computer-readable storage medium stores a computer program, which is executed by a processor to perform the steps of the above method.

[0070] Figure 4The bar chart for the cell evaluation factors in this embodiment shows that there are 12 cells in the battery pack. The horizontal axis represents the number of each cell, the vertical axis represents the evaluation factor of each cell, and the dashed line represents the average value of the evaluation factors of the cells in the group. The smaller the standard deviation of the evaluation factors of the cells in the battery pack, the closer the value of the cell evaluation factors is to the average value, indicating that the consistency of the cells in the battery pack is better, and vice versa.

[0071] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, synonymous substitutions, and improvements made within the scope of the precision and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for evaluating the consistency of battery pack cells, characterized in that, Includes the following steps: Step 1: Perform AC impedance testing on each battery pack; Apply current signals of a certain amplitude and multiple frequencies to the battery pack, and collect the voltage response signals of the cells in the battery pack to plot the EIS curves of all cells in the battery pack. Step 2: Feature extraction; Step 2.1: Denoise the test data on the EIS curves of all cells to obtain the effective EIS test dataset for all cells; Step 2.2: Use the Distributed Relaxation Time (DRT) algorithm to extract multiple polarization resistances corresponding to the internal electrochemical reactions of each cell from the valid EIS test dataset. Then use the Principal Component Analysis (PCA) algorithm to extract the principal components corresponding to the mid-frequency data of each cell from the valid EIS test dataset. Combine the multiple polarization resistances corresponding to the internal electrochemical reactions of the cell and the principal components corresponding to the mid-frequency data of the cell to form the feature data for cell evaluation. Step 3: Based on the fluctuation of the feature data, obtain the weight parameters of each feature and calculate the evaluation factor of the battery cell. Step 3.1: Normalize the feature data to obtain the normalized feature matrix; Step 3.2: Use the range of each column of the feature quantity in the feature quantity matrix as the first indicator to measure the degree of fluctuation of the feature quantity data. When the ranges are equal, use the variance of each column of the feature quantity in the feature quantity matrix as the second indicator to measure the degree of fluctuation of the feature quantity data. Step 3.3: Calculate the weight w of the j-th characteristic of the i-th cell using equation (1). i,j ; In equation (1), s i,j represents the fluctuation level of the j-th column characteristic quantity of the i-th cell in the characteristic quantity matrix, and p represents the total number of characteristic quantities; Step 3.4: Calculate the evaluation factor z of the i-th cell using equation (2). i : In equation (2), y i,j This represents the element in the i-th row and j-th column of the normalized eigenvalue matrix Y; Step 4: Conduct a consistency assessment of the evaluation factors for all battery cells: Based on the evaluation factors of each cell within the battery pack, the standard deviation of all cell evaluation factors is calculated and used as the basis for evaluating the consistency of cells within the battery pack. When the standard deviation of the evaluation factors reaches the set threshold, it indicates that the consistency of cells within the battery pack does not meet the requirements; otherwise, it indicates that the consistency of cells within the battery pack meets the requirements.

2. An electronic device, comprising a memory and a processor, characterized in that, The memory is used to store a program that supports the processor in executing the battery pack cell consistency evaluation method of claim 1, and the processor is configured to execute the program stored in the memory.

3. A computer-readable storage medium storing a computer program, characterized in that, The computer program is executed by the processor to perform the steps of the battery pack cell consistency evaluation method of claim 1.

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