CN116298929A - Battery pack cell consistency assessment method - Google Patents
Battery pack cell consistency assessment method Download PDFInfo
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- CN116298929A CN116298929A CN202310510479.2A CN202310510479A CN116298929A CN 116298929 A CN116298929 A CN 116298929A CN 202310510479 A CN202310510479 A CN 202310510479A CN 116298929 A CN116298929 A CN 116298929A
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
- G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
- G01R31/36—Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC]
- G01R31/367—Software therefor, e.g. for battery testing using modelling or look-up tables
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
- G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
- G01R31/36—Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC]
- G01R31/389—Measuring internal impedance, internal conductance or related variables
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
- G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
- G01R31/36—Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC]
- G01R31/392—Determining battery ageing or deterioration, e.g. state of health
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
- G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
- G01R31/36—Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC]
- G01R31/396—Acquisition or processing of data for testing or for monitoring individual cells or groups of cells within a battery
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
- Y02E60/00—Enabling technologies; Technologies with a potential or indirect contribution to GHG emissions mitigation
- Y02E60/10—Energy storage using batteries
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Abstract
The invention discloses a battery cell consistency assessment method, which comprises the following steps: 1. performing alternating current impedance test on the battery cells in the battery pack; 2. extracting characteristics of an Electrochemical Impedance Spectroscopy (EIS) curve of an obtained battery cell in the battery pack; 3. taking the fluctuation degree of the data as a selection basis of the weight of each characteristic quantity, and taking the calculation result weighted by the characteristic quantity as a cell evaluation factor; 4. and obtaining a consistency evaluation result of the battery cells in the battery pack by calculating the standard deviation of the battery cell evaluation factors. The invention can improve the speed and accuracy of the consistency evaluation of the battery cells in the battery pack, thereby reducing the disassembly workload when the battery pack is recycled.
Description
Technical Field
The invention belongs to the field of echelon utilization of retired batteries, and particularly relates to a battery pack cell consistency assessment method.
Background
In practical applications, to meet power and energy requirements, the cells are typically connected in series-parallel to form a high voltage battery. Because of different manufacturing processes and positions in the battery pack, the aging conditions of the internal cells have differences, so that the cells have differences in terms of open-circuit voltage, internal resistance, temperature, capacity, state of charge (SOC) and the like, the consistency among the internal cells is poor, the energy utilization rate, safety, health state and the like of the battery pack are adversely affected, and therefore, the consistency of the cells needs to be evaluated, and the use safety and reliability of the battery pack are ensured.
The existing cell consistency evaluation methods mainly comprise three types: the method for evaluating the consistency of the battery cells in the battery pack through single indexes such as capacity, internal resistance and the like only considers the difference of partial indexes of the battery, and cannot comprehensively evaluate the consistency of the battery; the second is to evaluate the performance of the battery by combining the performance indexes such as capacity, internal resistance and the like, and compared with a single-parameter method, the method can more comprehensively analyze the performance of the battery, but measuring the performance parameters of a plurality of battery cores can take longer test time, and the difficulty is that selecting proper weights for a plurality of parameters; the third is to evaluate by using battery test curves such as charge-discharge voltage-current curve, EIS curve, etc., which can reflect the characteristics of the battery more comprehensively, but the battery test curve usually belongs to high-dimensional data, which affects the evaluation speed.
In the existing consistency evaluation method, the evaluation accuracy is not high due to the fact that only a single index is considered in the single parameter evaluation method, the evaluation time is long due to the fact that a plurality of parameters are measured in the multi-parameter evaluation method, the evaluation speed is low due to the fact that the test time is long in the voltage curve-based evaluation method and the data redundancy problem exists in the evaluation, and the method for evaluating the battery through the EIS curve is possibly influenced by the problem of contact impedance crosstalk in the battery.
Disclosure of Invention
The invention aims to avoid the defects of the prior art, and provides a battery cell consistency evaluation method which can improve the speed and accuracy of the consistency evaluation of the battery cells in the battery pack, so that the disassembly workload of the battery pack during recycling can be reduced.
The invention adopts the following technical scheme for solving the technical problems:
the invention discloses a battery cell consistency assessment method which is characterized by comprising the following steps:
step one, alternating current impedance testing is carried out on each battery pack;
applying current signals with certain amplitude and multiple frequencies to the battery pack, and collecting voltage response signals of cells in the battery pack, so as to draw EIS curves of all the cells in the battery pack;
step two, extracting features;
step 2.1, denoising test data on the EIS curves of all the battery cells so as to obtain EIS effective test data sets of all the battery cells;
step 2.2, extracting a plurality of polarized internal resistances corresponding to the electrochemical reaction in each cell from the EIS effective test data set of each cell by using a relaxation time distribution technology (DRT) algorithm, extracting a main component corresponding to the frequency data in the cell from the EIS effective test data set of each cell by using a Principal Component Analysis (PCA) algorithm, and forming characteristic quantity data of cell evaluation by using the plurality of polarized internal resistances corresponding to the electrochemical reaction in the cell and the main component corresponding to the frequency data in the cell;
step three, acquiring weight parameters of each characteristic quantity according to the fluctuation degree of the characteristic quantity data, and calculating to obtain an evaluation factor of the battery cell;
step 3.1, normalizing the feature quantity data to obtain a normalized feature quantity matrix;
step 3.2, taking the range of each column of characteristic quantity in the characteristic quantity matrix as a first index for measuring the fluctuation degree of the characteristic quantity data, and taking the variance of each column of characteristic quantity in the characteristic quantity matrix as a second index for measuring the fluctuation degree of the characteristic quantity data when the range is equal;
step 3.3, calculating the weight w of the j-th characteristic quantity of the i-th battery cell by using the method (1) i,j ；
In the formula (1), s i,j The fluctuation degree of the j-th column characteristic quantity of the i-th battery cell in the characteristic quantity matrix is represented, and p represents the total number of the characteristic quantities;
step 3.4, calculating the evaluation factor z of the ith cell by using the step (2) i ：
In the formula (2), y i,j An ith row and jth column element representing the normalized feature quantity matrix Y;
step four, carrying out consistency evaluation on evaluation factors of all the battery cells:
according to the evaluation factors of each cell in the battery pack, calculating the standard deviation of all the cell evaluation factors and taking the standard deviation as an evaluation basis of the consistency of the cells in the battery pack, when the standard deviation of the evaluation factors reaches a set threshold value, the consistency of the cells in the battery pack is not in accordance with the requirements, otherwise, the consistency of the cells in the battery pack is in accordance with the requirements.
The electronic device of the invention comprises a memory and a processor, wherein the memory is used for storing a program for supporting the processor to execute the battery cell consistency assessment method, and the processor is configured to execute the program stored in the memory.
The invention relates to a computer readable storage medium, on which a computer program is stored, characterized in that the computer program, when being run by a processor, performs the steps of the battery cell consistency assessment method.
Compared with the prior art, the invention has the beneficial effects that:
1. according to the evaluation method, only the EIS data of the battery cells are used for feature extraction, and the frequency domain response information of the polarized internal resistance part of the battery is mainly used, so that the interference of the contact impedance of the battery cells in the battery pack on consistency evaluation can be reduced, the battery cells in the battery pack do not need to be tested one by one, and the evaluation speed is increased.
2. The calculation basis of the feature quantity weight in the evaluation method is based on the fluctuation degree of the data, the fluctuation degree of the data can be objectively reflected, the consistency evaluation is more reasonable, and the weight coefficient can be adaptively updated along with the fluctuation degree of the data.
3. The evaluation method provided by the invention quantifies the consistency degree of the battery pack when the consistency evaluation is carried out on the battery pack, and is beneficial to quantitatively evaluating the consistency of the battery pack.
Drawings
FIG. 1 is a flow chart of the method of the present invention;
FIG. 2 is a flow chart of a feature extraction method of an embodiment;
FIG. 3 is a flow chart of a weight selection method of an embodiment;
fig. 4 is a bar graph of cell evaluation factors for an embodiment.
Detailed Description
In this embodiment, in order to improve reliability and rapidity of a battery cell consistency evaluation method, a battery cell consistency evaluation method is provided, as shown in fig. 1, where the battery cell consistency evaluation method specifically includes:
step one, alternating current impedance testing is carried out on each battery pack;
applying current signals with certain amplitude and multiple frequencies to the battery pack, and collecting voltage response signals of cells in the battery pack, so as to draw EIS curves of all the cells in the battery pack;
(1) Applying a current signal with a certain amplitude to the battery pack, if the capacity of the battery cell is C, taking 1/10-1/20C of excitation current to ensure that the injection current does not interfere with the original chemical reaction inside the battery cell, maintaining an excitation signal with the frequency f, and synchronously collecting voltage response signals of each battery cell in the a periods;
(2) Calculating the average value of the voltage and current amplitudes in a period according to the formula (3) so as to filter noise;
in formula (3), U n Is the average value of voltage signals of the battery cell at the nth injection frequency, I n The average value of the voltage signal of the battery cell at the nth injection frequency.
(3) Calculating according to the formula (4) to obtain the cell impedance Z corresponding to the frequency f n The real part Re (Z) n ) And imaginary part Im (Z) n )；
In the formula (4), the amino acid sequence of the compound,
is the phase of the voltage, ">
For the phase of the current, it can be calculated using a fast fourier analysis (FFT) algorithm.
(4) And (3) changing the frequency of the excitation signal, and repeating the steps (1) to (2) to obtain EIS curves taking the negative numbers of the real part and the imaginary part of the impedance as coordinate axes at different frequency points.
Step two, extracting features;
fig. 2 is a flowchart of a feature extraction method according to an embodiment, and specifically includes:
step 2.1, denoising test data on the EIS curves of all the battery cells so as to obtain EIS effective test data sets of all the battery cells;
step 2.2, extracting a plurality of polarized internal resistances corresponding to the electrochemical reaction in each cell from the EIS effective test data set of each cell by using a relaxation time distribution technology (DRT) algorithm, extracting a main component corresponding to the frequency data in the cell from the EIS effective test data set of each cell by using a Principal Component Analysis (PCA) algorithm, and forming characteristic quantity data of cell evaluation by using the plurality of polarized internal resistances corresponding to the electrochemical reaction in the cell and the main component corresponding to the frequency data in the cell;
the EIS curve of the battery cell can reflect the alternating current impedance information of the battery cell, and the ohmic impedance and the inductive impedance of the high frequency band are easily deviated from the consistency result due to the influence of the contact impedance, so that the consistency evaluation of the battery cell is mainly carried out by adopting the impedance of the middle frequency band polarization part.
Because the inside of the battery core has a plurality of chemical reactions with different rates, the equivalent circuit model of the battery core has a plurality of polarization internal resistances, a plurality of circular arcs can be correspondingly generated on the EIS curve of the battery core, and the plurality of polarization internal resistances of the battery core can be extracted by using a DRT analysis method.
The frequency band data in the electric core EIS curve not only reflects the polarization reaction of the electric core, but also reflects the internal diffusion effect of the electric core, and partial electric core low-frequency information is extracted, so that the main component is extracted from partial data by using a PCA method, the dimension reduction treatment is realized, and the calculation amount of subsequent operation is facilitated to be simplified.
Step three, acquiring weight parameters of each characteristic quantity according to the fluctuation degree of the characteristic quantity data, and calculating to obtain an evaluation factor of the battery cell;
fig. 3 is a flowchart of a weight selection method according to an embodiment, which includes:
step 3.1, normalizing the feature quantity data to obtain a normalized feature quantity matrix so as to eliminate the influence of factors such as dimension of the feature quantity on consistency evaluation;
when the cell has m sections, the normalized formula is shown as formula (5):
in the formula (5), x ij Representing the feature quantity of the ith row and jth column in the feature quantity data, y ij An ith row and jth column element representing the normalized feature quantity matrix Y;
step 3.2, taking the range of each column of characteristic quantity in the characteristic quantity matrix as a first index for measuring the fluctuation degree of the characteristic quantity data, and taking the variance of each column of characteristic quantity in the characteristic quantity matrix as a second index for measuring the fluctuation degree of the characteristic quantity data when the range is equal;
step 3.3, calculating the weight w of the j-th characteristic quantity of the i-th battery cell by using the formula (6) according to the larger data fluctuation degree and the larger allocated weight i,j ：
In the formula (6), s i,j The fluctuation degree of the j-th column characteristic quantity of the i-th battery cell in the characteristic quantity matrix is represented, and p represents the total number of the characteristic quantities;
according to the calculated weight, when the feature quantity is p, a weight matrix w is formed px1 ；
Step 3.4, calculating the evaluation factor z of the ith cell by using the step (7) i ：
In formula (7), y i,j An ith row and jth column element representing the normalized feature quantity matrix Y;
step four, carrying out consistency evaluation on evaluation factors of all the battery cells:
according to the evaluation factors of each cell in the battery pack, calculating the standard deviation of all the cell evaluation factors and taking the standard deviation as the evaluation basis of the consistency of the cells in the battery pack, and when the standard deviation is smaller, the better the consistency is, and otherwise, the worse the consistency is. When the standard deviation of the evaluation factor reaches a set threshold, the consistency of the battery cells in the battery pack is not met, otherwise, the consistency of the battery cells in the battery pack is met.
In this embodiment, an electronic device includes a memory for storing a program supporting the processor to execute the above method, and a processor configured to execute the program stored in the memory.
In this embodiment, a computer-readable storage medium stores a computer program that, when executed by a processor, performs the steps of the method described above.
Fig. 4 is a bar chart of cell evaluation factors in an embodiment, 12 cells are arranged in a battery pack, the abscissa is the number corresponding to each cell, the ordinate is the evaluation factor corresponding to each cell, the broken line is the average value of the cell evaluation factors in the battery pack, when the standard deviation of the evaluation factors of the cells in the battery pack is smaller, the value of the cell evaluation factor is closer to the average value, which indicates that the uniformity of the cells in the battery pack is better, and otherwise, the value of the cell evaluation factor is worse.
The foregoing description of the preferred embodiments of the invention is not intended to be limiting, but rather is intended to cover all modifications, equivalents, and alternatives falling within the spirit and scope of the invention.
Claims (3)
1. The battery cell consistency evaluation method is characterized by comprising the following steps of:
step one, alternating current impedance testing is carried out on each battery pack;
applying current signals with certain amplitude and multiple frequencies to the battery pack, and collecting voltage response signals of cells in the battery pack, so as to draw EIS curves of all the cells in the battery pack;
step two, extracting features;
step 2.1, denoising test data on the EIS curves of all the battery cells so as to obtain EIS effective test data sets of all the battery cells;
step 2.2, extracting a plurality of polarized internal resistances corresponding to the electrochemical reaction in each cell from the EIS effective test data set of each cell by using a relaxation time distribution technology (DRT) algorithm, extracting a main component corresponding to the frequency data in the cell from the EIS effective test data set of each cell by using a Principal Component Analysis (PCA) algorithm, and forming characteristic quantity data of cell evaluation by using the plurality of polarized internal resistances corresponding to the electrochemical reaction in the cell and the main component corresponding to the frequency data in the cell;
step three, acquiring weight parameters of each characteristic quantity according to the fluctuation degree of the characteristic quantity data, and calculating to obtain an evaluation factor of the battery cell;
step 3.1, normalizing the feature quantity data to obtain a normalized feature quantity matrix;
step 3.2, taking the range of each column of characteristic quantity in the characteristic quantity matrix as a first index for measuring the fluctuation degree of the characteristic quantity data, and taking the variance of each column of characteristic quantity in the characteristic quantity matrix as a second index for measuring the fluctuation degree of the characteristic quantity data when the range is equal;
step 3.3, calculating the weight w of the j-th characteristic quantity of the i-th battery cell by using the method (1) i,j ；
In the formula (1), s i,j The fluctuation degree of the j-th column characteristic quantity of the i-th battery cell in the characteristic quantity matrix is represented, and p represents the total number of the characteristic quantities;
step 3.4, calculating the evaluation factor z of the ith cell by using the step (2) i ：
In the formula (2), y i,j An ith row and jth column element representing the normalized feature quantity matrix Y;
step four, carrying out consistency evaluation on evaluation factors of all the battery cells:
according to the evaluation factors of each cell in the battery pack, calculating the standard deviation of all the cell evaluation factors and taking the standard deviation as an evaluation basis of the consistency of the cells in the battery pack, when the standard deviation of the evaluation factors reaches a set threshold value, the consistency of the cells in the battery pack is not in accordance with the requirements, otherwise, the consistency of the cells in the battery pack is in accordance with the requirements.
2. An electronic device comprising a memory and a processor, wherein the memory is configured to store a program that supports the processor to perform the battery cell consistency assessment method of claim 1, the processor being configured to execute the program stored in the memory.
3. A computer readable storage medium having a computer program stored thereon, characterized in that the computer program when executed by a processor performs the steps of the battery cell consistency assessment method of claim 1.
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116908720A (en) * | 2023-09-07 | 2023-10-20 | 中国华能集团清洁能源技术研究院有限公司 | Battery pack consistency state diagnosis method, device and storage medium |
| CN117339913A (en) * | 2023-10-31 | 2024-01-05 | 科立鑫(珠海)新能源有限公司 | Waste battery recovery system |
| CN117706403A (en) * | 2023-12-16 | 2024-03-15 | 北京绿能环宇低碳科技有限公司 | Intelligent rapid disassembly method and system for new energy lithium battery |
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Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116908720A (en) * | 2023-09-07 | 2023-10-20 | 中国华能集团清洁能源技术研究院有限公司 | Battery pack consistency state diagnosis method, device and storage medium |
| CN116908720B (en) * | 2023-09-07 | 2023-12-26 | 中国华能集团清洁能源技术研究院有限公司 | Battery pack consistency state diagnosis method, device and storage medium |
| CN117339913A (en) * | 2023-10-31 | 2024-01-05 | 科立鑫(珠海)新能源有限公司 | Waste battery recovery system |
| CN117706403A (en) * | 2023-12-16 | 2024-03-15 | 北京绿能环宇低碳科技有限公司 | Intelligent rapid disassembly method and system for new energy lithium battery |
| CN117706403B (en) * | 2023-12-16 | 2024-05-24 | 北京绿能环宇低碳科技有限公司 | Intelligent rapid disassembly method and system for new energy lithium battery |
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