CN113484764B - Retired battery SOH and consistency assessment method based on multidimensional impedance spectrum - Google Patents
Retired battery SOH and consistency assessment method based on multidimensional impedance spectrum Download PDFInfo
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Abstract
The invention discloses an evaluation method of SOH and consistency of retired batteries based on multi-dimensional impedance spectroscopy, which utilizes a fingerprint identification algorithm to identify multi-dimensional EIS curve clusters of batteries to be evaluated, and finds out multi-dimensional EIS curve clusters matched with the multi-dimensional EIS curve clusters in a fingerprint spectrum library formed by multi-dimensional EIS curve clusters of sample batteries, so that SOH and SOC states of the batteries to be evaluated can be determined; dividing the batteries with SOH consistency into a group; and then the similarity between the maps is used for further evaluation of consistency in the batteries in the group. The invention utilizes the multidimensional EIS curve to represent the retired battery state, has higher accuracy, and considers that the performance can still have larger deviation under the condition of consistent SOH of the batteries, so that the consistency evaluation is carried out by utilizing two indexes of the SOH and the similarity of the battery fingerprints in the same group, and the evaluation result has higher reliability.
Description
Technical Field
The invention belongs to the technical field of batteries, and particularly relates to a retired battery SOH and consistency evaluation method based on multidimensional impedance spectroscopy.
Background
In recent years, electric automobiles are rapidly developed due to energy conservation, emission reduction and environmental friendliness, and the rapid development of the electric automobiles drives the output of power batteries to be greatly increased. However, when the capacity of the power battery for the vehicle is reduced to 80%, the power battery cannot meet the power requirement of running of the electric vehicle and is out of service, so that the problem of the elimination of the out-of-service power battery is caused. From the battery perspective, if the retired power battery is directly scrapped and disassembled, the service life of the battery can be seriously shortened, the energy utilization efficiency is reduced, and the serious waste of resources is caused. The problem can be well solved by the gradient utilization, the performance of the power battery can be fully exerted by the gradient utilization, and the cost can be reduced.
Factors involved in service life attenuation of the battery are complex, consistency difference of the retired battery is large, and in order to ensure safety of the battery system used in a gradient manner, performance and service life of the battery pack are improved, and the retired power battery can be reused after SOH and consistency evaluation; the accurate SOH and consistency evaluation method of the battery can improve the production benefit of the battery cascade utilization enterprises and reduce the yield of unqualified products, so that the accurate SOH and consistency evaluation of the retired power battery is particularly important.
The SOH and consistency assessment method in the related art is used for assessing consistency by using open-circuit voltage, capacity, internal resistance and the like of the battery, and the method is judged only according to the external characteristics of the battery and has the characteristics of simplicity in operation and low accuracy; the method also utilizes factors such as a battery charge-discharge curve to evaluate, but has long test time, can not realize rapid and accurate identification of consistency of a large number of batteries, and is unfavorable for commercial utilization. In addition, in the related art, an evaluation method based on electrochemical alternating current impedance spectrum is also provided, but the EIS curve is measured at normal temperature and in a full charge state, only the impedance state of the battery under a certain condition can be reflected, the real condition of the battery can not be accurately reflected, and the reliability of the consistency evaluation result is low.
Disclosure of Invention
The invention aims to solve the defects of the prior art, and provides a retired battery SOH and consistency assessment method for achieving consistency assessment by using two indexes, namely SOH and fingerprint similarity, so that the assessment accuracy and reliability are improved, and the problems of long assessment time and low accuracy in the prior method are solved.
In order to achieve the aim of the invention, the invention adopts the following technical scheme:
the invention relates to a retired battery SOH and consistency assessment method based on multidimensional impedance spectrum, which is characterized by comprising the following steps:
s1, randomly selecting a plurality of batteries, measuring SOH of each battery by using a typical cyclic charge-discharge method under a constant temperature state, and measuring multidimensional EIS curve clusters of each battery under different SOC states; repeating the testing process after changing the temperature, so as to obtain a multi-dimensional EIS curve cluster library of all batteries under different SOC states and temperatures T, and forming a sample battery map library;
s2, acquiring an EIS curve cluster of the battery to be evaluated under excitation currents with different magnitudes and with real parts and imaginary parts of impedance as coordinate axes, and matching the EIS curve cluster with the spectrum in the sample battery spectrum library;
if the matching is successful, taking SOH and SOC states corresponding to the map which is successfully matched as SOH and SOC states of the battery to be evaluated;
if the matching fails, measuring SOH of the battery to be evaluated and a multi-dimensional EIS curve cluster under different conditions of SOC and temperature T according to the process of the step S1, and adding the SOH and the multi-dimensional EIS curve cluster into a sample battery map library;
s3, matching all the batteries to be evaluated according to the process of the step 2;
s4, taking SOH as a first index of consistency evaluation, setting a value range of SOH, and grouping all the batteries to be evaluated within the value range into a group;
s5, numbering the batteries to be evaluated in the same group, taking the battery 1 to be evaluated as a reference battery, and calculating the similarity between the fingerprints of the other batteries to be evaluated and the fingerprints of the reference battery;
s6, taking the similarity as a second index of consistency evaluation, judging the batteries to be evaluated with the similarity higher than the set threshold value as batteries with consistency, classifying the batteries to be evaluated into one class, and continuously classifying the rest batteries to be evaluated according to the processes of the step S5 and the step S6 until all the batteries to be evaluated are classified.
The method for evaluating the SOH and the consistency of the retired battery based on the multidimensional impedance spectrum is also characterized in that the pattern matching method in the step S2 is a minutiae pattern fingerprint identification algorithm or a texture pattern fingerprint identification algorithm.
The minutiae pattern fingerprint recognition algorithm is to convert each pattern in the sample battery pattern library and the battery pattern to be evaluated into respective point sets formed by characteristic points, calculate the distance between each characteristic point in the point set converted by the battery pattern to be evaluated and the corresponding characteristic point in the point set converted by each pattern in the pattern library, if the distance is smaller than a set offset distance threshold value between the points, represent that the two corresponding characteristic points are matched, count the number of characteristic points matched between the point set converted by the battery to be evaluated and the point set converted by each pattern in the pattern library, and take the pattern with the largest number of matched points as a candidate pattern, and if the number of matched points is larger than the set number threshold value, the candidate pattern is the pattern successfully matched; otherwise, the matching is failed.
The fingerprint identification algorithm based on the texture mode is to convert each pattern in the sample battery pattern library and the pattern of the battery to be evaluated into a line set formed by lines, calculate the dynamic bending distance between adjacent lines in each line set and serve as the characteristic vector of the pattern, then calculate the distance between the characteristic vector of the pattern of the battery to be evaluated and the characteristic vector of each pattern in the sample battery pattern library, select the minimum distance and judge whether the minimum distance is smaller than the set pattern offset distance threshold, and if the minimum distance is smaller than the set pattern offset distance threshold, take the pattern corresponding to the minimum distance as the pattern successfully matched; otherwise, the matching is failed.
Compared with the prior art, the invention has the beneficial effects that:
1. the invention utilizes the multidimensional EIS impedance spectrum to characterize the battery state, and can more comprehensively and accurately reflect the real situation of the battery compared with the conventional characterization method.
2. The invention identifies the fingerprint formed by the impedance spectrum, and converts the consistency assessment into the similarity problem between the fingerprints, so that the consistency assessment is more reasonable and visual.
3. The invention is still applicable to the evaluation of the health status and consistency of a large number of retired power batteries, and has short test period and high working efficiency.
4. The invention adopts two indexes to evaluate consistency, has higher reliability, is beneficial to improving the overall performance of the battery pack after recombination and utilization, and prolongs the service life of the battery pack.
Drawings
FIG. 1 is a schematic diagram of the EIS curve of the present invention as a function of SOC;
FIG. 2 is a graph showing the variation of EIS curves with the number of charge and discharge cycles according to the present invention;
FIG. 3 is a flowchart of a minutiae pattern-based fingerprint matching algorithm of the present invention;
FIG. 4a is a three-dimensional EIS graph embodying the present invention for fingerprinting;
FIG. 4b is another three-dimensional EIS graph embodying the present invention for specifically elucidating fingerprint identification;
fig. 5 is a flowchart of the battery SOH to be evaluated and SOC determination method of the present invention.
Detailed Description
In this embodiment, the method for evaluating SOH and consistency of retired batteries based on multidimensional impedance spectrum includes:
s1, randomly selecting a plurality of batteries, measuring SOH of each battery by using a typical cyclic charge-discharge method under a constant temperature state, and measuring multidimensional EIS curve clusters of each battery under different SOC states; repeating the testing process after changing the temperature, so as to obtain a multi-dimensional EIS curve cluster library of all batteries under different SOC states and temperatures T, and forming a sample battery map library;
specifically, the method for acquiring the multidimensional EIS curve cluster comprises the following steps:
s1.1, applying frequency f to retired battery 1 ,f 2 ,f 3 …f b Amplitude is i 1 Is a sinusoidal current signal of (a);
s1.2, synchronously sampling voltage, current and temperature signals under the excitation signal, and obtaining the impedance Z of the battery under each frequency after Fourier transformation analysis 1 (f 1 ),Z 1 (f 2 ),Z 1 (f 3 )…Z 1 (f b )；
S1.3, fitting the real part and the imaginary part of the impedance to obtain the amplitude value i at the current temperature 1 An EIS curve under excitation signal of (a);
s1.4, changing the amplitude of the exciting current to be i 2 ,i 3 ...i a To obtain the impedance Z of the battery 2 (f 1 ),Z 2 (f 2 ),Z 2 (f 3 )…Z 2 (f b )，Z 3 (f 1 )、Z 3 (f 2 )、Z 3 (f 3 )…Z 3 (f b )，…，Z a (f 1 )、Z a (f 2 )、Z a (f 3 )…Z a (f b ) Fitting a corresponding EIS curve to form a multi-dimensional EIS curve cluster of the retired battery under different amplitude excitation currents with the real part and the imaginary part of impedance as coordinate axes.
As shown in fig. 1 and 2, it can be seen that the EIS curve changes significantly with the SOC and the number of cyclic charge and discharge, where the change in the number of cyclic charge and discharge is used instead of the change in the SOH of the battery.
S2, acquiring an EIS curve cluster of the battery to be evaluated under excitation currents with different magnitudes and with real parts and imaginary parts of impedance as coordinate axes, and matching the EIS curve cluster with the spectrum in the sample battery spectrum library;
the pattern matching method is a minutiae pattern-based fingerprint identification algorithm or a texture pattern-based fingerprint identification algorithm.
The fingerprint identification means that the fingerprint characteristics of a sample to be identified are compared with the fingerprint characteristics in a fingerprint library, and the fingerprint characteristics corresponding to or closest to the fingerprint characteristics are searched, and the specific identification process is simply described in mathematic mode, namely, assuming that m state modes exist in the fingerprint library D 1 ,S 2 ,…S m And each state pattern S i All have an n-dimensional fingerprint feature, and the fingerprint feature of the sample to be identified is y= (y) 1 ,y 2 ,…y n ) T Fingerprint feature recognition is to find the x closest to y in the fingerprint database D i And x is i The corresponding state pattern is the state pattern of y.
Converting each map in a sample battery map library and a battery map to be evaluated into respective point sets formed by characteristic points, calculating the distance between each characteristic point in the point set converted by the battery map to be evaluated and a corresponding characteristic point in the point set converted by each map in the map library, if the distance is smaller than a set offset distance threshold value between the points, representing that the two corresponding characteristic points are matched, counting the number of the characteristic points matched between the point set converted by the battery map to be evaluated and the point set converted by each map in the map library, taking the map with the largest number of the matched points as a candidate map, and if the number of the matched points is larger than the set number threshold value, obtaining the candidate map as a successfully matched map; otherwise, the matching is failed.
Specifically, as shown in FIG. 3, the cell impedance Z obtained during the acquisition of the multi-dimensional EIS curve cluster 1 (f 1 )、Z 1 (f 2 )、Z 1 (f 3 )…Z 1 (f b )，…Z a (f 1 )、Z a (f 2 )、Z a (f 3 )…Z a (f b ) As the characteristic points of the fingerprint, a characteristic point set of the fingerprint is formed, and whether the fingerprints are matched is judged by calculating the number of matching points among the characteristic point sets of the fingerprint. The distance between the feature points is obtained by the following formula (1):
d(Z i (f j ),Z i ′(f j ))＝(Re i (f j )-Re′ i (f j )) 2 +(Im i (f j )-Im′ i (f j )) 2 (1)
d(Z i (f j ),Z′ i (f j ) (Z) represents Z i (f j ) And Z' i (f j ) Distance between Z i (f j ) The battery corresponding to the point set A is represented to have the amplitude value i i Frequency f j Z 'of the excitation current' i (f j ) The battery corresponding to the point set B is represented to have the amplitude value i i Frequency f j Is a resistance at the exciting current of (a); re (Re) i (f j ),Im i (f j ) Respectively Z i (f j ) Corresponding real and imaginary parts of impedance, re' i (f j )，Im′ i (f j ) Respectively Z' i (f j ) Corresponding real and imaginary parts of the impedance.
And comparing the calculated distance between the feature points with a threshold value between the points, if the calculated distance between the feature points is smaller than the threshold value, considering that the two feature points are matched, and if the number of the matched points between the two point sets exceeds the number threshold value of fingerprint matching, considering that the two fingerprint patterns are matched. The method for determining the inter-point distance threshold comprises the following steps: under the condition that the battery is in the same SOH, SOC and temperature T state, multi-dimensional EIS curve clusters of the battery are acquired for many times, the distance between corresponding characteristic points is calculated, and the average value of the distances is used as a threshold value in the state; and obtaining judging thresholds corresponding to other SOH, SOC and temperature T states by the same method. The number threshold is determined by the accuracy required by the system.
More specifically, the identification process is illustrated in FIGS. 4a and 4b, which illustrate two to-be-evaluated excitation signal amplitudes I with real and imaginary impedance axes 1 、I 2 、I 3 Points on each EIS curve are impedance points calculated for each frequency during the fitting process. By calculating the distance between corresponding characteristic points, e.g. the excitation current amplitude is equal to I 1 The frequency on the EIS curve of (2) is f 1 And (3) judging whether the characteristic points are matched or not, counting the number of the matched points, and comparing the number of the matched points with a number threshold value to judge whether the patterns are matched or not.
The fingerprint matching algorithm based on the texture mode is to convert each pattern in the sample battery pattern library and the patterns of the battery to be evaluated into a line set formed by lines, calculate the dynamic bending distance between adjacent lines in each line set and serve as the characteristic vector of the pattern, calculate the distance between the characteristic vector of the pattern of the battery to be evaluated and the characteristic vector of each pattern in the sample battery pattern library, judge whether the minimum distance is smaller than the set pattern offset distance threshold value after selecting the minimum distance, and if the minimum distance is smaller than the set pattern offset distance threshold value, take the pattern corresponding to the minimum distance as the pattern successfully matched; otherwise, the matching is failed.
In particular, dynamic time warping (dynamic time wrapping, DTW) distances can effectively handle cases where there is a local shift in the sequence. DTW distance is calculated by constructing an alignment matrix and adopting a dynamic programming method at two time intervalsA curved path is found in the time series that minimizes the cumulative distance between the two time series. Define two time sequences as p= [ P ] 1 ,p 2 ,...p m ] T And q= [ Q ] 1 ,q 2 ,...q m ] T The sequence lengths are m and n, respectively. To align P and Q with DTW distance, a distance matrix A of m rows and n columns is first constructed, i.e
In the formula (2): element a in A ij ＝d(p i ,q j )＝(p i -q i ) 2 Representing a time series point p i And q j Is used for the alignment distance of the lens. The curved path is a continuous set of feature maps of P and Q in A, denoted as W= [ W ] 1 ,w 2 ,w k ,...,w K ]. The kth element in W is defined as W k ＝(a ij ) k 。
W is required to satisfy the following constraint conditions:
the limitation is max (m, n) is more than or equal to K and less than or equal to m+n-1.
Boundary: w (w) 1 ＝a 11 And w K ＝a mn Are used to represent the start and end points of W, respectively.
Continuity: for w k ＝a ij Its adjacent element w k-1 ＝a i'j' Satisfying i-i '1, j-j' 1, this constraint defines that the adjacent element in W is an adjacent element in A.
Monotonicity: i-i '. Gtoreq.0, j-j'. Gtoreq.0.
The DTW distance in which a plurality of W, P and Q satisfying the above constraint conditions exist means W whose cumulative distance is smallest, and the objective function is expressed as formula (3):
in the formula (3): f (f) DTW (P, Q) represents the DTW distance of P from Q; k represents the minimum curved pathA length; w (w) i Is the i-th element in the minimum curved path. Solving the DTW distance by using a dynamic programming algorithm, and the recursive algorithm is expressed as formula (4):
in the formula (4): d (i, j) represents element a ij And the minimum cumulative value of the length of the curved path section of the front section.
After calculating the dynamic bending distance between EIS curves, forming the feature vectors of the fingerprint, calculating the Euclidean distance between the feature vectors again, comparing the calculation result with the fingerprint offset threshold value, and judging whether the fingerprint is matched. The method for determining the map shift threshold value comprises the following steps: and under the condition that the battery is in the same SOH, SOC and temperature T state, acquiring the multidimensional EIS curve of the battery for a plurality of times, calculating the distance between the corresponding feature vectors, taking the average value of the distance as the threshold under the state, and obtaining the judging threshold corresponding to other SOH, SOC and temperature T states by the same method.
More specifically, still describing the recognition process with fig. 4a and 4b, the amplitude excitation in fig. 4a is calculated as I 1 And I 2 、I 2 And I 3 Dynamic bending distance dtw between corresponding EIS curves 1 、dtw 2 Form a feature vector l a ＝[dtw 1 ,dtw 2 ]The feature vector l of FIG. 4b can be obtained in the same way b ＝[dtw 1 ′,dtw 2 ′]And calculating the distance between the feature vectors of the two maps, and comparing with a map offset distance threshold value to judge whether the feature vectors are matched.
If the matching is successful, taking SOH and SOC states corresponding to the map which is successfully matched as SOH and SOC states of the battery to be evaluated;
if the matching fails, measuring SOH of the battery to be evaluated and a multi-dimensional EIS curve cluster under different conditions of SOC and temperature T according to the process of the step S1, and adding the SOH and the multi-dimensional EIS curve cluster into a sample battery map library;
s3, matching all the batteries to be evaluated according to the process of the step 2;
specifically, the matching flow of all the batteries to be evaluated is shown in fig. 5, and no matter whether the current battery is successfully matched or not, the rest batteries are continuously matched with the patterns in the pattern library, and the pattern library of the sample batteries is continuously perfect in the evaluation process.
For a new unmatched battery in the process of acquiring the SOH of the unmatched battery and the multi-dimensional EIS curve clusters under different SOCs and temperatures T, judging whether the new unmatched battery is matched with the spectrum of the battery under test, if the new unmatched battery is not matched with the spectrum of the battery under test, acquiring the SOH of the battery and the multi-dimensional EIS curve clusters under different SOCs and temperatures T, adding the SOH of the battery and the multi-dimensional EIS curve clusters into a sample spectrum library, and if the new unmatched battery is matched with the spectrum of the battery under test, the new unmatched battery is not required to be subjected to the process. The number of unmatched batteries is considered to be small, otherwise, the consistency of the whole batch of batteries to be detected is too bad, and the recycling value is lost.
S4, taking SOH as a first index of consistency evaluation, setting a value range of SOH, and grouping all the batteries to be evaluated within the value range into a group; the SOH value range is determined by the precision required by the evaluation system; for example, after the value of SOH is selected, setting the error to be within + -delta SOH;
s5, numbering the batteries to be evaluated in the same group, taking the battery 1 to be evaluated as a reference battery, and calculating the similarity between the fingerprints of the other batteries to be evaluated and the fingerprints of the reference battery; the similarity of the fingerprints is calculated by adopting a minutiae pattern-based fingerprint identification algorithm or a texture pattern-based fingerprint identification algorithm.
S6, taking the similarity as a second index of consistency evaluation, judging the batteries to be evaluated with the similarity higher than the set threshold value as batteries with consistency, classifying the batteries to be evaluated into one class, and continuously classifying the rest batteries to be evaluated according to the processes of the step S5 and the step S6 until all the batteries to be evaluated are classified.
If a minutiae pattern-based fingerprint identification algorithm is adopted, the number of matching points reaches a number threshold value of the pattern similarity requirement, the retired battery corresponding to the fingerprint pattern can be considered to have consistency, and the higher the number of the matching points is, the higher the similarity is, and the higher the consistency is; otherwise, the retired battery is not considered to have consistency.
If the texture pattern matching algorithm is adopted, the retired batteries corresponding to the fingerprint patterns can be considered to have consistency if the distance between the feature vectors is smaller than the distance threshold, and if the distance is smaller, the similarity is higher, the consistency is higher, otherwise, the retired batteries are considered to have no consistency.
The number threshold and the distance threshold used in the evaluation process are determined by the following methods: and calculating the similarity between curve clusters corresponding to SOH, SOC and temperature T states of the batteries to be compared in the sample battery map library, and taking the similarity as a threshold value. The retired power battery SOH and consistency assessment is completed.
Claims (4)
1. A method for evaluating SOH and consistency of retired batteries based on multi-dimensional impedance spectroscopy, comprising:
s1, randomly selecting a plurality of batteries, measuring SOH of each battery by using a typical cyclic charge-discharge method under a constant temperature state, and measuring multidimensional EIS curve clusters of each battery under different SOC states; repeating the testing process after changing the temperature, so as to obtain a multi-dimensional EIS curve cluster library of all batteries under different SOC states and temperatures T, and forming a sample battery map library;
s2, acquiring an EIS curve cluster of the battery to be evaluated under excitation currents with different magnitudes and with real parts and imaginary parts of impedance as coordinate axes, and matching the EIS curve cluster with the spectrum in the sample battery spectrum library;
if the matching is successful, taking SOH and SOC states corresponding to the map which is successfully matched as SOH and SOC states of the battery to be evaluated;
if the matching fails, measuring SOH of the battery to be evaluated and a multi-dimensional EIS curve cluster under different conditions of SOC and temperature T according to the process of the step S1, and adding the SOH and the multi-dimensional EIS curve cluster into a sample battery map library;
s3, matching all the batteries to be evaluated according to the process of the step 2;
s4, taking SOH as a first index of consistency evaluation, setting a value range of SOH, and grouping all the batteries to be evaluated within the value range into a group;
s5, numbering the batteries to be evaluated in the same group, taking the battery 1 to be evaluated as a reference battery, and calculating the similarity between the fingerprints of the other batteries to be evaluated and the fingerprints of the reference battery;
s6, taking the similarity as a second index of consistency evaluation, judging the batteries to be evaluated with the similarity higher than the set threshold value as batteries with consistency, classifying the batteries to be evaluated into one class, and continuously classifying the rest batteries to be evaluated according to the processes of the step S5 and the step S6 until all the batteries to be evaluated are classified.
2. The method for evaluating SOH and consistency of retired battery based on multi-dimensional impedance spectrum according to claim 1, wherein the pattern matching method in step S2 is minutiae pattern based fingerprint recognition algorithm or texture pattern based fingerprint recognition algorithm.
3. The method for evaluating the SOH and the consistency of the retired battery based on the multidimensional impedance spectrum according to claim 2, wherein the minutiae pattern fingerprint recognition algorithm is characterized in that each spectrum in the sample battery spectrum library and the battery spectrum to be evaluated are converted into respective point sets composed of characteristic points, the distance between each characteristic point in the point set converted by the battery spectrum to be evaluated and the corresponding characteristic point in the point set converted by each spectrum in the spectrum library is calculated, if the distance is smaller than a set offset distance threshold value between the points, the corresponding two characteristic points are represented to be matched, the number of characteristic points, with which the point set converted by the battery to be evaluated is matched with the point set converted by each spectrum in the spectrum library, is counted, the spectrum with the largest matching point number is taken as a candidate spectrum, and if the number of matching points is larger than a set number threshold value, the candidate spectrum is successfully matched; otherwise, the matching is failed.
4. The method for evaluating the SOH and the consistency of the retired battery based on the multidimensional impedance spectrum according to claim 2, wherein the fingerprint recognition algorithm based on the texture mode is characterized in that each spectrum in the sample battery spectrum library and the spectrum of the battery to be evaluated are respectively converted into a line set formed by lines, the dynamic bending distance between adjacent lines in each line set is calculated and is used as a characteristic vector of the spectrum, then the distance between the characteristic vector of the spectrum of the battery to be evaluated and the characteristic vector of each spectrum in the sample battery spectrum library is calculated, after the minimum distance is selected, whether the minimum distance is smaller than a set spectrum offset distance threshold value is judged, and if the minimum distance is smaller, the spectrum corresponding to the minimum distance is used as a spectrum successfully matched; otherwise, the matching is failed.
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