CN113484787B - Electrochemical impedance spectrum-based rapid sorting and recombining method for retired lithium ion battery - Google Patents
Electrochemical impedance spectrum-based rapid sorting and recombining method for retired lithium ion battery Download PDFInfo
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- G01—MEASURING; TESTING
- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
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Abstract
The invention provides a rapid sorting and recombining method of a retired lithium ion battery based on an electrochemical impedance spectrum, which is used for rapidly sorting and recombining the retired lithium ion battery and is characterized by comprising the following steps: step S1, carrying out electrochemical impedance spectrum test and relaxation time distribution analysis on the retired lithium ion battery, and calculating ohmic internal resistance R of the retired lithium ion battery from the intersection point of the impedance curve of the obtained electrochemical impedance spectrum Nyquist diagram and the horizontal axis O (ii) a Step S2, based on the pre-trained black box model and the relaxation time distribution data, rapidly estimating the battery capacity of the retired lithium ion battery; step S3, extracting impedance characteristic information of the retired lithium ion battery based on the relaxation time distribution data, and comparing the information with the estimated battery capacity and the ohm internal resistance R O Inputting the recombination criterion as a six-dimensional recombination criterion into a Gaussian mixture model; and step S4, the Gaussian mixture model carries out soft clustering recombination on the retired lithium ion battery to complete sorting recombination.
Description
Technical Field
The invention belongs to the technical field of battery sorting and recombination, and relates to a rapid sorting and recombination method of a retired lithium ion battery based on an electrochemical impedance spectrum.
Background
The lithium ion battery has the advantages of high energy density, long service life, environmental friendliness and the like, and is widely applied to new energy automobiles as a power source at present. However, after several years of use in electric vehicles, lithium ion batteries must be replaced to avoid safety issues and reduce mileage due to capacity degradation. In the next few years, the retired lithium ion batteries in China will rise greatly, and battery manufacturers and local governments will face tremendous recovery and disposal pressures. Due to poor consistency of the retired lithium ion battery, the retired lithium ion battery cannot be directly utilized in a gradient manner. Therefore, before the retired lithium ion battery is utilized in a ladder way, the battery needs to be sorted and recombined.
Relevant researches show that the capacity and the internal resistance of the retired lithium ion battery are two most basic sorting recombination indexes. The traditional method for obtaining the parameter indexes mainly adopts standard capacity test, and realizes high-precision and high-reliability data acquisition by charging and discharging the battery. However, this is a time consuming and power consuming process that makes large scale battery sorting difficult. In some existing research methods, special test equipment is needed for capacity estimation and screening of the retired lithium ion battery, a complex test process and long test time are involved, economy is poor, and efficiency is low, so that the large-scale retired lithium ion battery is difficult to sort and recombine.
Disclosure of Invention
In order to solve the problems, the invention provides a high-efficiency and low-cost sorting and recombining method for the retired lithium ion battery, which adopts the following technical scheme:
the invention provides a rapid sorting and recombining method of a retired lithium ion battery based on an electrochemical impedance spectrum, which is used for rapidly sorting and recombining the retired lithium ion battery and is characterized by comprising the following steps: step S1, performing electrochemical impedance spectrum test and relaxation time distribution analysis on the retired lithium ion battery to obtain a corresponding impedance curve of an electrochemical impedance spectrum Nyquist diagram and corresponding relaxation time distribution data, and then obtaining the impedance curve of the electrochemical impedance spectrum Nyquist diagramThe ohmic internal resistance R of the retired lithium ion battery is calculated by the intersection point of the line and the horizontal axis O (ii) a Step S2, based on the pre-trained black box model and the relaxation time distribution data, performing rapid capacity estimation on the retired lithium ion battery to obtain the estimated battery capacity of the retired lithium ion battery; step S3, extracting contact impedance R of the retired lithium ion battery based on relaxation time distribution data c SEI film resistance R SEI Charge exchange resistance R ct And lithium ion diffusion resistance R d And the characteristic information is compared with the estimated battery capacity and the ohm internal resistance R O Inputting the recombination criterion serving as a six-dimensional recombination criterion into a Gaussian mixture model; and step S4, performing soft clustering recombination on the retired lithium ion battery by using the Gaussian mixture model to obtain a final soft clustering result, and finishing sorting recombination.
The rapid sorting and recombining method for the retired lithium ion battery based on the electrochemical impedance spectrum can also have the technical characteristics that the physical condition of the electrochemical impedance spectrum test is that the temperature is 25 ℃, the applied disturbance voltage is 0.01V, and the test frequency range is 0.01Hz to 1000 Hz.
The rapid sorting and recombining method for the retired lithium ion battery based on the electrochemical impedance spectrum, provided by the invention, can also have the technical characteristics that during relaxation time distribution analysis, the SOC of each aged battery is adjusted to 20% SOC for 10 minutes of electrochemical impedance spectrum test, and the measured electrochemical impedance spectrum is subjected to relaxation time distribution analysis.
The invention provides a rapid sorting and recombining method for retired lithium ion batteries based on electrochemical impedance spectroscopy, which can also have the technical characteristics that a black box model is constructed based on a trained BPNN network, the BPNN network comprises an input layer, a hidden layer and an output layer, and the steps of pre-training are as follows: step S2-1, randomly selecting a small number of batteries to test and analyze the relaxation time distribution to obtain the corresponding standard capacity of the batteries 
And relaxation time distribution data as input variables for the BPNN networkMeasuring and sampling data, and carrying out normalization processing on input variables and the sampling data, wherein the formula is as follows:
in the formula, x max And x min Respectively inputting the maximum value and the minimum value in the vector x of the BPNN network; step S2-2, establishing an initial BPNN model, and performing random initialization on a connection weight matrix and a bias vector between each layer of the initial BPNN model based on a random function; and step S2-3, setting neurons and learning rate of the hidden layer, inputting the input variables and sample data after normalization processing to the initial BPNN model, and training the BPNN network by using an LM algorithm to obtain a black box model.
The invention provides a rapid sorting and recombining method for retired lithium ion batteries based on electrochemical impedance spectroscopy, which can also have the technical characteristics that the soft clustering and recombining method comprises the following steps: step S3-1, collecting data and input samples, and carrying out z-score normalization processing on the data and the samples, wherein the formula is as follows:
in the formula (I), the compound is shown in the specification,
the mean value of the original data is, and sigma is the standard deviation of the original data; s3-2, initializing K multivariate Gaussian distribution parameters by adopting a K-means algorithm; s3-3, solving parameters of the Gaussian mixture model by using an EM (effective minimum) optimization algorithm until the Gaussian mixture model iterates for 100 times based on the EM optimization algorithm; and step S3-4, outputting data of the Gaussian mixture model as a final clustering result obtained based on soft clustering recombination.
The invention also provides a rapid sorting and recombining method of the retired lithium ion battery based on the electrochemical impedance spectrum, which is used for rapidly sorting the retired lithium ion battery under different aging scenesRecombination, characterized in that it comprises the following steps: step S1, performing electrochemical impedance spectrum test and relaxation time distribution analysis on the retired lithium ion battery to obtain a corresponding electrochemical impedance spectrum Nyquist diagram impedance curve and corresponding relaxation time distribution data, and then calculating ohmic internal resistance R of the retired lithium ion battery from the intersection point of the electrochemical impedance spectrum Nyquist diagram impedance curve and the horizontal axis O (ii) a Step S2, based on the pre-trained black box model and the relaxation time distribution data, performing rapid capacity estimation on the retired lithium ion battery to obtain the estimated battery capacity of the retired lithium ion battery; step S3', extracting contact resistance R of the retired lithium ion battery based on relaxation time distribution data c SEI film resistance R SEI Charge exchange resistance R ct And lithium ion diffusion resistance R d Carrying out weight processing on the characteristic information; step S4', weighting the processed characteristic information and the estimated battery capacity and the ohm internal resistance R O Inputting the recombination criterion as a six-dimensional recombination criterion into a Gaussian mixture model; and step S5', the Gaussian mixture model carries out soft clustering recombination on the retired lithium ion batteries under different aging scenes to obtain a final soft clustering result, and sorting recombination is completed.
Action and effects of the invention
According to the rapid sorting and recombining method for the retired lithium ion battery based on the electrochemical impedance spectrum, provided by the invention, before sorting and recombining the retired lithium ion battery, the battery capacity of the retired lithium ion battery is rapidly estimated through a pre-trained black box model and by combining relaxation time distribution data, and then the estimated battery capacity, ohmic internal resistance obtained based on an electrochemical impedance spectrum test and impedance characteristic information are input into a Gaussian mixture model to be subjected to soft clustering to obtain a soft clustering result, so that the rapid sorting and recombining of the retired lithium ion battery is realized. The method can shorten the time for estimating the large-scale battery capacity from 3 hours to about 10 minutes without adopting a special capacity testing device, and has the advantages of high efficiency and low time consumption compared with the existing capacity testing device. The efficient battery capacity estimation also enables the sorting and recombining efficiency of the retired lithium ion batteries to be greatly improved, and meanwhile, the sorting and recombining of the retired lithium ion batteries are realized, so that the next echelon utilization can be performed according to different categories of the retired lithium ion batteries.
Drawings
FIG. 1 is a flow chart of a rapid sorting and recombining method for a retired lithium ion battery based on electrochemical impedance spectroscopy according to an embodiment of the present invention;
FIG. 2 is a graph of the test error results of the BPNN model in an embodiment of the present invention;
FIG. 3 is a graph of maximum likelihood function values for data as a function of iteration number for an embodiment of the invention;
FIG. 4 is a soft clustering result heatmap of a Gaussian mixture model in an embodiment of the invention;
FIG. 5 is a flowchart of a method for rapid sorting and recombining a retired lithium ion battery based on electrochemical impedance spectroscopy according to a second embodiment of the present invention;
FIG. 6 is a graph of profile coefficients of parameter combinations corresponding to three aging mode groups of an experimental battery according to a second embodiment of the present invention;
FIG. 7 is a graph comparing HPPC condition test results for 2-pack rebuilt battery packs of the present invention with 2-pack test battery packs.
Detailed Description
In order to make the technical means, creation features, achievement purposes and effects of the present invention easy to understand, the following embodiments and drawings are used to specifically describe the rapid sorting and recombining method for the retired lithium ion battery of the electrochemical impedance spectroscopy.
< example one >
Fig. 1 is a flowchart of a rapid sorting and recombining method for a retired lithium ion battery based on electrochemical impedance spectroscopy in an embodiment of the present invention.
As shown in fig. 1, the rapid sorting and recombining method for the retired lithium ion battery based on the electrochemical impedance spectrum comprises the following steps:
Step S1, performing electrochemical impedance spectrum test (EIS) and relaxation time distribution analysis (DRT) on the retired lithium ion battery to obtain a corresponding impedance curve of the electrochemical impedance spectrum Nyquist diagram and corresponding relaxation time distribution data, and then performing electric impedance spectrum Nyquist diagram test on the retired lithium ion battery and the corresponding relaxation time distribution dataThe ohmic internal resistance R of the retired lithium ion battery is calculated by the intersection point of the impedance curve of the chemical impedance spectrum Nyquist diagram and the horizontal axis O 。
In this embodiment, when performing the relaxation time distribution analysis, the SOC of each aged battery is first adjusted to 20% SOC, then the electrochemical impedance spectrum test is performed for 10 minutes, and finally the relaxation time distribution analysis is performed on the measured electrochemical impedance spectrum. Specifically, the method comprises the following steps:
the physical conditions of the electrochemical impedance spectrum test are that the temperature is 25 ℃, the applied disturbance voltage is 0.01V, and the test frequency range is 0.01Hz to 1000 Hz.
And step S2, based on the pre-trained black box model and the relaxation time distribution data, performing rapid capacity estimation on the retired lithium ion battery to obtain the estimated battery capacity of the retired lithium ion battery.
In this embodiment, the black box model is constructed based on a trained BPNN network, which includes three layers, namely an input layer, a hidden layer, and an output layer: the device comprises an input layer for representing input variables, a hidden layer consisting of a plurality of hidden layers and an output layer for representing output variables.
The BPNN network training steps are as follows:
step S2-1, randomly selecting a small number of batteries to test and analyze the relaxation time distribution to obtain the corresponding standard capacity of the batteries
(i < M) and relaxation time distribution data are used as input variables and sample data of the BPNN, normalization processing is carried out on the input variables and the sample data, and the formula is as follows:
in the formula, x max And x min Respectively, the maximum and minimum values in the BPNN network input vector x.
And step S2-2, establishing an initial BPNN model, and performing random initialization on a connection weight matrix and a bias vector between each layer of the initial BPNN model based on a random function.
In this embodiment, a sigmoid(s) function is used as an excitation function of the BP neural network model.
And step S2-3, setting neurons and learning rate of the hidden layer, inputting the input variables and sample data after normalization processing to the initial BPNN model, and training the BPNN network by using an LM algorithm to obtain a black box model.
In this embodiment, the initial BPNN model is trained. Specifically, the method comprises the following steps:
firstly, initializing a BPNN model, and using a log-sigmoid function as a transfer function for a hidden layer, wherein the formula is as follows:
the input method is as follows:
the ith node of the input layer is x p,i Inputting to the jth node of the hidden layer of the initial BPNN model:
in the formula, w j,i Is the weight of the input layer to the hidden layer, θ j,i Is the bias of the input layer to the hidden layer.
The output of the jth node in the hidden layer is:
the input of the jth node of the output layer is:
in the formula, w k,i 、θ k,i Hidden to output layer weights and offsets, respectively.
The k node output of the output layer is:
O p,k ＝sk(∑ j w k,j x p,k +θ k,j )。
and then carrying out error estimation on the BPNN model, and transmitting an error result of the error estimation from the output layer to the hidden layer. Specifically, the method comprises the following steps:
the output layer error calculation formula is as follows:
in the formula, T k Is the real output.
The error in the hidden layer is calculated as:
the error and deviation are then updated. Specifically, the method comprises the following steps:
the update equation of the weight is as follows:
w k,j ＝w k,j +Δw k,j
w j,i ＝w j,i +Δw j,i
in the formula, α is a learning rate.
The offset update equation is:
θ k,j ＝θ k,j +Δθ k,j
θ j,i ＝θ j,i +Δθ j,i 。
FIG. 2 is a graph of the test error results of the BPNN model in an embodiment of the present invention.
In this embodiment, the black box model is used to calculate and test the battery capacity of the same batch, and the errors of the test results are all lower than 4% as shown in fig. 2, so the trained BPNN model is used as the black box model.
Step S3, extracting impedance characteristic information of the retired lithium ion battery based on the relaxation time distribution data, and comparing the impedance characteristic information with the estimated battery capacity and the ohm internal resistance R O As a six-dimensional recombination criterion, to the gaussian mixture model.
In this embodiment, the impedance characteristic information includes contact impedance R of the retired lithium ion battery c SEI film resistance R SEI Charge exchange resistance R ct And lithium ion diffusion resistance R d 。
And step S4, the Gaussian mixture model performs soft clustering recombination on the retired lithium ion battery to obtain a final soft clustering result, and sorting recombination is completed.
In this embodiment, the soft clustering reorganization includes the following steps:
step S4-1, collecting data and input samples, and carrying out z-score normalization processing on the data and the samples, wherein the formula is as follows:
in the formula (I), the compound is shown in the specification,
the mean value of the raw data, and σ is the standard deviation of the raw data.
The observed data of the gaussian mixture model after normalization processing are as follows:
X＝(x 1 ,x 2 ,...,x M )。
and step S4-2, initializing K multivariate Gaussian distribution parameters by adopting a K-means algorithm.
In this example, the K multivariate Gaussian distribution parameters are measured k Sum Σ k An initialization process is performed.
And S4-3, solving the parameters of the Gaussian mixture model by using the EM optimization algorithm until the Gaussian mixture model iterates for 100 times based on the EM optimization algorithm.
In this embodiment, the EM algorithm expression is:
where the parameter θ is (p, μ, Σ), and through a series of iterations, θ can be obtained (0) ,θ (1) ,...,θ (t) Theta after iteration to a certain number of times (N) I.e. the final result of the iteration.
The joint probability and posterior probability formula is:
p(x,z)＝p(z)p(x|z)＝p z ·N(μ z ,∑ z )
wherein z is a discrete random variable comprising cluster C 1 ,C 2 ,...,C N The probability distribution of (a) is determined,
z is the probability that the corresponding sample X belongs to the gaussian distribution of the corresponding class.
Calculating observation data x according to parameters of the current Gaussian mixture model m Belong to cluster C k Probability of (2), i.e. Q (theta ) (t) )，
Solving a new iteration parameter theta again based on the maximum log-likelihood of the current parameter (t+1) The method comprises the following steps:
step 4-2 and step 4-3 are iterated until the results converge.
Fig. 3 is a graph of maximum likelihood function values for data as a function of iteration number in an embodiment of the invention.
The maximum likelihood function value of the experimental data model operation tends to be stable and convergent after the EM algorithm iterates for about 60 times, and in the embodiment, the number of times of EM algorithm iteration is selected to be 100 based on the experimental data model.
And step S4-4, outputting data of the Gaussian mixture model as a final clustering result obtained based on soft clustering recombination.
In this embodiment, the contour value s (x) and the contour coefficient (hereinafter referred to as SC) are used as indexes for measuring the cohesiveness and the inter-group separability of the clustering result groups i ) And the SC values are defined as follows:
In the formula, a (x) i ) Is x i Average of the degree of dissimilarity of vectors to other points within the same cluster, b (x) i ) Is x i The minimum of the average dissimilarity of the vectors to other clusters.
In this embodiment, the contour value s (x) i ) Has a value range of [ -1,1 [)]. Specifically, the method comprises the following steps:
when s (x) i ) E [ -1,0), b (x) i )＜a(x i ) The grouping is unreasonable, and s (x) i ) When-1, the grouping result is worst;
when s (x) i )∈[0,1]When, b (x) i )＞a(x i ) Reasonable grouping, and s (x) i ) When 1, the grouping results best.
And when the clustering effect of the Gaussian mixture model is judged, the high SC value is preferably used as the optimal model.
In this embodiment, the calculated contour value SC of the final clustering result is 0.278, and the clustering effect is reasonable.
FIG. 4 is a soft clustering result heatmap of a Gaussian mixture model in accordance with an embodiment of the present invention.
As shown in fig. 4, the probability that 17 batteries belong to each cluster in the soft clustering result heat map does not exceed 0.7, and the soft clustering result heat map has the characteristic of soft clustering, and two color ranks representing the grouping probability of the batteries are similar, so that the batteries can be flexibly recombined according to actual conditions.
Example one action and Effect
According to the method for rapidly sorting and recombining the retired lithium ion battery based on the electrochemical impedance spectrum, before sorting and recombining the retired lithium ion battery, the battery capacity of the retired lithium ion battery is rapidly estimated through a pre-trained black box model and by combining with relaxation time distribution data, and then the estimated battery capacity and ohmic internal resistance obtained based on the electrochemical impedance spectrum test are input into a Gaussian mixture model to be subjected to soft clustering to obtain a soft clustering result, so that rapid sorting and recombining of the retired lithium ion battery are achieved. Therefore, the method can shorten the time for estimating the battery capacity from 3 hours to about 10 minutes without adopting a special capacity testing device, is suitable for sorting and recombining large-scale retired lithium ion batteries, and has the advantages of high efficiency and low time consumption compared with the existing capacity testing device. And the efficient battery capacity estimation also greatly improves the sorting and recombining efficiency of the retired lithium ion battery.
< example II >
For convenience of expression, in the second embodiment, the same steps as those in the first embodiment are given the same reference numerals, and the same description is omitted.
In the first embodiment, in step S3, impedance feature information of the retired lithium ion battery is extracted based on the relaxation time distribution data, and the difference of the second embodiment is: in steps S3 'to S4', impedance feature information of the decommissioned lithium ion battery is extracted based on the relaxation time distribution data, and then weight processing is performed on the impedance features.
Fig. 5 is a flowchart of a method for rapidly sorting and recombining a retired lithium ion battery based on electrochemical impedance spectroscopy in an embodiment of the present invention.
As shown in fig. 5, the method for rapidly sorting and recombining the retired lithium ion battery based on the electrochemical impedance spectroscopy provided in this embodiment specifically includes the following steps:
step S1, carrying out electrochemical impedance spectrum test and relaxation time distribution analysis on the retired lithium ion battery to obtain a corresponding impedance curve of an electrochemical impedance spectrum Nyquist diagram and corresponding relaxation time distribution data, and then calculating ohmic internal resistance R of the retired lithium ion battery from the intersection point of the impedance curve of the electrochemical impedance spectrum Nyquist diagram and a horizontal axis O 。
And step S2, based on the pre-trained black box model and the relaxation time distribution data, performing rapid capacity estimation on the retired lithium ion battery to obtain the estimated battery capacity of the retired lithium ion battery.
Step S3', extracting contact resistance R of the retired lithium ion battery based on relaxation time distribution data c SEI film resistance R SEI Charge exchange resistance R ct And lithium ion diffusion resistance R d Impedance characteristic information ofAnd carrying out weight processing.
In this embodiment, the weighting process is performed on the extracted contact resistance R c SEI film resistance R SEI Charge exchange resistance R ct And lithium ion diffusion resistance R d And assigning a weight coefficient, wherein the assignment formula is as follows:
in the formula, X α Is input data of a weighted Gaussian mixture model, alpha i As a weight, when α i The influence of the weight factor can be amplified after the square operation is carried out, so that the clustering effect with the weight is better.
Step S4', weighting the processed characteristic information and the estimated battery capacity and the ohm internal resistance R O As a six-dimensional recombination criterion, to the gaussian mixture model.
And step S5', the Gaussian mixture model carries out soft clustering recombination on the retired lithium ion batteries under different aging scenes to obtain a final soft clustering result, and sorting recombination is completed.
In this embodiment, the retired lithium ion battery in the aging scenario includes three aging modes:
1) CL, i.e. conductivity loss, and ohmic internal resistance R O Contact resistance R C Correlation;
2) LLI, i.e. loss of lithium ions, and SEI film resistance R SEI A charge transfer resistance R ct Correlation;
3) LAM, i.e. loss of active material, and diffusion resistance R d And (4) correlating.
Fig. 6 is a profile coefficient chart of parameter combinations corresponding to three aging mode groups of the experimental battery in the second embodiment of the present invention.
In this embodiment, after the soft clustering result is obtained, the result is subjected to the calculation of the contour value and the contour coefficient, as shown in fig. 6, the value of the calculated contour coefficient is between 0.2 and 0.9, so that the clustering is reasonable.
HPPC working condition test is carried out on the batteries after sorting and recombination to check the consistency of the batteries after sorting and recombination of the retired lithium ion batteries of three aging modes in actual conditions, and the experimental process is as follows:
step a1, setting the full charge (SOC equal to 1) of the retired lithium ion battery as an initial state;
step A2, performing 10s constant current pulse charging on the retired lithium ion battery with 1C multiplying power;
step A3, standing for 30 s;
step A4, performing 10s constant current pulse discharge on the retired lithium ion battery at 1C multiplying power;
Step A5, standing for 30 s;
and step A6, performing constant current discharge on the retired lithium ion battery at the magnification of 1/3C.
In this embodiment, the test is performed once when the SOC of the retired lithium ion battery is 90%, 50%, and 10% (i.e., three SOC intervals of high, medium, and low), and the clustering results of two groups of batteries subjected to soft clustering (i.e., the recombined battery pack C1 and the recombined battery pack C2 in fig. 7) are detected, and two groups of verification experiments are performed on the two groups of batteries, and the clustering effect is evaluated. Specifically, the method comprises the following steps:
and randomly selecting the same number of retired lithium ion batteries to perform HPPC (high Performance Power programmable controller) test under the same condition, and comparing the test with the voltage curve of the battery subjected to soft clustering recombination.
FIG. 7 is a graph comparing HPPC condition test results for 2-pack rebuilt battery packs of the present invention with 2-pack test battery packs.
In this embodiment, the consistency of the voltage responses of the battery group is calculated by using the distance between the response vectors, that is, the average distance a of the voltage responses of the battery group is calculated, and the smaller the value is, the higher the consistency of the battery is, and the formula is as follows:
wherein k represents the total number of cells in the stack; gamma ray i Is the voltage vector of the corresponding battery.
The average distance of the C1 recombined battery pack is 0.0343, the average distance of the second group of the C1 test battery pack is 0.1402, the average distance of the C2 recombined battery pack is 0.0634, and the average distance of the C2 test battery pack is 0.0923, which shows that the average distance between the HPPC test results of the same group of batteries after clustering is smaller than that of the randomly-chosen battery pack, namely the recombined battery pack obtained by the clustering method has better consistency and clustering effect.
Example two actions and effects
Compared with the first embodiment, in the second embodiment, based on the electrochemical impedance spectrum characteristics inside the retired lithium ion battery, a clustering method with different battery aging modes and weights is provided to be applied to rapid sorting and recombination of the retired lithium ion battery under different aging scenes.
The above-described embodiments are merely illustrative of specific embodiments of the present invention, and the present invention is not limited to the description of the above-described embodiments.
Claims (7)
1. A rapid sorting and recombining method of a retired lithium ion battery based on an electrochemical impedance spectrum is used for rapidly sorting and recombining the retired lithium ion battery, and is characterized by comprising the following steps:
step S1, performing electrochemical impedance spectrum testing and relaxation time distribution analysis on the retired lithium ion battery to obtain a corresponding electrochemical impedance spectrum Nyquist diagram impedance curve and corresponding relaxation time distribution data, and then calculating ohmic internal resistance R of the retired lithium ion battery from the intersection point of the electrochemical impedance spectrum Nyquist diagram impedance curve and the horizontal axis O ；
Step S2, based on a pre-trained black box model and the relaxation time distribution data, performing rapid capacity estimation on the retired lithium ion battery to obtain the estimated battery capacity of the retired lithium ion battery;
step S3, extracting based on the relaxation time distribution dataImpedance characteristic information of the retired lithium ion battery, wherein the impedance characteristic information comprises contact impedance R of the retired lithium ion battery c SEI film resistance R SEI Charge exchange resistance R ct And lithium ion diffusion resistance R d And the impedance characteristic information and the estimated battery capacity and the ohm internal resistance R are compared O Inputting the recombination criterion as a six-dimensional recombination criterion into a Gaussian mixture model;
and step S4, the Gaussian mixture model performs soft clustering recombination on the retired lithium ion battery to obtain a final soft clustering result, and the sorting recombination is completed.
2. The method for rapidly sorting and recombining the retired lithium ion battery based on the electrochemical impedance spectroscopy as claimed in claim 1, wherein the method comprises the following steps:
the physical condition of the electrochemical impedance spectrum test is that the temperature is 25 ℃, the applied disturbance voltage is 0.01V, and the test frequency range is 0.01Hz to 1000 Hz.
3. The method for rapidly sorting and recombining the retired lithium ion battery based on the electrochemical impedance spectroscopy as claimed in claim 1, wherein the method comprises the following steps:
And when the relaxation time distribution analysis is carried out, adjusting the SOC of each aged battery to 20% SOC, carrying out the electrochemical impedance spectrum test for 10 minutes, and carrying out the relaxation time distribution analysis on the measured electrochemical impedance spectrum.
4. The method for rapidly sorting and recombining the retired lithium ion battery based on the electrochemical impedance spectroscopy as claimed in claim 1, wherein the method comprises the following steps:
wherein the black box model is constructed based on a trained BPNN network, the BPNN network comprises an input layer, a hidden layer and an output layer,
the pre-training steps are as follows:
step S2-1, randomly selecting a small number of batteries for testing and analyzing the relaxation time distribution to obtain the corresponding standard capacity of the batteries
And taking the relaxation time distribution data as an input variable and sample data of the BPNN, and carrying out normalization processing on the input variable and the sample data, wherein the formula is as follows:
in the formula, x max And x min Respectively inputting the maximum value and the minimum value in the vector x of the BPNN network;
step S2-2, establishing an initial BPNN model, and performing random initialization on a connection weight matrix and a bias vector between each layer of the initial BPNN model based on a random function;
and S2-3, setting the neurons and the learning rate of the hidden layer, inputting the input variables and the sample data after normalization processing into the initial BPNN model, and training the BPNN network by using an LM algorithm to obtain the black box model.
5. The method for rapidly sorting and recombining the retired lithium ion battery based on the electrochemical impedance spectroscopy according to claim 1, characterized in that:
wherein the soft cluster reorganization comprises the following steps:
step S4-1, collecting data and input samples, and carrying out z-score normalization processing on the data and the samples, wherein the formula is as follows:
in the formula (I), the compound is shown in the specification,
the mean value of the original data is, and sigma is the standard deviation of the original data;
s4-2, initializing K multivariate Gaussian distribution parameters by adopting a K-means algorithm;
s4-3, solving parameters of the Gaussian mixture model by using an EM (effective minimum) optimization algorithm until the Gaussian mixture model iterates for 100 times based on the EM optimization algorithm;
and step S4-4, outputting the data of the Gaussian mixture model as a final clustering result obtained based on the soft clustering recombination.
6. A rapid sorting and recombining method of a retired lithium ion battery based on an electrochemical impedance spectrum is used for rapidly sorting and recombining the retired lithium ion battery under different aging scenes, and is characterized by comprising the following steps:
step S1, performing electrochemical impedance spectrum testing and relaxation time distribution analysis on the retired lithium ion battery to obtain a corresponding electrochemical impedance spectrum Nyquist diagram impedance curve and corresponding relaxation time distribution data, and then calculating ohmic internal resistance R of the retired lithium ion battery from the intersection point of the electrochemical impedance spectrum Nyquist diagram impedance curve and the horizontal axis O ；
Step S2, based on a pre-trained black box model and the relaxation time distribution data, performing rapid capacity estimation on the retired lithium ion battery to obtain the estimated battery capacity of the retired lithium ion battery;
step S3', extracting contact resistance R of the retired lithium ion battery based on the relaxation time distribution data c SEI film resistance R SEI Charge exchange resistance R ct And lithium ion diffusion resistance R d Carrying out weight processing on the impedance characteristic information;
step S4', the characteristic information processed by the weight and the estimated battery capacity and the ohm internal resistance R O Inputting the recombination criterion as a six-dimensional recombination criterion into a Gaussian mixture model;
and step S5', the Gaussian mixture model performs soft clustering recombination on the retired lithium ion battery under different aging scenes to obtain a final soft clustering result, and the sorting recombination is completed.
7. The method for rapidly sorting and recombining the retired lithium ion battery based on the electrochemical impedance spectroscopy as claimed in claim 6, wherein the method comprises the following steps:
wherein the weight is processed to the contact resistance R c The SEI film resistance R SEI The charge exchange resistance R ct And the lithium ion diffusion resistance R d The assignment of the weight coefficients is performed,
the assignment formula is:
in the formula, X α Is input data of a weighted Gaussian mixture model, alpha i Are weights.
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