Lithium ion battery performance rapid evaluation method based on time domain impedance spectroscopy

Abstract

The invention relates to the technical field of battery performance evaluation, in particular to a lithium ion battery performance rapid evaluation method based on time-domain impedance spectroscopy, which comprises the following steps: acquiring current, voltage and impedance amplitude in the charging and discharging process of a lithium battery; the method comprises the following steps of: performing modal decomposition on impedance amplitude in a signal period, acquiring modal interference complexity according to data fluctuation conditions of each modal component and relevance between current data and voltage data, further calculating importance weight, obtaining impedance singularity by combining data abrupt change degrees in each modal component, and calculating the impedance singularity according to the impedance singularity. And analyzing the change difference of the impedance singularity, obtaining the impedance fault recognition degree of each signal period, obtaining an impedance fault evaluation value at the current moment by combining the fluctuation degree and the average level of the impedance amplitude, and performing fault evaluation on the lithium battery by combining a preset fault evaluation threshold. The lithium battery performance evaluation precision can be improved, and the fault problem in the charging and discharging process of the lithium battery can be accurately evaluated.

Description

Technical Field

[0001] This application relates to the technical field of battery performance evaluation, and particularly to a method for rapidly evaluating the performance of lithium-ion batteries based on time-domain impedance spectroscopy. Background Art

[0002] Lithium-ion batteries are also known as lithium batteries. The charge-discharge cycle of lithium batteries is a basic operation during the use of lithium batteries. The charge-discharge cycle of lithium batteries will cause capacity attenuation, internal resistance change, and change in cycle life, and it is easy to cause charge-discharge failures of lithium batteries. For example, the thermal runaway failure of lithium batteries. Since the internal temperature of lithium batteries will rise rapidly during use, and the temperature change rate during the thermal runaway process varies greatly in different stages, it will cause serious damage to the inside of lithium batteries, thus affecting the charge-discharge performance of lithium batteries. Therefore, it is necessary to predict the charge-discharge failures of lithium batteries and evaluate the performance of lithium batteries through the results of the failure prediction, so as to achieve the health management of lithium batteries and optimize the charge-discharge performance of lithium batteries.

[0003] Currently, the charge-discharge performance failures of lithium batteries can be predicted through electrochemical impedance spectroscopy (EIS). However, the measurement speed of electrochemical impedance spectroscopy is slow, and there is no mature technology that can accurately predict the charge-discharge failures of lithium batteries. At the same time, since the failure states of lithium battery charge and discharge are difficult to directly measure, the existing lithium battery failure detection is usually indirectly achieved through parameters such as voltage and current. However, due to the complex interference of influencing factors in the external environment on electrical parameters such as current and voltage during the charge-discharge process of lithium batteries, such as environmental temperature and humidity interference, external electromagnetic interference and other influencing factors, the existing technology cannot accurately measure the failure information during the charge-discharge process of lithium batteries, and thus cannot accurately predict the charge-discharge failures inside lithium batteries, resulting in poor accuracy in evaluating the performance of lithium batteries, and thus unable to effectively achieve the health management of lithium batteries. Summary of the Invention

[0004] In order to solve the above technical problems, this application provides a method for rapidly evaluating the performance of lithium-ion batteries based on time-domain impedance spectroscopy to solve the existing problems.

[0005] The method for rapidly evaluating the performance of lithium-ion batteries based on time-domain impedance spectroscopy in this application adopts the following technical solutions:

[0006] An embodiment of this application provides a method for rapidly evaluating the performance of lithium-ion batteries based on time-domain impedance spectroscopy, including the following steps:

[0007] Obtain the current, voltage, and impedance amplitude during the charge-discharge process of the lithium battery;

[0008] Perform frequency-domain analysis on the impedance amplitude to obtain the signal periods during the charge and discharge process of the lithium battery. Decompose the impedance amplitude within the signal period using modal decomposition. Based on the data fluctuations of each modal component and in combination with the correlation between each modal component and the current data and voltage data respectively, obtain the modal interference complexity of each modal component of the impedance amplitude within the signal period.

[0009] Use the modal interference complexity to determine the importance weights of each modal component of the impedance amplitude within the signal period. Combine the degree of data mutation in each modal component to obtain the impedance singularity of the impedance amplitude within the signal period, and analyze the difference in impedance singularity between each signal period and its adjacent signal periods to obtain the impedance fault recognition degree of each signal period.

[0010] Based on the impedance fault recognition degree, the fluctuation degree, and the average level of the impedance amplitude, and in combination with a neural network, obtain the impedance fault evaluation value at the current moment. Combine the preset fault evaluation threshold to evaluate the faults of the lithium battery.

[0011] Preferably, the method for obtaining each signal period during the charge and discharge process of the lithium battery is as follows:

[0012] Perform frequency-domain transformation on the impedance amplitude within a single acquisition duration to obtain the corresponding frequency spectrum diagram. Take the reciprocal of the frequency corresponding to the maximum amplitude in the frequency spectrum diagram as the signal period during the charge and discharge process of the lithium battery.

[0013] Preferably, the calculation method for the modal interference complexity of each modal component of the impedance amplitude within the signal period is as follows:

[0014] D t,j =α t,j ×exp[-(RI t,j +RU t,j )]; In the formula, D t,j , α t,j are respectively the modal interference complexity and the information entropy of the jth modal component of the impedance amplitude within the tth signal period. exp() is the exponential function with the natural constant as the base. RI t,j is the absolute value of the correlation between the jth modal component of the impedance amplitude within the tth signal period and the current vector, and RU t,j is the absolute value of the correlation between the jth modal component of the impedance amplitude within the tth signal period and the voltage vector. Among them, the current values and voltage values within each signal period are arranged in chronological order to form the current vector and voltage vector of each signal period.

[0015] Preferably, the correlation between the modal component and the current vector is the covariance between the modal component and the current vector, and the correlation between the modal component and the voltage vector is the covariance between the modal component and the voltage vector.

[0016] Preferably, the calculation method of the importance weight of each modal component of the impedance amplitude within the signal period is as follows:

[0017] β t,j = 1 - norm(D t,j ); In the formula, β t,j , D t,j are respectively the importance weight and the modal interference complexity of the j-th modal component of the impedance amplitude within the t-th signal period, and norm() is a normalization function.

[0018] Preferably, the calculation method of the impedance singularity of the impedance amplitude within the signal period is as follows:

[0019] In the formula, F t,j , M are respectively the impedance singularity and the number of modal components of the impedance amplitude within the t-th signal period, s t,j and x t,j are respectively the first dispersion and the first mean value of the first-order difference vector of the j-th modal component of the impedance amplitude within the t-th signal period. Among them, the standard deviation of all elements within the first-order difference vector of the j-th modal component is used as the first dispersion, and the mean value of the absolute values of all elements within the first-order difference vector is used as the first mean value.

[0020] Preferably, the method for obtaining the impedance fault recognition degree of each signal period is as follows:

[0021] Take multiple signal periods closest to each signal period as the adjacent signal periods of each signal period, calculate the average value of the absolute values of the differences in impedance singularity between each signal period and its adjacent signal periods, and the product of this average value and the impedance singularity of each signal period is used as the impedance fault recognition degree of each signal period.

[0022] Preferably, the further obtaining of the impedance fault evaluation value includes:

[0023] Arrange the normalized values of the variance of the impedance amplitude and the normalized values of the mean value of the impedance amplitude in each signal period within a preset first time period in chronological order to form a sequence, which is used as the training sample of the neural network. Arrange the normalized values of the impedance fault recognition degrees of all signal periods in chronological order to form a sequence, which is used as the label of the training sample, and train the neural network;

[0024] Arrange the normalized values of the variance of the impedance amplitude and the normalized values of the mean value of the impedance amplitude in each signal period within a preset second time period before the current moment in chronological order to form a sequence, input it into the trained neural network, obtain the impedance fault prediction values of all signal periods within the preset second time period before the current moment, and calculate the impedance fault evaluation value at the current moment.

[0025] Preferably, the impedance fault evaluation value at the current moment is the normalized result of the average value of the impedance fault prediction values of all signal cycles within a preset second duration before the current moment.

[0026] Preferably, the further fault evaluation of the lithium battery includes: if the impedance fault evaluation value at the current moment is greater than a preset fault evaluation threshold, it indicates that the lithium battery has a fault problem at the current moment; otherwise, the lithium battery has no fault problem at the current moment.

[0027] The present application has at least the following beneficial effects:

[0028] In order to eliminate the interference of external influencing factors on the fault information in the time-domain spectrogram, the present application analyzes the characteristics of each modal component in the time-domain spectrogram by means of modal decomposition, and measures according to the complexity interference characteristics in each modal component, which is beneficial to more accurately measure the singularity of the impedance during the charge and discharge process of the lithium battery in the follow-up, and improves the accuracy of measuring the fault information during the charge and discharge process of the lithium battery;

[0029] During the process of measuring the impedance singularity characteristics of the lithium battery, the importance weight of the modal component is set according to the complexity interference characteristics in each modal component, and the weighted summation method is used to eliminate the interference of external influencing factors on the fault information, making the measurement of the impedance singularity more accurate and improving the accuracy of predicting the charge and discharge faults inside the lithium battery;

[0030] Based on the impedance singularity degree and its change characteristics after eliminating the external interference, the impedance fault recognition degree is analyzed more accurately, eliminating the adverse interference caused by external interference factors, being able to more accurately predict the charge and discharge faults inside the lithium battery, improving the accuracy of evaluating the performance of the lithium battery, and effectively realizing the health management of the lithium battery at the same time;

[0031] According to the analysis results of the impedance fault recognition degree, the neural network model is trained and the fault is predicted, and then the charge and discharge performance of the lithium battery is quickly evaluated to realize the health management of the lithium battery. Since the present application eliminates the interference of external interference on the impedance fault in the impedance amplitude vector, it can more accurately measure the fault information during the charge and discharge process of the lithium battery, and thus more accurately predict the charge and discharge faults inside the lithium battery, improving the accuracy of evaluating the performance of the lithium battery, and thus more effectively realizing the health management of the lithium battery. Description of the Drawings

[0032] To more clearly illustrate the technical solutions and advantages in the embodiments of the present application or the prior art, the following will briefly introduce the drawings required for the description of the embodiments or the prior art. Obviously, the drawings in the following description are only some embodiments of the present application. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0033] Figure 1 It is a flowchart of the steps of the method for rapidly evaluating the performance of a lithium-ion battery based on time-domain impedance spectroscopy provided by the present application. Detailed implementation manners

[0034] To further elaborate on the technical means and effects adopted by the present application to achieve the intended invention purpose, the following will, in conjunction with the drawings and preferred embodiments, detail the specific implementation manners, structures, features, and effects of the method for rapidly evaluating the performance of a lithium-ion battery based on time-domain impedance spectroscopy proposed according to the present application. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. In addition, the specific features, structures, or characteristics in one or more embodiments can be combined in any suitable form.

[0035] Unless otherwise defined, terms such as "including", "comprising", or any other variant thereof are intended to cover non-exclusive inclusion, such that a circuit structure, article, or device including a series of elements not only includes those elements but also includes other elements not explicitly listed, or further includes elements inherent to such article or device. Without further limitation, an element defined by the statement "including one..." does not exclude the existence of additional identical elements in the article or device including the said element. Additionally, the term "and / or" used herein includes any and all combinations of one or more of the related listed items. All technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the technical field to which the present application belongs.

[0036] The following will specifically describe the specific solution of the method for rapidly evaluating the performance of a lithium-ion battery based on time-domain impedance spectroscopy provided by the present application in conjunction with the drawings.

[0037] The method for rapidly evaluating the performance of a lithium-ion battery based on time-domain impedance spectroscopy provided by an embodiment of the present application, specifically, please refer to Figure 1 , includes the following steps:

[0038] Step 1: Obtain the current, voltage, and impedance amplitude during the charge and discharge process of the lithium battery.

[0039] In the process of fault prediction for the charge and discharge state of a lithium-ion battery, in this embodiment, an impedance analyzer is used to measure the current value, voltage value, and impedance amplitude during the charge and discharge process of the lithium battery, and the sampling frequency is 100 Hz in all cases. The implementer can adaptively set the sampling frequency according to the actual situation.

[0040] Thus, according to the above process of this embodiment, the data during the charge and discharge process of the lithium battery can be collected.

[0041] Step 2: Perform frequency-domain analysis on the impedance amplitude to obtain each signal period during the charge and discharge process of the lithium battery. Decompose the impedance amplitude within the signal period by mode, and based on the data fluctuation of each mode component and in combination with the correlation between each mode component and the current data and voltage data respectively, obtain the mode interference complexity of each mode component of the impedance amplitude within the signal period.

[0042] Generally, during the charge and discharge process of a lithium battery, electrical parameters such as current, voltage, and impedance amplitude are easily affected by complex external factors, which will affect the accuracy of extracting fault information during the charge and discharge process of the lithium battery. As a result, it is impossible to accurately predict the fault state of the charge and discharge of the lithium battery, leading to poor accuracy in evaluating the performance of the lithium battery and being unable to effectively achieve the health management of the lithium battery. Therefore, in order to more accurately evaluate the performance of the lithium battery, it is necessary to accurately measure the fault information on the impedance amplitude vector, so as to more effectively predict the fault state of the charge and discharge of the lithium battery and then achieve health management.

[0043] Extract the impedance amplitude within a single acquisition duration during the charge and discharge process of the lithium battery, input the impedance amplitude within a single acquisition duration into the Fourier transform, and use the discrete Fourier transform to obtain the frequency spectrum diagram of the impedance amplitude vector. Take the reciprocal of the frequency corresponding to the maximum amplitude value in the frequency spectrum diagram as the signal period during the charge and discharge process of the lithium battery. Among them, the Fourier transform can be the DFT discrete Fourier transform (Discrete Fourier Transform) or the FFT fast Fourier transform (fast Fourier transform). The Fourier transform is a well-known technology and will not be elaborated further. In this embodiment, the single acquisition duration is 10 minutes.

[0044] At the same time, obtain the current value, voltage value, and impedance amplitude within each signal period during the charge and discharge process of the lithium battery. In this implementation, for the convenience of understanding and expression, the current value, voltage value, and impedance amplitude within each signal period during the charge and discharge process of the lithium battery are respectively arranged in chronological order to form vectors, which are used as the current vector, voltage vector, and impedance amplitude vector of each signal period during the charge and discharge process of the lithium battery. The current vector, voltage vector, and impedance amplitude vector of each signal period respectively reflect the change characteristics of the current, voltage, and impedance amplitude within the corresponding signal period.

[0045] When the charge and discharge state is normal, there is a strong correlation among the current, voltage, and resistance impedance in the internal circuit of the lithium battery. However, due to the complex interference of external environmental influencing factors, interference modal components will be generated in the changes of the current, voltage, and impedance amplitude, causing a certain change in the correlation among the current, voltage, and resistance impedance. To eliminate the interference of external influencing factors on the fault information in the time-domain frequency spectrum diagram, it is necessary to analyze the modal component characteristics of the impedance amplitude vector.

[0046] Taking the t-th signal period as an example, the impedance amplitude vector of the t-th signal period is respectively input into the modal decomposition algorithm. After modal decomposition, each modal component of the impedance amplitude within the t-th signal period is obtained. The modal decomposition algorithm can be the EMD modal decomposition algorithm (Empirical Mode Decomposition) or the VMD modal decomposition algorithm (Variational Mode Decomposition). This embodiment does not make specific limitations. In this embodiment, the EMD modal decomposition algorithm is selected for modal decomposition. The EMD modal decomposition algorithm uses an iterative convergence method to respectively obtain each modal component corresponding to the impedance amplitude vector. The modal decomposition algorithm is a well-known technology and will not be elaborated further.

[0047] If the correlation between the modal components of the impedance amplitude vector of the signal period and the signal period current vector and voltage vector is worse, it indicates that the modal component can less reflect the correlation between the resistance impedance and the current and voltage. Therefore, this modal component is more likely to be generated by the interference of external influencing factors. At the same time, if the degree of chaos of the data within the modal component of the impedance amplitude vector is higher, it can better reflect the complexity of the interference of external influencing factors.

[0048] Through the above analysis, for each signal period, calculate the modal interference complexity of each modal component of the impedance amplitude within the signal period. In this embodiment, the specific calculation formula is:

[0049] D t,j =α t,j ×exp[-(RI t,j +RU t,j )];

[0050] In the formula, D t,j is the modal interference complexity of the j-th modal component of the impedance amplitude within the t-th signal period, β t,j is the information entropy of the j-th modal component of the impedance amplitude within the t-th signal period, exp() is the exponential function with the natural constant as the base, RI t,j is the absolute value of the correlation degree between the j-th modal component of the impedance amplitude within the t-th signal period and the current vector, RUt,j It is the absolute value of the correlation between the j-th modal component of the impedance amplitude and the voltage vector within the t-th signal period.

[0051] Among them, the measurement method of the correlation can be the Pearson correlation coefficient or covariance. In this embodiment, covariance is used to measure the correlation, and the specific calculation process of covariance is a well-known technology and will not be elaborated further.

[0052] The complexity of modal interference can reflect the complexity of the impedance amplitude vector being interfered by external influencing factors. If the complexity of modal interference on the modal components in the impedance amplitude vector is higher, it is more difficult to accurately reflect the fault information of the lithium battery charge and discharge state. Therefore, when measuring the singularity of the impedance during the lithium battery charge and discharge process, a smaller importance weight should be set to eliminate the interference of external influencing factors on the fault information in the time-domain frequency spectrum diagram.

[0053] Step 3: Use the modal interference complexity to determine the importance weights of the modal components of the impedance amplitude within the signal period, combine the degree of data mutation in each modal component to obtain the impedance singularity of the impedance amplitude within the signal period, and analyze the differences in impedance singularity between each signal period and adjacent signal periods to obtain the impedance fault recognition degree of each signal period.

[0054] Furthermore, calculate the first-order difference vectors of the modal components of the impedance amplitude vector for each signal period. The element magnitudes within the first-order difference vectors can reflect the mutation characteristics and singularity characteristics on the modal components of the impedance amplitude vector. If the degree of mutation on the modal components is higher and the degree of singularity is greater, to a certain extent, it can reflect the abnormal characteristics on the impedance amplitude vector, thereby reflecting the impedance fault information of the lithium battery charge and discharge state.

[0055] In this embodiment, first, use the modal interference complexity to determine the importance weights of the modal components of the impedance amplitude within the signal period. The calculation method in this embodiment is as follows:

[0056] β t,j = 1 - norm(D t,j )

[0057] In the formula, β t,j is the importance weight of the j-th modal component of the impedance amplitude within the t-th signal period, and norm() is the normalization function.

[0058] Further, in order to accurately measure the impedance fault information of the charge and discharge state of the lithium battery, the dispersion degree of all elements in the first-order difference vector of the j-th modal component is calculated, denoted as the first dispersion degree. Among them, the dispersion degree can be variance or standard deviation. In this embodiment, the standard deviation is used to measure the dispersion degree. The greater the dispersion degree, the greater the singularity degree on the modal component of the impedance amplitude vector, and at this time, the fault information of the impedance amplitude vector can be more reflected. Further, the mean value of the absolute values of all elements in the first-order difference vector of the j-th modal component is calculated, denoted as the first mean value. The greater the mean value, to a certain extent, the greater the mutation degree on the modal component of the impedance amplitude vector. Therefore, the mutation and fluctuation degree of the data in the modal component are characterized by the dispersion degree of the elements in the first-order difference vector of the modal component and the mean value of the absolute values of the elements.

[0059] Therefore, according to the importance weight, combined with the dispersion degree of the elements in the first-order difference vector of the modal component and the mean value of the absolute values of the elements, the impedance singularity of the impedance amplitude in each signal period is calculated. The calculation method in this embodiment is:

[0060]

[0061] In the formula, F t,j is the impedance singularity of the impedance amplitude in the t-th signal period, M is the number of modal components of the impedance amplitude in the t-th signal period, s t,j and x t,j are respectively the first dispersion degree and the first mean value of the first-order difference vector of the j-th modal component of the impedance amplitude in the t-th signal period.

[0062] Among them, the normalization function can be an exponential normalization function or a range normalization function. In this embodiment, the range normalization function is used for normalization processing.

[0063] The impedance singularity reflects the singular change of the impedance during the charge and discharge process of the lithium battery. At this time, the error caused by external influencing factors is eliminated, and the impedance change singularity can be used to more accurately measure the internal abnormal faults during the charge and discharge process of the lithium battery.

[0064] When the performance of the lithium battery is excellent, the change of the impedance singularity in adjacent signal periods is small, which reflects the characteristic of the stable change of the internal impedance of the lithium battery. However, if the impedance singularity in adjacent signal periods changes greatly, and at this time the impedance singularity degree is relatively high, it indicates that the internal impedance of the lithium battery has a drastic singular change, and at this time, the impedance abnormal fault of the charge and discharge state of the lithium battery can be more accurately reflected.

[0065] Therefore, the n signal cycles closest to each signal cycle are denoted as the n adjacent signal cycles of each signal cycle. In this embodiment, n is set to 10, and the implementer can set the value according to the actual situation.

[0066] Further, calculate the average value of the absolute value of the difference in impedance singularity between each signal cycle and all its adjacent signal cycles. The product of this average value and the impedance singularity of each signal cycle is used as the impedance fault recognition degree of each signal cycle.

[0067] The impedance fault recognition degree reflects the characteristics of impedance faults during the charge and discharge process of the lithium battery, eliminates the adverse interference caused by external interference factors, can more accurately predict the charge and discharge faults inside the lithium battery, improves the accuracy of evaluating the performance of the lithium battery, and effectively realizes the health management of the lithium battery.

[0068] Step 4: Obtain the impedance fault evaluation value at the current moment through the impedance fault recognition degree, the fluctuation degree and the average level of the impedance amplitude, and combine it with the preset fault evaluation threshold to evaluate the faults of the lithium battery.

[0069] During the charge and discharge process of the lithium battery, the impedance amplitude vector and the impedance fault recognition degree of all signal cycles within a preset first time period are obtained. The sequence formed by arranging the normalized values of the impedance amplitude variances and the normalized values of the mean values of the impedance amplitudes in each signal cycle within the preset first time period in chronological order is used as the training sample of the neural network. The sequence formed by arranging the normalized values of the impedance fault recognition degrees of all signal cycles within the preset first time period in chronological order is used as the label of the training sample. In this embodiment, the preset first time period is 1 hour.

[0070] The training sample and the label of the training sample are input into the LSTM neural network model for training. Among them, the activation function uses the ReLU function, the optimizer uses the Adam optimizer, and the loss function uses the mean square error function. In this embodiment, for the convenience of understanding and expression, the trained LSTM neural network model is denoted as the fault prediction model.

[0071] Further, in this embodiment, impedance amplitude vectors of all signal cycles within a preset second time period before the current moment are obtained. A sequence formed by arranging the normalized values of the variance and mean of the impedance amplitudes within the impedance amplitude vectors of all signal cycles within 1 minute before the current moment in chronological order is denoted as two impedance spectrum feature sequences. The two impedance spectrum feature sequences are input into a fault prediction model, and the fault prediction model outputs impedance fault prediction values of all signal cycles within the preset second time period before the current moment, completing the fault prediction during the charge and discharge process of the lithium battery. In this embodiment, the preset second time period is 1 minute.

[0072] It should be noted that the specific processes of training and prediction of the neural network model are both well-known technologies and will not be elaborated in this embodiment.

[0073] Further, based on the impedance fault prediction values of all signal cycles within the preset second time period before the current moment, the impedance fault evaluation value at the current moment is calculated. Specifically, in this embodiment, the normalized result of the average value of the impedance fault prediction values of all signal cycles within the preset second time period before the current moment is used as the impedance fault evaluation value at the current moment.

[0074] Further, a preset fault evaluation threshold is set. In this embodiment, the fault evaluation threshold is set to 0.5. If the impedance fault evaluation value at the current moment is greater than the fault evaluation threshold, it indicates that the charge and discharge performance of the lithium battery at the current moment is poor. At this time, the lithium battery has a fault problem, and corresponding diagnosis should be carried out to take preventive maintenance measures to achieve the health management of the lithium battery; otherwise, it indicates that the charge and discharge performance of the lithium battery at the current moment is good and no fault problem occurs, and the lithium battery can continue to be charged and discharged, which can avoid overcharging or over-discharging problems during the charge and discharge process of the lithium battery and more effectively achieve the health management of the lithium battery.

[0075] It can be understood that referring to "one embodiment" or "some embodiments" described in the specification of this application means that specific features, structures, or characteristics described in combination with this embodiment are included in one or more embodiments of this application. Thus, when "in one embodiment", "in some embodiments", "in other some embodiments", "in still other embodiments", etc. appear in different places in this specification, they do not necessarily refer to the same embodiment, but mean "one or more but not all embodiments", unless otherwise specifically emphasized in other ways. The terms "comprising", "including", "having" and their variants all mean "including but not limited to", unless otherwise specifically emphasized in other ways.

[0076] It should be noted that the above order of the embodiments of the present application is only for description and does not represent the superiority or inferiority of the embodiments. In addition, the above specific embodiments of the present specification have been described. Moreover, the processes depicted in the drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous. At the same time, the magnitude of the serial numbers of the steps in the embodiments does not mean the sequence of execution. The execution sequence of each process should be determined by its function and internal logic, and should not constitute any limitation to the implementation process of the embodiments in this specification.

[0077] The above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them; although the present application has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions recorded in the foregoing embodiments, or perform equivalent replacements for some of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the scope of the technical solutions of the embodiments of the present application, and should all be included in the protection scope of the present application.

Claims

1. A rapid evaluation method for the performance of lithium-ion batteries based on time-domain impedance spectroscopy, characterized in that Including the following steps: Obtain the current, voltage, and impedance amplitude during the charge and discharge process of the lithium battery; Perform frequency-domain analysis on the impedance amplitude to obtain each signal period during the charge and discharge process of the lithium battery. Perform modal decomposition on the impedance amplitude within the signal period. According to the data fluctuation conditions of each modal component and in combination with the correlation between each modal component and the current data and voltage data respectively, obtain the modal interference complexity of each modal component of the impedance amplitude within the signal period; Use the modal interference complexity to determine the importance weight of each modal component of the impedance amplitude within the signal period. Combine the degree of data mutation in each modal component to obtain the impedance singularity of the impedance amplitude within the signal period. Analyze the difference in impedance singularity between each signal period and its adjacent signal periods to obtain the impedance fault recognition degree of each signal period; Through the impedance fault recognition degree, the fluctuation degree and average level of the impedance amplitude, and in combination with the neural network, obtain the impedance fault evaluation value at the current moment. Combine the preset fault evaluation threshold to evaluate the fault of the lithium battery.

2. The rapid evaluation method for the performance of a lithium-ion battery based on time-domain impedance spectroscopy according to claim 1, wherein The method for obtaining each signal period during the charge and discharge process of the lithium battery is as follows: Perform frequency-domain transformation on the impedance amplitude within a single acquisition duration to obtain the corresponding frequency spectrum diagram. Take the reciprocal of the frequency corresponding to the maximum amplitude in the frequency spectrum diagram as the signal period during the charge and discharge process of the lithium battery.

3. The rapid evaluation method for the performance of lithium-ion batteries based on time-domain impedance spectroscopy according to claim 1, wherein The calculation method for the modal interference complexity of each modal component of the impedance amplitude within the signal period is as follows: D t,j = α t,j × exp[-(RI t,j + RU t,j )]; where D t,j , α t,j are respectively the modal interference complexity and information entropy of the j-th modal component of the impedance amplitude within the t-th signal period, exp() is the exponential function with the natural constant as the base, RI t,j is the absolute value of the correlation between the j-th modal component of the impedance amplitude and the current vector within the t-th signal period, RU t,j is the absolute value of the correlation between the j-th modal component of the impedance amplitude and the voltage vector within the t-th signal period, where the current values and voltage values within each signal period are respectively arranged in chronological order to form the current vector and voltage vector of each signal period.

4. The rapid evaluation method for the performance of a lithium-ion battery based on time-domain impedance spectroscopy according to claim 3, characterized in that, The correlation degree between the modal component and the current vector is the covariance between the modal component and the current vector, and the correlation degree between the modal component and the voltage vector is the covariance between the modal component and the voltage vector.

5. The rapid evaluation method for the performance of a lithium-ion battery based on time-domain impedance spectroscopy according to claim 1, wherein, The calculation method for the importance weight of each modal component of the impedance amplitude within the signal period is as follows: β t,j = 1 - norm(D t,j ); where β t,j , D t,j are the importance weight and modal interference complexity of the j-th modal component of the impedance amplitude in the t-th signal period respectively, and norm() is the normalization function.

6. The rapid evaluation method for the performance of a lithium-ion battery based on time-domain impedance spectroscopy according to claim 1, wherein The calculation method for the impedance singularity of the impedance amplitude within the signal period is as follows: In the formula, F t,j , M are respectively the impedance singularity degree of the impedance amplitude and the number of modal components in the t-th signal period, s t,j and x t,j are respectively the first dispersion and the first mean of the first-order difference vector of the j-th modal component of the impedance amplitude in the t-th signal period. Among them, the standard deviation of all elements in the first-order difference vector of the j-th modal component is used as the first dispersion, and the mean value of the absolute values of all elements in the first-order difference vector is used as the first mean.

7. The rapid evaluation method for the performance of lithium-ion batteries based on time-domain impedance spectroscopy according to claim 1, characterized in that The method for obtaining the impedance fault recognition degree of each signal period is as follows: Take multiple signal periods closest to each signal period as the adjacent signal periods of each signal period. Calculate the average value of the absolute value of the difference in impedance singularity between each signal period and its adjacent signal periods. The product of this average value and the impedance singularity of each signal period is used as the impedance fault recognition degree of each signal period.

8. The rapid evaluation method for the performance of lithium-ion batteries based on time-domain impedance spectroscopy according to claim 1, wherein The obtaining of the impedance fault evaluation value further includes: Arrange the normalized values of the variance of the impedance amplitude and the normalized values of the mean of the impedance amplitude in each signal period within the preset first duration in chronological order to form a sequence, which is used as the training sample of the neural network. Arrange the normalized values of the impedance fault recognition degrees of all signal periods in chronological order to form a sequence, which is used as the label of the training sample to train the neural network; Input the sequence formed by arranging the normalized values of the variance of the impedance amplitude and the normalized values of the mean of the impedance amplitude in each signal period within the preset second duration before the current moment in chronological order into the trained neural network to obtain the impedance fault prediction values of all signal periods within the preset second duration before the current moment, so as to calculate the impedance fault evaluation value at the current moment.

9. The rapid evaluation method for the performance of a lithium-ion battery based on time-domain impedance spectroscopy according to claim 8, characterized in that The impedance fault evaluation value at the current moment is the normalized result of the average value of the impedance fault prediction values of all signal periods within the preset second duration before the current moment.

10. The rapid evaluation method for the performance of a lithium-ion battery based on time-domain impedance spectroscopy according to claim 1, characterized in that, The fault assessment of the lithium battery further includes: if the impedance fault assessment value at the current moment is greater than the preset fault assessment threshold, it indicates that the lithium battery has a fault problem at the current moment; on the contrary, the lithium battery does not have a fault problem at the current moment.

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