A rapid performance evaluation method for lithium-ion batteries based on time-domain impedance spectroscopy

Abstract

This application relates to the field of battery performance evaluation technology, specifically to a rapid performance evaluation method for lithium-ion batteries based on time-domain impedance spectroscopy. The method includes: acquiring the current, voltage, and impedance amplitudes during the charging and discharging process of the lithium battery; performing mode decomposition on the impedance amplitude within a signal period; obtaining the mode interference complexity based on the data fluctuations of each mode component and its correlation with current and voltage data; calculating the importance weights; obtaining the impedance singularity by combining the degree of data abrupt changes in each mode component; analyzing the differences in impedance singularity changes; obtaining the impedance fault identification degree for each signal period; obtaining the impedance fault assessment value at the current moment by combining the fluctuation degree and average level of the impedance amplitude; and performing fault assessment on the lithium battery by combining a preset fault assessment threshold. This application can improve the accuracy of lithium battery performance evaluation and accurately assess fault problems during the charging and discharging process of lithium batteries.

Description

Technical Field

[0001] This application relates to the field of battery performance evaluation technology, specifically to a rapid performance evaluation method for lithium-ion batteries based on time-domain impedance spectroscopy. Background Technology

[0002] Lithium-ion batteries, also known as lithium batteries, require charge-discharge cycles as a fundamental operation during use. These cycles cause capacity decay, changes in internal resistance, and variations in cycle life, making lithium batteries susceptible to charge-discharge failures, such as thermal runaway. During use, the internal temperature of a lithium battery rises rapidly, and the rate of temperature change during thermal runaway varies significantly at different stages, causing severe damage to the battery's internal structure and affecting its charge-discharge performance. Therefore, it is necessary to predict lithium battery charge-discharge failures and evaluate battery performance based on the prediction results, thereby achieving healthy battery management and optimizing charge-discharge performance.

[0003] Currently, electrochemical impedance spectroscopy (EIS) can be used to predict faults in the charge and discharge performance of lithium batteries. However, EIS measurement is slow, and there is no mature technology that can accurately predict lithium battery charge and discharge faults. Furthermore, since the fault state of lithium batteries during charge and discharge is difficult to measure directly, existing lithium battery fault detection is usually achieved indirectly through parameters such as voltage and current. However, these electrical parameters are easily affected by complex external environmental factors during charge and discharge, such as ambient temperature and humidity interference, and external electromagnetic interference. This makes it impossible for existing technologies to accurately measure fault information during the charge and discharge process, and consequently, to accurately predict charge and discharge faults within the lithium battery. This results in poor accuracy in evaluating lithium battery performance and hinders effective health management of lithium batteries. Summary of the Invention

[0004] To address the aforementioned technical problems, this application provides a rapid performance evaluation method for lithium-ion batteries based on time-domain impedance spectroscopy, thereby resolving existing issues.

[0005] The rapid performance evaluation method for lithium-ion batteries based on time-domain impedance spectroscopy in this application adopts the following technical solution:

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

[0007] Obtain the current, voltage, and impedance amplitude during the charging and discharging process of a lithium battery;

[0008] Frequency domain analysis of impedance amplitude is performed to obtain each signal period during the charging and discharging process of lithium battery. Mode decomposition of impedance amplitude within the signal period is performed. Based on the data fluctuation of each mode component and the correlation between each mode component and current data and voltage data, the mode interference complexity of each mode component of impedance amplitude within the signal period is obtained.

[0009] The importance weight of each modal component of the impedance amplitude within a signal period is determined by using the modal interference complexity. The impedance singularity of the impedance amplitude within a signal period is obtained by combining the degree of data mutation in each modal component. The difference between each signal period and its adjacent signal periods with respect to impedance singularity is analyzed to obtain the impedance fault identification degree of each signal period.

[0010] By combining impedance fault identification, impedance amplitude fluctuation and average level, and neural network, the current impedance fault assessment value is obtained. Combined with the preset fault assessment threshold, the lithium battery is fault assessed.

[0011] Preferably, the method for obtaining each signal cycle during the charging and discharging process of the lithium battery is as follows:

[0012] The impedance amplitude within a single acquisition time is transformed in the frequency domain to obtain the corresponding spectrum. The reciprocal of the frequency corresponding to the largest amplitude in the spectrum is used as the signal period during the charging and discharging process of the lithium battery.

[0013] Preferably, the method for calculating 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 Let be the modal interference complexity and information entropy of the j-th modal component of the impedance amplitude during the t-th signal period, respectively, and exp() be an exponential function with the natural constant as the base, RI. t,j Let RU be the absolute value of the correlation between the j-th mode component of the impedance amplitude and the current vector during the t-th signal period. t,j Let be the absolute value of the correlation between the j-th mode component of the impedance amplitude and the voltage vector in the t-th signal period, where the current and voltage values ​​in each signal period are arranged in chronological order to form the current and voltage vectors of each signal period.

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

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

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

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

[0019] In the formula, F t,j M and s represent the impedance singularity and the number of modal components of the impedance amplitude during the t-th signal period, respectively. t,j and x t,j Let be the first discreteness and the first mean of the first-order difference vector of the impedance amplitude of the j-th mode component within the t-th signal period, respectively. The standard deviation of all elements in the first-order difference vector of the j-th mode component is used as the first discreteness, and the mean of the absolute values ​​of all elements in the first-order difference vector is used as the first mean.

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

[0021] The nearest multiple signal cycles to each signal cycle are taken as the adjacent signal cycles of each signal cycle. The absolute values ​​of the differences in impedance singularity between each signal cycle and its adjacent signal cycles are calculated and averaged. The product of this average value and the impedance singularity of each signal cycle is used as the impedance fault identification degree of each signal cycle.

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

[0023] The normalized values ​​of the impedance amplitude variance and the normalized values ​​of the impedance amplitude mean in each signal cycle within the preset first time period are arranged in chronological order to form a sequence, which is used as the training sample of the neural network. The normalized values ​​of the impedance fault recognition degree of all signal cycles are arranged in chronological order to form a sequence, which is used as the label of the training sample to train the neural network.

[0024] The normalized values ​​of the impedance amplitude variance and the normalized values ​​of the impedance amplitude mean in each signal cycle within the preset second time period before the current time are arranged in chronological order and input into the trained neural network to obtain the impedance fault prediction values ​​for all signal cycles within the preset second time period before the current time, so as to calculate the impedance fault assessment value at the current time.

[0025] Preferably, the impedance fault assessment 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 time period prior to the current moment.

[0026] Preferably, 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; otherwise, the lithium battery has no fault problem at the current moment.

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

[0028] To eliminate the interference of external factors on fault information in the time-domain spectrum, this application uses mode decomposition to analyze the characteristics of each mode component in the time-domain spectrum and measures the complexity interference characteristics in each mode component. This is beneficial for more accurate measurement of impedance singularity during lithium battery charging and discharging, and improves the accuracy of measuring fault information during lithium battery charging and discharging.

[0029] In the process of measuring the impedance singularity characteristics of lithium batteries, the importance weight of the modal components 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 factors on fault information, so as to make the measurement of impedance singularity more accurate and improve the accuracy of predicting charging and discharging faults in lithium batteries.

[0030] Based on the impedance singularity and its change characteristics after eliminating the influence of external interference, the impedance fault identification is analyzed more accurately. The adverse interference caused by external interference factors is eliminated, which can more accurately predict the charging and discharging faults in the lithium battery, improve the accuracy of the lithium battery performance evaluation, and effectively realize the health management of the lithium battery.

[0031] Based on the analysis results of impedance fault identification, a neural network model is trained and fault prediction is performed. Subsequently, the charging and discharging performance of the lithium battery is rapidly evaluated, thereby achieving health management of the lithium battery. Since this application eliminates the interference of external interference on impedance faults in the impedance amplitude vector, it can more accurately measure fault information during the charging and discharging process of the lithium battery, and thus more accurately predict charging and discharging faults in the lithium battery, improving the accuracy of lithium battery performance evaluation and thus more effectively achieving health management of the lithium battery. Attached Figure Description

[0032] To more clearly illustrate the technical solutions and advantages in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0033] Figure 1 A flowchart illustrating the steps of the rapid performance evaluation method for lithium-ion batteries based on time-domain impedance spectroscopy provided in this application. Detailed Implementation

[0034] To further illustrate the technical means and effects adopted by this application to achieve the intended purpose of the invention, the following, in conjunction with the accompanying drawings and preferred embodiments, details the specific implementation, structure, features, and effects of the rapid performance evaluation method for lithium-ion batteries based on time-domain impedance spectroscopy proposed in this application. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. Furthermore, specific features, structures, or characteristics in one or more embodiments can be combined in any suitable form.

[0035] Unless otherwise defined, terms such as “comprising,” “including,” or any other variations thereof are intended to cover a non-exclusive inclusion, such that a circuit structure, article, or device that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such an article or device. Without further limitation, an element defined by the phrase “comprising one…” does not exclude the presence of other identical elements in the article or device that includes said element. Furthermore, the term “and / or” as used herein includes any and all combinations of one or more of the associated listed items. All technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application pertains.

[0036] The following description, in conjunction with the accompanying drawings, details the specific scheme of the rapid performance evaluation method for lithium-ion batteries based on time-domain impedance spectroscopy provided in this application.

[0037] This application provides an embodiment of a rapid performance evaluation method for lithium-ion batteries based on time-domain impedance spectroscopy. For details, please refer to [link to relevant documentation]. Figure 1 This includes the following steps:

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

[0039] In the process of fault prediction of the charging and discharging state of lithium-ion batteries, this embodiment uses an impedance analyzer to measure the current value, voltage value and impedance amplitude during the charging and discharging process of lithium batteries. The sampling frequency is 100Hz. The implementer can adaptively select the sampling frequency according to the actual situation.

[0040] Thus, according to the above process in this embodiment, data can be collected during the charging and discharging process of a lithium battery.

[0041] Step 2: Perform frequency domain analysis on the impedance amplitude to obtain each signal period during the charging and discharging process of the lithium battery. Perform mode decomposition on the impedance amplitude within the signal period. Based on the data fluctuation of each mode component and the correlation between each mode component and the current data and voltage data, obtain the mode interference complexity of each mode component of the impedance amplitude within the signal period.

[0042] Generally, during the charging and discharging process of lithium batteries, electrical parameters such as current, voltage, and impedance amplitude are easily affected by complex external factors. This can impact the accuracy of fault information extraction during the charging and discharging process, making it difficult to accurately predict faults in the charging and discharging state of the lithium battery. Consequently, the accuracy of lithium battery performance evaluation is poor, and effective health management of the lithium battery cannot be achieved. Therefore, to more accurately evaluate the performance of lithium batteries, it is necessary to accurately measure fault information on the impedance amplitude vector, thereby enabling more effective fault prediction and health management of the lithium battery's charging and discharging state.

[0043] The impedance amplitude is extracted within a single acquisition time during the charging and discharging process of the lithium battery. This impedance amplitude is then input into a Fourier transform (FT). A discrete Fourier transform (DFT) is used to obtain the spectrum of the impedance amplitude vector. The reciprocal of the frequency corresponding to the maximum amplitude value in the spectrum is taken as the signal period during the charging and discharging process of the lithium battery. The Fourier transform can be either a Discrete Fourier Transform (DFT) or a Fast Fourier Transform (FFT). The Fourier transform is a well-known technique and will not be elaborated further. In this embodiment, the single acquisition time is 10 minutes.

[0044] Simultaneously, the current, voltage, and impedance amplitudes within each signal cycle during the lithium battery charging and discharging process are acquired. In this embodiment, for ease of understanding and description, the current, voltage, and impedance amplitudes within each signal cycle during the lithium battery charging and discharging process are arranged into vectors according to their chronological order, serving as the current vector, voltage vector, and impedance amplitude vector for each signal cycle during the lithium battery charging and discharging process. The current vector, voltage vector, and impedance amplitude vector for each signal cycle respectively reflect the changing characteristics of the current, voltage, and impedance amplitudes within the corresponding signal cycle.

[0045] Under normal charging and discharging conditions, there is a strong correlation between current, voltage, and resistance impedance in the internal circuitry of a lithium battery. However, due to complex interference from external environmental factors, changes in the amplitudes of current, voltage, and impedance can generate interfering modal components, causing alterations in the correlation between current, voltage, and resistance impedance. To eliminate the interference of external factors on fault information in the time-domain spectrum, 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 input into the mode decomposition algorithm. The mode decomposition algorithm obtains the mode components of the impedance amplitude in the t-th signal period. The mode decomposition algorithm can be Empirical Mode Decomposition (EMD) or Variational Mode Decomposition (VMD). This embodiment does not make a specific limitation. In this embodiment, the EMD mode decomposition algorithm is selected for mode decomposition. The EMD mode decomposition algorithm obtains the mode components corresponding to the impedance amplitude vector by means of iterative convergence. The mode decomposition algorithm is a well-known technology and will not be described in detail.

[0047] The weaker the correlation between the modal components of the impedance amplitude vector of a signal period and the current and voltage vectors of the signal period, the less the modal component reflects the relationship between resistance and current / voltage. Therefore, the modal component is more likely to be caused by external interference. Furthermore, the higher the degree of disorder within the modal components of the impedance amplitude vector, the more complex the interference from external factors is reflected.

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

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

[0050] In the formula, D t,j Let β be the modal interference complexity of the j-th modal component of the impedance amplitude during the t-th signal period. t,j Let be the information entropy of the j-th mode component of the impedance amplitude during the t-th signal period, and exp() be an exponential function with base RI. t,j Let RU be the absolute value of the correlation between the j-th mode component of the impedance amplitude and the current vector during the t-th signal period.t,j It is the absolute value of the correlation between the j-th mode component of the impedance amplitude and the voltage vector during the t-th signal period.

[0051] The correlation can be measured by Pearson correlation coefficient or covariance. In this embodiment, covariance is used to measure the correlation. The specific calculation process of covariance is a well-known technique and will not be elaborated further.

[0052] The complexity of modal interference reflects the complexity of the impedance amplitude vector being affected by external factors. The higher the complexity of modal interference on the modal components of the impedance amplitude vector, the less accurately the fault information of the lithium battery charging and discharging state can be reflected. Therefore, when measuring the singularity of impedance during the charging and discharging process of lithium battery, a smaller importance weight should be set in order to eliminate the interference of external factors on the fault information in the time domain spectrum.

[0053] Step 3: Determine the importance weight of each modal component of the impedance amplitude within the signal period using the modal interference complexity, obtain the impedance singularity of the impedance amplitude within the signal period by combining the degree of data mutation in each modal component, and analyze the difference in impedance singularity between each signal period and adjacent signal periods to obtain the impedance fault identification degree of each signal period.

[0054] Furthermore, the first-order difference vector of each modal component of the impedance amplitude vector in each signal period is calculated. The size of the elements in the first-order difference vector can reflect the abrupt change and singularity characteristics of the modal components of the impedance amplitude vector. If the degree of abrupt change and the degree of singularity on the modal components are higher, the abnormal characteristics on the impedance amplitude vector can be reflected to a certain extent, thereby reflecting the impedance fault information of the lithium battery charging and discharging state.

[0055] In this embodiment, the importance weight of each modal component of the impedance amplitude within the signal period is first determined using modal interference complexity. 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 Let be the importance weight of the j-th mode component of the impedance amplitude within the t-th signal period, and norm() be the normalization function.

[0058] Furthermore, to accurately measure the impedance fault information of the lithium battery's charge / discharge state, the dispersion of all elements within the first-order difference vector of the j-th mode component is calculated, denoted as the first dispersion. The dispersion can be variance or standard deviation; in this embodiment, standard deviation is used. A larger dispersion indicates a greater singularity in the mode components of the impedance amplitude vector, thus better reflecting the fault information of the impedance amplitude vector. Further, the mean of the absolute values ​​of all elements within the first-order difference vector of the j-th mode component is calculated, denoted as the first mean. A larger mean indicates a greater degree of abrupt change in the mode components of the impedance amplitude vector. Therefore, the dispersion of elements within the first-order difference vector of the mode component and the mean of their absolute values ​​characterize the degree of abrupt fluctuations in the data within the mode component.

[0059] Therefore, based on the importance weights, combined with the dispersion of elements within the first-order difference vector of the modal components and the mean of the absolute values ​​of the elements, the impedance singularity of the impedance amplitude within each signal period is calculated. In this embodiment, the calculation method is as follows:

[0060]

[0061] In the formula, F t,j Let s be the impedance singularity of the impedance amplitude during the t-th signal period, M be the number of modal components of the impedance amplitude during the t-th signal period, and s be the impedance singularity. t,j and x t,j These are the first discreteness and first mean of the first-order difference vector of the j-th mode component of the impedance amplitude during the t-th signal period, respectively.

[0062] 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.

[0063] Impedance singularity reflects the singularity of impedance changes during the charging and discharging process of a lithium battery. This eliminates errors caused by interference from external factors, and using impedance change singularity can more accurately measure internal abnormal faults during the charging and discharging process of a lithium battery.

[0064] When the lithium battery has excellent performance, the change in impedance singularity between adjacent signal cycles is small, which reflects the stable change in the internal impedance of the lithium battery. However, if the impedance singularity between adjacent signal cycles changes significantly and the degree of impedance singularity is high, it indicates that the internal impedance of the lithium battery has undergone a drastic singularity change. In this case, the impedance abnormality fault of the lithium battery in the charging and discharging state 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 10, but the implementer can choose a value according to the actual situation.

[0066] Furthermore, the average of the absolute values ​​of the differences in impedance singularity between each signal period and all its adjacent signal periods is calculated. The product of this average value and the impedance singularity of each signal period is used as the impedance fault identification degree of each signal period.

[0067] Impedance fault identification reflects the characteristics of impedance faults during the charging and discharging process of lithium batteries. It eliminates adverse interference caused by external factors, enabling more accurate prediction of charging and discharging faults within lithium batteries. This improves the accuracy of lithium battery performance evaluation and effectively achieves health management of lithium batteries.

[0068] Step 4: By combining the impedance fault identification degree, the fluctuation level and average level of impedance amplitude, and the neural network, the impedance fault assessment value at the current moment is obtained. Combined with the preset fault assessment threshold, the lithium battery is assessed for fault.

[0069] The charging and discharging state of a lithium battery is predicted using a neural network. During the charging and discharging process, the impedance amplitude vector and impedance fault identification degree of all signal cycles within a preset first time period are obtained. The normalized values ​​of the impedance amplitude variance and the normalized values ​​of the impedance amplitude mean in each signal cycle within the preset first time period are arranged in chronological order to form a sequence, which is used as the training sample of the neural network. The normalized values ​​of the impedance fault identification degree of all signal cycles within the preset first time period are arranged in chronological order to form a sequence, which is used as the label of the training sample. In this embodiment, the preset first time period is 1 hour.

[0070] The training samples and their labels are input into the LSTM neural network model for training. The activation function is the ReLU function, the optimizer is the Adam optimizer, and the loss function is the mean squared error function. In this embodiment, for ease of understanding and description, the trained LSTM neural network model is referred to as the fault prediction model.

[0071] Furthermore, in this embodiment, the impedance amplitude vectors of all signal cycles within a preset second time period before the current moment are obtained. 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 are arranged in chronological order and recorded as two impedance spectrum feature sequences. The two impedance spectrum feature sequences are input into the fault prediction model, and the fault prediction model outputs the impedance fault prediction values ​​of all signal cycles within the preset second time period before the current moment, thus completing the fault prediction during the charging and discharging 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 well-known technologies and will not be described in detail in this embodiment.

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

[0074] Furthermore, a fault assessment threshold is preset. In this embodiment, the fault assessment threshold is set to 0.5. If the impedance fault assessment value at the current moment is greater than the fault assessment threshold, it indicates that the charging and discharging 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 health management of the lithium battery. Conversely, it indicates that the charging and discharging performance of the lithium battery at the current moment is good, no fault problem has occurred, and the lithium battery can continue to be charged and discharged. This can avoid overcharging or over-discharging problems during the charging and discharging process of the lithium battery, and more effectively achieve health management of the lithium battery.

[0075] It is understood that references to "one embodiment" or "some embodiments" in this specification mean that one or more embodiments of this application include the specific features, structures, or characteristics described in connection with that embodiment. Therefore, the appearance of phrases such as "in one embodiment," "in some embodiments," "in other embodiments," or "in still other embodiments" in different parts of this specification does not necessarily refer to the same embodiment, but rather means "one or more, but not all, embodiments," unless otherwise specifically emphasized. The terms "comprising," "including," "having," and variations thereof all mean "including but not limited to," unless otherwise specifically emphasized.

[0076] It should be noted that the order of the embodiments described above is merely for descriptive purposes and does not represent the superiority or inferiority of the embodiments. Furthermore, the above description focuses on specific embodiments of this specification. Additionally, the processes depicted in the accompanying drawings do not necessarily require a specific or sequential order to achieve the desired results. In some implementations, multitasking and parallel processing are possible or may be advantageous. Moreover, the sequence numbers of the steps in the embodiments do not imply a specific order of execution; the execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments in this specification.

[0077] The above embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of this application, and should all be included within the protection scope of this application.

Claims

1. A rapid performance evaluation method for lithium-ion batteries based on time-domain impedance spectroscopy, characterized in that, Includes the following steps: Obtain the current, voltage, and impedance amplitude during the charging and discharging process of a lithium battery; Frequency domain analysis of impedance amplitude is performed to obtain each signal period during the charging and discharging process of lithium battery. Mode decomposition of impedance amplitude within the signal period is performed. Based on the data fluctuation of each mode component and the correlation between each mode component and current data and voltage data, the mode interference complexity of each mode component of impedance amplitude within the signal period is obtained. The importance weight of each modal component of the impedance amplitude within a signal period is determined by using the modal interference complexity. The impedance singularity of the impedance amplitude within a signal period is obtained by combining the degree of data mutation in each modal component. The difference between each signal period and its adjacent signal periods with respect to impedance singularity is analyzed to obtain the impedance fault identification degree of each signal period. By combining impedance fault identification, impedance amplitude fluctuation and average level, and neural network, the impedance fault assessment value at the current moment is obtained, and the lithium battery is assessed for fault by combining the preset fault assessment threshold. The method for calculating 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 In the formula, D t,j α t,j Let be the modal interference complexity and information entropy of the j-th modal component of the impedance amplitude during the t-th signal period, respectively, and exp() be an exponential function with the natural constant as the base, RI. t,j Let RU be the absolute value of the correlation between the j-th mode component of the impedance amplitude and the current vector during the t-th signal period. t,j Let be the absolute value of the correlation between the j-th mode component of the impedance amplitude and the voltage vector in the t-th signal period, where the current and voltage values ​​in each signal period are arranged in chronological order to form the current and voltage vectors of each signal period. The method for calculating the importance weights 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 , where are the importance weight of the j-th mode component of the impedance amplitude in the t-th signal period, and are the mode interference complexity, respectively, and norm() is the normalization function; The method for calculating the impedance singularity of the impedance amplitude within the signal period is as follows: In the formula, F t,j M and s represent the impedance singularity and the number of modal components of the impedance amplitude during the t-th signal period, respectively. t,j and x t,j Let be the first discreteness and the first mean of the first-order difference vector of the impedance amplitude of the j-th mode component within the t-th signal period, respectively. The standard deviation of all elements in the first-order difference vector of the j-th mode component is used as the first discreteness, and the mean of the absolute values ​​of all elements in the first-order difference vector is used as the first mean. The method for obtaining the impedance fault identification degree of each signal cycle is as follows: The nearest multiple signal cycles to each signal cycle are taken as the adjacent signal cycles of each signal cycle. The absolute values ​​of the differences in impedance singularity between each signal cycle and its adjacent signal cycles are calculated and averaged. The product of this average value and the impedance singularity of each signal cycle is used as the impedance fault identification degree of each signal cycle.

2. The method for rapid performance evaluation of lithium-ion batteries based on time-domain impedance spectroscopy as described in claim 1, characterized in that, The method for obtaining each signal cycle during the charging and discharging process of the lithium battery is as follows: The impedance amplitude within a single acquisition time is transformed in the frequency domain to obtain the corresponding spectrum. The reciprocal of the frequency corresponding to the largest amplitude in the spectrum is used as the signal period during the charging and discharging process of the lithium battery.

3. The method for rapid performance evaluation of lithium-ion batteries based on time-domain impedance spectroscopy as described in claim 1, characterized in that, The correlation between the modal components and the current vector is the covariance between the modal components and the current vector, and the correlation between the modal components and the voltage vector is the covariance between the modal components and the voltage vector.

4. The method for rapid performance evaluation of lithium-ion batteries based on time-domain impedance spectroscopy as described in claim 1, characterized in that, The acquisition of the impedance fault assessment value further includes: The normalized values ​​of the impedance amplitude variance and the normalized values ​​of the impedance amplitude mean in each signal cycle within the preset first time period are arranged in chronological order to form a sequence, which is used as the training sample of the neural network. The normalized values ​​of the impedance fault recognition degree of all signal cycles are arranged in chronological order to form a sequence, which is used as the label of the training sample to train the neural network. The normalized values ​​of the impedance amplitude variance and the normalized values ​​of the impedance amplitude mean in each signal cycle within the preset second time period before the current time are arranged in chronological order and input into the trained neural network to obtain the impedance fault prediction values ​​for all signal cycles within the preset second time period before the current time, so as to calculate the impedance fault assessment value at the current time.

5. The method for rapid performance evaluation of lithium-ion batteries based on time-domain impedance spectroscopy as described in claim 4, characterized in that, The impedance fault assessment 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 time period prior to the current moment.

6. The method for rapid performance evaluation of lithium-ion batteries based on time-domain impedance spectroscopy as described in 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; otherwise, the lithium battery has no fault problem at the current moment.

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