Battery broadband electrochemical impedance spectroscopy time domain rapid detection method and system

Abstract

The invention provides a battery broadband electrochemical impedance spectroscopy time domain rapid detection method and system, and the method comprises the steps: S1, dividing a target signal frequency band into a low frequency band, a middle frequency band and a high frequency band from low to high, and setting the parameters of an excitation signal DIBS of each frequency band; s2, enabling the discrete binary sequence to have spectrum distribution corresponding to the target signal through an optimization algorithm, and obtaining a DIBS signal containing all set frequency points; s3, performing current excitation on the battery by adopting DIBS excitation signals of different frequency bands to obtain excitation current and response voltage; and S4, respectively calculating the frequency spectrums of the excitation current and the response voltage under the three frequency bands, and obtaining the final impedance. A multi-band excitation mode is adopted, the sequence length of a single-segment signal is shortened, the clock period of the single-segment signal is prolonged, and the storage burden of excitation equipment and the data processing complexity of a time-frequency conversion algorithm are effectively prevented from being aggravated due to the long sequence period.

Description

Technical Field

[0001] The present invention relates to the technical field of electrochemical nondestructive testing, and in particular to a method and system for rapid time-domain detection of a battery using broadband electrochemical impedance spectroscopy. Background Art

[0002] In recent years, with the shift in energy mix and the continuous rise of alternative energy sources, batteries have been widely used in energy storage systems and electric vehicles. Battery energy storage utilizes electrochemical reactions to convert electrical energy into chemical energy. Lithium-ion batteries, with their high energy density, low self-discharge rate, and lack of memory, are currently the preferred power source for electric vehicles (EVs). However, battery safety incidents caused by battery failure are becoming increasingly common, and effective early warning of battery failure has become a key research topic in the automotive industry.

[0003] Due to the limitations of lithium-ion battery manufacturing technology, in order to meet the needs of current, voltage, power and energy, a large number of battery cells need to be connected in series and parallel to form battery modules in the battery system. The differences between these battery cells, that is, the consistency of the battery, are closely related to the performance and safety of the entire battery system.

[0004] From the perspective of battery system performance, battery consistency is positively correlated with battery system performance. Differences in production processes and operating conditions lead to differences in capacity, internal resistance, and self-discharge rate between different battery cells. Due to the short board effect, the overall capacity of the battery pack is limited by the smallest cell. When this cell is fully charged / discharged, the entire battery pack stops charging / discharging, but the overall capacity of the battery pack is not actually fully utilized. Furthermore, smaller cells are more susceptible to overcharge and overdischarge, leading to battery misuse and a significantly increased probability of failure.

[0005] From a battery system safety perspective, battery consistency is a concrete indicator of a battery system failure. Most power cell failures in a battery system occur in a small number of cells. When a failure occurs, the faulty cell is inevitably different from the normal cells, resulting in poor battery consistency and often abnormal changes in external battery parameters.

[0006] Apart from sudden failures caused by collisions and other reasons, most other failures evolve from mild to severe. Therefore, timely and accurate detection and replacement of inconsistent battery cells in the battery pack are of great significance to maintaining the safe and stable operation of the battery pack. It is practical to use battery consistency to achieve early warning of battery failures in the cloud.

[0007] Patent document CN113933736B discloses a battery pack consistency evaluation method based on cloud-based discharge data. This method performs envelope processing on the discharge voltage data of battery cells, generating a cumulative curve of the maximum and minimum differences at each moment of the upper envelope and a cumulative curve of the maximum and minimum differences at each moment of the lower envelope. These two curves are summed to form a final sum curve, and the slope of the curve is used to quantitatively evaluate the consistency of the battery pack. This patent document uses envelopes when evaluating voltage consistency, which enhances the algorithm's generalization capabilities. However, it does not consider the selection of curve segments when fitting the final sum curve, resulting in limited ability for the algorithm to identify local anomalies in the final sum curve.

[0008] Patent document CN113765179B discloses a cloud-based battery pack consistency analysis method based on hierarchical clustering and cluster center distance. This method uses hierarchical clustering to identify locations with the greatest temperature inconsistency in the battery pack and individual cells with the greatest voltage inconsistency. The cluster center distance metric is used to quantitatively analyze the temporal trends of battery pack temperature and voltage consistency, as well as the correlation between the two. This patent document utilizes hierarchical clustering to overcome the low accuracy of cloud-based data and the potential for misjudgment when directly analyzing measured voltage, current, or temperature data. However, using the voltage and temperature of the entire discharge process to calculate the Euclidean distance as the basis for scoring limits its ability to identify local inconsistencies. Summary of the Invention

[0009] In view of the defects in the prior art, the purpose of the present invention is to provide a method and system for rapid time-domain detection of battery broadband electrochemical impedance spectroscopy.

[0010] According to the present invention, a rapid time-domain detection method for a battery using broadband electrochemical impedance spectroscopy is provided, comprising:

[0011] Step S1: Divide the target signal frequency band into three segments from low to high: low frequency band, medium frequency band and high frequency band, and set the parameters of the excitation signal DIBS of each frequency band respectively;

[0012] Step S2: Using an optimization algorithm, the discrete binary sequence is made to have a spectrum distribution corresponding to the target signal, and a DIBS signal containing all set frequency points is obtained;

[0013] Step S3: using DIBS excitation signals of different frequency bands to perform current excitation on the battery to obtain the excitation current and response voltage;

[0014] Step S4: Use the Goertzel algorithm to calculate the spectra of the excitation current and response voltage in three frequency bands respectively to obtain the final impedance.

[0015] Preferably, the sampling frequencies of the three frequency bands are at least twice the upper limit of the frequency band; the parameters of the excitation signal DIBS include sequence length N, sampling frequency fs , frequency resolution df, effective frequency point freqv, clock period dt and sequence period T.

[0016] Preferably, step S2 includes the following sub-steps:

[0017] Step S2.1: Define the error function J with respect to the Fourier coefficients;

[0018]

[0019] Among them, C gk is the Fourier coefficient of the kth harmonic of the constructed sequence g(t), C sk It is the Fourier coefficient of all target frequency signals. s(t) indicates that the Fourier coefficient of the kth harmonic is C sk A periodic signal, g(t) represents any discrete binary signal, which can be expressed as g(t)=Ah(t), where A is the signal amplitude, h(t) is a binary signal, and its value is +1 or -1; t represents the signal sampling time point, and j represents the imaginary unit;

[0020] Step S2.2: Obtain the optimal point of the error function;

[0021] g(t)=Ah(t),h 2 (t)=1

[0022]

[0023] Among them, s n and h n are the values ​​of s(t) and h(t) in the discrete interval ((n-1 / 2)T / N, (n+1 / 2)T / N); when When , J reaches its minimum value:

[0024]

[0025] Define the square term of the sum in the error function J as I, and transform the process of minimizing J into the process of maximizing I;

[0026] Step S2.3: Obtain the signal amplitude and binary distribution of the target sequence;

[0027]

[0028] in, represents the binary distribution of the target sequence, represents the signal amplitude and binary distribution of the target sequence; s n and S k is a set of Fourier transform pairs, and S is obtained by the parameters of the excitation signal DIBS defined in step S1.k and s n , and then obtain and Finally, a discrete binary signal with the same spectrum distribution is obtained

[0029] Preferably, step S3 includes the following sub-steps:

[0030] Step S3.1: In the EIS test, the device directly collects the current i(t) flowing through the battery and the terminal voltage u(t) of the battery;

[0031] Step S3.2: Subtract the DC voltage of the battery from the collected voltage to obtain the corresponding voltage, i.e. u v (t)=u(t)-u bat , the excitation current i(t) is equal to the collected current; where u v (t) represents the response voltage, u bat Indicates the DC voltage of the battery.

[0032] Preferably, step S4 includes the following sub-steps:

[0033] Step S4.1: Excitation current i(t) and response voltage u v (t) are sampled separately to obtain discrete sequences i(n) and u v (n), where n = 0, 1, 2, ..., N-1, N, where N is the number of sampling points;

[0034] Step S4.2: Determine the frequency point k of interest, and apply the discrete series of excitation current and response voltage to the second-order recursive formula of the Goertzel algorithm to calculate the spectrum values ​​respectively;

[0035] v k (n)=2cos(2πk / N)v k (n-1)-v k (n-2)+x(n)

[0036]

[0037] in, is the angular frequency corresponding to the frequency point of interest, is the rotation factor, Represents the excitation current i(t) or the response voltage u v (t) The spectrum of the discrete sequence at the kth point frequency is respectively and v k (n) is the intermediate variable introduced by the second-order recursive formula of the Goertzel algorithm;

[0038] Step S4.3: For each frequency point, calculate its impedance Repeat the above steps to obtain the final complete impedance spectrum

[0039] According to the present invention, a battery broadband electrochemical impedance spectroscopy time domain rapid detection system is provided, comprising:

[0040] Module M1: divides the target signal frequency band into three segments from low to high: low frequency band, medium frequency band and high frequency band, and sets the parameters of the excitation signal DIBS for each frequency band respectively;

[0041] Module M2: Through the optimization algorithm, the discrete binary sequence has a spectrum distribution corresponding to the target signal, and a DIBS signal containing all set frequency points is obtained;

[0042] Module M3: Use DIBS excitation signals of different frequency bands to excite the battery current and obtain the excitation current and response voltage;

[0043] Module M4: Use the Goertzel algorithm to calculate the spectra of the excitation current and response voltage in three frequency bands to obtain the final impedance.

[0044] Preferably, the sampling frequencies of the three frequency bands are at least twice the upper limit of the frequency band; the parameters of the excitation signal DIBS include sequence length N, sampling frequency f s , frequency resolution df, effective frequency point freqv, clock period dt and sequence period T.

[0045] Preferably, the module M2 includes the following submodules:

[0046] Module M2.1: Define the error function J with respect to the Fourier coefficients;

[0047]

[0048] Among them, C gk is the Fourier coefficient of the kth harmonic of the constructed sequence g(t), C sk It is the Fourier coefficient of all target frequency signals, s(t) indicates that the Fourier coefficient of the kth harmonic is C sk A periodic signal, g(t) represents any discrete binary signal, which can be expressed as g(t)=Ah(t), where A is the signal amplitude, h(t) is a binary signal, and its value is +1 or -1; t represents the signal sampling time point, and j represents the imaginary unit;

[0049] Module M2.2: Obtaining the optimal point of the error function;

[0050] g(t)=Ah(t),h 2 (t)=1

[0051]

[0052] Among them, s n and h n are the values ​​of s(t) and h(t) in the discrete interval ((n-1 / 2)T / N, (n+1 / 2)T / N); when When , J reaches its minimum value:

[0053]

[0054] Define the square term of the sum in the error function J as I, and transform the process of minimizing J into the process of maximizing I;

[0055] Module M2.3: Obtain the signal amplitude and binary distribution of the target sequence;

[0056]

[0057] in, represents the binary distribution of the target sequence, represents the signal amplitude and binary distribution of the target sequence; s n and S k is a set of Fourier transform pairs, and S is obtained by the parameters of the excitation signal DIBS defined in module M1. k and s n , and then obtain and Finally, a discrete binary signal with the same spectrum distribution is obtained

[0058] Preferably, the module M3 includes the following submodules:

[0059] Module M3.1: In the EIS test, the device directly collects the current i(t) flowing through the battery and the terminal voltage u(t) of the battery;

[0060] Module M3.2: Subtract the DC voltage of the battery from the collected voltage to obtain the corresponding voltage, i.e. u v (t)=u(t)-u bat , the excitation current i(t) is equal to the collected current; where u v (t) represents the response voltage, u bat Indicates the DC voltage of the battery.

[0061] Preferably, the module M4 includes the following submodules:

[0062] Module M4.1: Excitation current i(t) and response voltage u v (t) are sampled separately to obtain discrete sequences i(n) and u v(n), where n = 0, 1, 2, ..., N-1, N, where N is the number of sampling points;

[0063] Module M4.2: Determine the frequency point k of interest, apply the discrete series of excitation current and response voltage to the second-order recursive formula of the Goertzel algorithm, and calculate the spectrum values ​​respectively;

[0064] v k (n)=2cos(2πk / N)v k (n-1)-v k (n-2)+x(n)

[0065]

[0066] in, is the angular frequency corresponding to the frequency point of interest, is the rotation factor, Represents the excitation current i(t) or the response voltage u v (t) The spectrum of the discrete sequence at the kth point frequency is respectively and v k (n) is the intermediate variable introduced by the second-order recursive formula of the Goertzel algorithm;

[0067] Module M4.3: Calculate the impedance at each frequency point Repeat the above modules to obtain the final complete impedance spectrum

[0068] Compared with the prior art, the present invention has the following beneficial effects:

[0069] 1. The present invention adopts a multi-band excitation method to divide the signal into three frequency bands from low frequency to high frequency, shortening the sequence length of a single-segment signal and increasing its clock period; effectively avoiding the increased storage burden of the excitation device and the data processing complexity of the time-frequency conversion algorithm caused by the long sequence period, and better meeting the requirements of rapid impedance detection.

[0070] 2. The present invention adopts DIBS signal as the excitation signal, which can concentrate the signal energy at a specific frequency point through the optimization algorithm, and maintain a good signal-to-noise ratio without increasing the amplitude of the excitation signal; at the same time, the DIBS signal has a short injection time, which is conducive to the need for rapid impedance testing.

[0071] 3. The present invention adopts the Goertzel time-frequency conversion algorithm to realize the targeted calculation of the spectrum of the frequency points of interest, improve the operation efficiency and effectively suppress the spectrum leakage; at the same time, for DIBS signals whose energy is concentrated at known effective frequency points, Goertzel can combine its characteristics to realize spectrum analysis efficiently and quickly, which is conducive to online application.

[0072] 4. The multi-segment DIBS signal excitation combined with the Goertzel time-frequency conversion method adopted in the present invention can realize the impedance test of the battery in the wide frequency range of 0.001Hz to 1000Hz within 88.125s, which can greatly improve the test efficiency compared with the traditional swept frequency impedance test method based on the electrochemical workstation. BRIEF DESCRIPTION OF THE DRAWINGS

[0073] Other features, objects and advantages of the present invention will become more apparent upon reading the detailed description of non-limiting embodiments with reference to the following drawings:

[0074] Figure 1 Flow chart of the method of the present invention.

[0075] Figure 2 This is a spectrum distribution diagram of signals in three different frequency bands of a battery broadband electrochemical impedance spectroscopy time domain rapid detection method in an embodiment of the present invention.

[0076] Figure 3 Graphs showing impedance test current excitation signals and corresponding voltage response signals in three different frequency bands of a time-domain rapid detection method for broadband electrochemical impedance spectroscopy of a battery in an embodiment of the present invention.

[0077] Figure 4 ] are the spectra of the excitation current and the response voltage respectively obtained by the Goertzel algorithm in the embodiment of the present invention at the target frequency points of the three frequency bands of low frequency, medium frequency and high frequency.

[0078] Figure 5 This is a schematic diagram of the impedance spectra of the battery in three frequency bands obtained by the Goertzel algorithm in a time-domain rapid detection method for wide-band electrochemical impedance spectroscopy of a battery in an embodiment of the present invention. DETAILED DESCRIPTION

[0079] The present invention will be described in detail below with reference to specific embodiments. The following examples will help those skilled in the art to further understand the present invention, but are not intended to limit the present invention in any form. It should be noted that, for those skilled in the art, several changes and improvements can be made without departing from the scope of the present invention. These all fall within the scope of protection of the present invention.

[0080] like Figure 1As shown, a battery broadband electrochemical impedance spectroscopy time domain rapid detection method is based on segmented discrete binary signal excitation signal and Goertzel time-frequency conversion algorithm to realize rapid online impedance test of the battery, and includes the following steps:

[0081] Step S1: as shown in Figure 2 and Figure 3 , the figure from top to bottom is low, medium and high frequency. The target signal frequency band is segmented from low frequency to high frequency, and the main parameters of the excitation signal DIBS in the low, medium and high frequency bands are designed.

[0082] In step S1, the main parameter design method of the excitation signal is as follows: the DIBS signal is divided into low, medium and high frequency bands. In order to prevent the occurrence of spectrum aliasing phenomenon, the sampling frequency of the excitation signal in the low, medium and high frequency bands is at least twice the upper limit of the frequency band. The main parameters of the DIBS signal are designed, including sequence length N, sampling frequency f s , frequency resolution df, effective frequency point freqv, clock period dt and sequence period T.

[0083] In one embodiment, the frequency range of the low frequency band signal is 0.00125-0.125Hz, N=1024, f s =1.28; the frequency range of the medium frequency band signal is 0.125-12Hz, N=400, f s =50; the frequency range of the high frequency band signal is 16-1000, N=250, f s =2000. For impedance test in the broadband range of about 1mHz-1kHz, only 88.125s is needed to complete. Compared with the frequency sweeping test of the electrochemical workstation for tens of minutes, the DIBS signal can greatly shorten the test time.

[0084] Step S2: the discrete binary sequence has a frequency spectrum distribution similar to the target signal by optimizing the algorithm, and the DIBS signal containing all the designed frequency points is obtained.

[0085] In step S2, the error function of the optimization algorithm is defined as follows:

[0086] The Fourier coefficient C gk of the kth harmonic of g(t) is defined, the error function J about C gk is defined, and by minimizing the error function J, any kth Fourier series C gk is as close as possible to the Fourier coefficient C sk containing all the target frequency points, and the formula is as follows:

[0087]

[0088] Among them, C gk is the Fourier coefficient of the kth harmonic of the constructed sequence g(t), C sk It is the Fourier coefficient of all target frequency signals. s(t) indicates that the Fourier coefficient of the kth harmonic is C sk A periodic signal, g(t) represents any discrete binary signal, which can be expressed as g(t)=Ah(t), where A is the signal amplitude, h(t) is a binary signal, and its value is +1 or -1; t represents the signal sampling time point, and j represents the imaginary unit;

[0089] According to Parseval's theorem and the definition g(t)=Ah(t), h 2 (t) = 1, the error function J can be rewritten as:

[0090]

[0091] Since J is a quadratic function of A, when When , J reaches its minimum value. According to the definition, A>0, and it is substituted into the error function expression to obtain:

[0092]

[0093] Among them, s n and h n where s(t) and h(t) are the values ​​of s(t) and h(t) in the discrete interval ((n-1 / 2)T / N, (n+1 / 2)T / N). By defining the square term of the sum in the error function J as I, the process of minimizing J can be transformed into the process of maximizing I.

[0094] In step S2, the DIBS signal containing all design frequency points is obtained as follows:

[0095] Assume that any discrete binary signal is g(t)=Ah(t), where A is the signal amplitude and h(t) is a binary signal with a value of +1 or -1. The length of the h(t) sequence is N, and only at t=(n+1 / 2)T / N (n=0,…,N-1) is the target sequence In this example, because the battery's internal resistance is small, even small voltage fluctuations can cause significant current surges, potentially damaging the measurement equipment. Therefore, constant current mode is used for the battery impedance test. The excitation current signal amplitude is set to 0.5A. Under current perturbations, the corresponding voltage amplitude is tens of millivolts, which is considered to meet the linearity and stability requirements.

[0096] According to the main parameters of the DIBS signal defined in step S1, the target sequence and It can be expressed as:

[0097]

[0098] in, It is h n One of the solutions of , after substituting it into I, we get s n and S k is a set of Fourier transform pairs, and S is obtained by the main parameters of the DIBS signal defined in step S1. k and s n , and then obtain and Finally, a discrete binary signal with the same spectrum distribution is obtained

[0099] Step S3: Use DIBS excitation signals of different frequency bands to excite the battery to obtain the excitation current i(t) and response voltage u v (t).

[0100] In step S3, the excitation current i(t) and the response voltage u v (t) is obtained as follows: In the EIS test, the device directly collects the current i(t) flowing through the battery and the terminal voltage u(t) of the battery. The collected current is the excitation current i(t). Since the impedance test time is short and the excitation current alternates between positive and negative directions, it is assumed that the battery SOC remains unchanged during the test. Therefore, the response voltage u v (t) is the collected voltage minus the DC voltage of the battery. In one embodiment, it is replaced by the average value of the battery voltage, that is,

[0101] Step S4: Refer to Figure 4 and Figure 5 As shown in the figure, from top to bottom they are low, medium and high frequencies. The Goertzel algorithm is used to calculate the excitation current i(t) and the response voltage u v (t) Spectra in three frequency bands, respectively and The final impedance is

[0102] In step S4, the second-order recursive formula of the Goertzel algorithm is used, that is, the convolution operation of the discrete Fourier transform is obtained by recursion, and the spectra of the excitation current and the response voltage in the three frequency bands are calculated respectively, and the final impedance spectrum is further obtained. The method used is as follows:

[0103] According to the second-order recursive formula of the Goertzel algorithm:

[0104] v k (n)=2cos(2πk / N)v k(n-1)-v k (n-2)+x(n)

[0105]

[0106] Among them, y k (-2)=0,y k (-1)=0,y k (0) = x(0), when n = N, then X[k] = y k (n)| n＝N . For the excitation current i(t) and the response voltage u v (t) are sampled separately to obtain discrete sequences i(n) and u v (n), where n=0,1,2,…,N-1,N, where N is the number of sampling points. Determine the frequency point k of interest, and the corresponding angular frequency is Substitute the discrete sequence into v k (n)=2cos(2πk / N)v k (n-1)-v k (n-2)+x(n) is recursively calculated, and the spectrum value is calculated after the recursion is completed. The rotation factor Represents the excitation current i(t) or the response voltage u v (t) The spectrum of the discrete sequence at the kth point frequency is respectively and v k (n) is the intermediate variable introduced by the second-order recursive formula of the Goertzel algorithm; for each frequency point, its impedance is calculated Repeat the above steps to obtain the final complete impedance spectrum

[0107] In step S4, for N sampling points, the Goertzel algorithm needs to calculate 4N+4 real number addition operations and 2N+4 real number multiplication operations for each time to complete the time-frequency conversion of one frequency point. For the case where the frequency points to be measured are limited, the time complexity of the Goertzel algorithm is T(N)=O(kN), where k is the number of frequency points to be measured.

[0108] The present invention also provides a battery broadband electrochemical impedance spectroscopy time-domain rapid detection system. The battery broadband electrochemical impedance spectroscopy time-domain rapid detection system can be implemented by executing the process steps of the battery broadband electrochemical impedance spectroscopy time-domain rapid detection method. That is, those skilled in the art can understand the battery broadband electrochemical impedance spectroscopy time-domain rapid detection method as a preferred embodiment of the battery broadband electrochemical impedance spectroscopy time-domain rapid detection system.

[0109] Specifically, a battery broadband electrochemical impedance spectroscopy time domain rapid detection system includes:

[0110] Module M1: divides the target signal frequency band into three segments from low to high: low frequency band, medium frequency band and high frequency band, and sets the parameters of the excitation signal DIBS for each frequency band respectively;

[0111] Module M2: Through the optimization algorithm, the discrete binary sequence has a spectrum distribution corresponding to the target signal, and a DIBS signal containing all set frequency points is obtained;

[0112] Module M3: Use DIBS excitation signals of different frequency bands to excite the battery and obtain the excitation current and response voltage;

[0113] Module M4: Use the Goertzel algorithm to calculate the spectra of the excitation current and response voltage in three frequency bands to obtain the final impedance.

[0114] The sampling frequencies of the three frequency bands are at least twice the upper limit of the frequency band; the parameters of the excitation signal DIBS include sequence length N, sampling frequency f s , frequency resolution df, effective frequency point freqv, clock period dt and sequence period T.

[0115] The module M2 includes the following submodules:

[0116] Module M2.1: Define the error function J with respect to the Fourier coefficients;

[0117]

[0118] Among them, C gk is the Fourier coefficient of the kth harmonic of the constructed sequence g(t), C sk It is the Fourier coefficient of all target frequency signals, s(t) indicates that the Fourier coefficient of the kth harmonic is C sk A periodic signal, g(t) represents any discrete binary signal, which can be expressed as g(t)=Ah(t), where A is the signal amplitude, h(t) is a binary signal, and its value is +1 or -1; t represents the signal sampling time point, and j represents the imaginary unit;

[0119] Module M2.2: Obtaining the optimal point of the error function;

[0120] g(t)=Ah(t),h 2 (t)=1

[0121]

[0122] Among them, s n and h nare the values ​​of s(t) and h(t) in the discrete interval ((n-1 / 2)T / N, (n+1 / 2)T / N); when When , J reaches its minimum value:

[0123]

[0124] Define the square term of the sum in the error function J as I, and transform the process of minimizing J into the process of maximizing I;

[0125] Module M2.3: Obtain the signal amplitude and binary distribution of the target sequence;

[0126]

[0127] in, represents the binary distribution of the target sequence, represents the signal amplitude and binary distribution of the target sequence; s n and S k is a set of Fourier transform pairs, and S is obtained by the parameters of the excitation signal DIBS defined in module M1. k and s n , and then obtain and Finally, a discrete binary signal with the same spectrum distribution is obtained

[0128] The module M3 includes the following submodules:

[0129] Module M3.1: In the EIS test, the device directly collects the current i(t) flowing through the battery and the terminal voltage u(t) of the battery;

[0130] Module M3.2: Subtract the DC voltage of the battery from the collected voltage to obtain the corresponding voltage, i.e. u v (t)=u(t)-u bat , the excitation current i(t) is equal to the collected current; where u v (t) represents the response voltage, u bat Indicates the DC voltage of the battery.

[0131] The module M4 includes the following submodules:

[0132] Module M4.1: Excitation current i(t) and response voltage u v (t) are sampled separately to obtain discrete sequences i(n) and u v (n), where n = 0, 1, 2, ..., N-1, N, where N is the number of sampling points;

[0133] Module M4.2: Determine the frequency point k of interest, apply the discrete series of excitation current and response voltage to the second-order recursive formula of the Goertzel algorithm, and calculate the spectrum values ​​respectively;

[0134] v k (n)=2cos(2πk / N)v k (n-1)-v k (n-2)+x(n)

[0135]

[0136] in, is the angular frequency corresponding to the frequency point of interest, is the rotation factor, Represents the excitation current i(t) or the response voltage u v (t) The spectrum of the discrete sequence at the kth point frequency is respectively and v k (n) is the intermediate variable introduced by the second-order recursive formula of the Goertzel algorithm;

[0137] Module M4.3: Calculate the impedance at each frequency point Repeat the above modules to obtain the final complete impedance spectrum

[0138] Those skilled in the art will appreciate that, in addition to implementing the system and its various devices, modules, and units provided by the present invention in purely computer-readable program code, it is entirely possible to implement the same functions of the system and its various devices, modules, and units provided by the present invention in the form of logic gates, switches, application-specific integrated circuits, programmable logic controllers, and embedded microcontrollers by logically programming the method steps. Therefore, the system and its various devices, modules, and units provided by the present invention can be considered a hardware component, and the devices, modules, and units included therein for implementing various functions can also be considered as structures within the hardware component; the devices, modules, and units for implementing various functions can also be considered as both software modules implementing the method and structures within the hardware component.

[0139] The above describes specific embodiments of the present invention. It should be understood that the present invention is not limited to the specific embodiments described above, and those skilled in the art may make various changes or modifications within the scope of the claims, which do not affect the essence of the present invention. The embodiments of this application and the features in the embodiments may be combined with each other in any manner unless there is a conflict.

Claims

1. A rapid time-domain detection method for battery broadband electrochemical impedance spectroscopy, characterized in that: include: Step S1: Divide the target signal frequency band into three segments from low to high: low frequency band, medium frequency band and high frequency band, and set the parameters of the excitation signal DIBS of each frequency band respectively; Step S2: Using an optimization algorithm, the discrete binary sequence is made to have a spectrum distribution corresponding to the target signal, and a DIBS signal containing all set frequency points is obtained; Step S3: using DIBS excitation signals of different frequency bands to perform current excitation on the battery to obtain the excitation current and response voltage; Step S4: Use the Goertzel algorithm to calculate the spectra of the excitation current and response voltage in three frequency bands respectively to obtain the final impedance.

2. The battery broadband electrochemical impedance spectroscopy time domain rapid detection method according to claim 1, characterized in that: The sampling frequencies of the three frequency bands are at least twice the upper limit of the frequency band; the parameters of the excitation signal DIBS include sequence length N, sampling frequency f s , frequency resolution df, effective frequency point freqv, clock period dt and sequence period T.

3. The battery broadband electrochemical impedance spectroscopy time domain rapid detection method according to claim 2, characterized in that: The step S2 includes the following sub-steps: Step S2.1: Define the error function J with respect to the Fourier coefficients; Among them, C gk is the Fourier coefficient of the kth harmonic of the constructed sequence g(t), C sk is the Fourier coefficient of all target frequency signals; s(t) indicates that the Fourier coefficient of the kth harmonic is C sk periodic signal, g(t) represents any discrete binary signal, expressed as g(t)=Ah(t), where A is the signal amplitude, h(t) is a binary signal, and its value is +1 or -1; t represents the signal sampling time point, and j represents the imaginary unit; Step S2.2: Obtain the optimal point of the error function; g(t)=Ah(t),h 2 (t)=1 Among them, s n and h n are the values ​​of s(t) and h(t) in the discrete interval ((n-1 / 2)T / N, (n+1 / 2)T / N); when When , J reaches its minimum value: Define the square term of the sum in the error function J as I, and transform the process of minimizing J into the process of maximizing I; Step S2.3: Obtain the signal amplitude and binary distribution of the target sequence; in, represents the binary distribution of the target sequence, represents the signal amplitude and binary distribution of the target sequence; s n and S k is a set of Fourier transform pairs, and S is obtained by the parameters of the excitation signal DIBS defined in step S1. k and s n , and then obtain and Finally, a discrete binary signal with the same spectrum distribution is obtained 4. The battery broadband electrochemical impedance spectroscopy time domain rapid detection method according to claim 3, characterized in that: The step S3 includes the following sub-steps: Step S3.1: In the EIS test, the device directly collects the current i(t) flowing through the battery and the terminal voltage u(t) of the battery; Step S3.2: Subtract the DC voltage of the battery from the collected voltage to obtain the corresponding voltage, i.e. u v (t)=u(t)-u bat , the excitation current i(t) is equal to the collected current; where u v (t) represents the response voltage, u bat Indicates the DC voltage of the battery.

5. The battery broadband electrochemical impedance spectroscopy time domain rapid detection method according to claim 4, characterized in that: The step S4 includes the following sub-steps: Step S4.1: Excitation current i(t) and response voltage u v (t) are sampled separately to obtain discrete sequences i(n) and u v (n), where n = 0, 1, 2, ..., N-1, N, where N is the number of sampling points; Step S4.2: Determine the frequency point k of interest, and apply the discrete series of excitation current and response voltage to the second-order recursive formula of the Goertzel algorithm to calculate the spectrum values ​​respectively; v k (n)=2cos(2πk / N)v k (n-1)-v k (n-2)+x(n) in, is the angular frequency corresponding to the frequency point of interest, is the rotation factor, Represents the excitation current i(t) or the response voltage u v (t) The spectrum of the discrete sequence at the kth point frequency is respectively and v k (n) is the intermediate variable introduced by the second-order recursive formula of the Goertzel algorithm; Step S4.3: For each frequency point, calculate its impedance Repeat the above steps to obtain the final complete impedance spectrum 6. A battery broadband electrochemical impedance spectroscopy time domain rapid detection system, characterized in that: include: Module M1: divides the target signal frequency band into three segments from low to high: low frequency band, medium frequency band and high frequency band, and sets the parameters of the excitation signal DIBS for each frequency band respectively; Module M2: Through the optimization algorithm, the discrete binary sequence has a spectrum distribution corresponding to the target signal, and a DIBS signal containing all set frequency points is obtained; Module M3: Use DIBS excitation signals of different frequency bands to excite the battery current and obtain the excitation current and response voltage; Module M4: Use the Goertzel algorithm to calculate the spectra of the excitation current and response voltage in three frequency bands to obtain the final impedance.

7. The battery broadband electrochemical impedance spectroscopy time domain rapid detection system according to claim 6, characterized in that: The sampling frequencies of the three frequency bands are at least twice the upper limit of the frequency band; the parameters of the excitation signal DIBS include sequence length N, sampling frequency f s , frequency resolution df, effective frequency point freqv, clock period dt and sequence period T.

8. The battery broadband electrochemical impedance spectroscopy time domain rapid detection system according to claim 7, characterized in that: The module M2 Includes the following submodules: Module M2.1: Define the error function J with respect to the Fourier coefficients; Among them, C gk is the Fourier coefficient of the kth harmonic of the constructed sequence g(t), C sk It is the Fourier coefficient of all target frequency signals, s(t) indicates that the Fourier coefficient of the kth harmonic is C sk periodic signal, g(t) represents any discrete binary signal, expressed as g(t)=Ah(t), where A is the signal amplitude, h(t) is a binary signal, and its value is +1 or -1; t represents the signal sampling time point, and j represents the imaginary unit; Module M2.2: Obtaining the optimal point of the error function; g(t)=Ah(t),h 2 (t)=1 Among them, s n and h n are the values ​​of s(t) and h(t) in the discrete interval ((n-1 / 2)T / N, (n+1 / 2)T / N); when When , J reaches its minimum value: Define the square term of the sum in the error function J as I, and transform the process of minimizing J into the process of maximizing I; Module M2.3: Obtain the signal amplitude and binary distribution of the target sequence; in, represents the binary distribution of the target sequence, represents the signal amplitude and binary distribution of the target sequence; s n and S k is a set of Fourier transform pairs, and S is obtained by the parameters of the excitation signal DIBS defined in module M1. k and s n , and then obtain and Finally, a discrete binary signal with the same spectrum distribution is obtained 9. The battery broadband electrochemical impedance spectroscopy time domain rapid detection system according to claim 8, characterized in that: The module M3 includes the following submodules: Module M3.1: In the EIS test, the device directly collects the current i(t) flowing through the battery and the terminal voltage u(t) of the battery; Module M3.2: Subtract the DC voltage of the battery from the collected voltage to obtain the corresponding voltage, i.e. u v (t)=u(t)-u bat , the excitation current i(t) is equal to the collected current; where u v (t) represents the response voltage, u bat Indicates the DC voltage of the battery.

10. The battery broadband electrochemical impedance spectroscopy time domain rapid detection method according to claim 9, characterized in that: The module M4 includes the following submodules: Module M4.1: Excitation current i(t) and response voltage u v (t) are sampled separately to obtain discrete sequences i(n) and u v (n), where n = 0, 1, 2, ..., N-1, N, where N is the number of sampling points; Module M4.2: Determine the frequency point k of interest, apply the discrete series of excitation current and response voltage to the second-order recursive formula of the Goertzel algorithm, and calculate the spectrum values ​​respectively; v k (n)=2cos(2πk / N)v k (n-1)-v k (n-2)+x(n) in, is the angular frequency corresponding to the frequency point of interest, is the rotation factor, Represents the excitation current i(t) or the response voltage u v (t) The spectrum of the discrete sequence at the kth point frequency is respectively and v k (n) is the intermediate variable introduced by the second-order recursive formula of the Goertzel algorithm; Module M4.3: Calculate the impedance at each frequency point Repeat the above modules to obtain the final complete impedance spectrum

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