Fourier transform and S transform combined battery impedance spectroscopy measurement method and device

Abstract

The invention discloses a Fourier transform and S transform combined battery impedance spectroscopy measurement method and device, and aims to improve the precision, speed and reliability of impedance measurement of a battery energy storage system. According to the method, a multi-frequency component excitation signal is generated through a controllable current source, and current and voltage response signals of a battery are synchronously acquired and are respectively input into a Fourier transform module and an S transform module for frequency domain analysis. The Fourier transform module extracts impedance spectrums of low and medium frequency bands, and the S transform module extracts impedance spectrums of medium and high frequency bands through Gaussian window time-frequency analysis. And a data fusion algorithm is adopted to perform composite processing on results of the two modules, including a direct selection method, an arithmetic average method, an amplitude and phase calibration method and a weighted average method, so that smooth transition and optimization between frequency bands are realized, and finally the electrochemical impedance spectrum is generated. Through collaborative analysis of Fourier transform and S transform, parameter optimization and a fusion algorithm, the efficiency and accuracy of impedance spectroscopy measurement are remarkably improved, and the method is suitable for rapid monitoring and diagnosis of the battery state.

Description

Technical Field

[0001] The present invention belongs to the technical field of batteries, and in particular relates to a battery impedance spectrum measurement method and device combining Fourier transform and S transform. Background Art

[0002] With the rapid development of electric vehicles and renewable energy storage, batteries, as core energy storage units, are crucial for the accuracy of their condition monitoring and health assessment, which is crucial to system safety and reliability. Electrochemical impedance spectroscopy (EIS), as an important battery diagnostic technology, can accurately reflect the internal electrochemical state (such as electrode aging, electrolyte loss, etc.) of the battery by analyzing its impedance response at different frequencies. However, traditional impedance spectroscopy measurement methods have significant limitations: using a single frequency domain analysis method, such as Fourier transform, has high accuracy but has high hardware requirements; using S transform can calculate simple step waveforms, but the measurement time is long because sufficient data length is required in the low-frequency region.

[0003] In practical applications, the demand for rapid acquisition of battery impedance spectra is becoming increasingly urgent. For example, the battery management system of electric vehicles needs to monitor impedance changes in real time to predict faults. However, traditional methods are difficult to meet the requirements of efficient diagnosis in dynamic scenarios due to a series of problems such as long measurement time, large and expensive measuring instruments, etc. At the same time, existing technologies do not adequately optimize the excitation signal. The dispersion of low-frequency energy leads to a decrease in low-frequency impedance accuracy, and the high-frequency band is susceptible to interference due to the number of data points and sampling frequency limitations, further affecting the measurement reliability. Therefore, there is an urgent need for a method that can combine the advantages of Fourier transform and S transform, optimize the energy distribution of the excitation signal, and realize fast and high-precision measurement of wide-band impedance spectra, so as to improve the real-time and accuracy of battery status monitoring and provide technical support for the safe operation of energy storage systems. Summary of the Invention

[0004] The present invention aims to provide a battery impedance spectrum measurement method and device combining Fourier transform and S transform to solve the problems of high hardware requirements or long measurement time in the prior art and to improve the reliability, speed and accuracy of battery status monitoring.

[0005] In order to achieve the above object, the present invention adopts the following technical solutions:

[0006] A battery impedance spectrum measurement method combining Fourier transform and S transform, comprising:

[0007] Generate a current excitation signal I containing multiple frequency components through a controllable current source m (t), and apply the current excitation signal to the positive and negative electrodes of the battery to be tested. While injecting the current excitation signal, the real-time current response signal I(t) and voltage response signal V(t) of the battery are filtered and differentially amplified before being synchronously collected;

[0008] The digitized current response signal and voltage response signal are input into the Fourier transform module and the S transform module respectively, wherein: the signal is transformed into the frequency domain to extract the impedance amplitude and phase information of the characteristic frequency point, and the Fourier transform module calculates the first impedance spectrum Z DFT (t), the S-transformation module calculates the second impedance spectrum Z ST (t);

[0009] The first impedance spectrum Z is obtained by data fusion algorithm. DFT (t) and the second impedance spectrum Z ST (t) Perform composite analysis and processing, and select the combination of Fourier transform and S transform impedance spectrum data in the frequency domain according to the preset rules to obtain the optimized electrochemical impedance spectrum Z EIS (f) The final result.

[0010] A further improvement of the present invention is that the current excitation signal I m (t) is a broadband signal, including a multi-frequency sinusoidal signal, a chirp signal, a pseudo-random binary sequence, and a discrete interval binary sequence.

[0011] A further improvement of the present invention is that the Fourier transform module algorithm includes filtering the impedance calculation result obtained and interpolating the impedance value of the frequency point.

[0012] A further improvement of the present invention is that the S-transform module algorithm parameter setting satisfies the Gaussian window time width of the S-transform to be 3σ or greater. When the excitation signal contains multiple step moments, the calculation process includes performing S-transform on each rising edge and falling edge of the waveform to obtain each impedance value, and the multiple values ​​are averaged to obtain the second impedance spectrum Z. ST The final calculation result of (t).

[0013] A further improvement of the present invention is that the Fourier transform module and the S transform module algorithm include: determining the analysis frequency range interval (f DFT_min , f DFT_max ) and (f ST_min , f ST_max ), the union of the two interval ranges should cover the full frequency band that the impedance spectrum needs to measure.

[0014] A further improvement of the present invention is that (f DFT_min , f DFT_max ) is the low-intermediate frequency, (f ST_min , f ST_max ) is the medium and high frequency.

[0015] A further improvement of the present invention is that the fusion processing of the Fourier transform and S transform calculation results includes: the intersection of the frequencies calculated by the two methods (fboth_min , f both_max ) within the range of , take one of the calculation results, calculate the average value, perform amplitude calibration and phase matching, or use weighted averaging to achieve smooth transition between the overlapping frequency bands;

[0016] (1) Direct selection method

[0017] Select Z in the intersection band DFT (t) or Z ST (t) one of them as output;

[0018] Z EIS (f) = Z DFT (f) or Z EIS (f) = Z ST (f)

[0019] (2) Arithmetic mean method

[0020]

[0021] (3) Amplitude calibration and phase matching method

[0022] In order to eliminate the amplitude / phase offset caused by the algorithm differences between the two methods, normalization calibration is performed;

[0023] The calibration coefficients α and β are calculated by reference impedance, and the impedance amplitude in subsequent calculations can be obtained after calibration;

[0024]

[0025] |Z ′ DFT (f)|=α(f)|Z DFT (f)|

[0026] |Z ′ ST (f)|=β(f)|Z ST (f)|

[0027] Calculate the phase difference between DFT and ST and obtain the compensation value. In subsequent calculations, the ST calculation phase can be adjusted to align the DFT phase value.

[0028]

[0029] The calibrated impedance value is shown in the following formula:

[0030]

[0031] (4) Weighted average method

[0032] Dynamically allocate weights within the intersection frequency band. The weight function is designed as shown below

[0033] According to the frequency position, we can get

[0034]

[0035] According to the signal-to-noise ratio, we can get

[0036]

[0037] According to the weight coefficient, the calculation result of the intersection frequency band can be obtained as follows:

[0038] Z EIS (f) = ω DFT (f)·Z ′ DFT (f)+ω ST (f)·Z ′ ST (f).

[0039] A further improvement of the present invention is that when a discrete interval binary sequence is used as an excitation signal, a DIBS current excitation signal is constructed by using the library function dibs in MATLAB, and only (f DFT_min , f DFT_max ) low- and medium-frequency power spectrum amplitude; inject the generated DIBS signal into the battery as the excitation current waveform, process the collected response signal, calculate the low- and medium-frequency impedance through the Fourier module, and calculate the medium- and high-frequency impedance through the S-transform module by averaging the calculation results of multiple edges of the collected response signal, which can reduce the wave frequency while speeding up the measurement speed;

[0040] Using Fourier transform alone to measure impedance spectrum requires the DIBS waveform to be used for the full frequency band (f DFT_min , f ST_max ) spectrum to ensure that it has sufficient excitation energy density within the target frequency band;

[0041] When the Fourier transform measurement target is limited to the impedance characteristics of the low and medium frequency bands, the spectrum optimization strategy of DIBS is adjusted in a targeted manner. Through the waveform iteration generation algorithm, the signal energy is concentrated in (f DFT_min , f DFT_max ) within the target frequency band.

[0042] A battery impedance spectrum measuring device combining Fourier transform and S transform, comprising:

[0043] The data generation unit generates a current excitation signal I containing multiple frequency components through a controllable current source. m (t), and applying a current excitation signal to the positive and negative electrodes of the battery under test;

[0044] The data acquisition unit filters and differentially amplifies the real-time current response signal I(t) and voltage response signal V(t) of the battery while injecting the current excitation signal, and then synchronously collects them;

[0045] The data preprocessing unit inputs the digitized current response signal and voltage response signal into the Fourier transform module and the S transform module respectively, wherein: the signal is transformed in the frequency domain to extract the impedance amplitude and phase information of the characteristic frequency point, and the Fourier transform module calculates the first impedance spectrum Z DFT (t), the S-transformation module calculates the second impedance spectrum Z ST (t);

[0046] The data processing unit uses the data fusion algorithm to process the first impedance spectrum Z DFT (t) and the second impedance spectrum Z ST (t) Perform composite analysis and processing, and select the combination of Fourier transform and S transform impedance spectrum data in the frequency domain according to the preset rules to obtain the optimized electrochemical impedance spectrum Z EIS (f) The final result.

[0047] A further improvement of the present invention is that the current excitation signal I in the data generating unit m (t) is a broadband signal, including a multi-frequency sinusoidal signal, a chirp signal, a pseudo-random binary sequence, and a discrete interval binary sequence.

[0048] Compared with the prior art, the present invention has at least the following beneficial technical effects:

[0049] The core creative contributions of the present invention are:

[0050] In terms of data acquisition and processing, the system innovatively employs dual-module processing. The Fourier transform module focuses on low and medium frequency bands, rapidly measuring impedance spectra through filtering and interpolation. The S-transform module analyzes multiple step transients in the medium and high frequency bands and performs averaging to enhance the extracted impedance spectrum's anti-interference capability. Furthermore, when combining data, a fusion strategy (direct selection / arithmetic averaging / amplitude and phase calibration / weighted averaging) is dynamically selected in the frequency band intersection region to achieve a smooth transition of the impedance spectrum. This strategic combination, rather than simple superposition, ensures full-frequency-domain battery impedance spectrum accuracy across the entire frequency band.

[0051] Regarding the excitation signal, a discrete interval binary sequence (DIBS) is used. An innovative iterative algorithm enhances only the power spectrum amplitude in the low and medium frequency bands, enabling DIBS generation at a significantly reduced fundamental frequency. This optimizes the transmission frequency while ensuring a short excitation time. This eliminates the dependency of Fourier transform full-frequency domain calculations on a high-frequency current excitation signal generator, reducing hardware costs.

[0052] This approach significantly improves measurement speed, reduces hardware costs, enhances measurement reliability, and improves the accuracy of battery impedance spectrum measurements. Ultimately, this approach enables fast, low-cost, and high-precision battery impedance spectrum measurements, overcoming the inherent drawbacks of traditional Fourier transforms (high hardware requirements) and S-transforms (long processing time). BRIEF DESCRIPTION OF THE DRAWINGS

[0053] In order to more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the specific embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0054] Figure 1 This is a structural diagram of the battery impedance spectrum measurement system of the present invention.

[0055] Figure 2 This is a waveform diagram of the DIBS signal generated in the specific implementation.

[0056] Figure 3 FIG. 4 is a spectrum diagram of the DIBS signal generated in a specific implementation.

[0057] Figure 4 This is a comparison chart of the impedance calculation value and reference value of the 0.1-1 Hz Fourier transform in the specific implementation.

[0058] Figure 5 This is a comparison chart of the impedance calculation value and reference value for each edge S-transformation at 1-1kHz in a specific implementation.

[0059] Figure 6 This is a comparison chart of the impedance calculation value after 1-1kHz averaging and S-transformation and the reference value in the specific implementation.

[0060] Figure 7 This is the impedance spectrum curve of 0.1Hz-1kHz calculated in the specific implementation.

[0061] Figure 8 This is a structural block diagram of a battery impedance spectrum measurement device combining Fourier transform and S transform according to the present invention. DETAILED DESCRIPTION

[0062] Hereinafter, only certain exemplary embodiments are briefly described. As will be appreciated by those skilled in the art, the described embodiments may be modified in various ways without departing from the spirit or scope of the present invention. Therefore, the drawings and description are to be considered as illustrative in nature and not restrictive.

[0063] In the description of the present invention, it is to be understood that when used in this specification and the appended claims, the terms “include” and “comprise” indicate the presence of the described features, wholes, steps, operations, elements and / or components, but do not exclude the presence or addition of one or more other features, wholes, steps, operations, elements, components and / or collections thereof.

[0064] It should also be understood that the terms used in the present specification are only for the purpose of describing particular embodiments and are not intended to limit the present invention. As used in the present specification and the appended claims, the singular forms "a", "an", and "the" are intended to include the plural forms unless the context clearly indicates otherwise.

[0065] It should be further understood that the term "and / or" used in the present description and the appended claims refers to and includes any and all possible combinations of one or more of the associated listed items.

[0066] The accompanying drawings illustrate various schematic diagrams of structures according to embodiments disclosed herein. These figures are not drawn to scale; for clarity, some details are exaggerated and some details may be omitted. The shapes of the various regions and layers shown in the figures, as well as their relative sizes and positional relationships, are merely exemplary and may deviate in practice due to manufacturing tolerances or technical limitations. Those skilled in the art may design regions / layers with different shapes, sizes, and relative positions as needed.

[0067] The embodiments of the present invention are described in detail below with reference to the accompanying drawings.

[0068] Example 1

[0069] The present invention provides a battery impedance spectrum measurement method combining Fourier transform and S transform, comprising:

[0070] Generate a current excitation signal I containing multiple frequency components through a controllable current source m (t), and apply the current excitation signal to the positive and negative electrodes of the battery to be tested. While injecting the current excitation signal, the real-time current response signal I(t) and voltage response signal V(t) of the battery are filtered and differentially amplified before being synchronously collected;

[0071] The digitized current response signal and voltage response signal are input into the Fourier transform module and the S transform module respectively, wherein: the signal is transformed into the frequency domain to extract the impedance amplitude and phase information of the characteristic frequency point, and the Fourier transform module calculates the first impedance spectrum Z DFT (t), the S-transformation module calculates the second impedance spectrum Z ST (t).

[0072] The first impedance spectrum Z is obtained by data fusion algorithm. DFT (t) and the second impedance spectrum Z ST (t) Perform composite analysis and processing, and select the combination of Fourier transform and S transform impedance spectrum data in the frequency domain according to the preset rules to obtain the optimized electrochemical impedance spectrum Z EIS (f) The final result.

[0073] In this embodiment, the current excitation signal I m (t) includes but is not limited to multi-frequency sinusoidal signals, chirped signals, pseudo-random binary sequences (PRBS) and discrete interval binary sequences (DIBS).

[0074] In this embodiment, the Fourier transform module algorithm includes filtering the impedance calculation result and interpolating the impedance value of the frequency point.

[0075] In this embodiment, the S-transformation module algorithm parameter setting satisfies the Gaussian window time width of the S-transformation to be 3σ or greater. When the excitation signal contains multiple step moments, the calculation process includes performing S-transformation on each rising edge and falling edge of the waveform to obtain each impedance value, and averaging multiple values ​​to obtain the second impedance spectrum Z. ST (t), thereby suppressing the influence of noise on the calculation results and improving accuracy.

[0076] In this embodiment, the Fourier transform module and the S transform module algorithm include: determining the analysis frequency range interval (f DFT_min , f DFT_max ) and (f ST_min , f ST_max ), the union of the two interval ranges should cover the full frequency band that the impedance spectrum needs to measure. In general, (f DFT_min , f DFT_max ) is the low-intermediate frequency, (f ST_min , f ST_max ) is the medium and high frequency.

[0077] In this embodiment, the fusion processing of the Fourier transform and S transform calculation results includes but is not limited to: the intersection of the frequencies calculated by the two methods (f both_min , f both_max ) range, take one of the calculation results, calculate the average value, perform amplitude calibration and phase matching, or use weighted averaging to achieve smooth transition between overlapping frequency bands.

[0078] (1) Direct selection method

[0079] Select Z in the intersection band DFT (t) or Z ST(t) one of them as output;

[0080] Z EIS (f) = Z DFT (f) or Z EIS (f) = Z ST (f)

[0081] (2) Arithmetic mean method

[0082]

[0083] (3) Amplitude calibration and phase matching method

[0084] In order to eliminate the amplitude / phase offset caused by the algorithm differences between the two methods, normalization calibration is required.

[0085] The calibration coefficients α and β are calculated using the reference impedance, and the impedance amplitude in subsequent calculations can be obtained after calibration.

[0086]

[0087] |Z ′ DFT (f)|=α(f)|Z DFT (f)|

[0088] |Z ′ ST (f)|=β(f)|Z ST (f)|

[0089] Calculate the phase difference between DFT and ST and obtain the compensation value. In subsequent calculations, adjust the ST calculation phase to align the DFT phase value.

[0090]

[0091] The calibrated impedance value is shown in the following formula:

[0092]

[0093] (4) Weighted average method

[0094] Dynamically allocate weights within the intersection frequency band. The weight function is designed as shown below

[0095] According to the frequency position, we can get

[0096]

[0097] According to the signal-to-noise ratio, we can get

[0098]

[0099] According to the weight coefficient, the calculation result of the intersection frequency band can be obtained as follows:

[0100] Z EIS (f) = ω DFT (f)·Z ′ DFT (f)+ω ST (f)·Z ′ ST (f)

[0101] In this embodiment, when the discrete interval binary sequence (DIBS) is used as the excitation signal, the DIBS current excitation signal is constructed by the library function dibs in MATLAB, and only the (f DFT_min , f DFT_max ) The power spectrum amplitude in the low- and medium-frequency band. The generated DIBS signal is injected into the battery as the excitation current waveform, and the collected response signal is processed to make the energy distribution more concentrated in the low- and medium-frequency bands, improve the accuracy of the low- and medium-frequency measurement results, and reduce the frequency of the DIBS excitation signal.

[0102] The generated DIBS signal is injected into the battery as the excitation current waveform, and the collected response signal is processed. The low and medium frequency band impedance is calculated by the Fourier module, and the medium and high frequency band impedance is obtained by averaging the calculation results of multiple edges of the collected response signal through the S transform module. This can reduce the wave frequency while speeding up the measurement speed.

[0103] Example 2

[0104] To measure the impedance spectrum of the battery under test at 0.1Hz-1kHz, an optimized discrete interval binary sequence (DIBS) is generated as a broadband excitation signal. A cutoff frequency is determined, and an iterative algorithm is used to enhance the power spectrum amplitude in the low-frequency band (0.1Hz to the cutoff frequency). A transmission frequency of 2 times the cutoff frequency or above, and a data point number and waveform length of approximately 10s are selected to improve the measurement speed. A controllable current source is used to inject the current excitation generated by the determined DIBS signal into the battery under test.

[0105] The voltage value after differential amplification of the battery voltage and the reference voltage and the voltage value converted by the current sensor are collected to obtain the current and voltage response signals of the battery to be tested.

[0106] All sampling data are calculated by Fourier transform, and the impedance spectrum of the low frequency band (0.1Hz-demarcation frequency) is extracted after filtering; the sampling data are digitally filtered and then the impedance spectrum of the medium and high frequency band (demarcation frequency-1kHz) is extracted by S transform; the direct selection method in the fusion algorithm is used to select the calculation result of S transform at the demarcation point, and finally the optimized impedance spectrum Z covering the entire frequency band is generated. EIS (f).

[0107] Example 3

[0108] Figure 1 2 is a structural diagram of the impedance spectrum measurement system of the present invention, which includes an excitation generation module, a data acquisition module and an impedance calculation module.

[0109] Set the demarcation frequency to 1Hz, use the MATLAB library function dibs to enhance the power spectrum amplitude in the range of 0.1Hz-1Hz, set the wave frequency to 3Hz, and generate a DIBS current excitation sequence with a data volume of 31 and a waveform time of about 10.24s. Its waveform and spectrum are as follows Figure 2 and Figure 3 shown.

[0110] A KIKUSUI-PBZ20-20 programmable current source generates an excitation current waveform and injects it into the battery under test. To more accurately capture subtle changes in the response voltage, voltage sampling is achieved by taking the difference between the battery under test and a reference voltage and amplifying it 100 times. The current sample is converted into a voltage signal using a current sensor. Using a Dewesoft Siriusi-HS-8xHV data acquisition card with a sampling frequency of 100 kHz, voltage and current data (V(n) and I(n)) are acquired synchronously during the excitation current injection.

[0111] Fourier transforms are performed on 100V(n) and 5I(n) (the current sensor has a scaling factor of 5). The two are divided according to Ohm's law as shown below. The impedance spectrum is then obtained by mean filtering. The calculated results in the 0.1Hz-1Hz range are extracted, and the impedance values ​​at the low-frequency points are obtained by cubic spline interpolation. The reference value of the battery impedance is measured by Gamry. The two are compared. Figure 4 shown.

[0112]

[0113] After Butterworth second-order filtering of 100V(n) and 5I(n), each edge (7 rising edges and 7 falling edges) of V'(n) and I'(n) is subjected to S-transformation at each frequency point. The results are as follows: Figure 5 As shown, according to the Ohm's law shown in the following formula, the impedance value of the frequency range of 1Hz-1kHz can be directly obtained by dividing it. Finally, the average value of the results obtained at each edge is obtained to obtain Z ST (f), the results are as follows Figure 6 shown.

[0114]

[0115] According to the direct selection method in the data fusion algorithm, the calculation result of the S transform is selected at the 1Hz frequency point, and the impedance spectrum Z of 0.1Hz-1kHz is finally generated.EIS (f), and draw a curve such as Figure 7 It can be seen that the final full-band impedance calculation result is almost consistent with the standard EIS curve, and the accuracy of the method proposed in the present invention is very high.

[0116] Example 4

[0117] like Figure 8 As shown, the present invention provides a battery impedance spectrum measurement device combining Fourier transform and S transform, comprising:

[0118] The data generation unit generates a current excitation signal I containing multiple frequency components through a controllable current source. m (t), and applying a current excitation signal to the positive and negative electrodes of the battery under test;

[0119] The data acquisition unit filters and differentially amplifies the real-time current response signal I(t) and voltage response signal V(t) of the battery while injecting the current excitation signal, and then synchronously collects them;

[0120] The data preprocessing unit inputs the digitized current response signal and voltage response signal into the Fourier transform module and the S transform module respectively, wherein: the signal is transformed in the frequency domain to extract the impedance amplitude and phase information of the characteristic frequency point, and the Fourier transform module calculates the first impedance spectrum Z DFT (t), the S-transformation module calculates the second impedance spectrum Z ST (t);

[0121] The data processing unit uses the data fusion algorithm to process the first impedance spectrum Z DFT (t) and the second impedance spectrum Z ST (t) Perform composite analysis and processing, and select the combination of Fourier transform and S transform impedance spectrum data in the frequency domain according to the preset rules to obtain the optimized electrochemical impedance spectrum Z EIS (f) The final result.

[0122] In this embodiment, the current excitation signal I m (t) Includes multi-frequency sinusoidal signals, chirped signals, pseudo-random binary sequences and discrete interval binary sequences.

[0123] The above shows and describes the basic principles and main features of the present invention and the advantages of the present invention. It is obvious to those skilled in the art that the present invention is not limited to the details of the above exemplary embodiments, and that the present invention can be implemented in other specific forms without departing from the spirit or essential characteristics of the present invention. Therefore, from all points of view, the embodiments should be regarded as illustrative and non-restrictive. The scope of the present invention is defined by the appended claims rather than the above description, and it is intended that all changes that fall within the meaning and range of equivalents of the claims are included in the present invention. Any reference signs in the claims should not be construed as limiting the claim to which they relate.

[0124] In addition, it should be understood that although this specification describes the embodiments, not every embodiment contains only one independent technical solution. This description is for clarity only. Those skilled in the art should consider the specification as a whole. The technical solutions in each embodiment can also be appropriately combined to form other embodiments that can be understood by those skilled in the art. The above content is only for the purpose of illustrating the technical concept of the present invention and cannot be used to limit the scope of protection of the present invention. Any changes made based on the technical solution in accordance with the technical concept proposed by the present invention fall within the scope of protection of the claims of the present invention.

Claims

1. A battery impedance spectrum measurement method combining Fourier transform and S transform, characterized in that: include: Generate a current excitation signal I containing multiple frequency components through a controllable current source m (t), and apply the current excitation signal to the positive and negative electrodes of the battery to be tested. While injecting the current excitation signal, the real-time current response signal I(t) and voltage response signal V(t) of the battery are filtered and differentially amplified before being synchronously collected; The digitized current response signal and voltage response signal are input into the Fourier transform module and the S transform module respectively, wherein: the signal is transformed into the frequency domain to extract the impedance amplitude and phase information of the characteristic frequency point, and the Fourier transform module calculates the first impedance spectrum Z DFT (t), the S-transformation module calculates the second impedance spectrum Z ST (t); The first impedance spectrum Z is obtained by data fusion algorithm. DFT (t) and the second impedance spectrum Z ST (t) Perform composite analysis and processing, and select the combination of Fourier transform and S transform impedance spectrum data in the frequency domain according to the preset rules to obtain the optimized electrochemical impedance spectrum Z EIS (f) The final result.

2. The battery impedance spectrum measurement method combining Fourier transform and S transform according to claim 1, characterized in that: Current excitation signal I m (t) is a broadband signal, including a multi-frequency sinusoidal signal, a chirp signal, a pseudo-random binary sequence, and a discrete interval binary sequence.

3. The battery impedance spectrum measurement method combining Fourier transform and S transform according to claim 1, characterized in that: The Fourier transform module algorithm includes filtering the impedance calculation results and interpolating the impedance values ​​at the frequency points.

4. The battery impedance spectrum measurement method combining Fourier transform and S transform according to claim 1, characterized in that: The S-transformation module algorithm parameter setting satisfies the Gaussian window time width of the S-transformation to be 3σ or above. When the excitation signal contains multiple step moments, the calculation process includes performing S-transformation on each rising and falling edge of the waveform to obtain each impedance value. The average of multiple values ​​is the second impedance spectrum Z. ST The final calculation result of (t).

5. The battery impedance spectrum measurement method combining Fourier transform and S transform according to claim 1, characterized in that: The Fourier transform module and the S transform module algorithm include: determining the analysis frequency range interval (f DFT_min , f DFT_max ) and (f ST_min , f ST_max ), the union of the two interval ranges should cover the full frequency band that the impedance spectrum needs to measure.

6. The battery impedance spectrum measurement method combining Fourier transform and S transform according to claim 5, characterized in that: (f DFT_min , f DFT_max ) is the low-intermediate frequency, (f ST_min , f ST_max ) is the medium and high frequency.

7. The battery impedance spectrum measurement method combining Fourier transform and S transform according to claim 1, characterized in that: The fusion processing of Fourier transform and S transform calculation results includes: the intersection of the frequencies calculated by the two methods (f both_min , f both_max ) within the range of , take one of the calculation results, calculate the average value, perform amplitude calibration and phase matching, or use weighted averaging to achieve smooth transition between the overlapping frequency bands; (1) Direct selection method Select Z in the intersection band DFT (t) or Z ST (t) one of them as output; Z EIS f = Z DFT f or Z EIS f = Z ST f (2) Arithmetic mean method (3) Amplitude calibration and phase matching method In order to eliminate the amplitude / phase offset caused by the algorithm differences between the two methods, normalization calibration is performed; The calibration coefficients α and β are calculated by reference impedance, and the impedance amplitude in subsequent calculations can be obtained after calibration; |Z ′ DFT (f)|=α(f)|Z DFT (f)| |Z ′ ST (f)|=β(f)|Z ST (f)| Calculate the phase difference between DFT and ST and obtain the compensation value. In subsequent calculations, the ST calculation phase can be adjusted to align the DFT phase value. The calibrated impedance value is shown in the following formula: (4) Weighted average method Dynamically allocate weights within the intersection frequency band. The weight function is designed as shown below According to the frequency position, we can get According to the signal-to-noise ratio, we can get According to the weight coefficient, the calculation result of the intersection frequency band can be obtained as Z EIS (f) = ω DFT (f)·Z ′ DFT (f)+ω ST (f)·Z ′ ST (f).

8. The battery impedance spectrum measurement method combining Fourier transform and S transform according to claim 2, characterized in that: When the discrete interval binary sequence is used as the excitation signal, the DIBS current excitation signal is constructed by the library function dibs in MATLAB, and only the (f DFT_min , f DFT_max ) low- and medium-frequency power spectrum amplitude; inject the generated DIBS signal into the battery as the excitation current waveform, process the collected response signal, calculate the low- and medium-frequency impedance through the Fourier module, and calculate the medium- and high-frequency impedance through the S-transform module by averaging the calculation results of multiple edges of the collected response signal, which can reduce the wave frequency while speeding up the measurement speed; Using Fourier transform alone to measure impedance spectrum requires the DIBS waveform to be used for the full frequency band (f DFT_min , f ST_max ) spectrum to ensure that it has sufficient excitation energy density within the target frequency band; When the Fourier transform measurement target is limited to the impedance characteristics of the low and medium frequency bands, the spectrum optimization strategy of DIBS is adjusted in a targeted manner. Through the waveform iteration generation algorithm, the signal energy is concentrated in (f DFT_min , f DFT_max ) within the target frequency band.

9. A battery impedance spectrum measurement device combining Fourier transform and S transform, characterized in that: include: The data generation unit generates a current excitation signal I containing multiple frequency components through a controllable current source. m (t), and applying a current excitation signal to the positive and negative electrodes of the battery under test; The data acquisition unit filters and differentially amplifies the real-time current response signal I(t) and voltage response signal V(t) of the battery while injecting the current excitation signal, and then synchronously collects them; The data preprocessing unit inputs the digitized current response signal and voltage response signal into the Fourier transform module and the S transform module respectively, wherein: the signal is transformed in the frequency domain to extract the impedance amplitude and phase information of the characteristic frequency point, and the Fourier transform module calculates the first impedance spectrum Z DFT (t), the S-transformation module calculates the second impedance spectrum Z ST (t); The data processing unit uses the data fusion algorithm to process the first impedance spectrum Z DFT (t) and the second impedance spectrum Z ST (t) Perform composite analysis and processing, and select the combination of Fourier transform and S transform impedance spectrum data in the frequency domain according to the preset rules to obtain the optimized electrochemical impedance spectrum Z EIS (f) The final result.

10. The battery impedance spectrum measuring device combining Fourier transform and S transform according to claim 9, characterized in that: The current excitation signal I in the data generation unit m (t) is a broadband signal, including a multi-frequency sinusoidal signal, a chirp signal, a pseudo-random binary sequence, and a discrete interval binary sequence.

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