Electrochemical impedance spectroscopy measurement method of energy storage battery

Abstract

The invention provides an electrochemical impedance spectroscopy measurement method for an energy storage battery, which comprises the following steps: step 1, respectively optimizing M frequencies fi excited by current into optimally corrected frequencies which are multiples of an optimal fundamental frequency; 2, the amplitude Ai corresponding to the frequency fi is optimized to be Ai ', so that the response voltage signal amplitudes of the energy storage battery to be measured under different frequencies fi are all V; step 3, sampling the current excitation in the step 1A to obtain a discrete multi-sine composite signal sequence, and then performing phase iterative optimization on the current excitation in the step 1A based on the multi-sine composite signal sequence and Ai; step 4, applying the optimized current excitation to the energy storage battery to be tested to obtain a response voltage signal and a current signal of the battery end; and step 5, calculating the complex impedance of the battery under different frequencies fMi, and finally obtaining the complete impedance spectrum of the energy storage battery to be measured in the selected frequency range. The method is high in measurement speed and high in measurement precision.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of battery detection, and particularly relates to an electrochemical impedance spectroscopy measurement method for an energy storage battery. BACKGROUND

[0002] Lithium ion batteries are widely used in vehicles and consumer electronic products due to their high specific energy, environmental protection, and no memory effect. Electrochemical impedance spectroscopy (EIS) can reflect the dynamic characteristics of the complex physical and electrochemical processes of the battery. Processes such as passivation film growth, electrolyte degradation, and lithium dendrite growth, which are closely related to the life and safety, can be reflected in EIS. Therefore, EIS is reported to be used for SOH estimation, temperature estimation, and lithium precipitation diagnosis. In the traditional case, EIS is mainly used in the laboratory, for example, integrating this function in an electrochemical workstation, using multiple single-frequency sinusoidal signals to excite the battery to obtain the impedance at each frequency. Although this measurement method has high accuracy, the measurement time is relatively long. At the same time, vehicle-mounted applications also require real-time and rapid acquisition of EIS for control. Therefore, improving the EIS measurement speed has become a key problem.

[0003] CN109459465B discloses an electrochemical impedance spectroscopy measurement method based on M sequence. However, this method has the following disadvantages: (1) the low-frequency spectrum in the M sequence is relatively sparse, and it is difficult to customize the target frequency in actual measurement; (2) the M sequence has a serious high-frequency attenuation problem, and a considerable amount of excitation energy is distributed at frequency points that are not of interest, which reduces the signal-to-noise ratio of the measurement and thus the EIS measurement accuracy. CN117872191A discloses an electrochemical impedance spectroscopy measurement method based on multiple sinusoidal signals. However, this method only optimizes the amplitude of the superimposed sinusoidal signal and does not solve the problem of excessively high peak value and long measurement time of the multiple sinusoidal signals under certain conditions.

[0004] Therefore, the prior art still lacks a fast and high-precision electrochemical impedance spectroscopy measurement scheme. SUMMARY

[0005] The purpose of the present application is to provide an electrochemical impedance spectroscopy measurement method for an energy storage battery, which reduces the measurement time of electrochemical impedance spectroscopy, improves the signal-to-noise ratio, and reduces the instantaneous power demand of the excitation source. The technical solution adopted is as follows:

[0006] An electrochemical impedance spectroscopy measurement method for an energy storage battery, comprising the following steps:

[0007] Step 1, optimizing M frequencies f i to optimal modified frequencies, which are multiples of the optimal base frequency; i = 1 ~ M.

[0008] Step 1 is to optimize the frequency of the multi-sine signal to obtain a new impedance measurement frequency f Mi , thereby reducing the measurement time.

[0009] Step 2, the frequency f i corresponding to the amplitude A i is optimized to A i′ , so that the response voltage signal amplitudes of the different frequencies f i of the to-be-measured energy storage battery are all V.

[0010] Specifically: initially obtain the impedance modulus distribution Z i of the to-be-measured impedance spectrum, and optimize the amplitudes of the multi-sine signals according to the impedance characteristics, so that the response voltage amplitudes of the battery at different frequencies are the same.

[0011] Step 3, sample the current excitation in step 1A to obtain a discrete multi-sine composite signal sequence, and then perform phase iterative optimization on the multi-sine composite signal sequence and A i to the current excitation in step 1A.

[0012] Specifically: according to the highest frequency of the to-be-measured impedance, sample the multi-sine composite signal at a sampling frequency f s1 to obtain a discrete multi-sine signal sequence, and perform phase optimization on the multi-sine signal to reduce the peak value.

[0013] The sampling frequency f s1 is greater than the highest frequency of the to-be-measured impedance.

[0014] Step 4, apply the optimized current excitation to the to-be-measured energy storage battery to obtain the response voltage signal and the current signal at the battery end.

[0015] Specifically: use the power supply to output the optimized multi-sine signal to charge and discharge the battery, and use a sampling frequency f s2 to collect the end voltage and current sequence of the to-be-measured battery.

[0016] Step 5, perform time-frequency analysis on the obtained battery end voltage and current sequence to calculate the complex impedance of the battery at different frequencies f Mi , and finally obtain the complete impedance spectrum of the to-be-measured energy storage battery in the selected frequency range.

[0017] Preferably, step 1 specifically comprises:

[0018] Step 1A, to obtain the electrochemical impedance between the frequency range Ω1~Ω2, select M sinusoidal signals with different frequencies and amplitudes in the frequency range Ω1~Ω2 at equal intervals, and superimpose them as the current excitation of the to-be-measured energy storage battery;

[0019] Step 1B: Establish a frequency correction model. The frequency correction model consists of the fundamental frequency f0 and f... Mi Mapping between;

[0020] Step 1C: Based on the frequency correction model, establish the maximum relative error model, where the maximum relative error model is f. Mi and maximum relative error err max Mapping between;

[0021] Step 1D: Select any one of the M frequencies and assign it to the fundamental frequency f0. Then, substitute the M frequencies into the error model one by one. Iterate through the M maximum relative errors to obtain the maximum relative error.

[0022] The frequency corresponding to the minimum maximum relative error is taken as the optimal fundamental frequency. The corrected frequency f corresponding to the optimal fundamental frequency in the frequency correction model is... Mi That is, the optimal corrected frequency.

[0023] Preferably, the optimized amplitude A in step 2 i′ for:

[0024]

[0025] Among them, Z i -Energy storage battery at frequency f i The impedance below;

[0026] |Z i |- The magnitude of the impedance.

[0027] Preferably, step 3 specifically includes the following steps:

[0028] Step 3A: Sample the current excitation from Step 1A to obtain a discrete multi-sine composite signal sequence;

[0029] Step 3B: Based on the discrete multi-sine synthesized signal sequence, obtain the initial phase φ of the i-th sine signal. i ;

[0030] Step 3C: Based on the amplitude A of each sine signal i and initial phase φ i To obtain the new initial phase of each sinusoidal signal;

[0031] Step 3D, using amplitude A i With a new initial phase, repeat step 3C until the number of iterations k. times Increase to the specified number of iterations.

[0032] Preferably, step 3C specifically includes the following steps:

[0033] Step 3C1, Amplitude A of each sine signali and initial phase φ i , inverse discrete Fourier transform IDFT is performed to calculate the time domain solution of the multi-sine synthesis signal;

[0034] Step 3C2, peak current I peak′ of the multi-sine synthesis signal in the time domain is calculated peak and is replaced by I peak′ ;

[0035] Step 3C3, the multi-sine synthesis signal modified in step 3C2 is calculated by discrete Fourier transform DFT to obtain a new initial phase.

[0036] Compared with the prior art, the present application has the following advantages:

[0037] The method optimizes the frequency, amplitude and phase of the multi-sine synthesis signal, reduces the measurement time of the electrochemical impedance spectrum, improves the signal-to-noise ratio, and reduces the instantaneous power demand of the excitation source. It has the characteristics of fast measurement speed and high measurement accuracy. It is of great significance for the on-board implementation of the electrochemical impedance spectrum technology. BRIEF DESCRIPTION OF DRAWINGS

[0038] Figure 1 is a flow chart of the electrochemical impedance spectrum measurement method of the energy storage battery;

[0039] Figure 2 is a test result graph. DETAILED DESCRIPTION

[0040] The electrochemical impedance spectrum measurement method of the energy storage battery will be described in more detail below in conjunction with the accompanying drawings, in which a preferred embodiment of the present application is shown. It should be understood that those skilled in the art can modify the present application described herein while still achieving the advantageous effects of the present application. Therefore, the following description should be understood as a broad knowledge to those skilled in the art, and not as a limitation of the present application.

[0041] As shown in Figure 1 , an electrochemical impedance spectrum measurement method of an energy storage battery specifically comprises the following steps:

[0042] Step 1, M frequencies f i of the current excitation are respectively optimized to optimal modified frequencies, and the optimal modified frequencies are multiples of the optimal base frequency; i = 1 ~ M.

[0043] Step 1A, to obtain the electrochemical impedance between frequency range Ω1~Ω2, M frequency different and amplitude different sinusoidal signals are selected at equal intervals in the frequency range Ω1~Ω2, and are superimposed as the current excitation of the energy storage battery to be measured.

[0044] Step 1B: Establish a frequency correction model. The frequency correction model consists of the fundamental frequency f0 and f... Mi The mapping between them.

[0045] Frequency correction model:

[0046]

[0047] in, - The floor operator, f Mi - Corrected frequency;

[0048] That is, at the frequencies f of the M electrochemical impedance spectra to be measured i Reselect and optimize the frequency.

[0049] For the frequency f originally selected at logarithmic intervals i Make corrections. Design all frequencies to be multiples of a certain fundamental frequency f0.

[0050] Step 1C: Based on the frequency correction model, establish the maximum relative error model, where the maximum relative error model is f. Mi and maximum relative error err max The mapping between them.

[0051]

[0052] Calculate different err based on different f0 values. max The fundamental frequency is selected as f0 when the error is smaller.

[0053] Step 1D: Select any one of the M frequencies and assign it to the fundamental frequency f0. Then, substitute the M frequencies into the error model one by one. Iterate through the M maximum relative errors to obtain the maximum relative error.

[0054] The frequency corresponding to the minimum maximum relative error is taken as the optimal fundamental frequency. The corrected frequency f corresponding to the optimal fundamental frequency in the frequency correction model is... Mi That is, the optimal corrected frequency.

[0055] That is, the maximum relative error err between the modified frequency and the original frequency. max Calculate as shown in equation (2). Calculate different err based on different f0 values. max The fundamental frequency is selected as f0 when the maximum relative error is smaller.

[0056] Step 2, set the frequency f i The corresponding amplitude A i Optimized to A i′ This makes different frequencies f i The amplitude of the response voltage signal of the energy storage battery under test is V. Where V is 10mV.

[0057]

[0058] Z i -Energy storage battery at frequency f i The impedance is obtained from the impedance modulus of the energy storage battery under test as a function of frequency.

[0059] |Z i |- The magnitude of the impedance.

[0060] Adjust the frequency f as shown in equation (3) i The amplitude of the excitation signal A below i This ensures that the battery's response signal amplitude is the same, always V, under different excitation frequencies.

[0061] The variation of the battery's impedance mode with frequency was obtained by measuring it using pre-experimental methods such as DCR with an electrochemical workstation.

[0062] Step 3: Determine the sampling frequency f based on frequency Ω2. s1 With sampling frequency f s1 The current excitation in step 1A is sampled to obtain a discrete multi-sine composite signal sequence. Then, based on the multi-sine composite signal sequence and A... i Phase iteration optimization is performed on the current excitation in step 1A to reduce the peak value.

[0063] Multi-sine composite signal, i.e. current excitation signal.

[0064] The acquired multi-sine composite signal sequence includes: the initial phase φ1 of the fundamental wave, the power p of the i-th harmonic, and the initial phase φ1 of the fundamental wave. j .

[0065] Step 3A: Determine the sampling frequency f based on frequency Ω2. s1 With sampling frequency f s1 The current excitation in step 1A is sampled to obtain a discrete multi-sine composite signal sequence.

[0066] Step 3B: Based on the discrete multi-sine synthesized signal sequence, obtain the initial phase φ of the i-th sine signal. i .

[0067]

[0068] φ i - The initial phase of the i-th sinusoidal signal, φ1 - The initial phase of the fundamental wave, p j The power of the -i harmonic.

[0069] "Fundamental frequency" refers to the sine wave corresponding to the optimal fundamental frequency in step 1D.

[0070] Step 3C: Based on the amplitude A of each sine signal i and initial phase φ i To obtain the new initial phase of each sinusoidal signal.

[0071] Step 3C1, Amplitude A of each sine signal i and initial phase φ i Perform inverse discrete Fourier transform (IDFT) to compute the time-domain solution of the multisine composite signal (current excitation in step 1A).

[0072] The time-domain solution is a complete representation of the signal, including its shape and characteristics in the time domain.

[0073] Time-domain solution: The horizontal axis represents time, and the vertical axis represents the magnitude of the current.

[0074] The peak current I in step 3C2 peak The time-domain current data is obtained from this step.

[0075] Step 3C2, Calculate the peak current I peak′ And the peak current I of the multi-sinusoidal synthesized signal in the time domain peak Replace with I peak′ .

[0076]

[0077] The peak current I of the multisine composite signal is replaced in the time domain using the logarithmic clipping function in equation (5). peak .

[0078] Peak current I peak The method for determining "" is: first take the absolute value, then take the maximum value.

[0079] When V = 10mV, I peak >I peak′ .

[0080] The steps for "replacement" are as follows: The multi-sine composite signal is displayed on the computer, and the peak value can be modified on the computer.

[0081] Step 3C3: Calculate the modified multi-sine composite signal from step 3C2 using Discrete Fourier Transform (DFT) to obtain a new initial phase.

[0082] This phase solution (the new initial phase) corresponds to a current excitation with a lower peak value.

[0083] Step 3D, using amplitude A i With a new initial phase, repeat step 3C until the number of iterations k. times Increase to the specified number of iterations.

[0084] In summary, regarding step 3:

[0085] First, using A i and φ i An inverse discrete Fourier transform (IDFT) is performed to compute the time-domain solution of the multisine synthesized signal. Next, the peak current I of the multisine synthesized signal is replaced in the time domain using the logarithmic clipping function in equation (5). peak Then, the corrected multi-sine composite signal is calculated using Discrete Fourier Transform (DFT) to obtain the new phase φ. i This phase solution corresponds to a multi-sine composite signal with a lower peak value. The algorithm uses a new φ. i And the original A i Repeat the process from IDFT to DFT until the number of iterations k. times Increase to the specified number of iterations.

[0086] Step 4: Apply the optimized current excitation to the energy storage battery under test, using a sampling frequency f. s2 The response voltage and current signals at the battery terminal are obtained.

[0087] The optimized current excitation includes M sinusoidal signals, whose frequency, amplitude, and initial phase are respectively the optimal corrected frequency f. Mi A i′ The new initial phase is obtained after step 3D is completed.

[0088] Among them, "applying current excitation to the energy storage battery under test" and "using sampling frequency f" s2 The solution involved in "obtaining the response voltage and current signals from the battery terminal" is existing technology.

[0089] Step 5: Perform time-frequency analysis on the obtained battery terminal voltage and current sequences, and calculate different frequencies f. Mi By analyzing the complex impedance of the battery, the complete impedance spectrum of the energy storage battery under test within the selected frequency range is finally obtained.

[0090] In this embodiment, the electrochemical impedance spectroscopy is measured on an 18650 lithium-ion battery. The measurement frequency is 41 frequency points with equal logarithmic spacing in the range of 0.1Hz to 1kHz. Different f0 values ​​are set as shown in equation (2) to calculate the frequency error. A suitable f0 = 0.0323 is selected. Through frequency optimization, the measurement time of the multi-sine composite signal is reduced to 30.9598 seconds.

[0091] The impedance mode of the battery as a function of frequency, as measured by an electrochemical workstation, is shown in equation (3). The frequency f is adjusted with a target voltage of 10mV. i The amplitude of the excitation signal A below iThe signal-to-noise ratio is improved by amplitude optimization.

[0092] Finally, the sampling frequency f is selected based on the highest frequency of 1kHz. S1 Using sampling frequency f S1 =5kHz multi-sine composite signal is acquired and phase optimized as shown in step 3 to reduce the multi-sine current peak by 25.72%.

[0093] The battery is charged and discharged using a multi-sine synthesized signal optimized for power output, with a sampling frequency f. S2 =100kHz to acquire the terminal voltage and current of the battery under test, and obtain the discrete voltage sequence u(m) and discrete current sequence i(m).

[0094] As shown in equations (6) and (7), FFT processing is performed to obtain the Fourier transform results of the voltage response and the excitation current.

[0095]

[0096] The complex impedance Z(jω) is calculated as shown in equation (8).

[0097]

[0098] As shown in equations (9) and (10), the real part Z of the impedance in equation (8) is taken. Re (ω) and the imaginary part of impedance Z Im (ω),

[0099] Z Re (ω)=Re[Z(jω)] (9)

[0100] Z Im (ω)=Im[Z(jω)] (10)

[0101] The complex impedances obtained at each frequency point can be used to construct the electrochemical impedance spectrum of the battery under test.

[0102] Test results are as follows Figure 2 As shown. Figure 2 In Figure (a), the horizontal axis represents the real part of the impedance, and the vertical axis represents the imaginary part of the impedance. Figure 2 In Figure (b), the horizontal axis “EIS frequency” represents the frequency point.

[0103] Figure 2 The results show a comparison between the electrochemical impedance spectroscopy (EIS) data obtained by the method described in this patent and the EIS data obtained by an electrochemical workstation. The results indicate that the total measurement time for the proposed method in the 1 kHz–0.1 Hz range is 30.9598 seconds. Furthermore, the maximum modulus relative error is 0.47%, and the maximum phase absolute error is 0.23°.

[0104] The above are merely preferred embodiments of the present invention and do not constitute any limitation on the present invention. Any equivalent substitutions or modifications made by those skilled in the art to the technical solutions and content disclosed in the present invention without departing from the scope of the present invention shall be deemed to have remained within the protection scope of the present invention.

Claims

1. A method for measuring the electrochemical impedance spectroscopy of an energy storage battery, characterized in that, Includes the following steps: Step 1: Apply current excitation at M frequencies f i Each frequency is optimized to the optimal corrected frequency, which is a multiple of the optimal fundamental frequency; i = 1 to M; Step 2, set the frequency f i The corresponding amplitude A i Optimized to A i′ This makes different frequencies f i The amplitude of the response voltage signal of the energy storage battery under test is V; Step 3: Sample the current excitation from Step 1A to obtain a discrete multi-sine composite signal sequence. Then, based on the multi-sine composite signal sequence and A... i Phase iteration optimization is performed on the current excitation in step 1A; Step 4: Apply the optimized current excitation to the energy storage battery under test to obtain the response voltage and current signals at the battery terminals; Step 5: Perform time-frequency analysis on the obtained battery terminal voltage and current sequences, calculate the complex impedance of the battery at different frequencies, and finally obtain the complete impedance spectrum of the energy storage battery under test in the selected frequency range.

2. The electrochemical impedance spectroscopy measurement method for energy storage batteries according to claim 1, characterized in that, Step 1 specifically includes: Step 1A: To obtain the electrochemical impedance within the frequency range Ω1 to Ω2, select M sinusoidal signals with different frequencies and amplitudes at equal intervals within the frequency range Ω1 to Ω2, and superimpose them as the current excitation of the energy storage battery under test. Step 1B: Establish a frequency correction model. The frequency correction model consists of the fundamental frequency f0 and f... Mi Mapping between; Step 1C: Based on the frequency correction model, establish the maximum relative error model, where the maximum relative error model is f. Mi and maximum relative error err max Mapping between; Step 1D: Select any one of the M frequencies and assign it to the fundamental frequency f0. Then, substitute the M frequencies into the error model one by one. Iterate through the M maximum relative errors to obtain the maximum relative error. The frequency corresponding to the minimum maximum relative error is taken as the optimal fundamental frequency. The corrected frequency f corresponding to the optimal fundamental frequency in the frequency correction model is... Mi That is, the optimal corrected frequency.

3. The electrochemical impedance spectroscopy measurement method for energy storage batteries according to claim 1, characterized in that, The optimized amplitude A in step 2 i′ for: Among them, Z i -Energy storage battery at frequency f i The impedance below; |Z i |- The magnitude of the impedance.

4. The electrochemical impedance spectroscopy measurement method for energy storage batteries according to claim 1, characterized in that, Step 3 specifically includes the following steps: Step 3A: Sample the current excitation from Step 1A to obtain a discrete multi-sine composite signal sequence; Step 3B: Based on the discrete multi-sine synthesized signal sequence, obtain the initial phase φ of the i-th sine signal. i ; Step 3C: Based on the amplitude A of each sine signal i and initial phase φ i To obtain the new initial phase of each sinusoidal signal; Step 3D, using amplitude A i With the new initial phase, repeat step 3C until the iteration number k. times Increase to the specified number of iterations.

5. The electrochemical impedance spectroscopy measurement method for energy storage batteries according to claim 1, characterized in that, Step 3C specifically includes the following steps: Step 3C1, Amplitude A of each sine signal i and initial phase φ i Perform inverse discrete Fourier transform (IDFT) to compute the time-domain solution of the multi-sine composite signal; Step 3C2, Calculate the peak current I peak′ And the peak current I of the multi-sinusoidal synthesized signal in the time domain peak Replace with I peak′ ; Step 3C3: Calculate the modified multi-sine composite signal from step 3C2 using Discrete Fourier Transform (DFT) to obtain a new initial phase.

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