CN116243176A - Electrochemical impedance spectrum-based lithium battery modeling and parameter identification method - Google Patents

Electrochemical impedance spectrum-based lithium battery modeling and parameter identification method Download PDF

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CN116243176A
CN116243176A CN202310211924.5A CN202310211924A CN116243176A CN 116243176 A CN116243176 A CN 116243176A CN 202310211924 A CN202310211924 A CN 202310211924A CN 116243176 A CN116243176 A CN 116243176A
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cpe
drt
lithium battery
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赖纪东
苏志鹏
苏建徽
周晨光
施永
解宝
王祥
董磊
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Hefei University of Technology
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Abstract

The invention discloses a lithium battery modeling and parameter identification method based on electrochemical impedance spectroscopy, which is performed based on lithium battery EIS test data, and is used for effectively and quickly decomposing an EIS spectrum in a digital method, decoupling the dynamic process in a battery and introducing an improved relaxation time distribution method to establish a multi-order composite equivalent circuit model for a lithium battery system. The method for realizing model parameter identification by carrying out low-high frequency partition on the battery is adopted, the impedance spectrum of the high-frequency area is usually considered to be related to the ohmic impedance of the battery, the low-frequency area is considered to reflect the semi-diffusion effect of the lithium battery, the CPE of a plurality of normal phase elements is used for representing, the problem that error is caused by DRT analysis caused by a non-polarized reaction structure part in the actual battery is effectively avoided, and finally the modeling is completed by fitting a DRT distribution function gamma (lntau) through a regularization method. The equivalent circuit model with high physical significance, which is established by the method, can be effectively applied to occasions such as battery life prediction, battery classification screening, battery echelon utilization and the like.

Description

Electrochemical impedance spectrum-based lithium battery modeling and parameter identification method
Technical Field
The invention relates to a lithium battery modeling and parameter identification method based on electrochemical impedance spectroscopy.
Background
Lithium ion batteries are considered as one of the most promising important vehicle-mounted energy sources of Electric Vehicles (EVs) in view of the characteristics of high energy density, high power capability, long cycle life, lighter weight and the like. The pursuit of safer, more reliable, more efficient and longer-lived electrochemical energy storage and conversion technologies is independent of the support of electrochemical characterization technologies. The existing lithium battery modeling methods mainly comprise a cyclic voltammetry method, a timing current/voltage/electric quantity method, a special performance test working condition method, an electrochemical impedance spectroscopy method and the like.
The electrochemical impedance analysis mainly applies a plurality of sinusoidal signals with different frequencies to the battery, analyzes the acquired data information, predicts the current performance of the battery, and has the advantages of short time consumption, high precision, wide frequency band, simple operation, no damage and the like compared with other methods. In EIS diagnosis analysis, EIS test data are often fitted to a given equivalent circuit to extract circuit model parameters, so that the characteristics of a physical electrochemical system, such as diffusion coefficient, chemical reaction rate, microstructure characteristics and the like, are analyzed, and the EIS test data have good sensitivity to external parameters and internal parameters of the electrochemical system, so that the EIS test data are widely applied to the fields of new energy, electrocatalysis, battery modeling, parameter identification and the like.
The current mainstream EIS modeling method comprises digital modeling (such as a deep learning network) and mechanism modeling, wherein the digital modeling method has the problems that a large amount of data is required to be input and learned, the relation between the impedance spectrum of the battery and the result state parameter is not concerned, and the interpretation of the battery mechanism is weak. And the mechanism modeling selection can adopt an electrochemical model, a thermodynamic model, a coupling model, an equivalent circuit model and the like. The equivalent circuit model rule has the advantage of no need of in-depth analysis of electrochemical reaction inside the battery, and describes open-circuit voltage, direct-current internal resistance and polarized internal resistance of the battery through a circuit so as to realize characterization of external characteristics of the battery.
However, the existing equivalent circuit model method still has various defects and shortcomings such as prior assumption, for example, the combined action result of internal parameters and external parameters of a lithium battery system can be displayed on a limited bandwidth, so that characteristic peaks of the lithium battery system are overlapped together or characteristic time constants are not greatly different and are not easy to separate, and therefore ambiguity and uncertainty of EIS diagnosis analysis results are caused. On the other hand, since the characteristic time constants of the physicochemical processes are relatively close, the impedance spectrum arcs obtained by the test are seriously overlapped, thus making the corresponding impedance spectrum analysis difficult.
Disclosure of Invention
The invention aims to avoid the defects of the prior art, and provides a lithium battery modeling and parameter identification method based on electrochemical impedance spectroscopy, so as to realize DRT analysis and parameter identification on the electrochemical impedance spectroscopy of the lithium battery, thereby realizing high-efficiency and high-precision modeling on a lithium battery system.
The invention adopts the following technical scheme for solving the technical problems:
the invention discloses a lithium battery modeling and parameter identification method based on electrochemical impedance spectroscopy, which is characterized by comprising the following steps:
step 1, charging a lithium battery to be tested to a state of charge soc=100%, and standing for a period of time;
step S2, standingAfter being connected with a voltage and current sensor, the lithium battery is subjected to electrochemical impedance spectrum test to obtain impedance data at each frequency point, wherein the impedance data comprises frequency distribution { f } 1 ,f 2 ,…,f m ,…,f M Real part of impedance { Z' 1 ,Z′ 2 ,…,Z′ m ,…,Z′ M Inverse result of imaginary part of impedance { Z } 1 ,Z″ 2 ,…,Z″ m ,…,Z″ M -a }; wherein f m Represents the mth frequency, Z' m Represents the mth frequency f m The real part of the impedance, Z m Represents the mth frequency f m Inverting the imaginary part of the lower impedance;
s3, constructing a DRT model of the lithium battery to be tested, and sequentially using a high-frequency inductor L and an ohmic resistor R 0 DRT distribution function gamma (lnτ) and constant phase element { Q CPE_1 ,Q CPE_2 ,…,Q CPE_k ,…,Q CPE_K A series connection; wherein Q is CPE_k Representing the kth normal phase element;
s4, respectively processing high frequency and low frequency data of impedance data in a DRT model of the lithium battery to be detected so as to finish parameter identification;
step S4.1, according to { f 1 ,f 2 ,…,f m ,…,f M From { Z' 1 ,Z′ 2 ,…,Z′ m ,…,Z′ M Selecting the s real impedance pairs with the highest frequency to the ohmic resistor R 0 Identifying to obtain ohmic resistor R 0 Is a recognition resistance value of (a); meanwhile, the high-frequency inductor L is identified by utilizing s impedance imaginary parts with highest selected frequencies, so that an identification inductance value of the high-frequency inductor L is obtained;
step S4.2, according to { f 1 ,f 2 ,…,f m ,…,f M From { Z' 1 ,Z′ 2 ,…,Z′ m ,…,Z′ M Selecting v real impedance parts with lowest frequency to respectively subtract ohmic resistance R 0 The identification resistance value of the high-frequency inductor L and the identification inductance value of the high-frequency inductor L are obtained, and v corrected impedance real parts are obtained;
from { Z' 1 ,Z′ 2 ,…,Z′ m ,…,Z′ M Selection among }V impedance imaginary parts with lowest selection frequency minus ohmic resistance R 0 The identification resistance value of the high-frequency inductor L and the identification inductance value of the high-frequency inductor L are obtained, and v corrected impedance imaginary parts are obtained;
fitting the corrected v real parts and imaginary parts of the impedance by a least square method or nonlinear regression to obtain each constant-phase element Q CPE_1 ,Q CPE_2 ,…,Q CPE_k ,…,Q CPE_K Resistance value of (2);
step S4.3, the mth frequency f m The lower real part Z 'of impedance' m Respectively subtracting ohmic resistance R 0 Is a constant phase element Q CPE_1 ,Q CPE_2 ,…,Q CPE_k ,…,Q CPE_K After the real part of the impedance, the mth frequency f is obtained m The lower DRT impedance real part
Figure BDA0004113008680000021
Thereby obtaining DRT impedance real part +.>
Figure BDA0004113008680000022
Will be the mth frequency f m The inverse Z' of the imaginary part of the impedance m The identification inductance value of the high frequency inductance L and the normal phase element Q are subtracted respectively CPE_1 ,Q CPE_2 ,…,Q CPE_k ,…,Q CPE_K After the impedance imaginary part of (2) to obtain the mth frequency f m The inversion of the imaginary part of the DRT impedance
Figure BDA0004113008680000031
Thereby obtaining DRT impedance imaginary part +.>
Figure BDA0004113008680000032
Step S5, utilizing the frequency distribution { f 1 ,f 2 ,…,f m ,...,f M Real part of DRT impedance
Figure BDA0004113008680000033
And DRT impedance imaginary part->
Figure BDA0004113008680000034
And solving the DRT model of the lithium battery to be tested to obtain a fitted DRT distribution function gamma (lntau), thereby realizing the parameter identification of the DRT model of the lithium battery to be tested.
The invention provides an electronic device, which comprises a memory and a processor, wherein the memory is used for storing a program for supporting the processor to execute the lithium battery modeling and parameter identification method, and the processor is configured to execute the program stored in the memory.
The invention relates to a computer readable storage medium, wherein a computer program is stored on the computer readable storage medium, and the computer program is executed by a processor to execute the steps of the lithium battery modeling and parameter identification method.
Compared with the prior art, the invention has the beneficial effects that:
1. compared with the existing cyclic voltammetry, timing current/voltage/electric quantity method and the like, the EIS test technology is adopted, and the method has the advantages of high precision, wide frequency bandwidth (theoretically, all physical electrochemical processes can be covered), simplicity in operation, no damage and the like, and the EIS has sensitivity to external parameters and internal parameters of an electrochemical system.
2. The method solves the problem that the prior assumption is always needed to be considered when the equivalent circuit model is adopted, namely, the fact that a plurality of characteristic time constants exist in the impedance spectrum is needed to be known in advance or presupposed, but the characteristic time constants are not easy to determine, the DRT method is adopted to more accurately fit different types of lithium batteries, trial fitting is not needed, and the training model is high in fitting precision and good in convergence.
3. According to the method, only the improved DRT method is adopted to fit the polarized part of the lithium battery, and meanwhile decoupling treatment is carried out on the unpolarized and non-converged ohmic internal resistance and normal phase part, so that the DRT test precision is improved, and rapid modeling is realized by matching with a special DRT function identification algorithm.
4. The method fully utilizes EIS data in the full frequency band to carry out partition processing to obtain element parameters with strong physical significance, and combines linear fitting to identify the element parameters.
5. The invention introduces the DRT method to effectively distinguish different electrochemical processes in the device, thereby not only maintaining the advantages of EIS test and analytical model, but also overcoming the defects of difficult analysis caused by the ambiguity and uncertainty of diagnostic analysis results and serious overlapping of impedance spectrum arcs.
Drawings
FIG. 1 is a diagram of the measured impedance spectrum Nyquist of an EIS test of a lithium battery in accordance with the present invention;
FIG. 2a is a circuit model under a typical DRT parsing technique;
FIG. 2b is a circuit model under DRT analysis technique in the present invention;
FIG. 3 is a graph showing the current flow analysis of the circuit model according to the invention as the frequency of the AC impedance changes;
FIG. 4a is a graph showing the variation of EIS impedance data based on improved DRT analysis in the present invention
Fig. 4b is a graph of a fitted relaxation time distribution function gamma (lnτ) in accordance with the present invention.
Detailed Description
In this embodiment, a method for modeling and parameter identification of a lithium battery based on electrochemical impedance spectroscopy (Electrochemical Impedance Spectroscopy, EIS) performs equivalent circuit modeling extension on a polarization reaction part of the lithium battery by introducing a relaxation time distribution (Relaxation Time Distribution, DRT) function concept, and divides an acquisition frequency into a low frequency region and a high frequency region for parameter identification and mutual combination, including the following specific steps:
step 1, charging a lithium battery to be tested to a state of charge soc=100%, and standing for a period of time;
and S2, connecting the lithium battery after standing with a voltage and current sensor, performing electrochemical impedance spectrum test, and respectively reducing the description points from high frequency to low frequency from left to right according to EIS data obtained by the test. Obtaining impedance data at each frequency point by Fourier transformation, and repeating for multiple times to ensure accuracy of the impedance dataEIS test, and according to K-K conversion relation, checking reliability of 3 groups of test data, selecting a group with minimum average relative error as final obtained EIS data, including frequency distribution { f 1 ,f 2 ,...,f m ,...,f M Real part of impedance { Z' 1 ,Z′ 2 ,...,Z′ m ,...,Z′ M Inverse result of imaginary part of impedance { Z } 1 ,Z″ 2 ,...,Z″ m ,…,Z″ M -a }; wherein f m Represents the mth frequency, Z' m Represents the mth frequency f m The real part of the impedance, Z m Represents the mth frequency f m The inverse of the imaginary part of the impedance. At this time, a Nyquist chart of the EIS test impedance spectrum of the lithium battery may be drawn, as shown in fig. 1, and this step is mainly for obtaining original EIS impedance data;
step S3, constructing a DRT model of the lithium battery to be tested, wherein the model mainly comprises a high-frequency inductance L and an ohmic resistance R in sequence as shown in a figure 2b 0 DRT distribution function gamma (lnτ) and constant phase element { Q CPE_1 ,Q CPE_2 ,…,Q CPE_k ,…,Q CPE_K A series connection; wherein Q is CPE_k Representing the kth normal phase element. Comparing the model with a typical DRT model, as shown in fig. 2a, the lithium battery DRT model provided by the invention is more accurate and rich, can better embody the complex chemical reaction process in the lithium battery system, and has better fitting effect on EIS impedance data. The DRT distribution function y (lnτ) represents an infinite number of RC loops in series with each other, and the relaxation time constant is at (0, ++ infinity) in is uniformly distributed and is provided with a plurality of grooves, for describing the polarization process where the number and intensity of the cells are unknown. The impedance of each part can be calculated at this time,
high frequency inductance resistance part:
Z RL ＝R 0 +j2πfL (1)
an infinite number N of polarization RCs produces an impedance of:
Figure BDA0004113008680000041
here γ (lnτ) =τg (τ);
the k normal phase elements generate impedance which is decomposed according to the Euler formula:
Figure BDA0004113008680000051
s4, respectively processing high frequency and low frequency data of impedance data in a DRT model of the lithium battery to be detected so as to finish parameter identification;
step S4.1, according to { f 1 ,f 2 ,…,f m ,…,f M From { Z' 1 ,Z′ 2 ,…,Z′ m ,…,Z′ M Selecting the s real impedance pairs with the highest frequency to the ohmic resistor R 0 Identifying to obtain ohmic resistor R 0 Is a recognition resistance value of (a); meanwhile, the high-frequency inductor L is identified by utilizing s impedance imaginary parts with highest selected frequencies, so that an identification inductance value of the high-frequency inductor L is obtained;
as shown in FIG. 3, a simple analysis can now result
Figure BDA0004113008680000052
Z CPEk Approximately equal to 0, it is believed that the input impedance may be approximately considered to be Z only RL The effect may be that at this point, starting from the highest test frequency, e.g. 5 consecutive impedances { Z }, are selected 1 ,Z 2 ,Z 3 ,Z 4 ,Z 5 Point-to-high frequency inductance L, ohmic resistance R 0 Performing least square fitting;
Figure BDA0004113008680000053
step S4.2, according to { f 1 ,f 2 ,…,f m ,…,f M From { Z' 1 ,Z′ 2 ,…,Z′ m ,…,Z′ M Selecting v real impedance parts with lowest frequency to respectively subtract ohmic resistance R 0 The identification resistance value of the high-frequency inductor L and the identification inductance value of the high-frequency inductor L to obtain v corrected resistorsAn anti-real part;
from { Z' 1 ,Z′ 2 ，…,Z′ m ,…,Z′ M Selecting v impedance imaginary parts with lowest frequency to respectively subtract ohmic resistance R 0 The identification resistance value of the high-frequency inductor L and the identification inductance value of the high-frequency inductor L are obtained, and v corrected impedance imaginary parts are obtained;
fitting the corrected v real parts and imaginary parts of the impedance by a least square method or nonlinear regression to obtain each constant-phase element Q CPE_1 ,Q CPE_2 ,...,Q CPE_k ,…,Q CPE_K Resistance value of (2);
as in the current flow and impedance formula of FIG. 3, a simple analysis can be considered as
Figure BDA0004113008680000054
And Z is CPEk Is a purely resistive element and at this point: />
Figure BDA0004113008680000061
For example, assuming that k=5 is taken at this time, the imaginary part of the impedance is considered to be entirely composed of the imaginary part of the normal phase element, and the imaginary part of the impedance is considered to be mainly composed of the normal phase element, and after n (positive correlation with the impedance angle) of the normal phase element is determined at this time, at least 6 continuous frequency points are taken as the known variable parameters according to the EIS data in the low frequency region, the imaginary part of the impedance can be taken at this time according to the least square method or other suboptimal solution method
Figure BDA0004113008680000062
Constant phase element { Q 1 ,Q 2 ,Q 3 ,Q 4 ,Q 5 Actual values of the parameter, thereby constructing the values of the parameters of the constant phase element of the equivalent circuit.
Step S4.3, the mth frequency f m The lower real part Z 'of impedance' m Respectively subtracting ohmic resistance R 0 Is a constant phase element Q CPE_1 ,Q CPE_2 ,…,Q CPE_k ,…,Q CPE_K After the real part of the impedance, the mth frequency is obtainedf m The lower DRT impedance real part
Figure BDA0004113008680000063
Thereby obtaining DRT impedance real part +.>
Figure BDA0004113008680000064
Will be the mth frequency f m The inverse Z' of the imaginary part of the impedance m The identification inductance value of the high frequency inductance L and the normal phase element Q are subtracted respectively CPE_1 ,Q CPE_2 ,…,Q CPE_k ,…,Q CPE_K After the impedance imaginary part of (2) to obtain the mth frequency f m The inversion of the imaginary part of the DRT impedance
Figure BDA0004113008680000065
Thereby obtaining DRT impedance imaginary part +.>
Figure BDA0004113008680000066
Is composed of three parts in the circuit model of the invention as shown in figure 3, and the ohmic resistance R is determined according to the high-frequency inductance L of the battery in the previous two steps 0 Constant phase element { Q 1 ,Q 2 ,Q 3 ,Q 4 ,Q 5 Specific parameters are obtained by fitting, because the DRT analysis technology is data processing of an ideal multi-series-parallel RC network, the reliability of functions can be further improved by carrying out data preprocessing on non-convergent parts and pure resistive resistors, and therefore, the impedance composition of the multi-series-parallel RC network under pure DRT representation can be obtained after circuit correction:
Figure BDA0004113008680000067
the modified impedance spectrum and thus the DRT function is obtained, as shown in fig. 4a for the modified EIS impedance spectrum for DRT analysis.
Step S5, utilizing the frequency distribution { f 1 ,f 2 ,…,f m ,…,f M Real part of DRT impedance
Figure BDA0004113008680000068
And DRT impedance imaginary part->
Figure BDA0004113008680000069
And solving the DRT model of the lithium battery to be tested to obtain a fitted DRT distribution function gamma (lntau), thereby realizing the parameter identification of the DRT model of the lithium battery to be tested.
The DRT function is a distribution function of polarization resistance in a time constant domain, at the moment, the solving process of the DRT function is a problem of solving a large number of unknowns through a small amount of known information, the DRT function is a very typical mathematical uncertainty problem, a common numerical integration method cannot obtain a stable solution of the problem, and the method mainly comprises a Fourier transform method and a regularization method.
The method is mainly calculated by minimizing the following sum of squares:
Figure BDA0004113008680000071
finally, according to the DRT analysis result, the distribution condition of the polarization resistance inside the battery can be briefly analyzed, and the application of the battery in various application occasions is discussed to have superiority. As shown in FIG. 4b, the DRT distribution function condition drawn after the DRT analysis is provided, the distribution condition can be seen to have several characteristic peaks representing the aggregation distribution condition of polarization resistance, and the polarization reaction distribution condition in the battery can be well reflected. At this time, the complete lithium battery model based on electrochemical impedance spectrum is obtained, and each parameter of the model and the provided DRT analytic function are identified.
In this embodiment, an electronic device includes a memory for storing a program supporting the processor to execute the above method, and a processor configured to execute the program stored in the memory.
In this embodiment, a computer-readable storage medium stores a computer program that, when executed by a processor, performs the steps of the method described above.
In summary, the lithium battery modeling and parameter identification estimation method based on electrochemical impedance spectroscopy is performed based on lithium battery EIS test data, in order to effectively and rapidly decompose the EIS spectroscopy by a digital method and decouple the dynamic process inside the battery, an improved relaxation time distribution method is introduced to build a multi-order composite equivalent circuit model for a lithium battery system, a model parameter identification method is realized by carrying out low-high frequency partition on the battery, the impedance spectroscopy in a high-frequency area is always considered to be related to the ohmic impedance of the battery, the impedance spectroscopy in a low-frequency area is considered to reflect the semi-diffusion effect of the lithium battery, a plurality of common phase elements CPE are used for characterization, the problem that error is caused by DRT analysis caused by a non-polarized reaction structure part in the actual battery is effectively avoided, and finally modeling is completed by fitting a DRT distribution function gamma (lnτ) by a regularization method. The method provides method guidance for mechanism research and mathematic and physical modeling of the battery, and the established equivalent circuit model with high physical significance can be effectively applied to occasions such as battery life prediction, battery classification screening, battery echelon utilization and the like.

Claims (3)

1. The lithium battery modeling and parameter identification method based on electrochemical impedance spectrum is characterized by comprising the following steps of:
step 1, charging a lithium battery to be tested to a state of charge soc=100%, and standing for a period of time;
step S2, connecting the lithium battery after standing with a voltage and current sensor, and then performing electrochemical impedance spectrum test to obtain impedance data at each frequency point, wherein the impedance data comprises frequency distribution { f } 1 ,f 2 ,…,f m ,…,f M Real part of impedance { Z' 1 ,Z′ 2 ,…,Z′ m ,…,Z′ M Inverse result of imaginary part of impedance { Z } 1 ,Z″ 2 ,…,Z″ m ,…,Z″ M -a }; wherein f m Represents the mth frequency, Z' m Represents the mth frequency f m The real part of the impedance, Z m Represents the mth frequencyRate f m Inverting the imaginary part of the lower impedance;
s3, constructing a DRT model of the lithium battery to be tested, and sequentially using a high-frequency inductor L and an ohmic resistor R 0 DRT distribution function gamma (lnτ) and constant phase element { Q CPE_1 ,Q CPE_2 ,…,Q CPE_k ,…,Q CPE_K A series connection; wherein Q is CPE_k Representing the kth normal phase element;
s4, respectively processing high frequency and low frequency data of impedance data in a DRT model of the lithium battery to be detected so as to finish parameter identification;
step S4.1, according to { f 1 ,f 2 ,…,f m ,…,f M From { Z' 1 ,Z′ 2 ,…,Z′ z ,...,Z′ M Selecting the s real impedance pairs with the highest frequency to the ohmic resistor R 0 Identifying to obtain ohmic resistor R 0 Is a recognition resistance value of (a); meanwhile, the high-frequency inductor L is identified by utilizing s impedance imaginary parts with highest selected frequencies, so that an identification inductance value of the high-frequency inductor L is obtained;
step S4.2, according to { f 1 ,f 2 ,…,f m ,…,f M From { Z' 1 ,Z′ 2 ,…,Z′ m ,...,Z′ M Selecting v real impedance parts with lowest frequency to respectively subtract ohmic resistance R 0 The identification resistance value of the high-frequency inductor L and the identification inductance value of the high-frequency inductor L are obtained, and v corrected impedance real parts are obtained;
from { Z' 1 ,Z′ 2 ,...,Z′ m ,…,Z′ M Selecting v impedance imaginary parts with lowest frequency to respectively subtract ohmic resistance R 0 The identification resistance value of the high-frequency inductor L and the identification inductance value of the high-frequency inductor L are obtained, and v corrected impedance imaginary parts are obtained;
fitting the corrected v real parts and imaginary parts of the impedance by a least square method or nonlinear regression to obtain each constant-phase element Q CPE_1 ,Q CPE_2 ,…,Q CPE_k ,…,Q CPE_K Resistance value of (2);
step S4.3, the mth frequency f m The lower real part Z 'of impedance' m Respectively subtracting ohmic resistance R 0 Is identified by (a)Resistance value and normal phase element Q CPE_1 ,Q CPE_2 ,…,Q CPE_k ,…,Q CPE_K After the real part of the impedance, the mth frequency f is obtained m The lower DRT impedance real part
Figure FDA0004113008670000012
Thereby obtaining DRT impedance real part +.>
Figure FDA0004113008670000011
Will be the mth frequency f m The inverse Z' of the imaginary part of the impedance m The identification inductance value of the high frequency inductance L and the normal phase element Q are subtracted respectively CPE_1 ,Q CPE_2 ,…,Q CPE_k ,…,Q CPE_K After the impedance imaginary part of (2) to obtain the mth frequency f m The inversion of the imaginary part of the DRT impedance
Figure FDA0004113008670000021
Thereby obtaining DRT impedance imaginary part +.>
Figure FDA0004113008670000022
Step S5, utilizing the frequency distribution { f 1 ,f 2 ,…,f m ,…,f M Real part of DRT impedance
Figure FDA0004113008670000023
And DRT impedance imaginary part->
Figure FDA0004113008670000024
And solving the DRT model of the lithium battery to be tested to obtain a fitted DRT distribution function gamma (lntau), thereby realizing the parameter identification of the DRT model of the lithium battery to be tested.
2. An electronic device comprising a memory and a processor, wherein the memory is configured to store a program that supports the processor to perform the lithium battery modeling and parameter identification method of claim 1, the processor being configured to execute the program stored in the memory.
3. A computer readable storage medium having a computer program stored thereon, characterized in that the computer program when run by a processor performs the steps of the lithium battery modeling and parameter identification method of claim 1.
CN202310211924.5A 2023-03-07 2023-03-07 Electrochemical impedance spectrum-based lithium battery modeling and parameter identification method Pending CN116243176A (en)

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CN116774051A (en) * 2023-06-28 2023-09-19 上海炙云新能源科技有限公司 Battery capacity quick estimation method considering time-frequency domain multidimensional data characteristics
CN116774051B (en) * 2023-06-28 2024-02-02 上海炙云新能源科技有限公司 Battery capacity quick estimation method considering time-frequency domain multidimensional data characteristics

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