Electrochemical impedance acquisition method based on time sequence model and electrochemical impedance model

Abstract

The invention relates to an electrochemical impedance acquisition method based on a time sequence model and an electrochemical impedance model. The method comprises the following steps: constructing an experimental test matrix taking a battery material system, temperature, SOC and the like as variables; performing charging and discharging test on the battery according to the experimental test matrix; after the battery stands, current excitation is applied through an electrochemical workstation, real electrochemical impedance spectroscopy data are obtained, parameter identification is carried out, and a model training label is extracted; extracting features of the process data of the charging and discharging test, and constructing an electrochemical impedance data set; based on a time sequence pre-training basic model, training by utilizing the electrochemical impedance data set to obtain an electrochemical impedance model; and predicting the electrochemical impedance spectrum of the battery to be detected by adopting the trained electrochemical impedance model. Compared with the prior art, the method has the advantages that the time sequence large model and the electrochemical impedance model are combined, efficient acquisition and accurate analysis of the electrochemical impedance spectrum are realized, and the method has wide application potential in real-time monitoring and dynamic application environments.

Description

Technical Field

[0001] The present invention relates to the technical field of electrochemical system parameter acquisition, and in particular to an electrochemical impedance acquisition method based on a timing model and an electrochemical impedance model. Background Art

[0002] Electrochemical impedance spectroscopy (EIS) is an important technique for characterizing the dynamic processes within electrochemical systems. It obtains the impedance characteristics of the system by applying a small-amplitude sinusoidal current or voltage perturbation to the electrochemical system and measuring the system's response to excitations of different frequencies. These impedance characteristics can reflect a variety of electrochemical processes within the battery, such as charge transfer, electrode processes, and diffusion, and therefore have important applications in battery health status diagnosis, life prediction, and electrochemical mechanism research.

[0003] However, the traditional electrochemical impedance spectroscopy measurement method has some inherent limitations. First, the measurement process usually takes a long time, which limits its real-time and practical application in practical applications, especially in portable devices and vehicle-mounted measurement scenarios. Second, the accurate acquisition of electrochemical impedance spectroscopy relies on complex experimental equipment, which limits its application in portable and dynamic measurement environments. In addition, with the continuous development of battery technology, the complexity and diversity of battery systems have also increased the difficulty of impedance spectroscopy analysis.

[0004] In order to solve these problems, model-based impedance analysis methods have gradually attracted attention in recent years. By establishing an equivalent circuit model of the electrochemical system, the analysis process of the impedance spectrum can be effectively simplified. However, the traditional equivalent circuit model may have the problem of insufficient accuracy when dealing with complex electrochemical systems. Especially under different aging states, battery materials and working conditions, the model's generalization ability is weak and it is difficult to adapt to the changing use environment. Summary of the invention

[0005] The purpose of the present invention is to overcome the defects of the above-mentioned prior art that traditional electrochemical impedance spectroscopy measurement takes a long time, the equipment is bulky and is not suitable for portable and vehicle-mounted environments, and to provide an electrochemical impedance acquisition method based on a timing model and an electrochemical impedance model, so as to greatly shorten the EIS data acquisition time and improve the analysis accuracy, making it suitable for a variety of measurement environments.

[0006] The purpose of the present invention can be achieved by the following technical solutions:

[0007] A method for obtaining electrochemical impedance based on a time series model and an electrochemical impedance model comprises the following steps:

[0008] Construct an experimental test matrix with battery material system, temperature, SOC range, current amplitude and battery specifications as variables;

[0009] Performing a systematic charge and discharge test on the battery using a charge and discharge device, covering all operating conditions of the experimental test matrix, and collecting current input and voltage response data under each operating condition;

[0010] After the battery is at rest, current excitation is applied through the electrochemical workstation to obtain real electrochemical impedance spectroscopy data at different frequencies;

[0011] Establish an equivalent circuit model of electrochemical impedance, perform parameter identification based on the acquired real electrochemical impedance spectrum data, and extract key parameters of the equivalent circuit model as labels for model training; extract features related to electrochemical impedance based on the collected current input and voltage response data, and thus construct an electrochemical impedance data set;

[0012] Based on the time series pre-trained basic model, the electrochemical impedance model is trained using the electrochemical impedance data set;

[0013] Using the trained electrochemical impedance model, the corresponding electrochemical impedance model characteristic parameters are predicted according to the current input and voltage response data of the battery to be tested, and the electrochemical impedance spectrum is reconstructed to evaluate the health status and battery life of the battery to be tested.

[0014] Furthermore, the method further comprises: dividing the constructed electrochemical impedance data set into a training set and a test set, and using the training set to train the electrochemical impedance model;

[0015] The trained electrochemical impedance model is tested using a test set to predict electrochemical impedance spectrum parameters, and the Nyquist diagram and Bode diagram of the corresponding equivalent circuit model are plotted based on the predicted electrochemical impedance spectrum parameters; and the Nyquist diagram and Bode diagram of the corresponding real electrochemical impedance spectrum data are compared to verify the prediction accuracy of the trained electrochemical impedance model. If the preset verification accuracy requirements are met, the final electrochemical impedance model is obtained. If the verification accuracy requirements are not met, the electrochemical impedance model is re-adjusted or retrained.

[0016] Furthermore, the test set includes samples at different temperatures, SOCs and current amplitudes.

[0017] Furthermore, among the variables of the experimental test matrix, the battery material system includes ternary materials and lithium iron phosphate, the temperature includes multiple temperature points in the range of -10-50°C, the SOC range includes multiple test points in the range of 0% to 100%, the current amplitude includes multiple charge and discharge rates in the range of 0.02-2C, and the battery specifications include multiple capacity specifications in the range of 0.1-70Ah.

[0018] Furthermore, during the charge and discharge test of the battery, the current input and voltage response data under each set of working conditions are recorded in real time. After each working condition test is completed, the battery is left to stand until a quasi-steady state is reached inside the battery. Then, current excitation is applied through the electrochemical workstation to obtain true electrochemical impedance spectroscopy data at different frequencies.

[0019] Furthermore, the process of acquiring the real electrochemical impedance spectroscopy data is specifically as follows:

[0020] A wide frequency range from 1 mHz to 1 MHz is adopted, and a current excitation is applied to the battery through an electrochemical workstation to perform a frequency sweep test, and the impedance data at each frequency point is recorded as the real electrochemical impedance spectrum data.

[0021] Furthermore, the key parameters of the equivalent circuit model extracted include electrolyte resistance, resistance and capacitance caused by SEI, double layer capacitance and charge transfer resistance.

[0022] Furthermore, the health status assessment process is specifically as follows:

[0023] According to the predicted electrochemical impedance spectrum, the frequency points are selected to fit the corresponding impedance curve. The aging degree and potential failures of the battery are identified according to the impedance changes, and the health status of the battery is evaluated.

[0024] Furthermore, the battery life evaluation process is specifically as follows:

[0025] Based on the predicted impedance parameters of the electrochemical impedance spectroscopy, the battery life is predicted using extrapolation technology.

[0026] Furthermore, the method is used for battery performance evaluation, health status monitoring and life prediction of portable devices and vehicle-mounted systems.

[0027] Compared with the prior art, the present invention has the following advantages:

[0028] (1) The present invention first provides an experimental test matrix that comprehensively considers variables such as battery material system, temperature, SOC range, current amplitude and battery specifications, and performs battery testing. The obtained electrochemical impedance data set can ensure that the data covers a variety of operating conditions and has a sufficient sample size, thereby ensuring the comprehensiveness and reliability of the test results. On this basis, the time series large model is combined with the electrochemical impedance equivalent circuit model to achieve rapid acquisition and accurate analysis of the electrochemical impedance spectrum, significantly improving the acquisition speed and analysis efficiency, and demonstrating its wide application potential in real-time monitoring and dynamic application environments.

[0029] (2) The method of the present invention is particularly suitable for scenarios that require real-time monitoring, such as portable devices and vehicle-mounted systems, and provides strong technical support for battery performance evaluation, health status monitoring and life prediction.

[0030] (3) Considering that the time series large model can extract complex features from a large amount of time series data and has strong learning and generalization capabilities, the present invention combines the time series large model with the electrochemical impedance model to significantly improve the acquisition efficiency of the electrochemical impedance spectrum while ensuring measurement accuracy and reduce dependence on equipment. This technical combination opens up new possibilities for real-time monitoring and dynamic applications of electrochemical impedance. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] Figure 1 A schematic flow chart of an electrochemical impedance acquisition method based on a timing model and an electrochemical impedance model provided in an embodiment of the present invention;

[0032] Figure 2 A schematic diagram of obtaining electrochemical impedance spectroscopy equivalent circuit model parameters through PatchTST provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0033] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Generally, the components of the embodiments of the present invention described and shown in the drawings here can be arranged and designed in various different configurations.

[0034] Therefore, the following detailed description of the embodiments of the present invention provided in the accompanying drawings is not intended to limit the scope of the invention claimed for protection, but merely represents selected embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0035] It should be noted that similar reference numerals and letters denote similar items in the following drawings, and therefore, once an item is defined in one drawing, further definition and explanation thereof is not required in subsequent drawings.

[0036] Example 1

[0037] like Figure 1 As shown, this embodiment provides an electrochemical impedance acquisition method based on a timing model and an electrochemical impedance model, comprising the following steps:

[0038] S1 Experimental Test Matrix Design:

[0039] Construct an experimental test matrix with battery material system, temperature, SOC range, current amplitude and battery specifications as variables;

[0040] Among the variables of the experimental test matrix, the battery material system includes ternary materials and lithium iron phosphate, the temperature includes multiple temperature points in the range of 0-50°C, the SOC range includes multiple test points in the range of 0% to 100%, the current amplitude includes multiple charge and discharge rates in the range of 0.1-2C, and the battery specifications include multiple capacity specifications in the range of 1-70Ah.

[0041] The specific settings in this embodiment are as follows:

[0042] (1) Battery material system: Considering the differences in electrochemical properties of different battery materials, the present invention selects two commonly used battery material systems, namely ternary materials (such as NCM) and lithium iron phosphate (LFP). These two materials have different characteristics in terms of energy density, cycle life and safety. Through comparative studies, we can better understand their electrochemical behaviors under different working conditions.

[0043] (2) Temperature is an important factor affecting battery performance. In order to comprehensively analyze the effect of temperature on electrochemical impedance, the present invention sets the test temperature to seven levels: -10°C, 0°C, 10°C, 20°C, 30°C, 40°C, and 50°C. These temperature points cover the ambient temperature range that the battery may encounter in actual use, which is helpful for studying the impedance characteristics of the battery under different temperature conditions.

[0044] (3) SOC range: The SOC (State of Charge) of a battery directly affects its electrochemical performance. The present invention sets the SOC in the range from 0% to 100%, with each 10% being an interval, and a total of 11 test points. Through this detailed SOC segmentation, the impedance variation law of the battery under different charging states can be obtained, providing more accurate model data.

[0045] (4) Current amplitude: The charge and discharge rate also has a significant effect on the impedance characteristics of the battery. The present invention selects five common charge and discharge rates, namely 0.02C, 0.1C, 0.5C, 1C, 1.5C, and 2C, to simulate the battery performance under different power requirements, covering the battery slow charging and fast charging scenarios. Through testing at different rates, we can have a more comprehensive understanding of the impact of current density on impedance.

[0046] (5) Battery specifications: In order to ensure the wide applicability of the test data, the present invention selected batteries with various capacity specifications for testing, namely 0.1Ah, 1Ah, 3Ah, 20Ah and 70Ah. These capacities cover different application scenarios from small portable devices to large-scale energy storage systems, and can provide reference data for various applications.

[0047] Ultimately, the experimental test matrix covered 4,620 operating conditions (2 types of battery materials × 7 temperature points × 11 SOC points × 6 current rates × 5 battery specifications), ensuring the comprehensiveness and diversity of the test.

[0048] S2 battery testing and data collection:

[0049] Use charging and discharging equipment to conduct systematic charging and discharging tests on the battery, covering all working conditions of the experimental test matrix, and collecting current input and voltage response data under each working condition;

[0050] During the charge and discharge test of the battery, the current input and voltage response data under each set of working conditions are recorded in real time. After each working condition test is completed, the battery is left to stand until a quasi-steady state is reached inside the battery.

[0051] The specific process in this embodiment is as follows:

[0052] (1) Test process: Under the set test conditions, standard charge and discharge cycles are performed for each operating condition. In order to obtain accurate impedance data, the test under each operating condition must strictly follow the specifications, including precise control of the charging current, setting of the discharge cut-off voltage, etc. Through this standardized test process, consistent and reliable data can be obtained under different conditions.

[0053] (2) Data acquisition: During the entire charge and discharge process, the current and voltage data under each set of working conditions are recorded in real time. These data can not only be used to analyze the charge and discharge characteristics of the battery, but also provide the necessary basic data for subsequent electrochemical impedance spectroscopy acquisition and model training. The generated charge and discharge curve will show the voltage change trend of the battery under different working conditions, which is helpful for analyzing the electrochemical reaction inside the battery.

[0054] (3) Static process: To ensure the accuracy of impedance measurement, the battery needs to be left to stand for at least 30 minutes after each charge and discharge cycle. This process allows the battery to reach a quasi-steady state, eliminates transient effects that may be introduced during the charge and discharge process, and provides stable initial conditions for subsequent electrochemical impedance spectroscopy measurements.

[0055] S3 electrochemical impedance spectroscopy acquisition:

[0056] After the battery is at rest, current excitation is applied through the electrochemical workstation to obtain real electrochemical impedance spectroscopy data at different frequencies;

[0057] Specifically, a wide frequency range from 1 mHz to 1 MHz is adopted, and current excitation is applied to the battery through an electrochemical workstation to perform frequency scanning, and the impedance spectrum data of each frequency point is recorded as the real electrochemical impedance spectrum data.

[0058] The specific process in this embodiment is as follows:

[0059] (1) Frequency scanning: A wide frequency range from 1mHz to 1MHz is used for scanning. The wide frequency range helps to fully capture the impedance characteristics of the battery at each time constant. The low-frequency part reflects the polarization impedance of the battery, while the high-frequency part is mainly related to the electrolyte resistance and contact resistance.

[0060] (2) Data recording: Detailed recording of the impedance spectrum data of each frequency point in the frequency scan, including its real and imaginary parts. These data will be used for subsequent equivalent circuit model construction and parameter extraction, and are the basis for the present invention to achieve high-precision impedance analysis.

[0061] S4 dataset construction:

[0062] Parameter identification is performed based on the acquired real electrochemical impedance spectroscopy data, an equivalent circuit model of electrochemical impedance is established, and key parameters of the equivalent circuit model are extracted as labels for model training; features related to electrochemical impedance are extracted based on the collected current input and voltage response data, thereby constructing an electrochemical impedance data set;

[0063] The data set construction process of this embodiment involves multiple links such as parameter extraction, model building and feature extraction. The specific process is as follows:

[0064] (1) Establishment of equivalent circuit model: Parameter identification is performed based on the acquired electrochemical impedance spectroscopy data, and the corresponding equivalent circuit model is established. This model usually includes key parameters such as electrolyte resistance, resistance and capacitance caused by SEI, double layer capacitance, and charge transfer resistance. These parameters can reflect the electrochemical reaction process inside the battery and provide reliable label data for model training; Figure 2As shown, PatchTST can be used to obtain the parameters of the electrochemical impedance spectroscopy equivalent circuit model.

[0065] (2) Feature extraction: Extract the current and voltage features related to electrochemical impedance from the previously collected charge and discharge processes. These features will form a complete data set together with the impedance spectrum data for training the time series model. The quality of feature extraction directly affects the training effect of the model, so it is necessary to carefully select features that are closely related to the impedance characteristics for extraction.

[0066] (3) Dataset construction: The extracted model parameters are used as label data, combined with input features such as current and voltage, to construct an electrochemical impedance dataset covering a wide range of working conditions. The richness and diversity of this dataset will directly affect the generalization ability and prediction accuracy of the model.

[0067] S5 dataset division:

[0068] The constructed electrochemical impedance dataset is divided into a training set and a test set for model training and performance evaluation. This process ensures the robustness and generalization ability of the model under different working conditions. This process ensures the robustness and generalization ability of the model under different working conditions;

[0069] In this embodiment, specifically:

[0070] (1) Training set: The training set accounts for 80% of the data set and is used for training the large model. Sufficient training samples are selected to ensure that the model can learn the electrochemical impedance characteristics under various working conditions.

[0071] (2) Test set: The test set accounts for 20% of the data set and is used to evaluate the generalization ability and prediction accuracy of the model. The test set data should include samples at different temperatures, SOCs, and charge and discharge rates to comprehensively evaluate the applicability of the model.

[0072] S6 large model training:

[0073] A time series pre-training basic model (such as PatchTST) is used to extract time series features related to electrochemical impedance from the electrochemical impedance dataset, and the electrochemical impedance model is trained using the extracted time series features and corresponding labels. The core of the time series pre-training basic model is the ability to efficiently process time series data, and extract key features related to the electrochemical impedance spectrum in the DC input through the encoder and translate them into electrochemical impedance parameters through the decoder.

[0074] The specific process in this embodiment is:

[0075] (1) Feature extraction: The time series pre-training basic model extracts time series features related to electrochemical impedance by analyzing the current and voltage data during the charging and discharging process. The extraction process of these features uses a multi-head attention mechanism to extract key information, effectively improving the model's ability to recognize complex electrochemical phenomena.

[0076] (2) Model training: The model is trained on a large-scale training set, and the extracted features are used to improve the model's prediction accuracy for electrochemical impedance spectroscopy. The training process uses a step-by-step optimization method to continuously adjust the model parameters to achieve the best prediction performance.

[0077] S7 Model Validation:

[0078] The trained electrochemical impedance model is tested using a test set to predict electrochemical impedance spectrum parameters, and the Nyquist diagram and Bode diagram of the corresponding equivalent circuit model are plotted based on the predicted electrochemical impedance spectrum parameters; the Nyquist diagram and Bode diagram of the corresponding real electrochemical impedance spectrum data are compared to verify the prediction accuracy of the trained electrochemical impedance model. If the preset verification accuracy requirements are met, the final electrochemical impedance model is obtained. If the verification accuracy requirements are not met, the electrochemical impedance model is re-adjusted or retrained.

[0079] By applying the trained electrochemical impedance spectroscopy model to the test set, its performance is fully verified, and compared with the actual measurement data, the prediction accuracy and applicability of the model can be evaluated. The specific process in this embodiment includes:

[0080] (1) Model prediction: Use the trained model to predict the electrochemical impedance spectrum, and draw the Nyquist diagram and Bode diagram of the corresponding equivalent circuit model based on the predicted impedance spectrum parameters. These graphical representations facilitate intuitive analysis of the model's prediction effect.

[0081] (2) Comparative verification: Compare the Nyquist and Bode plots predicted by the model with the Nyquist and Bode plots of the actual measured impedance, and evaluate the prediction accuracy of the model through indicators such as the real part, imaginary part, modulus, phase angle fit, root mean square error, and mean absolute error. The comparison results will show the performance of the model at different frequencies and help identify potential room for improvement.

[0082] S8 Model Application:

[0083] After model training and verification are completed, the model can be applied in actual scenarios to realize functions such as battery performance analysis, health status assessment and life prediction.

[0084] That is, the trained electrochemical impedance model is used to predict the corresponding electrochemical impedance spectrum based on the current input and voltage response data of the battery to be tested, which is used to evaluate the health status and battery life of the battery to be tested.

[0085] The specific process of this embodiment is as follows:

[0086] (1) Health status assessment: Using the electrochemical impedance spectrum predicted by the model, the impedance curve is fitted at the appropriate frequency point to assess the health status of the battery. By analyzing the impedance changes, the degree of battery aging and potential failures can be identified, providing a basis for maintenance strategies.

[0087] (2) Life prediction: Based on the impedance parameters calculated by the model, the battery life is predicted using extrapolation technology. This function is particularly suitable for long-term monitoring and life management of batteries, helping users optimize battery usage and maintenance strategies.

[0088] The preferred specific embodiments of the present invention are described in detail above. It should be understood that a person skilled in the art can make many modifications and changes based on the concept of the present invention without creative work. Therefore, any technical solution that can be obtained by a person skilled in the art through logical analysis, reasoning or limited experiments based on the concept of the present invention on the basis of the prior art should be within the scope of protection determined by the claims.

Claims

1. A method for obtaining electrochemical impedance based on a time series model and an electrochemical impedance model, characterized in that: The following steps are involved: Construct an experimental test matrix with battery material system, temperature, SOC range, current amplitude and battery specifications as variables; Performing a systematic charge and discharge test on the battery using a charge and discharge device, covering all operating conditions of the experimental test matrix, and collecting current input and voltage response data under each operating condition; After the battery is at rest, current excitation is applied through the electrochemical workstation to obtain real electrochemical impedance spectroscopy data at different frequencies; Establish an equivalent circuit model of electrochemical impedance, perform parameter identification based on the acquired real electrochemical impedance spectrum data, and extract key parameters of the equivalent circuit model as labels for model training; Extract features related to electrochemical impedance based on the collected current input and voltage response data, thereby constructing an electrochemical impedance data set; Based on the time series pre-trained basic model, an electrochemical impedance model is obtained by training the electrochemical impedance data set; Using the trained electrochemical impedance model, the corresponding electrochemical impedance model characteristic parameters are predicted according to the current input and voltage response data of the battery to be tested, and the electrochemical impedance spectrum is reconstructed to evaluate the health status and battery life of the battery to be tested.

2. The electrochemical impedance acquisition method based on a time series model and an electrochemical impedance model according to claim 1, characterized in that: The method further comprises: dividing the constructed electrochemical impedance data set into a training set and a test set, and using the training set to train the electrochemical impedance model; The trained electrochemical impedance model is tested using a test set to predict electrochemical impedance spectrum parameters, and the Nyquist diagram and Bode diagram of the corresponding equivalent circuit model are plotted based on the predicted electrochemical impedance spectrum parameters; and the Nyquist diagram and Bode diagram of the corresponding real electrochemical impedance spectrum data are compared to verify the prediction accuracy of the trained electrochemical impedance model. If the preset verification accuracy requirements are met, the final electrochemical impedance model is obtained. If the verification accuracy requirements are not met, the electrochemical impedance model is re-adjusted or retrained.

3. The electrochemical impedance acquisition method based on a time series model and an electrochemical impedance model according to claim 2, characterized in that: The test set includes samples at different temperatures, SOCs, and current amplitudes.

4. The electrochemical impedance acquisition method based on a time series model and an electrochemical impedance model according to claim 1, characterized in that: Among the variables of the experimental test matrix, the battery material system includes ternary materials and lithium iron phosphate, the temperature includes multiple temperature points in the range of -10-50°C, the SOC range includes multiple test points in the range of 0% to 100%, the current amplitude includes multiple charge and discharge rates in the range of 0.02-2C, and the battery specifications include multiple capacity specifications in the range of 0.1-70Ah.

5. The electrochemical impedance acquisition method based on a time series model and an electrochemical impedance model according to claim 1, characterized in that: During the charge and discharge test of the battery, the current input and voltage response data under each set of working conditions are recorded in real time. After each working condition test is completed, the battery is left to stand until a quasi-steady state is reached inside the battery. Then, current excitation is applied through the electrochemical workstation to obtain real electrochemical impedance spectroscopy data at different frequencies.

6. The electrochemical impedance acquisition method based on a time series model and an electrochemical impedance model according to claim 1, characterized in that: The process of obtaining the real electrochemical impedance spectroscopy data is specifically as follows: A wide frequency range from 1 mHz to 1 MHz is adopted, and a current excitation is applied to the battery through an electrochemical workstation to perform a frequency sweep test, and the impedance data at each frequency point is recorded as the real electrochemical impedance spectrum data.

7. The electrochemical impedance acquisition method based on a time series model and an electrochemical impedance model according to claim 1, characterized in that: The key parameters extracted from the equivalent circuit model include electrolyte resistance, resistance and capacitance caused by SEI, double layer capacitance and charge transfer resistance.

8. The electrochemical impedance acquisition method based on a time series model and an electrochemical impedance model according to claim 1, characterized in that: The health status assessment process is specifically as follows: According to the predicted electrochemical impedance spectrum, the frequency points are selected to fit the corresponding impedance curve. The aging degree and potential failures of the battery are identified according to the impedance changes, and the health status of the battery is evaluated.

9. The electrochemical impedance acquisition method based on a time series model and an electrochemical impedance model according to claim 1, characterized in that: The battery life evaluation process is specifically as follows: Based on the predicted impedance parameters of the electrochemical impedance spectroscopy, the battery life is predicted using extrapolation technology.

10. The electrochemical impedance acquisition method based on a time series model and an electrochemical impedance model according to claim 1, characterized in that: The method is used for battery performance evaluation, health status monitoring and life prediction of portable devices and vehicle-mounted systems.

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