Method for identifying abnormal state of aqueous zinc metal negative electrode based on relaxation time distribution

Abstract

The application discloses a kind of water system zinc metal negative electrode abnormal state identification methods based on relaxation time distribution, the method includes obtaining the electrochemical impedance spectroscopy data of water system test battery containing zinc metal negative electrode under preset test condition;Electrochemical impedance spectroscopy data is pretreated;Corresponding relaxation time distribution function is obtained based on relaxation time distribution method;From relaxation time distribution function, the characteristic parameter of the running state of zinc metal negative electrode is extracted;The characteristic parameter of the water system test battery to be measured is compared with reference characteristic parameter, and abnormal characteristic deviation is obtained;And based on abnormal characteristic deviation, the abnormal state of water system zinc metal negative electrode is identified.The detection process of the present application is lossless, can reduce the dependence of equivalent circuit model and artificial experience, improve the timeliness, explainability and reliability of abnormal state identification, suitable for water system zinc battery in-situ monitoring and engineering state diagnosis.

Description

Technical Field

[0001] This invention relates to the field of state detection and safety diagnosis technology for electrochemical energy storage devices, and more specifically to a method for identifying abnormal states of aqueous zinc metal negative electrodes based on relaxation time distribution. Background Technology

[0002] With the development of new energy storage technologies, aqueous zinc batteries have attracted widespread attention in large-scale energy storage, backup power, low-speed power supplies, and distributed energy systems due to their high safety, low raw material costs, environmental friendliness, and relatively mild assembly conditions. Among these, the zinc metal anode, with its high theoretical capacity, low redox potential, and good resource abundance, is a key component of aqueous zinc battery systems.

[0003] However, aqueous zinc metal anodes still face several abnormal failure issues during long-term operation. During repeated deposition / stripping processes, the zinc anode interface is prone to uneven local electric field distribution, uneven deposition, and dendrite growth, leading to increased polarization, deteriorated interfacial contact, and in severe cases, even short circuits. Simultaneously, hydrogen evolution reactions, corrosion reactions, and byproduct accumulation may occur at the interface between the aqueous electrolyte and the zinc metal anode, resulting in increased interfacial impedance, continuous consumption of active zinc, and reduced cycle stability. Furthermore, byproduct coverage, restricted ion diffusion, and localized mass transfer unevenness further exacerbate battery performance degradation.

[0004] Currently, the assessment of the operating status of aqueous zinc batteries typically relies on macroscopic parameters such as capacity retention, coulombic efficiency, voltage plateau, DC internal resistance, cycle life, or polarization voltage. While these parameters reflect the overall changes in battery performance, they usually exhibit a certain degree of lag, making it difficult to promptly identify early abnormal states at the zinc metal anode interface or during the deposition process. For example, by the time capacity decay or voltage anomalies become apparent, side reactions at the zinc anode interface, uneven deposition, contact deterioration, or limited mass transfer have often already progressed to a more severe stage.

[0005] Electrochemical impedance spectroscopy (EIS) is a testing method that reflects the charge transfer, interfacial reactions, and ion diffusion processes within a battery. By applying a small AC perturbation to the battery and acquiring the response signal, impedance information at different frequencies can be obtained. However, the internal processes of aqueous zinc batteries are complex, and the electrochemical responses in the high-frequency, mid-frequency, and low-frequency regions of the impedance spectrum often overlap. When directly identifying states using Nyquist plots or equivalent circuit fitting methods, the results are easily affected by model selection, parameter coupling, and human experience, leading to insufficient accuracy and consistency in the identification of abnormal states.

[0006] Relaxation time distribution methods can transform impedance spectra from the frequency domain to the time constant domain, allowing different electrochemical processes to be presented in the form of response peaks, which helps improve the interpretability of impedance process analysis. However, existing relaxation time distribution analyses are mostly used for battery health status assessment, impedance process analysis, or material system comparison, lacking feature extraction methods and judgment rules for abnormal states of aqueous zinc metal anodes. In particular, it is still difficult to effectively identify early anomalies such as deposition / stripping anomalies, enhanced interfacial side reactions, hydrogen evolution corrosion, by-product accumulation, and limited mass transfer.

[0007] Therefore, it is necessary to provide a method for identifying abnormal states of aqueous zinc metal anodes based on relaxation time distribution, which transforms complex impedance spectra into interpretable time constant domain characteristic parameters and establishes the correspondence between these characteristic parameters and abnormal operation of zinc metal anodes, so as to improve the operational safety and engineering condition monitoring capabilities of aqueous zinc batteries. Summary of the Invention

[0008] Existing technologies for identifying abnormal states in aqueous zinc metal anodes primarily rely on macroscopic parameters such as capacity retention, coulombic efficiency, voltage plateau, or DC internal resistance. These methods suffer from drawbacks, including delayed early anomaly identification, insufficient feature interpretability, and limited applicability to in-situ monitoring and engineering diagnostics. This invention provides a method for identifying abnormal states in aqueous zinc metal anodes based on relaxation time distribution. This method analyzes the relaxation time distribution of electrochemical impedance spectroscopy data, transforming the frequency-domain impedance response into time-constant-domain features, thereby achieving non-destructive identification, early warning, and tiered judgment of abnormal states in zinc metal anodes.

[0009] To achieve the above objectives, this invention provides a method for identifying abnormal states of aqueous zinc metal anodes based on relaxation time distribution, comprising the following steps:

[0010] S1. Obtain electrochemical impedance spectroscopy data of an aqueous test battery containing a zinc metal negative electrode under preset test conditions;

[0011] S2. Preprocess the electrochemical impedance spectroscopy data to obtain preprocessed impedance spectroscopy data;

[0012] S3. Analyze the preprocessed impedance spectrum data based on the relaxation time distribution method to obtain the relaxation time distribution function corresponding to the water system test battery;

[0013] S4. Extract characteristic parameters representing the operating state of the zinc metal anode from the relaxation time distribution function;

[0014] S5. Compare the characteristic parameters of the test battery in the water system with the reference characteristic parameters to obtain the abnormal characteristic deviation.

[0015] S6. Based on the abnormal feature deviation, identify the abnormal state of the aqueous zinc metal negative electrode to obtain the state identification result.

[0016] Furthermore, the aqueous test cell containing a zinc metal anode includes one of an aqueous Zn||Zn symmetric cell, a Zn||Cu half-cell, or an aqueous full cell containing a zinc metal anode. By using an aqueous test cell containing a zinc metal anode, the obtained impedance response can reflect the electrochemical behaviors related to the state of the zinc metal anode, such as zinc deposition, zinc stripping, interfacial side reactions, corrosion hydrogen evolution, and mass transfer processes.

[0017] Furthermore, the preset test conditions include one or more of the following: cycling stage, deposition / stripping current density, deposition / stripping surface capacity, test temperature, settling time, electrolyte composition, electrolyte pH, and state of charge. By limiting or recording the test conditions, the interference of different operating conditions on the electrochemical impedance spectroscopy can be reduced, the comparability between different test data can be improved, and the reliability of abnormal state identification results can be enhanced.

[0018] Furthermore, the electrochemical impedance spectroscopy data is obtained by applying a small AC excitation signal to the aqueous test cell and acquiring the response signal. The amplitude of the AC excitation signal is 1 mV to 20 mV, and the test frequency range is 10 mHz to 1 MHz. More specifically, the AC excitation signal amplitude is 5 mV or 10 mV, and the test frequency range is 10 mHz to 100 kHz. Using a small AC excitation signal allows the testing process to be kept within a near-linear perturbation range, reducing the impact of the test itself on the cell state, thereby achieving non-destructive or near-non-destructive testing.

[0019] Furthermore, the preprocessing includes one or more of the following: outlier removal, data smoothing, ohmic resistance correction, impedance normalization, Kramers-Kronig consistency check, and test condition correction. This preprocessing reduces the impact of noise, outlier sampling points, differences in ohmic resistance, and initial battery variations on relaxation time distribution inversion and feature extraction, thereby improving the stability of subsequent analysis results.

[0020] Furthermore, the relaxation time distribution method includes one or more of the following: Tikhonov regularization, nonnegative least squares method, maximum entropy method, or Bayesian inversion method. By using the relaxation time distribution method, the superimposed or coupled frequency responses in the traditional electrochemical impedance spectroscopy can be transformed into response peaks in the time constant domain, thereby reducing the influence of equivalent circuit model selection and manual fitting experience on the analysis results.

[0021] Furthermore, the characteristic parameters include one or more of the following: peak amplitude, peak-corresponding time constant, peak area, peak width, number of peaks, peak position shift, and peak splitting characteristics. Among them, peak amplitude and peak area can reflect the strength of the impedance contribution of the corresponding electrochemical process; peak-corresponding time constant can reflect the kinetic characteristics of the process; peak width and peak splitting characteristics can reflect the coupling, diffusion distribution, or interfacial inhomogeneity between multiple processes; and changes in the number of peaks can reflect the emergence of new electrochemical processes or abnormal processes.

[0022] Furthermore, the relaxation time distribution function can be divided into high-frequency, mid-frequency, and low-frequency time constant intervals. The high-frequency time constant interval characterizes processes related to the zinc metal anode surface film, interfacial contact, or deposition state; the mid-frequency time constant interval characterizes processes related to charge transfer, interfacial side reactions, or hydrogen evolution corrosion; and the low-frequency time constant interval characterizes processes related to ion diffusion, byproduct accumulation, or mass transfer limitation. The changes in response peaks across different time constant intervals can reflect the state evolution of the zinc metal anode at different scales, thereby improving the interpretability of anomalous characteristics. The boundaries of each time constant interval can be determined based on the aqueous zinc battery system, the test frequency range, and the curve characteristics of the relaxation time distribution function.

[0023] Furthermore, the reference characteristic parameters are derived from the initial cycling stage and stable cycling stage of the same aqueous test battery, or from the relaxation time distribution characteristic parameters of the aqueous test battery in normal state of the same system. By setting the reference characteristic parameters, the absolute impedance difference between different battery samples or different cycling stages can be transformed into relative characteristic changes, thereby reducing the impact of initial sample differences on the state identification results.

[0024] Furthermore, the abnormal characteristic deviation includes one or more of the following: peak amplitude change rate, peak area change rate, peak corresponding time constant offset, peak width change, peak number change, peak splitting degree, and new peak appearance. By comparing the relaxation time distribution characteristics of the tested aqueous battery with those under reference conditions, a quantitative index characterizing the degree of abnormal evolution of the zinc metal anode can be obtained.

[0025] Furthermore, the state identification results include normal state, warning state, and abnormal state. When the abnormal feature deviation is less than a first threshold, the aqueous zinc metal anode is determined to be in a normal state; when the abnormal feature deviation is greater than or equal to the first threshold and less than a second threshold, the aqueous zinc metal anode is determined to be in a warning state; when the abnormal feature deviation is greater than or equal to the second threshold, the aqueous zinc metal anode is determined to be in an abnormal state; wherein, the second threshold is greater than the first threshold. The first and second thresholds can be determined based on the aqueous zinc battery system, test conditions, historical data, statistical characteristics of normal samples, or failure criteria.

[0026] The technical principle of this invention lies in the following: During the cyclic operation of an aqueous zinc metal anode, processes such as zinc deposition / stripping, interfacial film formation and evolution, charge transfer, hydrogen evolution corrosion, byproduct accumulation, and ion diffusion occur. When the zinc metal anode exhibits abnormal states such as dendrite growth, interfacial inhomogeneity, intensified corrosion, enhanced side reactions, limited mass transfer, or electrode deactivation, the kinetic characteristics of the aforementioned electrochemical processes change, further manifesting as changes in the impedance spectral response. However, in traditional frequency domain impedance spectroscopy, the responses of different electrochemical processes often overlap, making direct differentiation and interpretation difficult.

[0027] This technical solution transforms the electrochemical impedance spectroscopy from the frequency domain to the time constant domain using the relaxation time distribution method, allowing different kinetic processes to be presented as response peaks at different time constant positions. Abnormal evolution of the zinc metal anode can lead to changes in the peak amplitude, peak area, peak position, peak width, peak number, or peak splitting characteristics of the corresponding response peaks. For example, thickening of the interfacial film or deterioration of interfacial contact may cause enhancement or shift of the high-frequency response peak; impaired charge transfer, side reactions, or intensified hydrogen evolution corrosion may cause changes in the mid-frequency response peak; accumulation of byproducts, enhanced concentration polarization, or restricted ion diffusion may cause enhancement, broadening, or the generation of new response peaks in the low-frequency region. Therefore, by extracting characteristic parameters from the relaxation time distribution function and comparing them with benchmark characteristic parameters, the characteristic deviation quantity characterizing the abnormal state of the zinc metal anode can be obtained, enabling early identification and classification of abnormal states in aqueous zinc metal anodes.

[0028] Beneficial effects:

[0029] This invention identifies anomalous states in aqueous zinc metal anodes based on electrochemical impedance spectroscopy and relaxation time distribution methods. The detection process does not require disassembly or damage to the battery structure. It can acquire information related to the evolution of the zinc metal anode interface, deposition / stripping behavior, charge transfer, side reactions, and mass transfer processes without significantly disturbing the battery's operating state. Therefore, it is suitable for in-situ monitoring, continuous tracking, and engineering state diagnosis of aqueous zinc batteries. Compared to traditional methods that rely solely on macroscopic performance parameters such as capacity retention, coulombic efficiency, voltage plateau, or DC internal resistance, this invention can extract characteristic parameters from the time constant domain that better reflect the internal dynamic changes of the electrode. This helps identify early anomalies in the zinc metal anode before significant macroscopic performance degradation, improving the timeliness of anomalous state identification.

[0030] Meanwhile, this invention transforms the traditional frequency-domain impedance spectrum into response peaks in the time constant domain using a relaxation time distribution method. This allows processes such as interface film evolution, charge transfer, hydrogen evolution corrosion, byproduct accumulation, and ion diffusion, which were originally coupled and difficult to distinguish directly, to be presented in the form of peak values, peak positions, peak areas, peak widths, peak numbers, and peak splitting. This reduces the reliance on circuit model selection and human experience in traditional equivalent circuit fitting processes, and improves the objectivity and interpretability of feature extraction. By comparing the relaxation time distribution characteristic parameters under the test state with the benchmark characteristic parameters, this invention can transform the absolute impedance differences between different battery samples or different cycling stages into relative characteristic changes, reducing the impact of initial differences and test condition fluctuations on the identification results, thereby improving the stability and reliability of anomaly detection.

[0031] Furthermore, this invention can classify and identify the operating status of aqueous zinc metal anodes into normal, early warning, and abnormal categories based on abnormal characteristic deviations. It can not only determine whether an abnormality has occurred in the anode but also reflect the degree of abnormal evolution, providing a basis for battery operation and maintenance, failure early warning, life assessment, and optimization of electrolyte systems and anode protection strategies. Therefore, this invention has advantages such as non-destructive testing, sensitive response, strong feature interpretability, reliable diagnostic results, and suitability for engineering applications. Attached Figure Description

[0032] Figure 1 This is a flowchart of a method for identifying abnormal states of aqueous zinc metal anodes based on relaxation time distribution.

[0033] Figure 2 This is an in-situ electrochemical impedance spectroscopy (EIS) spectrum collected during the cycling process of the aqueous symmetric battery in the embodiment.

[0034] Figure 3 This is a relaxation time distribution curve of 9 groups of aqueous Zn||Zn symmetric battery samples in the embodiment.

[0035] Figure 4 This is a schematic diagram of state classification and anomaly type indication based on relaxation time distribution characteristic parameters in the embodiment.

[0036] Figure 5 This is a summary table of relaxation time distribution characteristic parameters for the baseline evolution group, early warning state group, and abnormal state group in Example 2. Detailed Implementation

[0037] To make the above-mentioned objects, features, and advantages of the present invention more apparent and understandable, specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of them. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the protection scope of the present invention.

[0038] Many specific details are set forth in the following description in order to provide a full understanding of the invention. However, the invention may also be practiced in other ways different from those described herein, and those skilled in the art can make similar extensions without departing from the spirit of the invention. Therefore, the invention is not limited to the specific embodiments disclosed below.

[0039] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The phrase "in one embodiment" appearing in different places throughout this specification does not necessarily refer to the same embodiment, nor is it a single or selective embodiment that mutually excludes other embodiments.

[0040] Example 1:

[0041] A method for identifying in-situ abnormal states of aqueous Zn||Zn symmetric cells based on relaxation time distribution, such as... Figure 1 As shown, the steps are as follows:

[0042] (1) Assemble an aqueous Zn||Zn symmetric cell. The aqueous Zn||Zn symmetric cell includes two zinc metal electrodes, a separator, and an aqueous zinc salt electrolyte. The aqueous zinc salt electrolyte can be zinc sulfate, zinc trifluoromethanesulfonate, zinc acetate, zinc chloride, or a composite electrolyte thereof;

[0043] (2) The aqueous Zn||Zn symmetric cell is subjected to constant current deposition / stripping cycling under preset current density and preset deposition / stripping surface capacity conditions. The preset current density and deposition / stripping surface capacity can be set according to the actual operating conditions of the aqueous zinc cell;

[0044] (3) Before cycling, at the beginning of cycling, during the middle of cycling, and at the end of cycling, or at each preset cycling time, the aqueous Zn||Zn symmetric cell was subjected to in-situ or quasi-in-situ electrochemical impedance spectroscopy (EIS) tests. The AC perturbation amplitude for the EIS test was 5 mV, and the test frequency range was 100 kHz to 10 mHz, to obtain impedance spectral data at different cycling stages.

[0045] (4) The impedance spectrum data is preprocessed, including outlier removal, ohmic resistance correction, and impedance normalization. For impedance spectrum data of the same battery at different cycling stages, the ohmic resistance of the initial cycling stage or the stable cycling stage is used as the correction reference;

[0046] (5) Based on the Tikhonov regularization method, the relaxation time distribution of the preprocessed impedance spectrum data is analyzed to obtain the relaxation time distribution function corresponding to different cycle stages.

[0047] (6) Extract the peak amplitude, peak corresponding time constant, peak area, peak width, peak number, peak position drift and peak splitting characteristics from the relaxation time distribution function as the characteristic parameters of the aqueous zinc metal anode state.

[0048] (7) Using the state characteristic parameters of the initial cycle stage, the stable cycle stage or the normal sample of the same system as the reference characteristic parameters, calculate the peak area change rate, peak corresponding time constant offset, peak width change and peak number change in the subsequent cycle stage to obtain the abnormal characteristic deviation.

[0049] (8) Compare the abnormal feature deviation with a preset threshold and output the state identification result of the aqueous zinc metal negative electrode. When the abnormal feature deviation is less than the first threshold, it is determined to be a normal state; when the abnormal feature deviation is greater than or equal to the first threshold and less than the second threshold, it is determined to be a warning state; when the abnormal feature deviation is greater than or equal to the second threshold, it is determined to be an abnormal state.

[0050] The above method can be used to identify early abnormal states of the zinc metal anode in aqueous Zn||Zn symmetric cells during cycling, caused by interfacial side reactions, uneven zinc deposition, or limited mass transfer.

[0051] Example 2:

[0052] A state classification method based on the relaxation time distribution spectra of 9 sets of aqueous Zn||Zn symmetric cells, the steps of which are as follows:

[0053] (1) Following the method in Example 1, in-situ or quasi-in-situ electrochemical impedance spectroscopy tests were performed on 9 groups of aqueous Zn||Zn symmetric battery samples during the cycling process to obtain impedance spectral data of 9 groups of samples at different cycling stages.

[0054] (2) The impedance spectrum data of the nine groups of samples were analyzed to obtain the relaxation time distribution curves corresponding to the nine groups of samples.

[0055] (3) Based on the peak amplitude, peak area, peak-corresponding time constant, peak width, peak number, and peak splitting characteristics in the relaxation time distribution curve, the nine groups of samples were divided into a baseline evolution group, a warning state group, and an abnormal state group. Among them, samples 1, 2, and 3 were classified as the baseline evolution group. The peak shape of the relaxation time distribution of this group of samples changed to some extent during the cycling process, but the overall peak area, peak position, and low-frequency response changes were within an acceptable range, and they can be used to establish a baseline for the relaxation time distribution evolution during normal cycling or initial activation. Samples 6, 7, and 9 were classified as the warning state group. This group of samples showed the characteristics of gradually increasing peak area in the mid-frequency or low-frequency time constant range, peak position shifting towards the long time constant direction, or increasing peak width during the cycling process, indicating that the polarization of the zinc metal anode interface was enhanced, the deposition / stripping uniformity decreased, or the interface side reactions began to intensify, which can be judged as a warning state. Samples 4, 5, and 8 were classified as the abnormal state group. During the cycling process, the samples exhibited characteristics such as a significant increase in peak area, a marked increase in peak width, peak splitting, or the appearance of new peaks in the low-frequency time constant range. This indicates that the aqueous zinc metal anode may have severe mass transfer limitation, by-product accumulation, deterioration of interfacial contact, or enhanced local side reactions, which can be identified as an abnormal state.

[0056] (4) Summarize the relaxation time distribution characteristic parameters of the baseline evolution group, early warning state group, and abnormal state group, and establish a state identification table. The results are as follows: Figure 5 As shown. By Figure 5 It can be seen that the relaxation time distribution characteristics can classify aqueous Zn||Zn symmetric cell samples into three categories: baseline evolution, early warning state, and abnormal state. Compared with direct observation of Nyquist plots, this method can more clearly reflect the changes in interfacial reactions, charge transfer, and ion diffusion processes in different cycling stages, and has better anomaly identification capabilities.

[0057] Example 3:

[0058] A rule for determining the abnormal state of aqueous zinc metal anodes based on relaxation time distribution characteristic parameters, comprising the following steps:

[0059] (1) Divide the relaxation time distribution function of the Zn||Zn symmetric cell of the water system under test into a high-frequency time constant interval, a medium-frequency time constant interval and a low-frequency time constant interval;

[0060] (2) Extract the peak area, peak amplitude, peak corresponding time constant, peak width, peak number and peak splitting features in each time constant interval to construct a state feature vector;

[0061] (3) Using the state feature vectors of the initial cycle stage, the stable cycle stage, or the baseline evolution group samples as the baseline feature vectors, calculate the abnormal feature deviation of the sample to be tested.

[0062] (4) The status shall be determined according to the following rules:

[0063] When the peak area change rate in the high-frequency, mid-frequency and low-frequency time constant intervals is small, the peak position does not shift continuously, and no new response peak or obvious peak splitting occurs, the aqueous zinc metal anode is judged to be in a normal state.

[0064] When the peak area in the medium or low frequency time constant range gradually increases, the peak position shifts towards the long time constant direction, or the peak width gradually increases, the aqueous zinc metal anode is determined to be in an early warning state.

[0065] When the peak area in the low-frequency time constant interval increases significantly, the peak width increases significantly, new peaks appear or peak splits occur, and the above characteristics persist in multiple cycle stages, the aqueous zinc metal anode is determined to be in an abnormal state.

[0066] (5) Output anomaly type prompts based on the state determination results. When the high-frequency time constant range is abnormally enhanced, it may indicate that there is deterioration of interfacial contact, changes in surface film, or abnormal deposition state; when the mid-frequency time constant range is abnormally enhanced, it may indicate that there is an increase in charge transfer resistance, enhanced interfacial side reactions, or intensified hydrogen evolution corrosion process; when the low-frequency time constant range is abnormally enhanced, it may indicate that there is restricted ion diffusion, accumulation of by-products, or uneven local mass transfer.

[0067] The above-mentioned judgment rules can be used to achieve graded identification of aqueous zinc metal negative electrodes from normal state, warning state to abnormal state.

[0068] Example 4:

[0069] A method for indicating the anomaly type of aqueous zinc metal negative electrode based on the relaxation time distribution state identification result, the steps of which are as follows:

[0070] (1) Following the methods of Examples 1 to 3, feature extraction and state determination are performed on the relaxation time distribution curve of the aqueous Zn||Zn symmetric cell to obtain the identification results of normal state, warning state or abnormal state;

[0071] (2) When the peak area or peak amplitude in the high frequency time constant range increases significantly, or the peak position shifts continuously, output prompts for abnormal interface contact, changes in zinc metal surface film, or abnormal deposition state.

[0072] (3) When the peak area in the intermediate frequency time constant range gradually increases, the peak width increases, or the peak position shifts towards the long time constant direction, the output charge transfer resistance increases, the interface side reaction is enhanced, or the hydrogen evolution corrosion process is aggravated.

[0073] (4) When the peak area in the low-frequency time constant range increases significantly, the peak width increases significantly, new peaks appear or peak splits occur, the output ion diffusion is restricted, by-products accumulate, local mass transfer is uneven or the interface contact deteriorates.

[0074] (5) When the above abnormal characteristics persist or intensify in multiple cycle stages, the state of the zinc metal negative electrode in the water system is upgraded from the warning state to the abnormal state, and the abnormal level is output.

[0075] This embodiment, by mapping relaxation time distribution characteristics to possible abnormal processes, can identify and classify abnormal types of aqueous zinc metal anodes without relying on complex equivalent circuit models, providing a basis for subsequent battery operation status monitoring and safety diagnosis. It should be noted that although this embodiment uses an aqueous Zn||Zn symmetric battery as an example, for Zn||Cu half-cells or aqueous full-cells containing zinc metal anodes, as long as electrochemical impedance spectroscopy data for the corresponding operating stage can be obtained, the relaxation time distribution analysis, characteristic parameter extraction, and abnormal state identification can also be performed according to the method of this invention.

[0076] As a further improvement, the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for identifying abnormal states of aqueous zinc metal anodes based on relaxation time distribution, characterized in that, The identification method includes the following steps: S1. Obtain electrochemical impedance spectroscopy data of an aqueous test battery containing a zinc metal negative electrode under preset test conditions; S2. Preprocess the electrochemical impedance spectroscopy data to obtain preprocessed impedance spectroscopy data; S3. Analyze the preprocessed impedance spectrum data based on the relaxation time distribution method to obtain the relaxation time distribution function corresponding to the water system test battery; S4. Extract characteristic parameters representing the operating state of the zinc metal anode from the relaxation time distribution function. The characteristic parameters include one or more of the following: peak amplitude, peak-corresponding time constant, peak area, peak width, number of peaks, peak position shift, and peak splitting characteristics. S5. Compare the characteristic parameters of the test battery in the water system with the reference characteristic parameters to obtain the abnormal characteristic deviation. S6. Based on the abnormal feature deviation, identify the abnormal state of the aqueous zinc metal negative electrode to obtain the state identification result.

2. The method for identifying abnormal states of aqueous zinc metal anodes based on relaxation time distribution according to claim 1, characterized in that, The aqueous test cell containing a zinc metal anode includes any one of the following: an aqueous Zn||Zn symmetric cell, a Zn||Cu half-cell, or an aqueous full cell containing a zinc metal anode.

3. The method for identifying abnormal states of aqueous zinc metal anodes based on relaxation time distribution according to claim 1, characterized in that, The preset test conditions include one or more of the following: cycle stage, deposition / stripping current density, deposition / stripping surface capacity, test temperature, settling time, electrolyte composition, electrolyte pH, and state of charge.

4. The method for identifying abnormal states of aqueous zinc metal anodes based on relaxation time distribution according to claim 1, characterized in that, The electrochemical impedance spectroscopy data were obtained by applying a small AC excitation signal to the aqueous test cell and collecting the response signal. The amplitude of the AC excitation signal was 1 mV to 20 mV, and the test frequency range was 10 mHz to 1 MHz.

5. The method for identifying abnormal states of aqueous zinc metal anodes based on relaxation time distribution according to claim 1, characterized in that, The preprocessing includes one or more of the following: outlier removal, data smoothing, ohmic resistance correction, impedance normalization, Kramers-Kronig compliance verification, and test condition correction.

6. The method for identifying abnormal states of aqueous zinc metal anodes based on relaxation time distribution according to claim 1, characterized in that, The relaxation time distribution method includes one or more of the following: Tikhonov regularization, nonnegative least squares method, maximum entropy method, or Bayesian inversion method.

7. The method for identifying abnormal states of aqueous zinc metal anodes based on relaxation time distribution according to claim 1, characterized in that, The relaxation time distribution function is divided into a high-frequency time constant interval, a mid-frequency time constant interval, and a low-frequency time constant interval. The high-frequency time constant interval is used to characterize processes related to the zinc metal anode surface film, interface contact, or deposition state. The mid-frequency time constant interval is used to characterize processes related to charge transfer, interface side reactions, or hydrogen evolution corrosion. The low-frequency time constant interval is used to characterize processes related to ion diffusion, byproduct accumulation, or mass transfer limitation.

8. The method for identifying abnormal states of aqueous zinc metal anodes based on relaxation time distribution according to claim 1, characterized in that, The reference characteristic parameters are derived from the initial cycling stage and stable cycling stage of the same water system test battery, or from the relaxation time distribution characteristic parameters of the same system water system test battery under normal state.

9. The method for identifying abnormal states of aqueous zinc metal anodes based on relaxation time distribution according to claim 1, characterized in that, The abnormal characteristic deviation includes one or more of the following: peak amplitude change rate, peak area change rate, peak corresponding time constant offset, peak width change, peak number change, peak splitting degree, and new peak appearance.

10. The method for identifying abnormal states of aqueous zinc metal anodes based on relaxation time distribution according to claim 1, characterized in that, The status recognition results include normal status, warning status, and abnormal status; when the abnormal feature deviation is less than a first threshold, it is determined to be a normal status; when the abnormal feature deviation is greater than or equal to the first threshold and less than a second threshold, it is determined to be a warning status; when the abnormal feature deviation is greater than or equal to the second threshold, it is determined to be an abnormal status, wherein the second threshold is greater than the first threshold.

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