Method and device for detecting health state of flow battery

Abstract

The invention discloses a method for detecting the health state of a flow battery, which determines the health state of the flow battery through attribute image data of a porous electrode of the flow battery and concentration data of an electrolyte in the charge and discharge process of the flow battery, and can realize real-time dynamic monitoring of the internal microstructure and the reaction process of the battery. The actual working state of the battery can be reflected more accurately, the health state of the flow battery can be determined more accurately, and guidance is provided for optimizing the electrode structure.

Description

[0001] This application is a divisional application of an invention patent application with an application date of October 22, 2024, Chinese application number 202411477733.4, and invention name “A method and device for detecting the health status of a flow battery”. Technical Field

[0002] The present invention relates to the field of battery technology, and in particular to a method and device for detecting the health status of a liquid flow battery. Background Art

[0003] With the rapid development of renewable energy, large-scale energy storage technology has become the key. As a new type of electrochemical energy storage technology, flow batteries have attracted much attention in the field of large-scale energy storage due to their advantages such as independent design of energy and power, long cycle life and rapid response. However, flow batteries still have shortcomings in core performance indicators such as energy density and power density, and their commercialization process is restricted. In the existing technology, the testing and characterization methods of flow batteries are single, and it is impossible to achieve simultaneous online monitoring of multiple physical fields, making it difficult to deeply understand the complex reaction mechanisms and performance evolution laws inside the battery. Therefore, there is an urgent need for a method for detecting the health status of flow batteries. Summary of the invention

[0004] The present invention provides a method and device for detecting the health status of a liquid flow battery, which can realize real-time dynamic monitoring of the internal microstructure and reaction process of the battery, can more accurately reflect the actual working state of the battery, and can more accurately determine the health status of the liquid flow battery.

[0005] In a first aspect, the present invention provides a method for detecting the health status of a flow battery, the method comprising:

[0006] Acquire attribute image data of the porous electrode of the target flow battery and concentration data of the electrolyte;

[0007] Determining target attribute information of the porous electrode of the target flow battery according to the attribute image data of the porous electrode;

[0008] Determining the electrolyte ion concentration, charge transfer data, and ion cross contamination degree data of the target flow battery according to the electrolyte concentration data;

[0009] Obtaining bubble characteristic data according to target attribute information of the porous electrode of the target flow battery;

[0010] Obtaining an electrolyte ion crossover signal according to the electrolyte ion concentration, charge transfer data, and ion crossover contamination degree data of the target flow battery;

[0011] If the bubble characteristic data or the electrolyte ion crossover signal meets a preset condition, determining the concentration data of the target type gas during the operation of the target flow battery;

[0012] The health status of the target flow battery is determined based on the electrolyte ion concentration, charge transfer data and ion cross contamination degree data of the target flow battery, as well as the concentration data of the target type gas during the operation of the target flow battery.

[0013] In a second aspect, the present invention provides a health status detection device for a flow battery, the device comprising: an optical imaging test unit, an electrolyte concentration test unit, a gas analysis unit and a battery performance test unit;

[0014] The optical imaging test unit is used to obtain attribute image data of the porous electrode of the target liquid flow battery; and determine target attribute information of the porous electrode of the target liquid flow battery according to the attribute image data of the porous electrode;

[0015] The electrolyte concentration testing unit is used to obtain the electrolyte concentration data of the target flow battery; according to the electrolyte concentration data; determine the electrolyte ion concentration, charge transfer data and ion cross contamination degree data of the target flow battery;

[0016] The gas analysis unit is used to obtain bubble characteristic data according to the target property information of the porous electrode of the target liquid flow battery; obtain an electrolyte ion crossover signal according to the electrolyte ion concentration, charge transfer data and ion crossover contamination degree data of the target liquid flow battery; if the bubble characteristic data or the electrolyte ion crossover signal meets the preset conditions, determine the concentration data of the target type gas of the target liquid flow battery during operation;

[0017] The battery performance testing unit is used to determine the health status of the target liquid flow battery based on the electrolyte ion concentration, charge transfer data and ion cross-contamination degree data of the target liquid flow battery, as well as the concentration data of the target type gas during the operation of the target liquid flow battery.

[0018] In a third aspect, the present invention provides a readable medium, comprising execution instructions. When a processor of an electronic device executes the execution instructions, the electronic device executes any method described in the first aspect.

[0019] In a fourth aspect, the present invention provides an electronic device, comprising a processor and a memory storing execution instructions, wherein when the processor executes the execution instructions stored in the memory, the processor executes any method described in the first aspect.

[0020] It can be seen from the above technical scheme that the method provided by the present invention can first obtain the attribute image data of the porous electrode of the target liquid flow battery and the concentration data of the electrolyte; then, the target attribute information of the porous electrode of the target liquid flow battery can be determined according to the attribute image data of the porous electrode; then, the electrolyte ion concentration, charge transfer data and ion cross-contamination degree data of the target liquid flow battery can be determined according to the electrolyte concentration data; then, the bubble characteristic data can be obtained according to the target attribute information of the porous electrode of the target liquid flow battery; next, the electrolyte ion crossover signal is obtained according to the electrolyte ion concentration, charge transfer data and ion cross-contamination degree data of the target liquid flow battery; if the bubble characteristic data or the electrolyte ion crossover signal meets the preset conditions, the concentration data of the target type gas of the target liquid flow battery during operation is determined; and, according to the electrolyte ion concentration, charge transfer data and ion cross-contamination degree data of the target liquid flow battery, and the concentration data of the target type gas of the target liquid flow battery during operation, the health status of the liquid flow battery is determined. It can be seen that the present application determines the health status of the flow battery through the attribute image data of the porous electrode of the flow battery during the charging and discharging process of the flow battery and the concentration data of the electrolyte, and can realize real-time dynamic monitoring of the internal microstructure and reaction process of the battery, which can more accurately reflect the actual working state of the battery, and can more accurately determine the health status of the flow battery, providing guidance for optimizing the electrode structure.

[0021] The further effects of the above-mentioned non-conventional preferred manner will be described below in conjunction with specific embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] In order to more clearly illustrate the embodiments of the present invention or the existing technical solutions, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0023] Figure 1a A schematic diagram of a flow chart of a method for detecting the health status of a flow battery provided in one embodiment of the present application;

[0024] Figure 1b This is a first schematic diagram of a flow battery testing platform provided in one embodiment of the present application;

[0025] Figure 1c is a schematic diagram of a visualization fixture and a flow battery provided in one embodiment of the present application;

[0026] Figure 1dThis is a second schematic diagram of a flow battery testing platform provided in one embodiment of the present application;

[0027] Figure 2 A schematic diagram of the structure of a health status detection device for a flow battery provided by an embodiment of the present invention;

[0028] Figure 3 A schematic diagram of the structure of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0029] In order to make the purpose, technical solution and advantages of the present invention clearer, the technical solution of the present invention will be clearly and completely described below in conjunction with specific embodiments and corresponding drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. 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.

[0030] Various non-limiting embodiments of the present invention are described in detail below in conjunction with the accompanying drawings.

[0031] See also Figure 1a , shows a method for detecting the health status of a flow battery in an embodiment of the present invention, which can be applied to a flow battery testing platform. For example, the flow battery testing platform can be used to obtain the attribute image data of the porous electrode of the target flow battery and the concentration data of the electrolyte, and determine the health status of the target flow battery based on the attribute image data of the porous electrode of the target flow battery and the concentration data of the electrolyte. Figure 1b As shown, the flow battery test platform 100 may include a flow battery system 10, a multi-physical field parameter coupling detection system 20, and a data acquisition system 30. In an embodiment of the present application, the flow battery system 10 includes: a flow battery 11 and a visualization fixture 12. The flow battery 11 includes an electrode 111 and a liquid storage tank 112, the electrode 111 includes a positive electrode 111 and a negative electrode 111 arranged in a stacked manner, and the liquid storage tank 112 includes a positive electrode liquid storage tank 112 and a negative electrode liquid storage tank 112 arranged on both sides of the electrode 111. Figure 1cAs shown, the visualization fixture 12 includes a diaphragm 121, two graphite plates 122 and two end plates 123. The diaphragm 121 is arranged between the positive electrode 111 and the negative electrode 111. The two graphite plates 122 are arranged on the opposite sides of the electrode 111 away from the diaphragm 121. The two end plates 123 are arranged on the opposite sides of the graphite plates 122 away from the electrode 111, wherein at least one of the two end plates 123 is a transparent end plate 123. The multi-physical field parameter coupling detection system 20 includes: an optical detection unit 21, an electrolyte detection unit 22, a gas detection unit 23 and a battery tester 24. The optical detection unit 21 is used to collect the internal image of the liquid flow battery 11 in real time to obtain the internal image data of the liquid flow battery 11. The electrolyte detection unit 22 is used to collect the electrolyte in the liquid storage tank 112 in real time through a micro-sampling circuit to obtain the concentration data of the electrolyte. The gas detection unit 23 is used to collect the gas in the liquid storage tank 112 in real time to obtain the concentration data of the gas. The battery tester 24 is used to charge and discharge the flow battery 11 and collect electrochemical data. Figure 1d As shown, the electrolyte detection unit 22 includes an in-situ ultraviolet spectrometer, and the electrolyte concentration data includes curve data of the concentration of anions and cations in the electrolyte changing with time.

[0032] In this embodiment, the method may include the following steps:

[0033] S101: Acquire attribute image data of a porous electrode and concentration data of an electrolyte of a target flow battery.

[0034] In this embodiment, the target property information of the porous electrode of the target liquid flow battery includes the porosity, specific surface area, liquid saturation of the porous electrode, and the change pattern of the porosity, specific surface area, and liquid saturation with time and current density.

[0035] As an example, the flow battery test platform can charge and discharge the visualization fixture, obtain image data such as porous electrodes, electrolyte flow, and bubble behavior inside the target flow battery through a microscope, and use digital image processing algorithms to enhance, segment, and extract features on the obtained images to obtain the porosity, specific surface area, and liquid saturation of the porous electrode, and obtain the change patterns of porosity, specific surface area, and liquid saturation over time and current density. The flow battery test platform can perform in-situ ultraviolet spectrometry tests on the inside of the target flow battery and measure the concentration changes of the electrolyte in real time, that is, the concentration data of the electrolyte.

[0036] S102: Determine target attribute information of the porous electrode of the target liquid flow battery according to the attribute image data of the porous electrode.

[0037] As an example, a histogram equalization algorithm can be used to perform global contrast enhancement on the attribute image data of the porous electrode, and an adaptive histogram equalization algorithm can be used to perform local contrast enhancement on the attribute image data of the porous electrode to obtain the enhanced attribute image data of the porous electrode. And, a non-local mean filtering algorithm can be used to remove image noise from the enhanced attribute image data of the porous electrode to obtain the denoised attribute image data of the porous electrode. It should be noted that this embodiment uses a histogram equalization algorithm to perform global contrast enhancement on the image (i.e., the attribute image data of the porous electrode), uses an adaptive histogram equalization algorithm to perform local contrast enhancement on the image, and uses a non-local mean filtering algorithm to remove image noise to improve the signal-to-noise ratio.

[0038] Then, the grayscale image corresponding to the denoised attribute image data can be globally segmented using the Otsu threshold segmentation algorithm, and the local adaptive segmentation can be performed using the region growing algorithm to obtain the mask image of the porous electrode region. Specifically, the enhanced grayscale image can be globally segmented using the Otsu threshold segmentation algorithm, and the local adaptive segmentation can be performed using the region growing algorithm to extract the segmentation mask images of the porous electrode region and the background region, and the segmentation mask images of the porous electrode region and the background region are used as the mask image of the porous electrode region.

[0039] Next, the segmented mask image of the porous electrode region is subjected to noise filtering and repair processing using morphological opening and closing operations, and the connectivity features and morphological features of the porous electrode region are extracted based on the mask image of the porous electrode region using a connected domain labeling algorithm. That is, the segmented binary image is subjected to noise filtering and repair processing using morphological opening and closing operations, and the connectivity features and morphological features of the porous electrode region are extracted using a connected domain labeling algorithm.

[0040] According to the connectivity characteristics and morphological characteristics of the porous electrode region, the porosity, specific surface area, and liquid saturation of the porous electrode are determined. Specifically, the quantitative analysis of the microstructural parameters such as the porosity, specific surface area, and liquid saturation of the porous electrode is achieved by the following steps:

[0041] (1) Calculate the porosity ε of the porous electrode using the volume fraction formula:

[0042]

[0043] Where V void is the pore volume, V total is the total volume, N void is the number of pore pixels, N total is the total number of pixels, which can be obtained by counting the pixels of the segmentation mask image.

[0044] (2) Calculate the specific surface area a of the porous electrode using the volume specific surface area formula:

[0045]

[0046] Where a interface is the solid-liquid interface area, L interface is the total length of the solid-liquid interface, and res is the image resolution, which can be obtained by extracting the edge pixels of the segmentation mask image and calculating their total length.

[0047] (3) Calculate the liquid saturation s of the porous electrode using the liquid volume fraction formula:

[0048]

[0049] Where V liquid is the volume of electrolyte, N liquid is the number of electrolyte pixels, which can be obtained by performing grayscale threshold segmentation on the pore area in the segmentation mask image.

[0050] Next, the variation of the porosity, specific surface area, and liquid saturation over time and current density can be determined based on the porosity, specific surface area, and liquid saturation of the porous electrode at multiple moments, as well as the current density data of the porous electrode. As an example, the variation of the porosity, specific surface area, and liquid saturation over time and current density can be achieved by the following steps:

[0051] (1) By charging and discharging at different discharge time points (such as 0, 5, 10, 20, 30, 60 min, etc.) and different constant current density (such as 100, 200, 300 mA cm -2 A series of porous electrode microscopic images are repeatedly collected under different working conditions, and the above steps of image enhancement, segmentation, feature extraction and parameter calculation are repeated for each image to obtain quantitative data of microstructure parameters under a series of different working conditions.

[0052] (2) The data on the changes of microstructure parameters with time and current density are interpolated, fitted and statistically analyzed to obtain the time evolution curves and current density dependence curves of parameters such as porosity, specific surface area, and liquid saturation, and to establish quantitative expressions for the microstructure parameters.

[0053] (3) Combining theoretical electrochemical models and multi-physics field numerical simulations, we analyze the dynamic changes of microstructure parameters and their intrinsic relationship with the battery charge and discharge characteristics, revealing the mechanism by which the evolution of microstructure affects battery capacity, current density, cycle performance, and other aspects.

[0054] S103: Determine the electrolyte ion concentration, charge transfer data and ion cross contamination degree data of the target flow battery according to the electrolyte concentration data.

[0055] In this embodiment, an in-situ ultraviolet spectrophotometric test can be performed on the inside of the target flow battery to measure the concentration change of the electrolyte in real time, and continuously monitor the changes in the concentration and valence of active ions, calculate the coulombic efficiency, energy efficiency, and self-discharge index of the battery, and obtain the spatial distribution of ion concentration by scanning and imaging different positions of the electrolyte, and obtain charge transfer and cross-contamination degree data. The charge transfer data includes: coulombic efficiency data, energy efficiency data, and self-discharge rate data of the target flow battery.

[0056] Specifically, the concentrations of the oxidized and reduced active ions in the electrolyte can be determined based on the Lambert-Beer law, the concentration data of the electrolyte, and the preset absorbance of the electrolyte at the characteristic absorption wavelength, and the concentrations of the oxidized and reduced active ions in the electrolyte are used as the electrolyte ion concentrations of the target flow battery, and the valence data of the electrolyte ions of the target flow battery are determined;

[0057] Then, the coulombic efficiency data, energy efficiency data, and self-discharge rate data of the target flow battery can be determined according to the electrolyte ion concentration and the valence state data of the electrolyte ions of the target flow battery, and the coulombic efficiency data, energy efficiency data, and self-discharge rate data of the target flow battery can be used as the charge transfer data of the target flow battery;

[0058] Next, the target flow battery ion cross contamination degree data can be determined based on the electrolyte ion concentration and electrolyte ion valence data of the target flow battery. As an example, the concentration of the electrolyte ions at the positive and negative electrodes, the preset Faraday constant, and the electrolyte volume of the positive and negative electrodes can be obtained; and the target flow battery ion cross contamination degree data can be determined based on the electrolyte ion concentration and electrolyte ion valence data of the target flow battery, and the concentration of the electrolyte ions at the positive and negative electrodes, the preset Faraday constant, and the electrolyte volume of the positive and negative electrodes.

[0059] Specifically, the concentrations of oxidized and reduced active ions in the electrolyte can be quantitatively calculated by measuring the absorbance of the electrolyte at the characteristic absorption wavelength and combining it with the Lambert-Beer law. The expression of the Lambert-Beer law is:

[0060] A=εbc

[0061] Where A is the absorbance, ε is the molar absorptivity, b is the optical path length, and c is the solution concentration.

[0062] For the oxidized and reduced ions, their characteristic absorption wavelengths λ are measured respectively. ox and λ red The absorbance A ox and A red , and then according to the pre-calibrated molar absorption coefficient ε ox and ε red , and the known optical path length b, calculate the concentration c of the oxidized and reduced ions respectively ox and c red :

[0063]

[0064] The degree of ion cross contamination is calculated by the following expression:

[0065]

[0066] In the formula, m and n are the valence states of ions A and B, t i The concentration of ions A and B at the positive and negative electrodes at the moment, t i The charge carried by the positive and negative electrodes at the moment, F is the Faraday constant, V pos , V neg is the volume of the electrolyte of the positive and negative electrodes, is the degree of cross contamination between positive and negative electrodes, is the degree of battery cross contamination.

[0067] Continuously monitor the changes in active ion concentration and valence state to evaluate the battery's Coulomb efficiency, energy efficiency, self-discharge and other performance indicators. The specific evaluation method is as follows:

[0068] (1) The calculation formula of Coulombic efficiency (CE) is:

[0069]

[0070] Among them, Q d is the cumulative discharge capacity of the discharge process, Q c It is the cumulative charging capacity of the charging process, which can be obtained by multiplying the change in active ion concentration by the volume of the electrolyte and integrating it over time.

[0071] (2) The calculation formula for energy efficiency (EE) is:

[0072]

[0073] Among them, E dis the cumulative output energy of the discharge process, E c It is the cumulative input energy of the charging process, which can be calculated by integrating the change in active ion concentration, battery voltage and time.

[0074] (3) The calculation formula for the self-discharge rate (SDR) is:

[0075]

[0076] Where c0 is the initial active ion concentration, c t is the active ion concentration after standing for t time. The self-discharge performance is evaluated by monitoring the decay rate of the active ion concentration in the open circuit state of the battery.

[0077] S104: Obtaining bubble characteristic data according to target attribute information of the porous electrode of the target liquid flow battery.

[0078] S105: Obtaining an electrolyte ion crossover signal according to the electrolyte ion concentration, charge transfer data and ion cross contamination degree data of the target liquid flow battery.

[0079] S106: If the bubble characteristic data or the electrolyte ion crossover signal meets a preset condition, determine the concentration data of the target type gas during the operation of the target liquid flow battery.

[0080] The preset condition is that the bubble characteristic data satisfies a preset data threshold, or the signal intensity of the electrolyte ion cross signal satisfies a preset intensity.

[0081] Specifically, the gas released during the operation of the target liquid flow battery can be collected first. Then, the chromatographic peak area of ​​hydrogen and the chromatographic peak area of ​​oxygen can be determined based on the gas released during the operation of the target liquid flow battery. Next, the concentration corresponding to the hydrogen can be determined based on the chromatographic peak area of ​​the hydrogen. Next, the concentration corresponding to the oxygen can be determined based on the chromatographic peak area of ​​the oxygen. Finally, the concentration corresponding to the hydrogen and the concentration corresponding to the oxygen can be used as the concentration data of the target type gas during the operation of the target liquid flow battery.

[0082] As an example, the processing of S104-S106 is as follows: by calculating and processing the obtained porosity, specific surface area, and liquid saturation data, bubble characteristic data is obtained, and by calculating and processing the electrolyte ion concentration, charge transfer, and ion cross contamination degree data, the electrolyte ion cross signal is obtained. At this time, when the liquid flow battery test platform monitors and obtains the bubble characteristics or electrolyte ion cross signal, it will start the gas chromatograph to monitor the gas components released during the battery operation of the target liquid flow battery and obtain gas concentration data such as hydrogen or oxygen.

[0083] Then, the concentration data of gases such as hydrogen or oxygen in the liquid flow battery storage tank can be obtained. In this step, the gas components released during the operation of the target liquid flow battery are monitored by the following steps:

[0084] (1) Gas sampling: Use the sampler that comes with the gas chromatograph to collect a certain volume (e.g., 0.5-1 mL) of gas sample from the gas phase space of the flow battery storage tank. When sampling, care should be taken to prevent liquid from entering the sampler to avoid affecting the analysis results.

[0085] (2) Chromatographic separation: The collected gas sample is injected into the gas chromatograph through the injection port. Driven by the carrier gas (such as helium, nitrogen, etc.), the gas sample interacts with the stationary phase through the chromatographic column to achieve component separation.

[0086] (3) Chromatographic detection: The separated gas components enter the detector in sequence and generate corresponding signal responses. Commonly used detectors include thermal conductivity detector (TCD), flame ionization detector (FID), etc. For hydrogen and oxygen released from flow batteries, TCD detector is suitable, which has good sensitivity and stability.

[0087] (4) Data acquisition and processing: The signal generated by the detector is amplified and digitized, and then collected and recorded by the chromatography workstation software. By comparing with standard samples and establishing calibration curves, the concentrations of components such as hydrogen and oxygen can be qualitatively and quantitatively analyzed. The chromatographic peak area or peak height is linearly related to the component concentration and can be used for quantitative calculations:

[0088] c i =f i ·A i

[0089] In the formula, c i is the concentration of gas component i, f i is the correction factor of component i, which is related to chromatographic conditions, detector type, etc. i is the chromatographic peak area.

[0090] S107: Determine the health status of the target liquid flow battery according to the electrolyte ion concentration, charge transfer data and ion cross contamination degree data of the target liquid flow battery, and the concentration data of the target type gas during the operation of the target liquid flow battery.

[0091] As an example, the electrolyte ion concentration, charge transfer data and ion cross-contamination degree data of the target liquid flow battery, as well as the concentration data of the target type gas during the operation of the target liquid flow battery can be preprocessed to obtain target data; wherein the preprocessing operation includes a cleaning operation, a filtering operation and a normalization operation.

[0092] Then, a deviation value between the target data and the target property information of the porous electrode of the target liquid flow battery may be determined.

[0093] Next, the health status of the target flow battery may be obtained according to the deviation value between the target data and the target attribute information of the porous electrode of the target flow battery, and the preset weight corresponding to the target attribute information.

[0094] Specifically, the electrolyte concentration data and gas concentration data obtained are sent to the data processing system through data transmission, and are summarized with the obtained energy efficiency, coulomb efficiency and self-discharge rate, and then pre-processed by cleaning, filtering, normalization and other operations. Then, the real-time data obtained by pre-processing is compared with the initial value in step 2 (such as: energy efficiency deviation = (real-time energy efficiency-initial energy efficiency) / initial energy efficiency × 100%), and the deviation value is calculated. Then, the ion crossover degree, hydrogen evolution side reaction degree, energy efficiency deviation, coulomb efficiency deviation, self-discharge rate and other indicators are weighted averaged (comprehensive score = ∑ (indicator weight × indicator score) / ∑ indicator weight), and the comprehensive score is compared with the set threshold to give a comprehensive score of the battery health status. When the evaluation result is lower than the preset threshold, a warning or alarm signal is issued in time. In this way, the diagnosis of the electrolyte ion concentration and the electrode surface state is realized, and the health status of the liquid flow battery system is obtained.

[0095] As an example, a mapping relationship between performance indicators and key parameters is established to build an evaluation system and prediction model for flow battery performance. Combined with the electrolyte concentration data and ion crossover degree data monitored by the UV spectrometer and the hydrogen concentration data monitored by the gas chromatograph, the health status of the battery is comprehensively evaluated, and the test analysis results are combined with the battery management system to achieve online monitoring of the battery operating status, fault diagnosis, life prediction and other functions, and to dynamically adjust the battery's pump speed, voltage window, current and other information in real time, forming a closed loop of data monitoring-data feedback-data mapping-data feedback-data monitoring to ensure the safe and efficient operation of the battery.

[0096] 1. Integrate the data obtained by various testing methods, establish the mapping relationship between performance indicators and key parameters, and build the evaluation system and prediction model of flow battery performance. The specific steps are:

[0097] (1) Collect and organize experimental data obtained by different testing methods, including ultraviolet spectroscopic data, gas chromatography data, electrochemical test data (such as charge and discharge curves, cyclic voltammetry curves, AC impedance spectra, etc.) and other physical and chemical characterization data, and establish a structured database.

[0098] (2) Perform data preprocessing and feature engineering, normalize and reduce the dimension of the original data, and extract key characteristic parameters that can reflect battery performance, such as active material concentration, ion crossover degree, hydrogen evolution rate, Coulomb efficiency, energy efficiency, capacity decay rate, etc.

[0099] (3) Use machine learning algorithms, such as multivariate linear regression, support vector machine, random forest, etc., to establish a quantitative mapping relationship between performance indicators and key characteristic parameters. Through training and verification, optimize the model's hyperparameters and generalization performance to obtain a more robust performance prediction model. Predict and evaluate the performance of new flow batteries or batteries under working conditions.

[0100] 2. Combine the electrolyte concentration data and ion crossover degree data monitored by the UV spectrometer with the hydrogen concentration data monitored by the gas chromatography to comprehensively evaluate the health status of the battery. Specifically:

[0101] (1) Receive monitoring data from the UV spectrometer and gas chromatograph in real time, extract key indicators such as electrolyte active ion concentration, ion crossover degree, and hydrogen evolution gas concentration, and perform data synchronization and alignment.

[0102] (2) According to the pre-established health status assessment model, analyze the changing trends and abnormal conditions of various indicators. Set quantitative indicators of health status, such as ion crossover degree less than 5%, hydrogen evolution gas concentration less than 1%, etc., as the basis for judging whether the battery is healthy or not.

[0103] (3) Comprehensively consider the evaluation results of various indicators and give a comprehensive score of the battery health status, that is, the health status of the target liquid flow battery, such as healthy, sub-healthy, abnormal, and failure levels.

[0104] (4) Continuously track changes in battery health status and issue warnings or alarms in a timely manner when the evaluation result is lower than the preset threshold. At the same time, analyze the causes of changes in health status, such as electrolyte degradation, membrane material aging, catalyst poisoning, etc., to provide a basis for fault diagnosis and maintenance strategies.

[0105] 3. Combine the test analysis results with the battery management system (i.e., flow battery test platform) to realize online monitoring of battery operation status, fault diagnosis, life prediction and other functions. Specifically, the steps are as follows:

[0106] (1) The multi-physical field monitoring data and performance evaluation results are transmitted to the platform battery management system in real time, and correlated with the battery operating parameters (such as voltage, current, temperature, etc.) for analysis.

[0107] (2) Build a monitoring interface for the battery operating status, and intuitively display the real-time performance and health status of the battery through charts, indicators, etc.

[0108] (3) Based on machine learning algorithms, a battery fault diagnosis model is established to perform feature learning and classification for common fault modes (such as membrane perforation, flow channel blockage, electrolyte leakage, etc.). When abnormal monitoring data occurs, the cause of the fault is analyzed in a timely manner and the diagnosis result is given.

[0109] (4) Feedback the analysis results of monitoring, diagnosis, and prediction to the online data collection platform to form a closed data loop. Based on the analysis results, the online data collection platform optimizes the control strategy of battery operation, adjusts the charging and discharging parameters, slows down battery aging, and extends battery life.

[0110] 4. The steps of dynamically adjusting the battery's pump speed, voltage window, current and other information in real time are as follows:

[0111] (1) Based on the monitoring data of ultraviolet spectrometry and gas chromatography, the severity of ion crossover and hydrogen evolution behavior inside the battery is evaluated. When the data processing system receives the ion crossover degree data and gas concentration data and determines that it exceeds the preset safety threshold (such as the ion crossover degree exceeds 1% or the hydrogen concentration exceeds 100ppm), the system will send a control instruction to the battery charge and discharge tester.

[0112] (2) After the online acquisition platform receives the warning signal, it starts the dynamic adjustment function of the pump speed and voltage window. A gradient adjustment strategy is adopted. For example, for every 1% the gas concentration exceeds the safety threshold, the cut-off voltage is reduced by 0.05V. At the same time, a control instruction is sent to the electrolyte circulation pump. For example, for every 1% the ion crossover degree exceeds the safety threshold, the pump speed is increased by 10% until the gas concentration returns to a safe range. By controlling the electrolyte flow rate, the reactant supply and product discharge are increased, and the performance degradation caused by limited mass transfer is alleviated. By adjusting the charge and discharge cut-off voltage, the loss of active substances and the intensification of hydrogen evolution side reactions caused by overcharging and over-discharging can be avoided.

[0113] (3) While adjusting the pump speed and voltage window, the online acquisition platform must also take into account energy efficiency and cost factors. By optimizing the control algorithm, while ensuring battery safety and performance, the pump power and voltage window losses are minimized to achieve optimal energy utilization.

[0114] (4) For the regulation of charge and discharge current, the online data collection platform needs to comprehensively consider the power demand and capacity attenuation of the battery. By reasonably setting the charge and discharge rate, while meeting the load demand, avoid excessive current density that accelerates battery aging. If necessary, the current regulation scheme can also be optimized through strategies such as peak and valley electricity price scheduling.

[0115] (5) The online data acquisition platform continuously monitors changes in battery performance based on the adjusted pump speed, voltage window, and current parameters. Through data analysis and feedback, the adjustment strategy is continuously optimized to achieve dynamic matching and adaptive optimization of the battery operating status and control strategy.

[0116] In one implementation, the health status of the flow battery is a health score of the flow battery; the method may further include:

[0117] If the health status of the flow battery is lower than a preset score threshold, a warning or alarm signal is issued.

[0118] It can be seen from the above technical scheme that the method provided by the present invention can first obtain the attribute image data of the porous electrode of the target liquid flow battery and the concentration data of the electrolyte; then, the target attribute information of the porous electrode of the target liquid flow battery can be determined according to the attribute image data of the porous electrode; then, the electrolyte ion concentration, charge transfer data and ion cross-contamination degree data of the target liquid flow battery can be determined according to the electrolyte concentration data; then, the bubble characteristic data can be obtained according to the target attribute information of the porous electrode of the target liquid flow battery; next, the electrolyte ion crossover signal is obtained according to the electrolyte ion concentration, charge transfer data and ion cross-contamination degree data of the target liquid flow battery; if the bubble characteristic data or the electrolyte ion crossover signal meets the preset conditions, the concentration data of the target type gas of the target liquid flow battery during operation is determined; and, according to the electrolyte ion concentration, charge transfer data and ion cross-contamination degree data of the target liquid flow battery, and the concentration data of the target type gas of the target liquid flow battery during operation, the health status of the liquid flow battery is determined. It can be seen that the present application determines the health status of the flow battery through the attribute image data of the porous electrode of the flow battery during the charging and discharging process of the flow battery and the concentration data of the electrolyte, and can realize real-time dynamic monitoring of the internal microstructure and reaction process of the battery, which can more accurately reflect the actual working state of the battery, and can more accurately determine the health status of the flow battery, providing guidance for optimizing the electrode structure.

[0119] like Figure 2As shown, it is a specific embodiment of the health status detection device of the flow battery described in this application. The device described in this embodiment is a physical device for executing the method described in the above embodiment. Its technical scheme is essentially consistent with the above embodiment, and the corresponding description in the above embodiment is also applicable to this embodiment. In this embodiment, the device includes: an optical imaging test unit 201, an electrolyte concentration test unit 202, a gas analysis unit 204 and a battery performance test unit 204;

[0120] The optical imaging test unit 201 is used to obtain attribute image data of the porous electrode of the target flow battery; and determine target attribute information of the porous electrode of the target flow battery according to the attribute image data of the porous electrode;

[0121] The electrolyte concentration testing unit 202 is used to obtain the electrolyte concentration data of the target flow battery; determine the electrolyte ion concentration, charge transfer data and ion cross contamination degree data of the target flow battery according to the electrolyte concentration data;

[0122] The gas analysis unit 203 is used to obtain bubble characteristic data according to the target property information of the porous electrode of the target liquid flow battery; obtain an electrolyte ion crossover signal according to the electrolyte ion concentration, charge transfer data and ion crossover contamination degree data of the target liquid flow battery; if the bubble characteristic data or the electrolyte ion crossover signal meets the preset conditions, determine the concentration data of the target type gas of the target liquid flow battery during operation;

[0123] The battery performance testing unit 204 is used to determine the health status of the target liquid flow battery according to the electrolyte ion concentration, charge transfer data and ion cross contamination degree data of the target liquid flow battery, as well as the concentration data of the target type gas during the operation of the target liquid flow battery.

[0124] Optionally, the target property information of the porous electrode of the target liquid flow battery includes the porosity, specific surface area, liquid saturation of the porous electrode, and the change patterns of the porosity, the specific surface area, and the liquid saturation with time and current density.

[0125] Optionally, determining target attribute information of the porous electrode of the target flow battery according to the attribute image data of the porous electrode includes:

[0126] Performing global contrast enhancement on the attribute image data of the porous electrode using a histogram equalization algorithm, and performing local contrast enhancement on the attribute image data of the porous electrode using an adaptive histogram equalization algorithm to obtain enhanced attribute image data of the porous electrode;

[0127] Using a non-local mean filtering algorithm to remove image noise from the enhanced attribute image data of the porous electrode to obtain denoised attribute image data of the porous electrode;

[0128] Performing global threshold segmentation on the grayscale image corresponding to the denoised attribute image data using the Otsu threshold segmentation algorithm, and performing local adaptive segmentation using the region growing algorithm to obtain a mask image of the porous electrode region;

[0129] Using morphological opening and closing operations to filter noise and repair the mask image of the segmented porous electrode region, and using a connected domain labeling algorithm to extract connectivity features and morphological features of the porous electrode region based on the mask image of the porous electrode region;

[0130] Determining the porosity, specific surface area, and liquid saturation of the porous electrode according to the connectivity characteristics and morphological characteristics of the porous electrode region;

[0131] According to the porosity, specific surface area, liquid saturation of the porous electrode at multiple moments and the current density data of the porous electrode, the variation patterns of the porosity, specific surface area and liquid saturation with time and current density are determined.

[0132] Optionally, the charge transfer data includes: Coulomb efficiency data, energy efficiency data, and self-discharge rate data of the target liquid flow battery.

[0133] Optionally, determining the electrolyte ion concentration, charge transfer data and ion cross contamination degree data of the target flow battery according to the electrolyte concentration data includes:

[0134] Based on the Lambert-Beer law, the concentration data of the electrolyte and the preset absorbance of the electrolyte at the characteristic absorption wavelength, the concentrations of the oxidized and reduced active ions in the electrolyte are determined, and the concentrations of the oxidized and reduced active ions in the electrolyte are used as the electrolyte ion concentrations of the target flow battery, and the valence data of the electrolyte ions of the target flow battery are determined;

[0135] Determine the coulombic efficiency data, energy efficiency data, and self-discharge rate data of the target flow battery according to the electrolyte ion concentration and the valence state data of the electrolyte ions of the target flow battery, and use the coulombic efficiency data, energy efficiency data, and self-discharge rate data of the target flow battery as the charge transfer data of the target flow battery;

[0136] According to the electrolyte ion concentration and electrolyte ion valence data of the target liquid flow battery, the target liquid flow battery ion cross contamination degree data is determined.

[0137] Optionally, determining the target flow battery ion cross contamination degree data according to the electrolyte ion concentration and electrolyte ion valence data of the target flow battery includes:

[0138] Obtaining the concentration of the electrolyte ions at the positive and negative electrodes, the preset Faraday constant, and the electrolyte volume of the positive and negative electrodes;

[0139] The target flow battery ion cross contamination degree data is determined based on the electrolyte ion concentration of the target flow battery, the valence data of the electrolyte ions, the concentration of the electrolyte ions at the positive and negative electrodes, the preset Faraday constant, and the electrolyte volume of the positive and negative electrodes.

[0140] Optionally, the preset condition is that the bubble characteristic data meets a preset data threshold, or the signal intensity of the electrolyte ion cross signal meets a preset intensity;

[0141] If the bubble characteristic data or the electrolyte ion crossover signal meets a preset condition, determining the concentration data of the target type gas during the operation of the target flow battery includes:

[0142] Collecting gas released during operation of the target flow battery;

[0143] Determining the chromatographic peak area of ​​hydrogen and the chromatographic peak area of ​​oxygen based on the gas released by the target liquid flow battery during operation;

[0144] Determining the concentration of the hydrogen according to the chromatographic peak area of ​​the hydrogen;

[0145] Determining the concentration of the oxygen according to the chromatographic peak area of ​​the oxygen;

[0146] The concentration corresponding to the hydrogen and the concentration corresponding to the oxygen are used as the concentration data of the target type gas during the operation of the target liquid flow battery.

[0147] Optionally, determining the health status of the target flow battery according to the electrolyte ion concentration, charge transfer data and ion cross contamination degree data of the target flow battery, and the concentration data of the target type gas during the operation of the target flow battery includes:

[0148] Performing a preprocessing operation on the electrolyte ion concentration, charge transfer data and ion cross contamination degree data of the target liquid flow battery, as well as the concentration data of the target type gas during the operation of the target liquid flow battery, to obtain target data; wherein the preprocessing operation includes a cleaning operation, a filtering operation and a normalization operation;

[0149] Determining a deviation value between the target data and target attribute information of the porous electrode of the target flow battery;

[0150] The health status of the target flow battery is obtained according to the deviation value between the target data and the target attribute information of the porous electrode of the target flow battery, and the preset weight corresponding to the target attribute information.

[0151] Optionally, the health status of the flow battery is a health score of the flow battery; the device further comprises: a prompting unit, configured to:

[0152] If the health status of the flow battery is lower than a preset score threshold, a warning or alarm signal is issued.

[0153] Figure 3 : is a schematic diagram of the structure of an electronic device provided by an embodiment of the present invention. At the hardware level, the electronic device includes a processor, and optionally also includes an internal bus, a network interface, and a memory. Among them, the memory may include a memory, such as a high-speed random access memory (Random-Access Memory, RAM), and may also include a non-volatile memory (non-volatile memory), such as at least one disk storage, etc. Of course, the electronic device may also include hardware required for other services.

[0154] The processor, network interface and memory can be interconnected through an internal bus, which can be an ISA (Industry Standard Architecture) bus, a PCI (Peripheral Component Interconnect) bus or an EISA (Extended Industry Standard Architecture) bus. The bus can be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 3 Only one bidirectional arrow is used in the diagram, but this does not mean that there is only one bus or only one type of bus.

[0155] The memory is used to store execution instructions. Specifically, the execution instructions are computer programs that can be executed. The memory may include internal memory and non-volatile memory, and provides execution instructions and data to the processor.

[0156] In a possible implementation, the processor reads the corresponding execution instructions from the non-volatile memory into the memory and then runs them, and can also obtain the corresponding execution instructions from other devices to form a healthy state detection device for a flow battery at a logical level. The processor executes the execution instructions stored in the memory to implement the healthy state detection method for a flow battery provided in any embodiment of the present invention by executing the execution instructions.

[0157] As described above, the present invention Figure 1a The method performed by the health status detection device of the flow battery provided in the illustrated embodiment can be applied to a processor or implemented by a processor. The processor may be an integrated circuit chip with signal processing capabilities. In the implementation process, each step of the above method can be completed by an integrated logic circuit of hardware in the processor or instructions in the form of software. The above processor may be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc.; it may also be a digital signal processor (DSP), an application specific integrated circuit (ASIC), a field programmable gate array (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components. The disclosed methods, steps and logic block diagrams in the embodiments of the present invention can be implemented or executed. The general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc.

[0158] The steps of the method disclosed in the embodiment of the present invention can be directly embodied as being executed by a hardware decoding processor, or can be executed by a combination of hardware and software modules in the decoding processor. The software module can be located in a mature storage medium in the art such as a random access memory, a flash memory, a read-only memory, a programmable read-only memory, or an electrically erasable programmable memory, a register, etc. The storage medium is located in a memory, and the processor reads the information in the memory and completes the steps of the above method in combination with its hardware.

[0159] An embodiment of the present invention also proposes a readable storage medium, which stores execution instructions. When the stored execution instructions are executed by a processor of an electronic device, the electronic device can execute the health status detection method of the liquid flow battery provided in any embodiment of the present invention, and is specifically used to execute the method described in the above-mentioned data query.

[0160] The electronic device described in the above embodiments may be a computer.

[0161] Those skilled in the art will appreciate that the embodiments of the present invention may be provided as methods or computer program products. Therefore, the present invention may be implemented in the form of a complete hardware embodiment, a complete software embodiment, or a combination of software and hardware.

[0162] The various embodiments of the present invention are described in a progressive manner, and the same or similar parts between the various embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the device embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment.

[0163] It should also be noted that the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, commodity or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, commodity or device. In the absence of more restrictions, the elements defined by the sentence "comprises a ..." do not exclude the existence of other identical elements in the process, method, commodity or device including the elements.

[0164] The above description is only an embodiment of the present invention and is not intended to limit the present invention. For those skilled in the art, the present invention may have various modifications and variations. Any modification, equivalent substitution, improvement, etc. made within the spirit and principle of the present invention should be included in the scope of the claims of the present invention.

Claims

1. A method for detecting the health status of a flow battery, characterized in that: The method comprises: Acquire attribute image data of the porous electrode of the target flow battery and concentration data of the electrolyte; Determining target attribute information of the porous electrode of the target flow battery according to the attribute image data of the porous electrode; Determining the electrolyte ion concentration, charge transfer data, and ion cross contamination degree data of the target flow battery according to the electrolyte concentration data; Obtaining bubble characteristic data according to target attribute information of the porous electrode of the target flow battery; Obtaining an electrolyte ion crossover signal according to the electrolyte ion concentration, charge transfer data, and ion crossover contamination degree data of the target flow battery; If the bubble characteristic data or the electrolyte ion crossover signal meets a preset condition, determining the concentration data of the target type gas during the operation of the target flow battery; The health status of the target flow battery is determined based on the electrolyte ion concentration, charge transfer data and ion cross contamination degree data of the target flow battery, as well as the concentration data of the target type gas during the operation of the target flow battery.

2. The method according to claim 1, characterized in that The target property information of the porous electrode of the target liquid flow battery includes the porosity, specific surface area, liquid saturation of the porous electrode, and the variation patterns of the porosity, specific surface area, and liquid saturation with time and current density.

3. The method according to claim 1, characterized in that Determining the electrolyte ion concentration, charge transfer data and ion cross contamination degree data of the target flow battery according to the electrolyte concentration data includes: An in-situ UV spectrophotometric test is performed on the interior of the target liquid flow battery to measure the concentration change of the electrolyte in real time, while continuously monitoring the changes in the concentration and valence state of the active ions, calculating the coulombic efficiency, energy efficiency, and self-discharge index of the battery, and obtaining the spatial distribution of the ion concentration by scanning and imaging different positions of the electrolyte, and obtaining charge transfer and cross-contamination degree data; wherein the charge transfer data includes: coulombic efficiency data, energy efficiency data, and self-discharge rate data of the target liquid flow battery.

4. The method according to claim 2, characterized in that: The step of determining target attribute information of the porous electrode of the target flow battery according to the attribute image data of the porous electrode comprises: Performing global contrast enhancement on the attribute image data of the porous electrode using a histogram equalization algorithm, and performing local contrast enhancement on the attribute image data of the porous electrode using an adaptive histogram equalization algorithm to obtain enhanced attribute image data of the porous electrode; Using a non-local mean filtering algorithm to remove image noise from the enhanced attribute image data of the porous electrode to obtain denoised attribute image data of the porous electrode; Performing global threshold segmentation on the grayscale image corresponding to the denoised attribute image data using the Otsu threshold segmentation algorithm, and performing local adaptive segmentation using the region growing algorithm to obtain a mask image of the porous electrode region; Using morphological opening and closing operations to filter noise and repair the mask image of the segmented porous electrode region, and using a connected domain labeling algorithm to extract connectivity features and morphological features of the porous electrode region based on the mask image of the porous electrode region; Determining the porosity, specific surface area, and liquid saturation of the porous electrode according to the connectivity characteristics and morphological characteristics of the porous electrode region; According to the porosity, specific surface area, liquid saturation of the porous electrode at multiple moments and the current density data of the porous electrode, the variation patterns of the porosity, specific surface area and liquid saturation with time and current density are determined.

5. The method according to claim 4, characterized in that Determining the porosity, specific surface area, and liquid saturation of the porous electrode according to the connectivity characteristics and morphological characteristics of the porous electrode region includes: The porosity ε of the porous electrode is calculated using the volume fraction formula: Where V void is the pore volume, V total is the total volume, N void is the number of pore pixels, N total is the total number of pixels, which can be obtained by counting the pixels of the segmentation mask image; The specific surface area a of the porous electrode is calculated using the volume specific surface area formula: Where a interface is the solid-liquid interface area, L interface is the total length of the solid-liquid interface, res is the image resolution, which can be obtained by extracting the edge pixels of the segmentation mask image and calculating its total length; The liquid saturation s of the porous electrode is calculated using the liquid volume fraction formula: Where V liquid is the volume of electrolyte, N liquid is the number of electrolyte pixels, which is obtained by performing grayscale threshold segmentation on the pore area in the segmentation mask image.

6. The method according to claim 4, characterized in that The method of determining the variation of the porosity, the specific surface area, and the liquid saturation with time and the current density according to the porosity, the specific surface area, and the liquid saturation of the porous electrode at multiple moments and the current density data of the porous electrode comprises: By repeatedly collecting a series of porous electrode microscopic images at different discharge time points and different constant current charge and discharge current densities, and repeating the steps of image enhancement, segmentation, feature extraction and parameter calculation for each image, a series of microstructural parameters under different working conditions are obtained; Interpolate, fit and statistically analyze the data of microstructure parameters changing with time and current density, obtain the time evolution curve and current density dependence curve of at least one parameter including porosity, specific surface area and liquid saturation, and establish quantitative expressions of microstructure parameters; Combining theoretical electrochemical models and multi-physics field numerical simulations, the dynamic changes of microstructure parameters and the intrinsic relationship between the battery charge and discharge characteristics are analyzed, and the influence mechanism of microstructure evolution on battery capacity, current density and cycle performance is determined.

7. A health status detection device for a flow battery, characterized in that: The device comprises: an optical imaging test unit, an electrolyte concentration test unit, a gas analysis unit and a battery performance test unit; The optical imaging test unit is used to obtain attribute image data of the porous electrode of the target liquid flow battery; and determine target attribute information of the porous electrode of the target liquid flow battery according to the attribute image data of the porous electrode; The electrolyte concentration testing unit is used to obtain the electrolyte concentration data of the target flow battery; according to the electrolyte concentration data; determine the electrolyte ion concentration, charge transfer data and ion cross contamination degree data of the target flow battery; The gas analysis unit is used to obtain bubble characteristic data according to the target property information of the porous electrode of the target liquid flow battery; obtain an electrolyte ion crossover signal according to the electrolyte ion concentration, charge transfer data and ion crossover contamination degree data of the target liquid flow battery; if the bubble characteristic data or the electrolyte ion crossover signal meets the preset conditions, determine the concentration data of the target type gas of the target liquid flow battery during operation; The battery performance testing unit is used to determine the health status of the target liquid flow battery based on the electrolyte ion concentration, charge transfer data and ion cross-contamination degree data of the target liquid flow battery, as well as the concentration data of the target type gas during the operation of the target liquid flow battery.

8. The health status detection device of a flow battery according to claim 7, characterized in that: The target property information of the porous electrode of the target liquid flow battery includes the porosity, specific surface area, liquid saturation of the porous electrode, and the change rules of the porosity, the specific surface area, and the liquid saturation with time and current density.

9. The health status detection device of a flow battery according to claim 7, characterized in that: The optical imaging test unit is also used for: Performing global contrast enhancement on the attribute image data of the porous electrode using a histogram equalization algorithm, and performing local contrast enhancement on the attribute image data of the porous electrode using an adaptive histogram equalization algorithm to obtain enhanced attribute image data of the porous electrode; Using a non-local mean filtering algorithm to remove image noise from the enhanced attribute image data of the porous electrode to obtain denoised attribute image data of the porous electrode; Performing global threshold segmentation on the grayscale image corresponding to the denoised attribute image data using the Otsu threshold segmentation algorithm, and performing local adaptive segmentation using the region growing algorithm to obtain a mask image of the porous electrode region; Using morphological opening and closing operations to filter noise and repair the mask image of the segmented porous electrode region, and using a connected domain labeling algorithm to extract connectivity features and morphological features of the porous electrode region based on the mask image of the porous electrode region; Determining the porosity, specific surface area, and liquid saturation of the porous electrode according to the connectivity characteristics and morphological characteristics of the porous electrode region; According to the porosity, specific surface area, liquid saturation of the porous electrode at multiple moments and the current density data of the porous electrode, the variation patterns of the porosity, specific surface area and liquid saturation with time and current density are determined.

10. The health status detection device of a flow battery according to claim 7, characterized in that: The optical imaging test unit is also used for: The porosity ε of the porous electrode is calculated using the volume fraction formula: Where V void is the pore volume, V total is the total volume, N void is the number of pore pixels, N total is the total number of pixels, which can be obtained by counting the pixels of the segmentation mask image; The specific surface area a of the porous electrode is calculated using the volume specific surface area formula: Where a interface is the solid-liquid interface area, L interface is the total length of the solid-liquid interface, res is the image resolution, which can be obtained by extracting the edge pixels of the segmentation mask image and calculating its total length; The liquid saturation s of the porous electrode is calculated using the liquid volume fraction formula: Where V liquid is the volume of electrolyte, N liquid is the number of electrolyte pixels, which is obtained by performing grayscale threshold segmentation on the pore area in the segmentation mask image.

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