A method for detecting the state of health of a flow battery

Abstract

The application discloses a health state detection method of a liquid flow battery. The method determines the health state of the liquid flow battery through attribute image data of a porous electrode of the liquid flow battery and concentration data of electrolyte in a charging and discharging process of the liquid flow battery, can realize real-time dynamic monitoring of a microstructure and a reaction process in the battery, can more accurately reflect an actual working state of the battery, can more accurately determine the health state of the liquid flow battery, and provides guidance for optimizing an electrode structure.

Description

[0001] This application is a divisional application of Chinese patent application No. 202411477733.4, filed on October 22, 2024, entitled "A method and device for detecting the health status of a flow battery". Technical Field

[0002] This invention relates to the field of battery technology, and in particular to a method and apparatus for detecting the health status of a flow battery. Background Technology

[0003] With the rapid development of renewable energy, large-scale energy storage technology has become crucial. Flow batteries, as a novel electrochemical energy storage technology, have attracted significant attention in the field of large-scale energy storage due to their advantages such as independent energy and power design, long cycle life, and rapid response. However, flow batteries still have shortcomings in core performance indicators such as energy density and power density, which hinders their commercialization. Existing technologies rely on limited testing and characterization methods for flow batteries, failing to achieve simultaneous online monitoring of multiple physical fields and hindering a deep understanding of the complex reaction mechanisms and performance evolution within the battery. Therefore, a method for detecting the health status of flow batteries is urgently needed. Summary of the Invention

[0004] This invention provides a method and apparatus for detecting the health status of a flow battery, which can realize real-time dynamic monitoring of the battery's internal microstructure and reaction process, more accurately reflect the actual working state of the battery, and more precisely determine the health status of the 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 the property image data of the porous electrode and the concentration data of the electrolyte of the target flow battery;

[0007] Based on the attribute image data of the porous electrode, the target attribute information of the porous electrode of the target flow battery is determined;

[0008] Based on the electrolyte concentration data, determine the electrolyte ion concentration, charge transport data, and ion cross-contamination level data of the target flow battery;

[0009] Based on the target attribute information of the porous electrode of the target flow battery, bubble characteristic data is obtained;

[0010] Based on the electrolyte ion concentration, charge transport data, and ion cross-contamination level data of the target flow battery, the electrolyte ion cross-contamination signal is obtained.

[0011] If the bubble characteristic data or the electrolyte ion cross signal meets the preset conditions, the concentration data of the target type gas in the target flow battery during operation is determined;

[0012] The health status of the target flow battery is determined based on the electrolyte ion concentration, charge transport data, and ion cross-contamination level 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 testing unit, an electrolyte concentration testing unit, a gas analysis unit, and a battery performance testing unit;

[0014] The optical imaging testing unit is used to acquire attribute image data of the porous electrode of the target flow battery; and to determine the target attribute information of the porous electrode of the target flow battery based on the attribute image data of the porous electrode.

[0015] The electrolyte concentration testing unit is used to acquire the electrolyte concentration data of the target flow battery; and based on the electrolyte concentration data, to determine the electrolyte ion concentration, charge transport data, and ion cross-contamination level data of the target flow battery.

[0016] The gas analysis unit is used to obtain bubble characteristic data based on the target attribute information of the porous electrode of the target flow battery; to obtain electrolyte ion cross-contamination signal based on the electrolyte ion concentration, charge transport data and ion cross-contamination degree data of the target flow battery; and to determine the concentration data of the target type gas during the operation of the target flow battery if the bubble characteristic data or the electrolyte ion cross-contamination signal meets the preset conditions.

[0017] The battery performance testing unit is used to determine the health status of the target flow battery based on the electrolyte ion concentration, charge transport data, and ion cross-contamination level 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. In a third aspect, the present invention provides a readable medium including executable instructions, which, when executed by a processor of an electronic device, cause the electronic device to perform any of the methods described in the first aspect.

[0018] Fourthly, the present invention provides an electronic device including a processor and a memory storing execution instructions, wherein when the processor executes the execution instructions stored in the memory, the processor performs the method as described in any of the first aspects.

[0019] As can be seen from the above technical solution, the method provided by the present invention can first acquire the attribute image data of the porous electrode of the target flow battery and the concentration data of the electrolyte; then, based on the attribute image data of the porous electrode, the target attribute information of the porous electrode of the target flow battery can be determined; next, based on the concentration data of the electrolyte, the electrolyte ion concentration, charge transport data, and ion cross-contamination degree data of the target flow battery can be determined; then, based on the target attribute information of the porous electrode of the target flow battery, bubble characteristic data can be obtained; next, based on the electrolyte ion concentration, charge transport data, and ion cross-contamination degree data of the target flow battery, the electrolyte ion cross-contamination signal can be obtained; if the bubble characteristic data or the electrolyte ion cross-contamination signal meets the preset conditions, the concentration data of the target type gas during the operation of the target flow battery can be determined; and, based on the electrolyte ion concentration, charge transport 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, the health status of the flow battery can be determined. As can be seen, this application determines the health status of a flow battery by using the attribute image data of the porous electrodes and the concentration data of the electrolyte during the charging and discharging process. This enables real-time dynamic monitoring of the battery's internal microstructure and reaction process, more accurately reflecting the actual working state of the battery and providing guidance for optimizing the electrode structure.

[0020] The further effects of the aforementioned non-conventional preferred method will be explained below in conjunction with specific embodiments. Attached Figure Description

[0021] To more clearly illustrate the embodiments of the present invention or the existing technical solutions, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0022] Figure 1a A schematic flowchart illustrating a method for detecting the health status of a flow battery according to an embodiment of this application;

[0023] Figure 1b This is a first schematic diagram of a flow battery testing platform provided in an embodiment of this application;

[0024] Figure 1c This is a schematic diagram of a visualization fixture and flow battery provided in one embodiment of this application;

[0025] Figure 1dThis is a second schematic diagram of a flow battery testing platform provided in one embodiment of this application;

[0026] Figure 2 This is a schematic diagram of a health status detection device for a flow battery according to an embodiment of the present invention;

[0027] Figure 3 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present invention. Detailed Implementation

[0028] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of this invention will be clearly and completely described below in conjunction with specific embodiments and corresponding drawings. Obviously, the described embodiments are only a part of the embodiments of this invention, and not all of them. Based on the embodiments of this invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this invention.

[0029] Various non-limiting embodiments of the present invention will now be described in detail with reference to the accompanying drawings.

[0030] See Figure 1a This invention illustrates a method for detecting the health status of a flow battery according to an embodiment of the present invention. The method can be applied to a flow battery testing platform. For example, the platform can be used to acquire attribute image data of the porous electrodes and electrolyte concentration data of a target flow battery, and to determine the health status of the target flow battery based on the attribute image data of the porous electrodes and the electrolyte concentration data. Figure 1b As shown, the flow battery testing platform 100 may include a flow battery system 10, a multiphysics parameter coupling detection system 20, and a data acquisition system 30. In an embodiment of this application, the flow battery system 10 includes a flow battery 11 and a visualization fixture 12. The flow battery 11 includes electrodes 111 and a storage tank 112. The electrodes 111 include a positive electrode 111 and a negative electrode 111 stacked together. The storage tank 112 includes a positive electrode storage tank 112 and a negative electrode storage tank 112 disposed on both sides of the electrodes 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 disposed between the positive electrode 111 and the negative electrode 111. The two graphite plates 122 are disposed on opposite sides of the electrode 111 away from the diaphragm 121. The two end plates 123 are disposed on opposite sides of the graphite plates 122 away from the electrode 111. At least one of the two end plates 123 is a transparent end plate 123. The multiphysics 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 acquire internal images of the flow battery 11 in real time to obtain internal image data of the flow battery 11. The electrolyte detection unit 22 is used to acquire electrolyte in the storage tank 112 in real time through a micro-sampling circuit to obtain electrolyte concentration data. The gas detection unit 23 is used to acquire gas in the storage tank 112 in real time to obtain gas concentration data. The battery tester 24 is used to charge and discharge the flow battery 11 and collect electrochemical data. For example... 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 over time.

[0031] In this embodiment, the method may include, for example, the following steps:

[0032] S101: Acquire the property image data of the porous electrode of the target flow battery and the concentration data of the electrolyte.

[0033] In this embodiment, the target attribute information of the porous electrode of the target flow battery includes the porosity, specific surface area, liquid saturation of the porous electrode, and the variation of the porosity, specific surface area, and liquid saturation with time and current density.

[0034] As an example, the flow battery testing platform can charge and discharge a visualization fixture, acquiring image data of the porous electrodes, electrolyte flow, and bubble behavior inside the target flow battery using a microscope. Digital image processing algorithms are then used to enhance, segment, and extract features from the acquired images to obtain the porosity, specific surface area, and liquid saturation of the porous electrodes, as well as the changes in porosity, specific surface area, and liquid saturation with time and current density. The platform can also perform in-situ ultraviolet spectrophotometry on the inside of the target flow battery, measuring the electrolyte concentration changes in real time, i.e., electrolyte concentration data.

[0035] S102: Determine the target attribute information of the porous electrode of the target flow battery based on the attribute image data of the porous electrode.

[0036] 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, resulting in enhanced attribute image data of the porous electrode. Furthermore, a nonlocal mean filtering algorithm is used to remove image noise from the enhanced attribute image data of the porous electrode, resulting in 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), an adaptive histogram equalization algorithm to perform local contrast enhancement on the image, and a nonlocal mean filtering algorithm to remove image noise, thereby improving the signal-to-noise ratio.

[0037] Then, the Otsu thresholding algorithm can be used to perform global thresholding on the grayscale image corresponding to the denoised attribute image data, and the region growing algorithm can be used for local adaptive segmentation to obtain the mask image of the porous electrode region. Specifically, the Otsu thresholding algorithm can be used to perform global thresholding on the enhanced grayscale image, and the region growing algorithm can be used for local adaptive segmentation to extract the segmented mask images of the porous electrode region and the background region. The segmented mask images of the porous electrode region and the background region are used as the mask image of the porous electrode region.

[0038] Next, morphological opening and closing operations are used to perform noise filtering and restoration on the mask image of the segmented porous electrode region. Then, a connected component labeling algorithm is used to extract the connectivity and morphological features of the porous electrode region based on the mask image. Specifically, morphological opening and closing operations are used to perform noise filtering and restoration on the segmented binarized image, and a connected component labeling algorithm is used to extract the connectivity and morphological features of the porous electrode region.

[0039] Based on the connectivity 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 porosity, specific surface area, and liquid saturation of the porous electrode is achieved through the following steps:

[0040] (1) Calculate the porosity ε of the porous electrode using the volume fraction formula:

[0041]

[0042] In the formula V void V is the pore volume. total For the total volume, N void N represents the number of aperture pixels. total The total number of pixels can be obtained by statistically analyzing the pixels of the segmented mask image.

[0043] (2) Calculate the specific surface area a of the porous electrode using the volumetric surface area formula:

[0044]

[0045] In the formula a interface L is the solid-liquid interface area. interface The total length of the solid-liquid interface is given by , and res is the image resolution, which can be obtained by extracting the edge pixels of the segmented mask image and calculating its total length.

[0046] (3) Calculate the liquid saturation s of the porous electrode using the liquid volume fraction formula:

[0047]

[0048] In the formula V liquid N is the volume of the electrolyte. liquid The number of pixels for the electrolyte can be obtained by performing grayscale thresholding on the pore areas in the segmentation mask image.

[0049] Next, based on the porosity, specific surface area, liquid saturation, and current density data of the porous electrode at multiple time points, the variation patterns of the porosity, specific surface area, and liquid saturation with time and current density can be determined. As an example, the variation patterns of the porosity, specific surface area, and liquid saturation with time and current density can be achieved through the following steps:

[0050] (1) By different discharge time points (e.g., 0, 5, 10, 20, 30, 60 min, etc.) and different constant current charge-discharge current densities (e.g., 100, 200, 300 mA·cm⁻¹), -2 A series of porous electrode microscopic images were repeatedly acquired under different operating conditions. For each image, the above steps of image enhancement, segmentation, feature extraction and parameter calculation were repeated to obtain a series of quantitative data of microstructure parameters under different operating conditions.

[0051] (2) Interpolate, fit and statistically analyze the data of microstructure parameters with time and current density to obtain the time evolution curves and current density dependence curves of parameters such as porosity, specific surface area and liquid saturation, and establish quantitative expressions for microstructure parameters.

[0052] (3) Combining theoretical electrochemical models and multiphysics numerical simulations, we analyze the dynamic changes of microstructure parameters and their intrinsic relationship with battery charge and discharge characteristics, revealing the influence mechanism of microstructure evolution on battery capacity, current density, cycle performance, etc.

[0053] S103: Based on the electrolyte concentration data, determine the electrolyte ion concentration, charge transport data, and ion cross-contamination level data of the target flow battery.

[0054] In this embodiment, in-situ ultraviolet spectrophotometry can be performed on the interior of the target flow battery to measure the concentration changes of the electrolyte in real time, while continuously monitoring the changes in the concentration and valence state of active ions. The coulombic efficiency, energy efficiency, and self-discharge rate of the battery can be calculated. Furthermore, by scanning and imaging different locations in the electrolyte, the spatial distribution of ion concentration can be obtained, yielding data on charge transport and cross-contamination levels. The charge transport data includes the coulombic efficiency, energy efficiency, and self-discharge rate of the target flow battery.

[0055] Specifically, based on Lambert-Beer's law, the concentration data of the electrolyte, and the preset absorbance of the electrolyte at the characteristic absorption wavelength, the concentrations of oxidized and reduced active ions in the electrolyte can be determined. The concentrations of oxidized and reduced active ions in the electrolyte can then be used as the electrolyte ion concentrations of the target flow battery. Finally, the valence state data of the electrolyte ions in the target flow battery can be determined.

[0056] Then, based on the electrolyte ion concentration and valence state data of the target flow battery, the coulombic efficiency data, energy efficiency data, and self-discharge rate data of the target flow battery can be determined, and the coulombic efficiency data, energy efficiency data, and self-discharge rate data of the target flow battery can be used as the charge transport data of the target flow battery.

[0057] Next, the degree of ion cross-contamination in the target flow battery can be determined based on the electrolyte ion concentration and valence state data of the electrolyte ions. As an example, the concentration of electrolyte ions at the positive and negative electrodes, a preset Faraday constant, and the electrolyte volume at the positive and negative electrodes can be obtained; and the degree of ion cross-contamination in the target flow battery can be determined based on the electrolyte ion concentration, valence state data of the electrolyte ions, and the electrolyte volume at the positive and negative electrodes.

[0058] Specifically, the concentrations of oxidized and reduced active ions in the electrolyte can be quantitatively calculated by measuring the absorbance of the electrolyte at its characteristic absorption wavelength and applying Lambert-Beer's law. The expression for Lambert-Beer's law is:

[0059] A=εbc

[0060] In the formula, A is absorbance, ε is molar absorptivity, b is optical path length, and c is solution concentration.

[0061] For oxidized and reduced ions, their characteristic absorption wavelengths λ are measured respectively. ox and λ red Absorbance A at point ox and A red Then, based on the pre-calibrated molar absorptivity ε ox and ε red Given the known optical path length b, calculate the concentrations c of oxidized and reduced ions, respectively. ox and c red :

[0062]

[0063] The degree of ion cross-contamination is calculated using the following expression:

[0064]

[0065] In the formula, m and n represent the valence states of ions A and B, respectively. For t i The concentrations of ions A and B at the positive and negative electrodes at any given time. For t i The charge carried by the positive and negative electrodes at time , where F is the Faraday constant, V pos V neg The electrolyte volume represents the volume of the positive and negative electrodes. The degree of cross-contamination between positive and negative electrodes. This indicates the degree of cross-contamination within the batteries.

[0066] Continuous monitoring of changes in active ion concentration and valence state is used to evaluate battery performance indicators such as coulombic efficiency, energy efficiency, and self-discharge. The specific evaluation methods are as follows:

[0067] (1) The formula for calculating Coulomb efficiency (CE) is:

[0068]

[0069] Among them, Q d Q represents the cumulative discharge capacity during the discharge process. c The cumulative charging capacity during the charging process can be obtained by multiplying the change in active ion concentration by the electrolyte volume and integrating over time.

[0070] (2) The formula for calculating energy efficiency (EE) is:

[0071]

[0072] Among them, E dE represents the cumulative output energy during the discharge process. c The cumulative input energy during the charging process can be calculated by integrating the changes in active ion concentration, battery voltage, and time.

[0073] (3) The formula for calculating the self-discharge rate (SDR) is:

[0074]

[0075] Where c0 is the initial active ion concentration, c t The active ion concentration is t after a settling time. The self-discharge performance is evaluated by monitoring the decay rate of the active ion concentration under open-circuit conditions.

[0076] S104: Obtain bubble characteristic data based on the target attribute information of the porous electrode of the target flow battery.

[0077] S105: Obtain the electrolyte ion cross-contamination signal based on the electrolyte ion concentration, charge transport data, and ion cross-contamination level data of the target flow battery.

[0078] S106: If the bubble characteristic data or the electrolyte ion cross signal meets the preset conditions, determine the concentration data of the target type gas during the operation of the target flow battery.

[0079] The preset condition is that the bubble feature data meets a preset data threshold, or the signal strength of the electrolyte ion cross signal meets a preset strength.

[0080] Specifically, the gases released during the operation of the target flow battery can be collected first. Then, based on the gases released during the operation, the chromatographic peak areas of hydrogen and oxygen can be determined. Next, the concentration of hydrogen can be determined based on the chromatographic peak area of ​​hydrogen. Following that, the concentration of oxygen can be determined based on the chromatographic peak area of ​​oxygen. Finally, the concentrations of hydrogen and oxygen can be used as the concentration data of the target type gas during the operation of the target flow battery.

[0081] As an example, the processing steps for S104-S106 are as follows: Bubble characteristic data are obtained by calculating and processing the porosity, specific surface area, and liquid saturation data. Electrolyte ion cross-contamination signals are obtained by calculating and processing the electrolyte ion concentration, charge transport, and ion cross-contamination levels. When the flow battery testing platform detects bubble characteristics or electrolyte ion cross-contamination signals, it will activate a gas chromatograph to monitor the gas components released during the target flow battery's operation, obtaining gas concentration data such as hydrogen or oxygen.

[0082] Then, the concentration data of gases such as hydrogen or oxygen in the flow battery's storage tank can be obtained. In this step, monitoring the gas components released during the operation of the target flow battery is achieved through the following steps:

[0083] (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 reservoir. Care should be taken to avoid liquid entering the sampler during sampling to prevent affecting the analytical results.

[0084] (2) Chromatographic separation: The collected gas sample is injected into the gas chromatograph through the injection port. Under the action of the carrier gas (such as helium, nitrogen, etc.), the gas sample interacts with the stationary phase through the chromatographic column to achieve the separation of components.

[0085] (3) Chromatographic detection: The separated gas components enter the detector sequentially, generating corresponding signal responses. Commonly used detectors include thermal conductivity detectors (TCD) and flame ionization detectors (FID). For hydrogen and oxygen evolved from flow batteries, TCD detectors are suitable due to their good sensitivity and stability.

[0086] (4) Data Acquisition and Processing: The signal generated by the detector is amplified and digitized, then acquired and recorded by the chromatography workstation software. Through comparison with standard samples and the establishment of calibration curves, the concentrations of components such as hydrogen and oxygen can be qualitatively and quantitatively analyzed. The chromatographic peak area or peak height has a linear relationship with the component concentration and can be used for quantitative calculations.

[0087] c i =f i ·A i

[0088] In the formula, c i f represents the concentration of gaseous component i. i A is the correction factor for component i, which is related to chromatographic conditions, detector type, etc. i This represents the chromatographic peak area.

[0089] S107: Determine the health status of the target flow battery based on the electrolyte ion concentration, charge transport data, and ion cross-contamination level 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.

[0090] As an example, the target flow battery's electrolyte ion concentration, charge transport data, and ion cross-contamination level data, as well as the target type gas concentration data during operation, can be preprocessed to obtain target data; wherein, the preprocessing operation includes cleaning operation, filtration operation, and normalization operation.

[0091] Then, the deviation value between the target data and the target attribute information of the porous electrode of the target flow battery can be determined.

[0092] Next, the health status of the target flow battery can be obtained based on the deviation between the target data and the target attribute information of the porous electrode of the target flow battery, as well as the preset weight corresponding to the target attribute information.

[0093] Specifically, the obtained electrolyte concentration data and gas concentration data are transmitted to the data processing system via data transmission. After being aggregated with the obtained energy efficiency, coulombic efficiency, and self-discharge rate, preprocessing operations such as cleaning, filtering, and normalization are performed. Then, the real-time data obtained from the preprocessing is compared with the initial values ​​from step two (e.g., energy efficiency deviation = (real-time energy efficiency - initial energy efficiency) / initial energy efficiency × 100%) to calculate the deviation value. Subsequently, a weighted average is calculated for indicators such as ion cross-linking degree, hydrogen evolution side reaction degree, energy efficiency deviation, coulombic efficiency deviation, and self-discharge rate (comprehensive score = ∑(indicator weight × indicator score) / ∑indicator weight). The comprehensive score is compared with a set threshold to give a comprehensive score for the battery's health status. When the evaluation result is lower than the preset threshold, a warning or alarm signal is issued promptly. This achieves the diagnosis of electrolyte ion concentration and electrode surface state, thus obtaining the health status of the flow battery system.

[0094] As an example, a mapping relationship between performance indicators and key parameters is established to construct an evaluation system and prediction model for flow battery performance. By combining electrolyte concentration data and ion cross-linking data monitored by ultraviolet spectrophotometer with hydrogen concentration data monitored by gas chromatography, the battery's health status is comprehensively evaluated. The test analysis results are integrated with the battery management system to achieve online monitoring of battery operating status, fault diagnosis, and lifespan prediction. Furthermore, the system dynamically adjusts battery pump speed, voltage window, current, and other information in real time, forming a closed-loop regulation system of data monitoring-data feedback-data mapping-data feedback-data monitoring to ensure the safe and efficient operation of the battery.

[0095] 1. The steps for establishing a mapping relationship between performance indicators and key parameters, and constructing an evaluation system and prediction model for flow battery performance, based on data obtained from multiple testing methods, are as follows:

[0096] (1) Collect and organize experimental data obtained by different testing methods, including ultraviolet spectrophotometry data, gas chromatography data, electrochemical test data (such as charge-discharge curves, cyclic voltammetry curves, AC impedance spectroscopy, etc.) and other physicochemical characterization data, and establish a structured database.

[0097] (2) Perform data preprocessing and feature engineering, normalize and reduce the dimensions of the original data, and extract key feature parameters that can reflect battery performance, such as active material concentration, ion cross-linking degree, hydrogen evolution rate, coulombic efficiency, energy efficiency, capacity decay rate, etc.

[0098] (3) Using machine learning algorithms, such as multiple linear regression, support vector machines, and random forests, establish a quantitative mapping relationship between performance indicators and key feature parameters. Through training and validation, optimize the hyperparameters and generalization performance of the model to obtain a more robust performance prediction model. Predict and evaluate the performance of the battery under new flow battery or operating conditions.

[0099] 2. The steps for comprehensively assessing the battery's health status by combining electrolyte concentration data and ion cross-linking data monitored by ultraviolet spectrophotometer with hydrogen concentration data monitored by gas chromatography are as follows:

[0100] (1) Real-time reception of monitoring data from ultraviolet spectrometer and gas chromatograph, extraction of key indicators such as active ion concentration, ion cross-linking degree and hydrogen evolution gas concentration in electrolyte, and data synchronization and alignment.

[0101] (2) Based on the pre-established health status assessment model, analyze the changing trends and abnormalities of various indicators. Set quantitative indicators for health status, such as ion cross-linking degree below 5% and hydrogen evolution gas concentration below 1%, as the basis for judging whether the battery is healthy.

[0102] (3) Taking into account the evaluation results of various indicators, a comprehensive score of the battery health status is given, namely the health status of the target flow battery, such as healthy, sub-healthy, abnormal, and failure level.

[0103] (4) Continuously track changes in battery health status and issue timely warnings or alarms when the assessment results fall below the preset threshold. At the same time, analyze the causes of changes in health status, such as electrolyte degradation, membrane material aging, and catalyst poisoning, to provide a basis for fault diagnosis and maintenance strategies.

[0104] 3. The steps to integrate the test analysis results with the battery management system (i.e., the flow battery test platform) to achieve online monitoring of battery operating status, fault diagnosis, and lifespan prediction are as follows:

[0105] (1) Transmit multiphysics field monitoring data and performance evaluation results to the platform battery management system in real time, and perform correlation analysis with the battery's operating parameters (such as voltage, current, temperature, etc.).

[0106] (2) Build a monitoring interface for battery operation status, and intuitively display the real-time performance and health status of the battery through charts, indicators and other forms.

[0107] (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 is detected, the cause of the fault is analyzed in a timely manner and a diagnosis result is given.

[0108] (4) The analysis results of monitoring, diagnosis, and prediction are fed back to the online data acquisition platform to form a data closed loop. Based on the analysis results, the online data acquisition platform optimizes the control strategy for battery operation, adjusts charging and discharging parameters, slows down battery aging, and extends battery life.

[0109] 4. The steps for real-time dynamic adjustment of battery pump speed, voltage window, current, and other information are as follows:

[0110] (1) Based on the monitoring data from ultraviolet spectrophotometry and gas chromatography, assess the severity of ion cross-linking and hydrogen evolution behavior inside the battery. When the data processing system receives the ion cross-linking degree data and gas concentration data and determines that they exceed the preset safety threshold (e.g., the ion cross-linking degree exceeds 1% or the hydrogen concentration exceeds 100ppm), the system will send a control command to the battery charge / discharge tester.

[0111] (2) Upon receiving the early warning signal, the online acquisition platform activates the dynamic adjustment function of the pump speed and voltage window. A gradient adjustment strategy is adopted, such as reducing the cutoff voltage by 0.05V for every 1% increase in gas concentration beyond the safety threshold, while simultaneously sending control commands to the electrolyte circulation pump, such as increasing the pump speed by 10% for every 1% increase in ion cross-linking degree beyond the safety threshold, until the gas concentration returns to the safe range. By controlling the electrolyte flow rate, the supply of reactants and the discharge of products are improved, mitigating the performance degradation caused by limited mass transfer. By adjusting the charge and discharge cutoff voltage, the loss of active materials and the aggravation of hydrogen evolution side reactions caused by overcharging and over-discharging are avoided.

[0112] (3) While adjusting the pump speed and voltage window, the online data acquisition platform also needs to consider energy efficiency and cost factors. By optimizing the control algorithm, the pump power and voltage window losses are minimized while ensuring battery safety and performance, thus achieving optimal energy utilization.

[0113] (4) For the regulation of charging and discharging current, the online data acquisition platform needs to comprehensively consider the battery's power demand and capacity degradation. By reasonably setting the charging and discharging rate, the load demand can be met while avoiding excessive current density that accelerates battery aging. If necessary, the current regulation scheme can also be optimized through strategies such as peak-valley electricity pricing.

[0114] (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 between the battery operating state and the control strategy.

[0115] In one implementation, the health status of the flow battery is defined as a health score of the flow battery; the method may further include:

[0116] If the health status of the flow battery is lower than a preset score threshold, a warning or alarm signal will be issued.

[0117] As can be seen from the above technical solution, the method provided by the present invention can first acquire the attribute image data of the porous electrode of the target flow battery and the concentration data of the electrolyte; then, based on the attribute image data of the porous electrode, the target attribute information of the porous electrode of the target flow battery can be determined; next, based on the concentration data of the electrolyte, the electrolyte ion concentration, charge transport data, and ion cross-contamination degree data of the target flow battery can be determined; then, based on the target attribute information of the porous electrode of the target flow battery, bubble characteristic data can be obtained; next, based on the electrolyte ion concentration, charge transport data, and ion cross-contamination degree data of the target flow battery, the electrolyte ion cross-contamination signal can be obtained; if the bubble characteristic data or the electrolyte ion cross-contamination signal meets the preset conditions, the concentration data of the target type gas during the operation of the target flow battery can be determined; and, based on the electrolyte ion concentration, charge transport 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, the health status of the flow battery can be determined. As can be seen, this application determines the health status of a flow battery by using the attribute image data of the porous electrodes and the concentration data of the electrolyte during the charging and discharging process. This enables real-time dynamic monitoring of the battery's internal microstructure and reaction process, more accurately reflecting the actual working state of the battery and providing guidance for optimizing the electrode structure.

[0118] like Figure 2The image shows a specific embodiment of the flow battery health status detection device described in this application. The device described in this embodiment is the physical device used to perform the method described in the above embodiments. Its technical solution is essentially the same as that of the above embodiments, and the corresponding descriptions in the above embodiments are also applicable to this embodiment. In this embodiment, the device includes: an optical imaging testing unit 201, an electrolyte concentration testing unit 202, a gas analysis unit 204, and a battery performance testing unit 204;

[0119] The optical imaging test unit 201 is used to acquire attribute image data of the porous electrode of the target flow battery; and to determine the target attribute information of the porous electrode of the target flow battery based on the attribute image data of the porous electrode.

[0120] Electrolyte concentration testing unit 202 is used to acquire the electrolyte concentration data of the target flow battery; and based on the electrolyte concentration data, determine the electrolyte ion concentration, charge transport data, and ion cross-contamination degree data of the target flow battery.

[0121] The gas analysis unit 203 is used to obtain bubble characteristic data based on the target attribute information of the porous electrode of the target flow battery; to obtain electrolyte ion cross-contamination signal based on the electrolyte ion concentration, charge transport data and ion cross-contamination degree data of the target flow battery; and to determine the concentration data of the target type gas during the operation of the target flow battery if the bubble characteristic data or the electrolyte ion cross-contamination signal meets the preset conditions.

[0122] The battery performance testing unit 204 is used to determine the health status of the target flow battery based on the electrolyte ion concentration, charge transport data, and ion cross-contamination level 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.

[0123] Optionally, the target attribute information of the porous electrode of the target flow battery includes the porosity, specific surface area, liquid saturation of the porous electrode, and the variation of the porosity, specific surface area, and liquid saturation with time and current density.

[0124] Optionally, determining the target attribute information of the porous electrode of the target flow battery based on the attribute image data of the porous electrode includes:

[0125] The attribute image data of the porous electrode is enhanced globally using a histogram equalization algorithm, and locally using an adaptive histogram equalization algorithm to enhance the attribute image data of the porous electrode, thereby obtaining the enhanced attribute image data of the porous electrode.

[0126] The enhanced attribute image data of the porous electrode is subjected to image noise removal using a nonlocal mean filtering algorithm to obtain the denoised attribute image data of the porous electrode.

[0127] The Otsu thresholding algorithm is used to perform global thresholding on the grayscale image corresponding to the denoised attribute image data, and the region growing algorithm is used for local adaptive segmentation to obtain the mask image of the porous electrode region.

[0128] Morphological opening and closing operations are used to perform noise filtering and repair processing on the mask image of the segmented porous electrode region. In addition, a connected component labeling algorithm is used to extract the connectivity features and morphological features of the porous electrode region based on the mask image of the porous electrode region.

[0129] Based on the connectivity and morphological characteristics of the porous electrode region, the porosity, specific surface area, and liquid saturation of the porous electrode are determined.

[0130] Based on the porosity, specific surface area, liquid saturation, and current density data of the porous electrode at multiple time points, the variation patterns of the porosity, specific surface area, and liquid saturation with time and current density are determined.

[0131] Optionally, the charge transfer data includes: coulombic efficiency data, energy efficiency data, and self-discharge rate data of the target flow battery.

[0132] Optionally, determining the electrolyte ion concentration, charge transport data, and ion cross-contamination level data of the target flow battery based on the electrolyte concentration data includes:

[0133] Based on Lambert-Beer's law, the concentration data of the electrolyte, and the preset absorbance of the electrolyte at the characteristic absorption wavelength, the concentrations of oxidized and reduced active ions in the electrolyte are determined, and the concentrations of oxidized and reduced active ions in the electrolyte are used as the electrolyte ion concentrations of the target flow battery, and the valence state data of the electrolyte ions of the target flow battery are determined.

[0134] Based on the electrolyte ion concentration and valence state data of the target flow battery, the coulombic efficiency data, energy efficiency data, and self-discharge rate data of the target flow battery are determined, and the coulombic efficiency data, energy efficiency data, and self-discharge rate data of the target flow battery are used as the charge transport data of the target flow battery.

[0135] Based on the electrolyte ion concentration and valence state data of the target flow battery, the degree of ion cross-contamination of the target flow battery is determined.

[0136] Optionally, determining the degree of cross-contamination of the target flow battery ions based on the electrolyte ion concentration and valence state data of the target flow battery includes:

[0137] The concentration of ions in the electrolyte at the positive and negative electrodes, the preset Faraday constant, and the electrolyte volume at the positive and negative electrodes are obtained.

[0138] Based on the electrolyte ion concentration, valence state data of the electrolyte ions, and the concentration of the electrolyte ions at the positive and negative electrodes, the preset Faraday constant, and the electrolyte volume at the positive and negative electrodes, the cross-contamination degree of the target flow battery is determined.

[0139] Optionally, the preset condition is that the bubble feature data meets a preset data threshold, or that the signal strength of the electrolyte ion cross signal meets a preset strength.

[0140] If the bubble characteristic data or the electrolyte ion cross signal meets preset conditions, the concentration data of the target type gas during the operation of the target flow battery is determined, including:

[0141] Collect the gas released during the operation of the target flow battery;

[0142] Based on the gases released during the operation of the target flow battery, the chromatographic peak areas of hydrogen and oxygen were determined.

[0143] The concentration of hydrogen gas is determined based on the chromatographic peak area of ​​the hydrogen gas.

[0144] The concentration of oxygen is determined based on the chromatographic peak area of ​​the oxygen.

[0145] The concentrations of hydrogen and oxygen are used as the concentration data of the target type gas during the operation of the target flow battery.

[0146] Optionally, determining the health status of the target flow battery based on the electrolyte ion concentration, charge transport data, and ion cross-contamination level 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, includes:

[0147] The target flow battery's electrolyte ion concentration, charge transport data, and ion cross-contamination level data, as well as the target flow battery's target type gas concentration data during operation, are preprocessed to obtain target data; wherein, the preprocessing operation includes cleaning operation, filtration operation, and normalization operation;

[0148] Determine the deviation value between the target data and the target attribute information of the porous electrode of the target flow battery;

[0149] The health status of the target flow battery is obtained based on the deviation 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.

[0150] Optionally, the health status of the flow battery is defined as a health score; the device further includes: a prompting unit, used for:

[0151] If the health status of the flow battery is lower than a preset score threshold, a warning or alarm signal will be issued.

[0152] Figure 3 This is a schematic diagram of the structure of an electronic device provided in 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. The memory may include main memory, such as high-speed random-access memory (RAM), or it may also include non-volatile memory, such as at least one disk storage device. Of course, the electronic device may also include other hardware required for other services.

[0153] The processor, network interface, and memory can be interconnected via 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, etc. This bus can be divided into address bus, data bus, control bus, etc. For ease of representation, Figure 3 The symbol is represented by a single double-headed arrow, but this does not mean that there is only one bus or one type of bus.

[0154] Memory is used to store instructions for execution. Specifically, instructions for execution are computer programs that can be executed. Memory can include main memory and non-volatile memory, and it provides the processor with execution instructions and data.

[0155] In one possible implementation, the processor reads the corresponding execution instructions from non-volatile memory into main memory and then executes them. Alternatively, it can obtain the corresponding execution instructions from other devices to form a flow battery health status detection device at the logical level. The processor executes the execution instructions stored in the memory to implement the flow battery health status detection method provided in any embodiment of the present invention through the executed execution instructions.

[0156] The above is as described in the present invention. Figure 1a The method executed by the flow battery health status detection device 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. During implementation, each step of the above method can be completed by integrated logic circuits in the processor's hardware or by instructions in software form. The processor can be a general-purpose processor, including a Central Processing Unit (CPU), a Network Processor (NP), etc.; it can 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 gate or transistor logic devices, or discrete hardware components. It can implement or execute the methods, steps, and logic block diagrams disclosed in the embodiments of this invention. The general-purpose processor can be a microprocessor or any conventional processor.

[0157] The steps of the method disclosed in the embodiments of this invention can be directly manifested as being executed by a hardware decoding processor, or executed by a combination of hardware and software modules in the decoding processor. The software modules can reside in random access memory, flash memory, read-only memory, programmable read-only memory, electrically erasable programmable memory, registers, or other mature storage media in the art. This storage medium is located in memory, and the processor reads information from the memory and, in conjunction with its hardware, completes the steps of the above method.

[0158] This invention also proposes a readable medium that stores execution instructions. When the stored execution instructions are executed by the processor of an electronic device, the electronic device can execute the flow battery health status detection method provided in any embodiment of this invention, and specifically execute the method described above for data query.

[0159] The electronic devices described in the foregoing embodiments may be computers.

[0160] Those skilled in the art will understand that embodiments of the present invention can be provided as methods or computer program products. Therefore, the present invention can be implemented in a completely hardware embodiment, a completely software embodiment, or a combination of software and hardware.

[0161] The various embodiments in this invention are described in a progressive manner. Similar or identical parts between embodiments can be referred to mutually. Each embodiment focuses on describing the differences from other embodiments. In particular, the device embodiments are basically similar to the method embodiments, so the description is relatively simple; relevant parts can be referred to the descriptions of the method embodiments.

[0162] It should also be noted that the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0163] The above description is merely an embodiment of the present invention and is not intended to limit the invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principle of the present invention should be included within 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 includes: Acquire the property image data of the porous electrode and the concentration data of the electrolyte of the target flow battery; Based on the attribute image data of the porous electrode, the target attribute information of the porous electrode of the target flow battery is determined; Based on the electrolyte concentration data, determine the electrolyte ion concentration, charge transport data, and ion cross-contamination level data of the target flow battery; Based on the target attribute information of the porous electrode of the target flow battery, bubble characteristic data is obtained; Based on the electrolyte ion concentration, charge transport data, and ion cross-contamination level data of the target flow battery, the electrolyte ion cross-contamination signal is obtained. If the bubble characteristic data or the electrolyte ion cross signal meets the preset conditions, the concentration data of the target type gas in the target flow battery during operation is determined; The health status of the target flow battery is determined based on the electrolyte ion concentration, charge transport data, and ion cross-contamination level 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. The preset condition is that the bubble feature data meets a preset data threshold, or the signal strength of the electrolyte ion cross signal meets a preset strength. The step of determining the health status of the target flow battery based on the electrolyte ion concentration, charge transport data, and ion cross-contamination level 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, includes: The target flow battery's electrolyte ion concentration, charge transport data, and ion cross-contamination level data, as well as the target flow battery's target type gas concentration data during operation, are preprocessed to obtain target data; wherein, the preprocessing operation includes cleaning operation, filtration operation, and normalization operation; Determine the deviation value between the target data and the target attribute information of the porous electrode of the target flow battery; The health status of the target flow battery is obtained based on the deviation 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.

2. The method according to claim 1, characterized in that, If the bubble characteristic data or the electrolyte ion cross signal meets preset conditions, the concentration data of the target type gas during the operation of the target flow battery is determined, including: Collect the gas released during the operation of the target flow battery; Based on the gases released during the operation of the target flow battery, the chromatographic peak areas of hydrogen and oxygen were determined. The concentration of hydrogen gas is determined based on the chromatographic peak area of ​​the hydrogen gas. The concentration of oxygen is determined based on the chromatographic peak area of ​​the oxygen. The concentrations of hydrogen and oxygen are used as the concentration data of the target type gas during the operation of the target flow battery.

3. The method according to claim 1, characterized in that, The step of obtaining the health status of the target flow battery based on 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, includes: The deviation value of the target attribute information is calculated by comparing the real-time data obtained from preprocessing with the initial value; the target attribute information includes: degree of ion cross-linking, degree of hydrogen evolution side reaction, energy efficiency, coulombic efficiency, and self-discharge rate. The weighted average of the deviations in ion cross-linking degree, hydrogen evolution side reaction degree, energy efficiency, coulombic efficiency, and self-discharge rate is used to obtain a comprehensive score. The comprehensive score is then compared with a set threshold to determine the overall health status of the battery.

4. The method according to claim 1, characterized in that, The health status of the flow battery is defined as the health score of the flow battery; the method further includes: If the health status of the flow battery is lower than a preset score threshold, a warning or alarm signal will be issued.

5. The method according to claim 1, characterized in that, Also includes: Establish a mapping relationship between performance indicators and key parameters, and construct an evaluation system and prediction model for flow battery performance; The health status of the battery is comprehensively assessed by combining the electrolyte concentration data and ion cross-linking data monitored by ultraviolet spectrophotometer with the hydrogen concentration data monitored by gas chromatography. By combining test analysis results with the battery management system, online monitoring of battery operating status, fault diagnosis, and life prediction can be achieved. It also dynamically adjusts at least one of the following information in real time: battery pump speed, voltage window, and current, forming a closed-loop adjustment system of data monitoring, data feedback, data mapping, data feedback, and data monitoring.

6. The method according to claim 5, characterized in that, The establishment of a mapping relationship between performance indicators and key parameters, and the construction of an evaluation system and prediction model for flow battery performance, include: Collect and organize experimental data obtained by different testing methods, including ultraviolet spectrophotometry data, gas chromatography data, electrochemical testing data, and other physicochemical characterization data, and establish a structured database; Data preprocessing and feature engineering are performed to normalize and reduce the dimensionality of the raw data and extract key feature parameters that can reflect battery performance. The key feature parameters include at least one of active material concentration, ion cross-linking degree, hydrogen evolution rate, coulombic efficiency, energy efficiency and capacity decay rate. Machine learning algorithms are used to establish a quantitative mapping relationship between performance metrics and key feature parameters; through training and validation, the hyperparameters and generalization performance of the model are optimized to obtain a performance prediction model.

7. The method according to claim 5, characterized in that, The battery health status is comprehensively assessed by combining electrolyte concentration data and ion cross-linking data monitored by ultraviolet spectrophotometer with hydrogen concentration data monitored by gas chromatography, including: It receives real-time monitoring data from ultraviolet spectrometer and gas chromatograph, extracts key indicators such as active ion concentration, ion cross-linking degree and hydrogen evolution gas concentration in electrolyte, and performs data synchronization and alignment. Based on a pre-established health status assessment model, analyze the changing trends and abnormalities of various indicators; Taking into account the evaluation results of various indicators, a comprehensive score for the battery health status is given to determine the health status of the target flow battery. It continuously tracks changes in battery health status and issues warnings or alarms when the assessment results fall below a preset threshold.

8. The method according to claim 5, characterized in that, The method of combining test analysis results with the battery management system to achieve online monitoring of battery operating status, fault diagnosis, and lifespan prediction includes: Multiphysics monitoring data and performance evaluation results are transmitted to the platform's battery management system in real time for correlation analysis with the battery's operating parameters. Build a monitoring interface for battery operating status, and intuitively display the real-time performance and health status of the battery through charts and / or indicators; Based on machine learning algorithms, a battery fault diagnosis model is established to perform feature learning and classification for common fault modes; when abnormal monitoring data occurs, the cause of the fault is analyzed and a diagnosis result is given; the common fault modes include membrane perforation, flow channel blockage, and electrolyte leakage. The analysis results of monitoring, diagnosis, and prediction are fed back to the online data acquisition platform to form a data closed loop; based on the analysis results, the online data acquisition platform optimizes the control strategy for battery operation and adjusts the charging and discharging parameters.

9. The method according to claim 5, characterized in that, The real-time dynamic adjustment of the battery's pump speed, voltage window, and current information forms a closed-loop adjustment mechanism of data monitoring-data feedback-data mapping-data feedback-data monitoring, including: Based on monitoring data from ultraviolet spectrophotometry and gas chromatography, the severity of ion cross-linking and hydrogen evolution behavior inside the battery is assessed; when the data processing system receives ion cross-linking degree data and gas concentration data and determines that one of them exceeds the preset safety threshold, the system sends a control command to the battery charge and discharge tester. After receiving the early warning signal, the online acquisition platform activates the dynamic adjustment function of the pump speed and voltage window; it adopts a gradient adjustment strategy and sends control commands to the electrolyte circulation pump until the gas concentration returns to a safe range. While adjusting the pump speed and voltage window, the online data acquisition platform considers energy efficiency and cost factors; by optimizing the control algorithm, it minimizes pump power and voltage window losses while ensuring battery safety and performance. For adjusting the charging and discharging current, the online acquisition platform is based on the battery's power requirements and capacity degradation status; by reasonably setting the charging and discharging rate, it can meet the load requirements while avoiding excessive current density that accelerates battery aging. The online data acquisition platform continuously monitors changes in battery performance based on adjusted pump speed, voltage window, and current parameters. Through data analysis and feedback, it optimizes the adjustment strategy to achieve dynamic matching and adaptive optimization between battery operating status and control strategy.

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