A method and system for monitoring the operating state of an AEM electrolytic water hydrogen production device

Abstract

The present invention discloses an operating state monitoring method and system for an AEM electrolysis water hydrogen production device, specifically relating to the technical field of electrolytic cell operation monitoring and control, and is used to solve the problem of non-linear efficiency fluctuations caused by the alternation of polarization modes and microstructure deterioration under low current density conditions in the prior art; by generating a dynamic response sequence to extract polarization fluctuation characteristics and calculating the dynamic coupling degree to identify the polarization alternation dominant mode; based on the frequency characteristic analysis of the local blockage probability and the temperature gradient spatial distribution characteristics to locate the sparse area of active sites and generate an efficiency fluctuation evaluation coefficient; by constructing a dynamic compensation factor matrix between the sparse area of active sites and the cathode flow field pressure gradient to synergistically adjust the anode current density distribution weight and the cathode pressure gradient parameter, so that the adjustment amount is accurately matched with the local blockage probability and the dynamic coupling degree; realizing the multi-scale correlation analysis of the microscopic deterioration state and the macroscopic efficiency fluctuation of the electrolytic cell, and significantly improving the operating stability under low current density.

Description

Technical Field

[0001] The present invention relates to the technical field of electrolytic cell operation monitoring and control, and more specifically, to a method and system for monitoring the operating state of an AEM electrolytic water hydrogen production device. Background Art

[0002] Currently, the anion exchange membrane (AEM) electrolytic water hydrogen production technology combines the high efficiency of the proton exchange membrane (PEM) and the low cost characteristics of the alkaline electrolytic cell (ALK). In the prior art, the AEM electrolytic cell conducts hydroxide ions through an alkaline anion exchange membrane and can operate in a weakly alkaline environment, adapting to non-precious metal catalysts (such as nickel-based materials), significantly reducing the dependence on precious metals and equipment costs. At the same time, its membrane electrode structure design allows for quick start-stop and dynamic response, especially suitable for scenarios with volatile renewable energy input. However, for large-scale applications, the AEM electrolytic cell needs to maintain stable operation in the low current density range (<0.5 A / cm²) to balance equipment costs and system efficiency.

[0003] In the prior art, when the AEM electrolytic cell operates at low current density, due to the sluggish reaction kinetics on the electrode surface and the non-uniform distribution of the ion transport path, the utilization rate of active sites is significantly reduced. At the same time, the alternating dominant effect of ohmic polarization and concentration polarization causes non-linear fluctuations in efficiency, resulting in the efficiency optimization falling into local extrema and unable to achieve global stable control, affecting the reliability of the AEM electrolytic cell under low load conditions. Summary of the Invention

[0004] In order to overcome the above-mentioned defects of the prior art, embodiments of the present invention provide a method and system for monitoring the operating state of an AEM electrolytic water hydrogen production device to solve the problems raised in the above background art.

[0005] To achieve the above object, the present invention provides the following technical solutions:

[0006] A method for monitoring the operating state of an AEM electrolytic water hydrogen production device, comprising the following steps:

[0007] S1. Real-time collect and perform time-series synchronization calibration on the anode-side voltage fluctuation parameter and the cathode-side hydrogen evolution rate parameter of the electrolytic cell to generate a dynamic response sequence;

[0008] S2. Extract the anode polarization fluctuation feature and the cathode concentration polarization fluctuation feature from the dynamic response sequence, and calculate the dynamic coupling degree to identify the polarization alternating dominant mode;

[0009] S3. Determine the local blockage probability of the ion transport path at the membrane electrode interface based on the frequency feature of the polarization alternating dominant mode;

[0010] S4. Collect and analyze the spatial distribution characteristics of the temperature gradient parameters at the membrane electrode interface, locate the sparse area of active site distribution according to the high-frequency mutation area, and generate an efficiency fluctuation evaluation coefficient in combination with the dynamic coupling degree;

[0011] S5. Establish the spatial correspondence between the sparse area of active site distribution and the pressure gradient of the cathode flow field, and construct a dynamic compensation factor matrix;

[0012] S6. According to the change trends of the efficiency fluctuation evaluation coefficient and the dynamic compensation factor matrix, adjust the anode current density distribution weight and the cathode flow field pressure gradient parameters so that the adjustment amount meets the cooperative matching condition of the local blocking probability and the dynamic coupling degree.

[0013] In a preferred embodiment, the voltage fluctuation parameters on the anode side and the hydrogen evolution rate parameters on the cathode side of the electrolytic cell are collected in real time and time-sequentially synchronized and calibrated to generate a dynamic response sequence, including:

[0014] Obtain the voltage fluctuation parameters on the anode side and the hydrogen evolution rate parameters on the cathode side through a preset sensor group with a periodic acquisition period;

[0015] Take the clock signal of the electrolytic cell controller as the synchronization reference to align the timestamps of the voltage fluctuation parameters on the anode side and the hydrogen evolution rate parameters on the cathode side;

[0016] Under the synchronization reference, intercept the data segments of the voltage fluctuation parameters on the anode side and the hydrogen evolution on the cathode side with a fixed time window to generate a dynamic response sequence.

[0017] In a preferred embodiment, extract the anode polarization fluctuation characteristics and the cathode concentration polarization fluctuation characteristics from the dynamic response sequence, and calculate the dynamic coupling degree to identify the polarization alternation dominant mode, including:

[0018] Extract the standard deviation of the anode voltage of the anode polarization fluctuation characteristics and the change slope of the hydrogen evolution rate of the cathode concentration polarization fluctuation characteristics from the dynamic response sequence;

[0019] Normalize the standard deviation of the anode voltage and the change slope of the hydrogen evolution rate according to the time window to generate a normalized anode polarization coefficient and a normalized cathode concentration polarization coefficient;

[0020] Calculate the product of the normalized anode polarization coefficient and the normalized cathode concentration polarization coefficient as the dynamic coupling degree;

[0021] When the dynamic coupling degree is greater than the preset threshold, it is determined as the ohmic polarization dominant mode, and when it is less than or equal to the preset threshold, it is determined as the concentration polarization dominant mode.

[0022] In a preferred embodiment, determine the local blocking probability of the ion transport path at the membrane electrode interface based on the frequency characteristics of the polarization alternation dominant mode, including:

[0023] Separate the energy of the high-frequency fluctuation component and the energy of the low-frequency fluctuation component from the frequency characteristics of the polarization-alternation dominant mode;

[0024] Calculate the ratio of the energy of the high-frequency fluctuation component to the energy of the low-frequency fluctuation component as the interaction coefficient of microcracks and pore blockage;

[0025] According to the deviation degree of the interaction coefficient from the preset reference value, map and generate the local blockage probability of the ion transport path at the membrane electrode interface;

[0026] The preset reference value is the calibrated ratio of the energy of the high-frequency fluctuation component to the energy of the low-frequency fluctuation component when the electrolytic cell is in an unblocked state.

[0027] In a preferred embodiment, collect and analyze the spatial distribution characteristics of the temperature gradient parameters at the membrane electrode interface, locate the sparse area of active site distribution according to the high-frequency mutation region, and generate an efficiency fluctuation evaluation coefficient in combination with the dynamic coupling degree, including:

[0028] Collect the temperature gradient parameters at the membrane electrode interface, analyze the spatial distribution characteristics of the temperature gradient parameters at the membrane electrode interface, calculate the temperature gradient change rate between adjacent temperature measurement points and identify the high-frequency mutation region;

[0029] According to the continuous distribution area and the number of mutations per unit area of the high-frequency mutation region, determine the boundary coordinates of the sparse area of active site distribution;

[0030] Multiply the area ratio of the sparse area of active site distribution by the dynamic coupling degree according to the preset weight coefficient to generate an efficiency fluctuation evaluation coefficient;

[0031] The preset weight coefficient is calibrated based on the influence degree of the area ratio of the sparse area and the dynamic coupling degree on the efficiency fluctuation in historical data.

[0032] In a preferred embodiment, establish the spatial correspondence relationship between the sparse area of active site distribution and the cathode flow field pressure gradient, and construct a dynamic compensation factor matrix, including:

[0033] Map the boundary coordinates of the sparse area of active site distribution to the spatial distribution grid of the cathode flow field pressure gradient;

[0034] Quantify the pressure gradient drop amplitude of the cathode flow field in the grid corresponding to the boundary coordinates of the sparse area to generate a local pressure compensation coefficient;

[0035] According to the local pressure compensation coefficients of the grids corresponding to all the boundary coordinates of the sparse area and the gradient correlation degree with adjacent grids, construct a dynamic compensation factor matrix.

[0036] In a preferred embodiment, the gradient correlation degree is the matching degree between the cathode flow field pressure gradient change rate of adjacent grids and the pressure compensation demand of the sparse area.

[0037] In a preferred embodiment, according to the changing trends of the efficiency fluctuation evaluation coefficient and the dynamic compensation factor matrix, the anode current density distribution weight and the cathode flow field pressure gradient parameter are adjusted so that the adjustment amount satisfies the cooperative matching condition of the local blocking probability and the dynamic coupling degree, including:

[0038] Quantify the periodic change amplitude of the efficiency fluctuation evaluation coefficient and the gradient change direction of the dynamic compensation factor matrix;

[0039] Generate an anode current density distribution weight adjustment instruction and a cathode flow field pressure gradient adjustment instruction according to the periodic change amplitude of the efficiency fluctuation evaluation coefficient and the gradient change direction of the dynamic compensation factor matrix;

[0040] Based on the cooperative matching condition of the local blocking probability and the dynamic coupling degree, the proportional relationship between the anode current density distribution weight adjustment amount and the cathode flow field pressure gradient adjustment amount is constrained.

[0041] In a preferred embodiment, the cooperative matching condition is a linear mapping relationship between the product of the local blocking probability and the dynamic coupling degree and the adjustment amount ratio.

[0042] On the other hand, the present invention provides an operating state monitoring system for an AEM electrolytic water hydrogen production device, including the following modules:

[0043] Acquisition synchronization module: used to collect in real time and perform time series synchronization calibration on the anode side voltage fluctuation parameters and the cathode side hydrogen evolution rate parameters of the electrolytic cell to generate a dynamic response sequence;

[0044] Feature coupling module: used to extract the anode polarization fluctuation feature and the cathode concentration polarization fluctuation feature from the dynamic response sequence, and calculate the dynamic coupling degree to identify the polarization alternation dominant mode;

[0045] Frequency resistance determination module: used to determine the local blocking probability of the ion transport path at the membrane electrode interface based on the frequency characteristics of the polarization alternation dominant mode;

[0046] Temperature analysis positioning module: used to collect and analyze the spatial distribution characteristics of the membrane electrode interface temperature gradient parameters, locate the sparse area of the active site distribution according to the high-frequency mutation area, and generate an efficiency fluctuation evaluation coefficient in combination with the dynamic coupling degree;

[0047] Air pressure mapping module: used to establish the spatial correspondence relationship between the sparse area of the active site distribution and the cathode flow field pressure gradient, and construct a dynamic compensation factor matrix;

[0048] Co-state adjustment module: used to adjust the anode current density distribution weight and the cathode flow field pressure gradient parameter according to the changing trends of the efficiency fluctuation evaluation coefficient and the dynamic compensation factor matrix, so that the adjustment amount satisfies the cooperative matching condition of the local blocking probability and the dynamic coupling degree.

[0049] Compared with the prior art, the present invention has the following beneficial effects:

[0050] 1. Through the analysis of dynamic coupling polarization characteristics and temperature gradient, the accurate capture and cross-field collaborative regulation of the microscopic degradation state of the membrane electrode interface are realized. It is based on the construction of a dynamic response sequence with time synchronization, effectively identifying the alternating dominant modes of ohmic polarization and concentration polarization. Combining the positioning of the active site sparse area in the high-frequency temperature mutation region, through the joint analysis of the dynamic coupling degree and the local blocking probability, a multi-scale state evaluation model of physical formation is established for the microscopic structure degradation (such as microcracks and pore blockage) and macroscopic efficiency fluctuation. Compared with the prior art, it can real-time sense the dynamic changes of the ion transport path at low current density, and through the cross-field mapping of the compensation factor matrix, significantly improve the utilization rate of active sites and suppress the non-linear oscillation of efficiency caused by the polarization mode switching.

[0051] 2. By dynamically adapting the spatial pressure gradient compensation and the current density distribution, the problem of the coupling superposition of local blockage and polarization effect in the traditional regulation method is solved. Based on the spatial correspondence between the active site sparse area and the pressure gradient, the constructed dynamic matrix can real-time match the microscopic blockage state and the flow field mechanical characteristics, realizing the coordinated regulation of the anode current density weight and the cathode pressure gradient. Through the coordinated constraint conditions of the local blocking probability and the dynamic coupling degree, it is ensured that the adjustment amount always matches the actual degradation degree of the electrolytic cell and the polarization fluctuation intensity, avoiding the control instability caused by over-compensation or under-compensation. While maintaining the stability of the electrolysis efficiency, it significantly reduces the risk of membrane electrode failure caused by the accumulation of microscopic structure damage, ensuring the long-term reliable operation of the AEM electrolytic cell in the renewable energy fluctuation scenario. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] Figure 1 It is a flowchart of a method for monitoring the operating state of an AEM electrolytic water hydrogen production device of the present invention;

[0053] Figure 2 It is a schematic structural diagram of a system for monitoring the operating state of an AEM electrolytic water hydrogen production device of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0054] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0055] Example 1: Figure 1A method for monitoring the operating state of an AEM electrolytic water hydrogen production device according to the present invention is provided, which includes the following steps:

[0056] S1. Collect in real time and perform time series synchronization calibration on the anode-side voltage fluctuation parameter and the cathode-side hydrogen evolution rate parameter of the electrolytic cell to generate a dynamic response sequence;

[0057] S2. Extract the anode polarization fluctuation characteristics and the cathode concentration polarization fluctuation characteristics from the dynamic response sequence, and calculate the dynamic coupling degree to identify the polarization alternation dominant mode;

[0058] S3. Determine the local blockage probability of the ion transport path at the membrane electrode interface based on the frequency characteristics of the polarization alternation dominant mode;

[0059] S4. Collect and analyze the spatial distribution characteristics of the temperature gradient parameter at the membrane electrode interface, locate the sparse area of the active site distribution according to the high-frequency mutation area, and generate an efficiency fluctuation evaluation coefficient in combination with the dynamic coupling degree;

[0060] S5. Establish the spatial correspondence relationship between the sparse area of the active site distribution and the cathode flow field pressure gradient, and construct a dynamic compensation factor matrix;

[0061] S6. According to the change trends of the efficiency fluctuation evaluation coefficient and the dynamic compensation factor matrix, adjust the anode current density distribution weight and the cathode flow field pressure gradient parameter so that the adjustment amount meets the coordinated matching condition of the local blockage probability and the dynamic coupling degree.

[0062] S1. Collect in real time and perform time series synchronization calibration on the anode-side voltage fluctuation parameter and the cathode-side hydrogen evolution rate parameter of the electrolytic cell to generate a dynamic response sequence. The specific implementation is as follows:

[0063] Obtain the anode-side voltage fluctuation parameter and the cathode-side hydrogen evolution rate parameter of the electrolytic cell through a preset sensor group with a periodic acquisition period. The sensor group includes but is not limited to a high-precision voltage sensor on the anode side and a gas mass flow sensor on the cathode side. The acquisition frequency of the anode-side voltage fluctuation parameter and the acquisition frequency of the cathode-side hydrogen evolution rate parameter are both set to a fixed period synchronized with the clock signal of the electrolytic cell controller. For example, the acquisition period of the anode-side voltage fluctuation parameter is set to be acquired once every 100 milliseconds, and the acquisition period of the cathode-side hydrogen evolution rate parameter is also set to be acquired once every 100 milliseconds, and the starting moments of both acquisition periods are aligned with the rising edge of the clock signal of the electrolytic cell controller.

[0064] Taking the electrolyzer controller clock signal as the synchronization reference, timestamp alignment is performed on the anode-side voltage fluctuation parameter and the cathode-side hydrogen evolution rate parameter. The synchronization reference is the standard time signal output by the internal clock of the electrolyzer controller, and the time resolution of this clock signal is 1 microsecond. During the timestamp alignment process, each data point of the anode-side voltage fluctuation parameter and each data point of the cathode-side hydrogen evolution rate parameter are marked with the corresponding clock signal timestamp, and the acquisition time deviation between the anode-side voltage fluctuation parameter and the cathode-side hydrogen evolution rate parameter is controlled within 1 millisecond through the timestamp matching algorithm. For example, if a data point of the anode-side voltage fluctuation parameter is acquired at the 1000th millisecond of the clock signal, and a corresponding data point of the cathode-side hydrogen evolution rate parameter is acquired at the 1000.5th millisecond of the clock signal, the time deviation between the two is corrected to the same clock signal period through timestamp alignment.

[0065] Under the synchronization reference, data segments of the anode-side voltage fluctuation parameter and the cathode-side hydrogen evolution rate parameter are intercepted with a fixed time window to generate a dynamic response sequence. The length of the fixed time window is set as an integer multiple of the period of the electrolyzer controller clock signal. For example, 10 consecutive clock periods (i.e., 1 second) are selected as the time window length. Within each time window, the data segment of the anode-side voltage fluctuation parameter is composed of all the acquisition data of the anode-side voltage fluctuation parameter within this time window arranged in chronological order, and the data segment of the cathode-side hydrogen evolution rate parameter is composed of all the acquisition data of the cathode-side hydrogen evolution rate parameter within this time window arranged in chronological order. The generation method of the dynamic response sequence is to merge the data segment of the anode-side voltage fluctuation parameter and the data segment of the cathode-side hydrogen evolution rate parameter within the same time window after timestamp alignment into a single data sequence. For example, within a time window, the anode-side voltage fluctuation parameter contains 10 voltage values arranged in chronological order, and the cathode-side hydrogen evolution rate parameter contains 10 gas flow values arranged in chronological order. The generation method of the dynamic response sequence is to combine each voltage value with the flow value of its corresponding timestamp into a data pair, and arrange all the data pairs in chronological order to form a dynamic response sequence.

[0066] The setting of the periodic acquisition period is based on the dynamic response characteristics of the electrolyzer operating conditions. For example, in the low current density range (less than 0.5 amperes per square centimeter), the electro-chemical reaction time constant of the electrolyzer is usually in the range of 100 milliseconds to 1 second. Therefore, the acquisition period is set to 100 milliseconds to cover the key frequency band of the reaction dynamic process. The setting of the time window length is based on the switching period of the polarization alternating dominant mode. For example, the alternating dominant period of ohmic polarization and concentration polarization is verified to be 1 to 5 seconds. Therefore, the time window length is set to 1 second to capture at least one complete polarization alternating cycle.

[0067] The time deviation correction method during timestamp alignment is the value method. For example, if there are missing data points in the anode-side voltage fluctuation parameter within a certain clock signal period, interpolation calculation is performed according to the numerical difference between two adjacent voltage data points in proportion to time to complete the missing values; the data completion method for the cathode-side hydrogen evolution rate parameter is the same as that of the anode-side voltage fluctuation parameter. The data storage format of the dynamic response sequence is a two-dimensional array, where the first dimension is the time axis and the second dimension contains the anode-side voltage fluctuation parameter value and the cathode-side hydrogen evolution rate parameter value. For example, the storage form of the nth data point of the dynamic response sequence is [timestamp n, voltage value n, flow value n], and all data points are arranged in ascending order of timestamps, where n is the number of the data points in the dynamic response sequence.

[0068] The calibration method of the sensor group is to regularly perform comparison with a standard signal source. For example, a standard voltage signal (such as 1.0 V, 1.5 V, 2.0 V) is applied to the anode-side voltage sensor every 24 hours and the output value of the sensor is recorded. If the deviation between the sensor output value and the standard voltage signal exceeds 0.5%, a calibration alarm is triggered; for the cathode-side gas mass flow sensor, a standard gas with a known flow rate (such as hydrogen with a purity of 99.999%) is introduced and the output value of the sensor is recorded. If the flow measurement deviation exceeds 1%, a calibration alarm is triggered. During the generation process of the dynamic response sequence, if data missing or abnormal values are detected, the continuity of the sequence is maintained by repeating the previous valid data point. For example, if the anode-side voltage fluctuation parameter at a certain moment is not collected due to a sensor failure, the voltage value at this moment is filled with the previous valid voltage value.

[0069] The generation method of the dynamic response sequence realizes data caching and arrangement through the built-in memory of the controller. The memory capacity needs to meet at least the requirement of storing the dynamic response sequence data for 1 hour continuously. For example, if each data point occupies 16 bytes of storage space and the acquisition period is 100 milliseconds, the total amount of dynamic response sequence data for 1 hour is 16 bytes × 10 data points / second × 3600 seconds = 576,000 bytes, and the memory capacity of the controller needs to be configured to be no less than 1 megabyte to support the data caching requirement.

[0070] S2. Extract the anode polarization fluctuation characteristics and the cathode concentration polarization fluctuation characteristics from the dynamic response sequence, and calculate the dynamic coupling degree to identify the polarization alternating dominant mode. The specific implementation is as follows:

[0071] The standard deviation of the anode voltage, which extracts the characteristics of anode polarization fluctuations from the dynamic response sequence, and the slope of the change in the hydrogen evolution rate, which characterizes the fluctuations in cathode concentration polarization. The method for calculating the standard deviation of the anode voltage is as follows: for the data segment of the anode-side voltage fluctuation parameters within each time window in the dynamic response sequence, calculate the standard deviation of all voltage values in this data segment. For example, if there are 10 anode-side voltage fluctuation parameter values within a time window, first calculate the average of these 10 voltage values, then calculate the square of the difference between each voltage value and the average, find the average of the squared differences and take the square root to obtain the standard deviation of the anode voltage. The method for calculating the slope of the change in the hydrogen evolution rate is as follows: for the data segment of the hydrogen evolution rate parameters on the cathode side within the same time window in the dynamic response sequence, perform a linear regression analysis with the time stamp as the horizontal axis and the hydrogen evolution rate value as the vertical axis, and take the slope value of the regression line as the slope of the change in the hydrogen evolution rate. For example, if the hydrogen evolution rate value increases with time within the time window, the slope of the change in the hydrogen evolution rate is positive; if it decreases, it is negative.

[0072] Normalize the standard deviation of the anode voltage and the slope of the change in the hydrogen evolution rate according to the time window to generate a normalized anode polarization coefficient and a normalized cathode concentration polarization coefficient. The method of normalization is as follows: for the standard deviation of the anode voltage within the same time window, divide it by the reference value of the maximum standard deviation of the anode voltage under the rated current density of the electrolytic cell to obtain the normalized anode polarization coefficient; for the slope of the change in the hydrogen evolution rate, divide it by the reference value of the maximum slope of the change in the hydrogen evolution rate under the rated current density of the electrolytic cell to obtain the normalized cathode concentration polarization coefficient. For example, if the reference value of the maximum standard deviation of the anode voltage under the rated current density is 0.1 V and the standard deviation of the anode voltage in the current time window is 0.05 V, the normalized anode polarization coefficient is 0.05 / 0.1 = 0.5; if the reference value of the maximum slope of the change in the hydrogen evolution rate is 0.2 L / min·s and the current slope of the change in the hydrogen evolution rate is 0.1 L / min·s, the normalized cathode concentration polarization coefficient is 0.1 / 0.2 = 0.5.

[0073] Calculate the product of the normalized anode polarization coefficient and the normalized cathode concentration polarization coefficient as the dynamic coupling degree. The calculation method of the product is to directly multiply the normalized anode polarization coefficient and the normalized cathode concentration polarization coefficient. If the normalized anode polarization coefficient is 0.5 and the normalized cathode concentration polarization coefficient is 0.5, the dynamic coupling degree is 0.5 × 0.5 = 0.25; if the normalized anode polarization coefficient is 0.8 and the normalized cathode concentration polarization coefficient is 0.3, the dynamic coupling degree is 0.8 × 0.3 = 0.24.

[0074] When the dynamic coupling degree is greater than the preset threshold, it is determined as the Ohmic polarization dominant mode; when it is less than or equal to the preset threshold, it is determined as the concentration polarization dominant mode. The preset threshold is set based on the critical value of the dynamic coupling degree when Ohmic polarization and concentration polarization alternate in historical operation data. For example, by analyzing the dynamic coupling degree data during multiple polarization mode switching processes in the low current density range of the electrolyzer, it is statistically obtained that when the dynamic coupling degree exceeds 0.3, Ohmic polarization dominates, and when it is less than or equal to 0.3, concentration polarization dominates. Therefore, the preset threshold is set to 0.3. Historical operation data includes, but is not limited to, the corresponding relationship data between the dynamic coupling degree and the polarization mode under different current densities, temperatures, and pressures.

[0075] The determination method of the reference value of the maximum standard deviation of the anode voltage and the reference value of the hydrogen evolution rate change slope at the rated current density is as follows: During the stable operation of the electrolyzer at the rated current density, multiple groups of anode voltage fluctuation parameter data and cathode hydrogen evolution rate parameter data are collected, and the maximum value of the standard deviation of the anode voltage and the maximum absolute value of the hydrogen evolution rate change slope are obtained, and this maximum value is used as the reference benchmark for normalization processing. For example, when the rated current density is 1.0 A / cm², by continuously collecting the anode voltage fluctuation data within 1 hour, the maximum standard deviation of the anode voltage is calculated to be 0.1 V; by continuously collecting the hydrogen evolution rate data during the same period, the maximum hydrogen evolution rate change slope is 0.2 L / min·s.

[0076] The time stamp interval in the linear regression analysis is in milliseconds, which is consistent with the acquisition period of the dynamic response sequence. For example, if the acquisition period of the dynamic response sequence is 100 milliseconds, the time stamp interval is 100 milliseconds, and the time difference on the horizontal axis in the regression analysis is incremented by 100 milliseconds. The calculation process of the linear regression analysis is as follows: According to the least squares principle, the slope value that minimizes the sum of the squared residuals between the hydrogen evolution rate value and the predicted value of the regression line is obtained. For example, for the hydrogen evolution rate data points within the time window, the slope is calculated through the least squares formula, and the specific calculation process is well-known technology in the art.

[0077] S3. Determine the local blocking probability of the ion transport path at the membrane electrode interface based on the frequency characteristics of the polarization alternating dominant mode. The specific implementation is as follows:

[0078] Separate the energy of the high-frequency fluctuation component and the energy of the low-frequency fluctuation component from the frequency characteristics of the polarization-alternation-dominated mode. The frequency range of the high-frequency fluctuation component energy is from 3 Hz to 5 Hz, corresponding to the tortuosity effect of the ion transport path caused by microcracks at the membrane electrode interface; the frequency range of the low-frequency fluctuation component energy is from 0.5 Hz to 2 Hz, corresponding to the extended reactant diffusion path effect caused by pore blockage in the catalyst layer. The frequency characteristics are obtained by converting the time-domain signal of the polarization-alternation-dominated mode through the fast Fourier transform method, where the high-frequency fluctuation component energy is the sum of the energies of all frequency components within the frequency band from 3 Hz to 5 Hz, and the low-frequency fluctuation component energy is the sum of the energies of all frequency components within the frequency band from 0.5 Hz to 2 Hz. For example, if the sum of the energies of the frequency characteristics of the polarization-alternation-dominated mode in the frequency band from 3 Hz to 5 Hz is 100 mJ and the sum of the energies in the frequency band from 0.5 Hz to 2 Hz is 80 mJ, then the high-frequency fluctuation component energy is 100 mJ and the low-frequency fluctuation component energy is 80 mJ.

[0079] Calculate the ratio of the high-frequency fluctuation component energy to the low-frequency fluctuation component energy as the interaction coefficient of microcracks and pore blockage. The ratio is calculated by dividing the high-frequency fluctuation component energy by the low-frequency fluctuation component energy. For example, if the high-frequency fluctuation component energy is 100 mJ and the low-frequency fluctuation component energy is 80 mJ, then the interaction coefficient is 100 / 80 = 1.25; if the high-frequency fluctuation component energy is 60 mJ and the low-frequency fluctuation component energy is 120 mJ, then the interaction coefficient is 60 / 120 = 0.5. The physical meaning of the interaction coefficient is the relative strength of the microcrack effect and the pore blockage effect. When the interaction coefficient is greater than 1, it indicates that the microcrack effect dominates; when it is less than 1, it indicates that the pore blockage effect dominates.

[0080] According to the deviation degree of the interaction coefficient from the preset reference value, map and generate the local blockage probability of the ion transport path at the membrane electrode interface. The preset reference value is the calibrated ratio of the high-frequency fluctuation component energy to the low-frequency fluctuation component energy of the electrolytic cell in the unblocked state. The determination method is as follows: In the unblocked state after the new installation or cleaning and maintenance of the electrolytic cell, continuously collect multiple groups of frequency characteristic data of the polarization-alternation-dominated mode, calculate the ratio of the high-frequency fluctuation component energy to the low-frequency fluctuation component energy for each group of data respectively, and take the average value of all ratios as the preset reference value. For example, if the ratios of the high-frequency fluctuation component energy to the low-frequency fluctuation component energy measured in three groups in the unblocked state of the electrolytic cell are 1.2, 1.3, and 1.1 respectively, then the preset reference value is (1.2 + 1.3 + 1.1) / 3 = 1.2. The calculation method of the deviation degree is the percentage of the absolute difference between the interaction coefficient and the preset reference value to the preset reference value. For example, if the preset reference value is 1.2 and the current interaction coefficient is 1.25, then the deviation degree is (1.25 - 1.2) / 1.2×100% = 4.17%.

[0081] The method for generating the mapping of the local blockage probability is as follows: Different blockage levels are divided according to the magnitude of the deviation degree, and linearly mapped to the probability interval from 0 to 1. For example, when the deviation degree is less than or equal to 5%, it is determined as the non-blockage state, and the local blockage probability is 0; when the deviation degree is greater than 5% and less than or equal to 20%, the local blockage probability is (deviation degree - 5%) / 15%; when the deviation degree is greater than 20%, the local blockage probability is 1. The basis for dividing the blockage levels is the empirical data of the operation and maintenance of the electrolytic cell. For example, when the deviation degree exceeds 20%, immediate shutdown for maintenance is required. The linear mapping relationship is determined through experimental calibration. Specifically, the corresponding relationship data between the deviation degree and the actual blockage degree (such as the proportion of the microcrack area observed by electron microscopy and the pore blockage rate) are collected under different blockage states of the electrolytic cell, and a linear regression model between the deviation degree and the blockage probability is established. For example, if the experimental data shows that for every 1% increase in the deviation degree, the corresponding actual blockage probability increases by 0.05, the formula for calculating the local blockage probability is 0.05 × deviation degree.

[0082] In the above embodiments, the basis for dividing the frequency ranges of the high-frequency and low-frequency fluctuation components is the characteristic frequency response of the microscopic defects at the membrane electrode interface of the electrolytic cell. For example, through electrochemical impedance spectroscopy tests, it is found that the tortuous effect of the ion transport path caused by microcracks shows significant impedance changes in the frequency band from 3 Hz to 5 Hz, while the diffusion resistance caused by pore blockage is more obvious in the frequency band from 0.5 Hz to 2 Hz. The fast Fourier transform method is a well-known signal processing technology in the art, and its specific application scenario is the frequency-domain energy analysis of the polarization alternation dominant mode. The mapping rule of the local blockage probability is achieved through experimental data calibration. For example, the blockage level division thresholds (such as 5%, 20%) are obtained based on the statistics of the historical operation fault data of the electrolytic cell to ensure the accuracy of early warning.

[0083] S4. Collect and analyze the spatial distribution characteristics of the temperature gradient parameters at the membrane electrode interface, locate the sparse area of the active site distribution according to the high-frequency mutation region, and generate an efficiency fluctuation evaluation coefficient in combination with the dynamic coupling degree. The specific implementation is as follows:

[0084] Collect the temperature gradient parameters at the membrane electrode interface, analyze the spatial distribution characteristics of the temperature gradient parameters at the membrane electrode interface, calculate the temperature gradient change rate between adjacent temperature measurement points, and identify high-frequency mutation regions. The temperature gradient parameters at the membrane electrode interface are collected in real time through an array of temperature measurement points distributed on the surface of the membrane electrode, and the temperature data of each temperature measurement point is updated according to a preset collection period (e.g., 100 milliseconds). The calculation method of the temperature gradient change rate is: for the temperature values of two adjacent temperature measurement points, calculate the ratio of the temperature difference to the physical distance between the two points. For example, if the temperatures of adjacent temperature measurement points are 80°C and 85°C respectively, and the distance between the two points is 5 millimeters, then the temperature gradient change rate is (85 - 80) / 5 = 1°C / mm. The identification condition for high-frequency mutation regions is: the absolute value of the temperature gradient change rate exceeds the mutation threshold within three consecutive collection periods at the same temperature measurement point. For example, if the mutation threshold is set to 0.8°C / mm, when the temperature gradient change rate of a certain temperature measurement point is 0.9°C / mm, 1.0°C / mm, and 1.1°C / mm for three consecutive collection values, this region is determined to be a high-frequency mutation region.

[0085] Determine the boundary coordinates of the sparse active site distribution area based on the continuous distribution area and the number of mutations per unit area in the high-frequency mutation region. The calculation method of the continuous distribution area is: count the connected regions formed by all adjacent high-frequency mutation regions, and calculate the number of temperature measurement points they cover multiplied by the physical area represented by a single temperature measurement point. For example, if a single temperature measurement point represents an area of 2 square millimeters, and a connected region contains 50 temperature measurement points, then the continuous distribution area is 50×2 = 100 square millimeters. The calculation method of the number of mutations per unit area is: count the total number of times the mutation threshold is reached by all temperature measurement points in the connected region within a unit time (e.g., 1 minute), and divide it by the area of the connected region. For example, if the area of a connected region is 100 square millimeters and 200 mutations are detected within 1 minute, then the number of mutations per unit area is 200 / 100 = 2 mutations per square millimeter. The rule for determining the boundary coordinates of the sparse area is: when the continuous distribution area is greater than or equal to the preset area threshold (e.g., 50 square millimeters) and the number of mutations per unit area is greater than or equal to the preset number threshold (e.g., 1 mutation per square millimeter), mark the coordinates of the temperature measurement points on the outer contour of the connected region as the boundary coordinates of the sparse area.

[0086] Multiply the area ratio of the sparse region of the active site distribution by the dynamic coupling degree according to a preset weight coefficient to generate an efficiency fluctuation evaluation coefficient. The calculation method of the area ratio is: the area of the sparse region divided by the total area of the membrane electrode interface. For example, if the area of the sparse region is 100 square millimeters and the total area of the membrane electrode is 500 square millimeters, then the area ratio is 100 / 500 = 0.2. The calibration method of the preset weight coefficient is: statistically analyze the influence weights of the area ratio of the sparse region and the dynamic coupling degree on the efficiency fluctuation through historical operation data. For example, by analyzing multiple groups of historical data, it is found that for every 0.1 increase in the area ratio of the sparse region, the efficiency decreases by 5%, and for every 0.1 increase in the dynamic coupling degree, the efficiency decreases by 3%. Then the weight coefficients are set to 0.5 and 0.3 respectively.

[0087] The generation method of the efficiency fluctuation evaluation coefficient is: Efficiency fluctuation evaluation coefficient = Area ratio × Weight coefficient α + Dynamic coupling degree × Weight coefficient β. For example, if the area ratio is 0.2, the weight coefficient α is 0.5, the dynamic coupling degree is 0.3, and the weight coefficient β is 0.3, then the efficiency fluctuation evaluation coefficient = 0.2×0.5 + 0.3×0.3 = 0.1 + 0.09 = 0.19.

[0088] The determination basis of the mutation threshold, the preset area threshold, and the preset number threshold is the statistical result of abnormal working conditions in the historical operation data of the electrolytic cell. For example, by analyzing the membrane electrode failure cases, it is found that when the temperature gradient change rate exceeds 0.8 °C / mm, the corresponding risk of microcrack propagation increases significantly. Therefore, the mutation threshold is set to 0.8 °C / mm. The calibration process of the weight coefficient α and the weight coefficient β is: collect the area ratio of the sparse region, the dynamic coupling degree, and the actual efficiency fluctuation data under different operating states of the electrolytic cell, and determine the contribution weights of each parameter through multiple linear regression analysis. For example, the regression analysis shows that the regression coefficient of the area ratio of the sparse region is 0.5, and the regression coefficient of the dynamic coupling degree is 0.3. Then the weight coefficient α = 0.5 / (0.5 + 0.3) = 0.625, and the weight coefficient β = 0.3 / (0.5 + 0.3) = 0.375.

[0089] In the above embodiments, the calculation of the temperature gradient change rate, the analysis of the connected region, and the calibration of the weight coefficient are all realized based on the existing data processing methods. For example, the flood filling algorithm in the field of image processing is used for the analysis of the connected region, and the specific application scenario is the boundary recognition of the high-frequency mutation region. The generation process of the efficiency fluctuation evaluation coefficient is realized by the arithmetic logic unit built in the controller, and its calculation rule can be dynamically adjusted according to the model of the electrolytic cell. For example, for different models of electrolytic cells, the characteristics can be adapted by updating the values of the weight coefficients α and β.

[0090] S5. Establish the spatial correspondence between the sparse region of the active site distribution and the cathode flow field pressure gradient, and construct a dynamic compensation factor matrix. The specific implementation is as follows:

[0091] Map the boundary coordinates of the sparse region of the active site distribution to the spatial distribution grid of the cathode flow field pressure gradient. The spatial distribution grid of the cathode flow field pressure gradient is a Cartesian grid divided based on the flow channel geometry. Each grid cell corresponds to a physical region of the flow channel and stores the real-time pressure gradient value of that region. The mapping method is as follows: Convert the boundary coordinates of the sparse region into grid indices to determine the grid cell where the boundary coordinates are located and its adjacent grid cells. For example, if the flow channel is divided into a 10×10 grid and each grid corresponds to a 5 mm×5 mm region, and the boundary coordinates of the sparse region are (15 mm, 25 mm), then it is mapped to the grid cell corresponding to the grid index (3, 5).

[0092] Quantify the magnitude of the decrease in the cathode flow field pressure gradient within the grid corresponding to the boundary coordinates of the sparse region to generate a local pressure compensation coefficient. The calculation method for the magnitude of the pressure gradient decrease is as follows: Compare the current pressure gradient value with the reference pressure gradient value in the unblocked state and calculate its relative decrease percentage. For example, if the reference pressure gradient value of a certain grid cell in the unblocked state is 0.5 MPa / m and the current pressure gradient value is 0.4 MPa / m, then the decrease magnitude is (0.5 - 0.4) / 0.5×100% = 20%. The generation rule for the local pressure compensation coefficient is: For every 1% increase in the decrease magnitude, the compensation coefficient increases by 0.01. For example, when the decrease magnitude is 20%, the local pressure compensation coefficient is 0.2.

[0093] Construct a dynamic compensation factor matrix based on the local pressure compensation coefficients of all sparse region grids and the gradient correlation degrees with adjacent grids. The calculation method for the gradient correlation degree is as follows: Statistically analyze the consistency between the pressure gradient change direction of adjacent grid cells and the pressure compensation demand direction of the sparse region. For example, if the sparse region requires an increase in the pressure gradient (positive correlation with the compensation coefficient), and the pressure gradient change direction of the adjacent grid is increasing, then the gradient correlation degree is 1; if the directions are opposite, it is 0. The construction rule for the dynamic compensation factor matrix is: Multiply the local pressure compensation coefficient of each sparse region grid by the sum of its gradient correlation degrees with adjacent grids as the compensation factor for that grid. For example, if the local pressure compensation coefficient of a certain sparse region grid is 0.2, and its gradient correlation degrees with three adjacent grids are 1, 0, and 1 respectively, then the compensation factor is 0.2×(1 + 0 + 1) = 0.4.

[0094] The method for determining the reference pressure gradient value in the unblocked state is as follows: After the new installation or cleaning and maintenance of the electrolytic cell, collect the pressure gradient data of each grid unit and calculate its average value as the reference value. For example, in a 10×10 grid, the pressure gradient data of each grid unit is continuously collected 10 times in the unblocked state, and the average value of the 10 data is taken as the reference pressure gradient value of the grid. The linear relationship between the compensation coefficient and the decrease amplitude is calibrated through historical data. For example, by analyzing the pressure gradient decrease amplitude and compensation effect of the electrolytic cell under different blockage degrees, determine the proportional relationship that a 0.01 compensation coefficient corresponds to every 1% decrease amplitude.

[0095] The basis for determining the direction consistency of the gradient correlation degree is the correlation between the pressure gradient change trend and the compensation requirement. For example, when the pressure gradient needs to be increased in the sparse area, if the pressure gradient of the adjacent grid shows an upward trend, it is determined as a positive correlation, and if it shows a downward trend, it is determined as a negative correlation. The update period of the dynamic compensation factor matrix is synchronized with the clock signal of the electrolytic cell controller to ensure the real-time nature of the compensation adjustment. For example, update the compensation factor matrix every 100 milliseconds according to the latest pressure gradient data.

[0096] In the above embodiments, the grid division, coordinate mapping, and compensation factor calculation are all implemented based on the flow channel physical structure and the existing data processing method. For example, the Cartesian grid division and index mapping are conventional techniques in the field of computational fluid dynamics, and their specific application scenarios are the spatial correlation analysis of the flow field pressure gradient and the active site sparse area. The direction determination of the gradient correlation degree is achieved by comparing the pressure gradient change trends of adjacent grids.

[0097] S6. According to the change trends of the efficiency fluctuation evaluation coefficient and the dynamic compensation factor matrix, adjust the anode current density distribution weight and the cathode flow field pressure gradient parameter so that the adjustment amount satisfies the collaborative matching condition of the local blockage probability and the dynamic coupling degree. The specific implementation is as follows:

[0098] Quantify the periodic change amplitude of the efficiency fluctuation evaluation coefficient and the gradient change direction of the dynamic compensation factor matrix. The calculation method of the periodic change amplitude is: perform a sliding window average process on the efficiency fluctuation evaluation coefficient, and calculate the difference between the maximum value and the minimum value within the window as the change amplitude. For example, select the efficiency fluctuation evaluation coefficients of 10 consecutive time windows, and calculate the difference of 0.1 between the maximum value of 0.25 and the minimum value of 0.15 as the change amplitude. The method for determining the gradient change direction of the dynamic compensation factor matrix is: compare the change trends of the compensation factor values of adjacent grid units. If the compensation factor increases along the flow channel direction, it is determined as a positive gradient, and if it decreases, it is a negative gradient. For example, if the compensation factor of grid A is 0.4, the downstream grid B is 0.5, and the upstream grid C is 0.3, then the gradient direction is positive from A to B and negative from A to C.

[0099] Generate the anode current density distribution weight adjustment command and the cathode flow field pressure gradient adjustment command according to the periodic change amplitude of the efficiency fluctuation evaluation coefficient and the gradient change direction of the dynamic compensation factor matrix. The generation rule of the adjustment command is as follows: when the change amplitude of the efficiency fluctuation evaluation coefficient is greater than or equal to the fluctuation threshold and the gradient direction of the dynamic compensation factor matrix is positive, generate a command to increase the anode current density distribution weight; when the gradient direction is negative, generate a command to decrease the cathode flow field pressure gradient. For example, if the change amplitude is 0.1 (the fluctuation threshold is set to 0.08) and the gradient direction is positive, the anode current density distribution weight increases by 5%; if the gradient direction is negative, the cathode flow field pressure gradient decreases by 3%.

[0100] Based on the cooperative matching condition of the local blocking probability and the dynamic coupling degree, constrain the proportional relationship between the adjustment amount of the anode current density distribution weight and the adjustment amount of the cathode flow field pressure gradient. The cooperative matching condition is the linear mapping relationship between the product of the local blocking probability and the dynamic coupling degree and the adjustment amount ratio. For example, if the local blocking probability is 0.3 and the dynamic coupling degree is 0.4, the product is 0.12, and the corresponding adjustment amount ratio is anode current density weight adjustment amount: cathode pressure gradient adjustment amount = 0.12:0.08 = 3:2. The mapping rule of the proportional relationship is determined by experimental calibration. Collect the corresponding relationship data of the local blocking probability, the dynamic coupling degree, and the adjustment amount ratio under different operating states of the electrolytic cell, and establish a linear regression model of the product and the ratio value. For example, the experimental data shows that for every 0.01 increase in the product, the anode adjustment amount ratio increases by 0.5%, and the cathode adjustment amount ratio increases by 0.3%.

[0101] The setting basis of the fluctuation threshold and the adjustment amount ratio is the requirement for the operating stability of the electrolytic cell. For example, by analyzing the operating data of the electrolytic cell in the efficiency fluctuation range of 0.08 to 0.12, it is determined that when the change amplitude exceeds 0.08, the adjustment command needs to be triggered to avoid efficiency oscillation. The calibration method of the linear mapping relationship is as follows: under different blocking degrees and polarization states of the electrolytic cell, gradually adjust the anode current density distribution weight and the cathode flow field pressure gradient parameters, record the optimal matching ratio of the local blocking probability, the dynamic coupling degree, and the adjustment amount, and determine the coefficient relationship between the product and the ratio by least squares fitting. For example, the fitting result shows that the relationship between the product and the anode adjustment amount ratio is y = 5x (x is the product value, y is the adjustment amount percentage), and the relationship with the cathode adjustment amount ratio is y = 3x.

[0102] In the above embodiments, the sliding window average processing, the gradient direction determination, and the linear regression model are all existing data processing methods. For example, the sliding window average processing is implemented through a circular buffer built into the controller. The buffer capacity matches the window length (such as 10 time windows). Each time an update occurs, the earliest data point is removed and the latest data point is added. The gradient direction determination is achieved by comparing the numerical differences of the compensation factors of adjacent grid cells. The execution mode of the adjustment instruction is to adjust the current distribution parameters of the anode power supply and the opening degree of the pressure regulating valve of the cathode flow channel through the analog output of the electrolyzer controller.

[0103] Embodiment 2: Figure 2 The structural schematic diagram of an operating state monitoring system for an AEM electrolytic water hydrogen production device of the present invention is given. An operating state monitoring system for an AEM electrolytic water hydrogen production device includes the following modules:

[0104] The acquisition synchronization module: used to collect in real time and perform time series synchronization calibration on the anode-side voltage fluctuation parameters and the cathode-side hydrogen evolution rate parameters of the electrolyzer to generate a dynamic response sequence;

[0105] The feature coupling module: used to extract the anode polarization fluctuation feature and the cathode concentration polarization fluctuation feature from the dynamic response sequence, and calculate the dynamic coupling degree to identify the polarization alternation dominant mode;

[0106] The frequency resistance determination module: used to determine the local blocking probability of the ion transport path at the membrane electrode interface based on the frequency characteristics of the polarization alternation dominant mode;

[0107] The temperature analysis and positioning module: used to collect and analyze the spatial distribution characteristics of the temperature gradient parameters at the membrane electrode interface, locate the sparse area of the active site distribution according to the high-frequency mutation area, and generate an efficiency fluctuation evaluation coefficient in combination with the dynamic coupling degree;

[0108] The air pressure mapping module: used to establish the spatial correspondence between the sparse area of the active site distribution and the pressure gradient of the cathode flow field, and construct a dynamic compensation factor matrix;

[0109] The co-state regulation module: used to adjust the anode current density distribution weight and the cathode flow field pressure gradient parameters according to the change trends of the efficiency fluctuation evaluation coefficient and the dynamic compensation factor matrix, so that the adjustment amount meets the cooperative matching conditions of the local blocking probability and the dynamic coupling degree.

[0110] All the above formulas are dimensionless and take their numerical calculations. The formula is obtained by software simulation of a large amount of collected data to get a formula closest to the actual situation. The preset parameters and threshold selection in the formula are set by those skilled in the art according to the actual situation.

[0111] It should be noted that the present invention can be deployed on the device itself to achieve embedded applications, or can also run on a PC or other terminal with a user interface, so as to meet various hardware environments and usage requirements.

[0112] The above embodiments can be implemented in whole or in part by software, hardware, firmware or any other combination. When implemented using software, the above embodiments can be implemented in whole or in part in the form of a computer program product.

[0113] Those of ordinary skill in the art can realize that the modules and algorithm steps of each example described in combination with the embodiments disclosed herein can be implemented by electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are executed in hardware or software depends on the specific application and invention constraints of the technical solution. Professional technicians can use different methods to implement the described functions for each specific application, but such implementation should not be considered to exceed the scope of this application.

[0114] In addition, in each embodiment of the present application, the functional modules can be integrated into one processing module, or each module can exist physically alone, or two or more modules can be integrated into one module.

[0115] In the several embodiments provided in the present application, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are only illustrative. For example, the division of the modules is only a logical function division. In actual implementation, there can be other division methods. For example, multiple modules or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the displayed or discussed coupling or direct coupling or communication connection to each other can be through some interfaces, and the indirect coupling or communication connection of the device or module can be in electrical, mechanical or other forms.

[0116] As mentioned above, the above is only the specific implementation manner of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art within the technical scope disclosed in the present application can easily think of changes or substitutions, which should all be covered by the protection scope of the present application. Therefore, the protection scope of the present application should be subject to the protection scope of the claims.

[0117] Finally: The above is only the preferred embodiment of the present invention and is not used to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principle of the present invention should all be included in the protection scope of the present invention.

Claims

1. A method for monitoring the operating state of an AEM electrolytic water hydrogen production device, characterized in that, It includes the following steps: S1. Real-time collect and perform time-sequence synchronous calibration on the anode-side voltage fluctuation parameters and the cathode-side hydrogen evolution rate parameters of the electrolytic cell to generate a dynamic response sequence; S2. Extract the anode polarization fluctuation characteristics and the cathode concentration polarization fluctuation characteristics from the dynamic response sequence, and calculate the dynamic coupling degree to identify the polarization alternation dominant mode, including: Extract the standard deviation of the anode voltage of the anode polarization fluctuation characteristics and the slope of the change in the hydrogen evolution rate of the cathode concentration polarization fluctuation characteristics from the dynamic response sequence; Normalize the standard deviation of the anode voltage and the slope of the change in the hydrogen evolution rate according to a time window to generate a normalized anode polarization coefficient and a normalized cathode concentration polarization coefficient; Calculate the product of the normalized anode polarization coefficient and the normalized cathode concentration polarization coefficient as the dynamic coupling degree; When the dynamic coupling degree is greater than the preset threshold, it is determined as the ohmic polarization dominant mode, and when it is less than or equal to the preset threshold, it is determined as the concentration polarization dominant mode; S3. Determine the local blockage probability of the ion transport path at the membrane electrode interface based on the frequency characteristics of the polarization alternation dominant mode, including: Separate the energy of the high-frequency fluctuation component and the energy of the low-frequency fluctuation component from the frequency characteristics of the polarization alternation dominant mode; Calculate the ratio of the energy of the high-frequency fluctuation component to the energy of the low-frequency fluctuation component as the interaction coefficient of microcracks and pore blockage; Map and generate the local blockage probability of the ion transport path at the membrane electrode interface according to the deviation degree of the interaction coefficient from the preset reference value; The preset reference value is the calibrated ratio of the energy of the high-frequency fluctuation component to the energy of the low-frequency fluctuation component when the electrolytic cell is in an unblocked state; S4. Collect and analyze the spatial distribution characteristics of the temperature gradient parameters at the membrane electrode interface, locate the sparse area of the active site distribution according to the high-frequency mutation area, and generate an efficiency fluctuation evaluation coefficient in combination with the dynamic coupling degree, including: Collect the temperature gradient parameters at the membrane electrode interface, analyze the spatial distribution characteristics of the temperature gradient parameters at the membrane electrode interface, calculate the temperature gradient change rate between adjacent temperature measurement points and identify the high-frequency mutation area; Determine the boundary coordinates of the sparse area of the active site distribution according to the continuous distribution area and the number of mutations per unit area of the high-frequency mutation area; Multiply the area ratio of the sparse area of the active site distribution by the dynamic coupling degree according to the preset weight coefficient to generate an efficiency fluctuation evaluation coefficient; The preset weight coefficient is calibrated based on the influence degree of the area ratio of the sparse area and the dynamic coupling degree on the efficiency fluctuation in historical data; S5. Establish the spatial correspondence relationship between the sparse area of the active site distribution and the cathode flow field pressure gradient, and construct a dynamic compensation factor matrix; S6. According to the change trends of the efficiency fluctuation evaluation coefficient and the dynamic compensation factor matrix, adjust the anode current density distribution weight and the cathode flow field pressure gradient parameters so that the adjustment amount meets the coordinated matching conditions of the local blockage probability and the dynamic coupling degree.

2. The operation state monitoring method of an AEM electrolytic water hydrogen production device according to claim 1, characterized in that, Real-time collect and perform time-sequence synchronous calibration on the anode-side voltage fluctuation parameters and the cathode-side hydrogen evolution rate parameters of the electrolytic cell to generate a dynamic response sequence, including: Obtain the anode-side voltage fluctuation parameters and the cathode-side hydrogen evolution rate parameters through a preset sensor group with a periodic acquisition period; Align the time stamps of the anode-side voltage fluctuation parameters and the cathode-side hydrogen evolution rate parameters with the clock signal of the electrolytic cell controller as the synchronization reference; Under the synchronous reference, the data segments of the anode-side voltage fluctuation parameters and the cathode-side hydrogen evolution are intercepted with a fixed time window to generate a dynamic response sequence.

3. A method for monitoring the operating state of an AEM electrolytic water hydrogen production device according to claim 1, characterized in that, Establish the spatial correspondence between the sparse regions of the active site distribution and the cathode flow field pressure gradient, and construct a dynamic compensation factor matrix, including: Map the boundary coordinates of the sparse regions of the active site distribution to the spatial distribution grid of the cathode flow field pressure gradient; Quantify the cathode flow field pressure gradient drop amplitude within the grid corresponding to the boundary coordinates of the sparse region to generate a local pressure compensation coefficient; Construct a dynamic compensation factor matrix based on the local pressure compensation coefficients of the grids corresponding to all the boundary coordinates of the sparse regions and the gradient correlation degree with the adjacent grids.

4. The operation state monitoring method of an AEM electrolytic water hydrogen production device according to claim 3, characterized in that, The gradient correlation degree is the matching degree between the change rate of the cathode flow field pressure gradient of the adjacent grids and the pressure compensation demand of the sparse region.

5. The operation status monitoring method of an AEM electrolytic water hydrogen production device according to claim 1, characterized in that According to the change trend of the efficiency fluctuation evaluation coefficient and the dynamic compensation factor matrix, adjust the anode current density distribution weight and the cathode flow field pressure gradient parameters so that the adjustment amount meets the cooperative matching condition of the local blocking probability and the dynamic coupling degree, including: Quantify the periodic change amplitude of the efficiency fluctuation evaluation coefficient and the gradient change direction of the dynamic compensation factor matrix; Generate an anode current density distribution weight adjustment instruction and a cathode flow field pressure gradient adjustment instruction according to the periodic change amplitude of the efficiency fluctuation evaluation coefficient and the gradient change direction of the dynamic compensation factor matrix; Based on the cooperative matching condition of the local blocking probability and the dynamic coupling degree, constrain the proportional relationship between the anode current density distribution weight adjustment amount and the cathode flow field pressure gradient adjustment amount.

6. The operating state monitoring method of an AEM electrolytic water hydrogen production device according to claim 5, characterized in that, The cooperative matching condition is the linear mapping relationship between the product of the local blocking probability and the dynamic coupling degree and the adjustment amount ratio.

7. An operating state monitoring system for an AEM electrolytic water hydrogen production device, which is used to implement the operating state monitoring method of an AEM electrolytic water hydrogen production device according to any one of claims 1-6, characterized in that, It includes the following modules: Acquisition synchronization module: used to collect in real time and perform time series synchronization calibration on the anode-side voltage fluctuation parameters and the cathode-side hydrogen evolution rate parameters of the electrolytic cell to generate a dynamic response sequence; Feature coupling module: used to extract the anode polarization fluctuation feature and the cathode concentration polarization fluctuation feature from the dynamic response sequence, and calculate the dynamic coupling degree to identify the polarization alternation dominant mode; Frequency resistance determination module: used to determine the local blocking probability of the ion transport path at the membrane electrode interface based on the frequency characteristics of the polarization alternation dominant mode; Temperature analysis and positioning module: used to collect and analyze the spatial distribution characteristics of the membrane electrode interface temperature gradient parameters, locate the sparse regions of the active site distribution according to the high-frequency mutation regions, and generate an efficiency fluctuation evaluation coefficient in combination with the dynamic coupling degree; Air pressure mapping module: used to establish the spatial correspondence between the sparse regions of the active site distribution and the cathode flow field pressure gradient, and construct a dynamic compensation factor matrix; Co-state adjustment module: used to adjust the anode current density distribution weight and the cathode flow field pressure gradient parameters according to the change trend of the efficiency fluctuation evaluation coefficient and the dynamic compensation factor matrix, so that the adjustment amount meets the cooperative matching condition of the local blocking probability and the dynamic coupling degree.

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