Method for predicting performance degradation of a hydrogen production device using electrolysis of water

Abstract

The present application relates to the technical field of electrochemical hydrogen production, and specifically provides a performance degradation prediction method for a water electrolysis hydrogen production device, which comprises the following steps: obtaining operation data and electrochemical indexes of the water electrolysis hydrogen production device under a target operation strategy; obtaining microstructure characteristics of a membrane electrode under the target operation strategy; constructing a performance-structure coupling dynamics model, which decouples the total cell voltage into multiple overpotential components, including activation, ohmic and mass transfer overpotentials; expressing each overpotential as a submodel of multiple microstructure damage parameters, which are used to represent the physical and chemical state evolution of the membrane electrode; and based on the operation data and the microstructure characteristics, performing iterative calculation through the performance-structure coupling model to output the voltage variation trajectory of the water electrolysis hydrogen production device. The present application realizes the prediction of the performance degradation trajectory and failure of the water electrolysis hydrogen production device in the whole life cycle through iterative calculation.

Description

Technical Field

[0001] This invention belongs to the field of hydrogen production device performance prediction technology, and particularly relates to a method for predicting the performance degradation of an electrolytic water hydrogen production device. Background Technology

[0002] Hydrogen energy, as a key carrier bridging renewable energy and deep decarbonization applications, holds significant strategic value in addressing the challenge of absorbing intermittent green electricity from wind and solar power. Hydrogen production through water electrolysis, with its rapid response characteristics, is widely recognized as an ideal hydrogen production technology adapted to the instantaneous power fluctuations of renewable energy sources. However, the large-scale commercial application of proton exchange membrane water electrolysis (PEMWE) remains limited by core bottlenecks such as the high cost of membrane electrode assemblies (MEAs) and their uncertain operational lifespan under dynamic conditions.

[0003] Existing commercial MEAs are mostly designed based on steady-state operating conditions, while actual photovoltaic-coupled hydrogen production systems require the hydrogen production unit to operate in a load-following mode for extended periods, facing complex operating conditions such as frequent start-ups and shutdowns, significant power ramp-ups, and prolonged low-load standby. This actual operating environment creates a complex stress field superimposed by drastic fluctuations in electrode potential, mechanical-chemical coupling stress, and transient changes in the multiphase flow field, which significantly accelerates the aging and degradation of MEA materials and structures.

[0004] Studies have confirmed that the irreversible decay rate of MEAs under dynamic operating conditions is much higher than that under steady-state conditions. However, the intrinsic structure-activity relationship of catalyst layer microstructure reconstruction, interfacial fatigue, and ionomer degradation induced by transient composite stress remains unclear. Existing accelerated stress tests mostly use simplified ideal waveforms such as square waves and triangular waves, which are difficult to reproduce the synergistic effect of multidimensional stresses under real operating conditions, and lack quantitative decoupling methods for reversible and irreversible decay. For example, membrane thinning or the exposure of new active sites by trace dissolution of the catalyst may mask performance degradation in the short term, causing traditional electrochemical diagnostic methods based on terminal voltage signals to fail. In addition, traditional lifetime prediction usually relies on simple linear extrapolation of short-term voltage decay rates, ignoring the nonlinear cumulative damage of microstructural damage such as interfacial microcracks and membrane thinning. It cannot predict sudden short-circuit faults such as interfacial physical delamination and mechanical damage of the proton exchange membrane caused by thermo-mechanical fatigue under dynamic operating conditions, thus posing a great challenge to the aging assessment, online diagnosis, and lifetime prediction of PEMWE devices. Summary of the Invention

[0005] To address the problems existing in the prior art, the present invention provides a method for predicting the performance degradation of a water electrolysis hydrogen production device, comprising the following steps: Step S1: Obtain the operating data and electrochemical indicators of the water electrolysis hydrogen production unit under the target operating strategy; Step S2: Obtain the microstructural features of the membrane electrode under the target operating strategy, wherein the microstructural features include physical structural features and chemical structural features; Step S3: Construct a performance-structure coupled dynamic model. The performance-structure coupled dynamic model decouples the total battery voltage into multiple overpotential components, including activation, ohmic, and mass transfer overpotentials. Step S4: Each overpotential is represented as a sub-model of multiple microstructural damage parameters, which are used to characterize the physical and chemical state evolution of the membrane electrode. Step S5: Based on the operating data and microstructural characteristics, iterative calculations are performed using the performance-structure coupling model to output the voltage change trajectory of the water electrolysis hydrogen production device.

[0006] Furthermore, the microstructure damage parameters include interfacial gap parameters characterizing the bonding state between the proton exchange membrane and the catalyst layer, membrane thickness loss parameters characterizing the degree of chemical degradation of the proton exchange membrane, anode catalyst solubility parameters characterizing the loss of active material from the anode catalyst, cathode catalyst solubility parameters characterizing the loss of active material from the cathode catalyst, crystallinity parameters characterizing changes in the micromorphology of the catalyst, porous diffusion layer passivation parameters characterizing the degree of oxidation on the surface of the porous diffusion layer, and mass transfer break-in parameters characterizing the break-in effect of the mass transfer channels.

[0007] Based on the above scheme, the performance-structure coupled dynamics model is used to describe the relationship between the overpotential decay rate and the structural damage parameters: , , in, β k This represents the sensitivity of overpotential to physical damage. C k It is the modulation coefficient under operating conditions. α k Used for adjustment C k orders of magnitude f ( t ) is the physical or chemical stress term that drives structural damage.

[0008] Specifically, the operating condition modulation coefficient is used to distinguish the impact of different load modes on the microstructure damage rate, and the load modes include at least constant current mode, square wave mode, triangular wave mode and photovoltaic fluctuation mode.

[0009] Based on the above scheme, the sub-model of step S4 includes an activation overpotential sub-model: , in, To activate the overpotential, , Sensitivity coefficients for catalyst dissolution at the anode and cathode, respectively. , These are the solubility coefficients of the anode and cathode catalysts, respectively. To reconstruct sensitivity for the initial active site, The degree of crystallinity of the catalyst, is the time constant for catalyst crystallization.

[0010] Based on the above scheme, the sub-model of step S4 includes an ohmic overpotential sub-model: , in, For Ohm overpotential, For the resistivity of the passivation layer, The passivation coefficient of the porous diffusion layer. The time constant for passivation of the porous diffusion layer. The interfacial contact resistivity, This is the three-phase interface gap width coefficient. The reverse film conductivity, This represents the film thickness loss rate.

[0011] Based on the above scheme, the sub-model of step S4 includes a mass transfer overpotential sub-model: , in, For mass transfer overpotential, For the resistivity of the passivation layer, The passivation degree of the porous diffusion layer, The bubble pinning coefficient is... The time constant for passivation of the porous diffusion layer. This is the improvement factor for the run-in period. The degree of integration in the mass transfer process. This is the time constant for the break-in effect.

[0012] Preferably, step S5 further includes the failure of the water electrolysis hydrogen production device, specifically including: A preset physical failure threshold is defined, which includes a cumulative film thickness reduction threshold, a cumulative interfacial gap increase threshold, and a cumulative catalyst dissolution threshold. During the iterative calculation process, the device is determined to be faulty when any physical failure threshold is reached.

[0013] Based on the above scheme, the voltage surge when the water electrolysis hydrogen production device fails is specifically as follows: When the cumulative reduction in membrane thickness reaches the threshold, it is determined that membrane perforation has occurred, causing a short circuit in the device, and the activation, ohmic, and mass transfer overpotentials are zero. When the threshold for the cumulative increase in interfacial gap or the threshold for the cumulative amount of catalyst dissolved are reached, it is determined that the interfacial contact is broken or the catalyst is exhausted, resulting in a short circuit in the device, and the activation, ohmic and mass transfer overpotentials tend to infinity.

[0014] Compared with the prior art, the present invention has the following beneficial effects: 1. By defining microstructural parameters including interfacial gap, film thickness loss, catalyst solubility, catalyst crystallinity, and passivation degree of porous diffusion layer, the physical / chemical state of membrane electrode can be comprehensively characterized, providing a basis for performance degradation modeling; 2. By establishing a quantitative correlation between macroscopic electrochemical properties such as activation, ohmic and mass transfer overpotential and microstructural damage parameters, dynamic simulation of the membrane electrode decay process was achieved, improving the physical interpretability of the prediction. 3. By setting the load condition modulation coefficient and the direct physical / chemical stress term, it is possible to distinguish the acceleration or inhibition effects of different load modes on the decay path, thereby improving the model's adaptability to complex load conditions; 4. This invention can simulate and decouple the degradation mechanism under different current density change rates, and predict the performance degradation trajectory and failure of the membrane electrode throughout its entire life cycle through iterative calculation. 5. The method of the present invention overcomes the limitations of traditional linear extrapolation based on short-term voltage decay rate, and can identify sudden failures caused by interface peeling or membrane perforation, providing support for the design of long-life membrane electrodes. Attached Figure Description

[0015] Figure 1 This is a flowchart illustrating the overall process of the decline prediction method of the present invention. Figure 2 This is a flowchart of the PEMWE device degradation prediction process of the present invention; Figure 3 This is a schematic diagram of the aging process of the PEMWE device under multiple operating conditions according to the present invention; Figure 4 Waveform diagrams are set for the operating conditions of this invention; Figure 5 This is the voltage evolution curve under multiple operating conditions of the present invention; Figure 6 This is a multi-condition overpotential prediction curve according to an embodiment of the present invention; Figure 7 This is a multi-condition total voltage prediction curve according to an embodiment of the present invention; Figure 8 This is a multi-condition overpotential prediction curve according to another embodiment of the present invention; Figure 9 This is a multi-condition total voltage prediction curve according to another embodiment of the present invention. Detailed Implementation

[0016] This invention provides a method for predicting the performance degradation of a water electrolysis hydrogen production device. By using mathematical modeling to quantitatively analyze the degradation patterns under different operating conditions, it achieves a leap from single performance monitoring to performance-structure coupled stability assessment.

[0017] The invention will be further described below with reference to the accompanying drawings and specific embodiments.

[0018] Those skilled in the art will understand that the method of the present invention is applicable to all water electrolysis hydrogen production devices, including alkaline solution hydrogen production devices, anion exchange membrane hydrogen production devices, proton exchange membrane water electrolysis devices, etc.

[0019] like Figure 1 and Figure 2 As shown, this embodiment provides a method for predicting the performance degradation of a water electrolysis hydrogen production device. Taking a proton exchange membrane water electrolysis device as an example, this embodiment includes the following steps: Step S1: Obtain the operating data and electrochemical indicators of the proton exchange membrane (PEM) electrolyzer under the target operating strategy. The electrochemical indicators include time t, current density j, electrolyzer voltage U, temperature T, and high-frequency resistance HFR obtained through electrochemical impedance spectroscopy. The operating data under the target operating strategy refers to the different operating conditions of the water electrolysis hydrogen production device during testing or actual operation, including constant current, square wave, triangular wave, and photovoltaic.

[0020] Step S2: Obtain the microstructural characteristics of the membrane electrode under the target operating strategy. The microstructural characteristics include physical structural characteristics and chemical structural characteristics. In this embodiment, the physical structural features include at least the membrane-catalyst interlayer gap width d. int and film thickness d mem Chemical structural features include at least the Ir dissolution rate (anode catalyst dissolution rate) v Ir Ir chemical valence state (anodic catalyst valence state) c Ir Pt dissolution rate (cathode catalyst dissolution rate) v Pt and PTL valence state (cathode catalyst valence state) c PTL .

[0021] like Figure 3 and Figure 4As shown, membrane electrodes will age under various operating conditions, such as proton exchange membrane (PEM) degradation, catalyst dissolution, and passivation of the porous transport layer (PTL). These damages will lead to a decline in device performance.

[0022] Step S3: Construct a performance-structure coupled dynamic model. The performance-structure coupled dynamic model decouples the total battery voltage into multiple overpotential components, including activation overpotential, ohmic overpotential and mass transfer overpotential, and establishes a quantitative relationship between overpotential evolution and microstructural damage.

[0023] Specifically, step S3 includes: S31: Based on the current-voltage relationship, the formula for calculating the total voltage of water electrolysis is: , in, V cell This represents the total voltage (electrolytic cell voltage U). E rev Indicates reversible potential. η kin Indicates activation overpotential, η ohm Indicates the ohmic overpotential. η mass It represents the mass transfer overpotential.

[0024] In step S31, the electrolytic cell voltage U is decomposed into activation, ohmic, and mass transfer overpotentials based on the HFR in S1 through impedance decoupling. Among them, the reversible potential is related to the Gibbs free energy of the reaction and is affected by temperature. Under standard atmospheric pressure, it is calculated using the following standard reversible potential formula: , In the formula, T is the thermodynamic temperature of the test system.

[0025] In step S31, the total voltage is decomposed into components with clear physical meaning, providing a mapping target for microstructural damage parameters.

[0026] S32: Establish a quantitative relationship between the rate of change of overpotential and the rate of change of microstructural damage parameters. The model is based on the following coupled kinetic equations: , , in, For overpotential, S k For microstructural damage parameters, β k This represents the sensitivity of overpotential to physical damage. Ck It is the modulation coefficient under operating conditions. α k Used for adjustment C k orders of magnitude f ( t ) are the physical or chemical stress terms that drive structural damage, as shown in Tables 1 and 2.

[0027] Table 1 Definition of Modulation Coefficient under Operating Conditions

[0028] Table 2 β k 、f ( t )definition

[0029] This model, based on microstructure damage parameters, treats the overpotential decay rate of the membrane electrode assembly as a weighted function of multiple independent and competing structural damage variables. In this embodiment, the operating condition modulation coefficient is used to distinguish the impact of different load modes on the microstructure damage rate. The load modes (operating conditions) include at least constant current mode, square wave mode, triangular wave mode, and photovoltaic fluctuation mode.

[0030] Because this embodiment uses experimental test data ranging from 0 to 500 hours, simplified assumptions are made regarding the stress term form of some parameters, and the microstructure damage parameters are... S gap , S thin 、S diss,Ir and S diss,Pt Simplified to linear form. Wherein, f gap = f thin = f diss,Ir = f diss,Pt =1 assumes that the gap expansion rate, film thinning rate, and Ir / Pt dissolution rate remain constant, leading to η It shows linear growth; f cr = f PTL = f break = This assumes that catalyst crystallization, PTL passivation, and mass transfer break-in effects have a greater impact in the initial stage, and that after a certain time, structural damage tends to saturate, thus affecting... η The impact has weakened; α gap =α thin = α diss,Ir = α diss,Pt = 10 -4 This was based on the assumption that the linear growth rate of overpotential is in the single digits, and was obtained with reference to the target value defined by the U.S. Department of Energy for PEMWE. τ k This indicates the time it takes for physical damage to reach the failure threshold.

[0031] Furthermore, based on the model established in step S3, step S4 is executed to represent each overpotential as a sub-model of multiple microstructural damage parameters. These microstructural damage parameters are used to characterize the physical and chemical state evolution of the membrane electrode, specifically including the interfacial gap parameter characterizing the bonding state between the proton exchange membrane and the catalyst layer. S gap Membrane thickness loss parameters characterizing the degree of chemical degradation of proton exchange membranes S thin Anode catalyst solubility parameters characterizing the loss of active material from the anode catalyst S diss,Ir Cathode catalyst solubility parameters characterizing the loss of active material from the cathode catalyst S diss,Pt Crystallinity parameters characterizing changes in the microstructure of catalysts S cr Passivation parameters of porous diffusion layers, characterizing the degree of oxidation on the surface of the porous diffusion layer. S PTL Mass transfer running-in parameters characterizing the running-in effect of mass transfer channels S break .

[0032] The sub-model in step S4 includes an activation overpotential sub-model, which decomposes the rate of change of activation overpotential into a long-term linear term characterizing catalyst dissolution and an initial exponential term characterizing catalyst crystallization: , in, To activate the overpotential, , These are the sensitivity coefficients of activation overpotential to anodic Ir dissolution and cathode Pt dissolution, respectively. , These are the solubility coefficients of the anode and cathode catalyst layers, respectively. To reconstruct sensitivity for the initial active site, This is the coefficient of catalyst crystallinity. is the time constant for catalyst crystallization.

[0033] According to this embodiment, the step of establishing the activated overpotential model in step S4 is as follows: S411: Activation overpotential dynamics model constructed based on the Tafel equation: , , in, b Let the Tafel slope be... j 0 represents the exchange current density.

[0034] S412: The exchange current density depends on the electrochemically active surface area (ECSA) and the inherent activity of the catalyst. σ ), assuming σ For constants: , ; S413: Catalyst dissolution reduces the electrochemical active surface area, and catalyst crystallization reduces the geometric area of ​​the catalyst layer, thus reducing the inherent activity of the catalyst. for: , , ; S414: To reduce the complexity of nonlinear coupling, If considered as an effective constant under a single time step or overall trend, then the activation overpotential sub-model is: , Among them, item (1) indicates that the overpotential increase caused by the loss of effective catalyst components constitutes irreversible material aging; item (2) indicates that the material recrystallization and microstructure changes caused by drastic voltage fluctuations and temperature field changes during the initial operation of the membrane electrode constitute the physical root cause of the significant increase in early kinetic overpotential.

[0035] Those skilled in the art should understand that this embodiment uses data from 0 to 500 hours. If a larger range of data is used, the form and parameters of the dynamic model and its sub-models will change accordingly.

[0036] Furthermore, the sub-model in step S4 includes an ohmic overpotential sub-model, which decomposes the rate of change of the ohmic overpotential into: , in, For Ohm overpotential, For the resistivity of the passivation layer, The passivation coefficient of the porous diffusion layer. The time constant for passivation of the porous diffusion layer. The interfacial contact resistivity, This is the three-phase interface gap width coefficient. The reverse film conductivity, This represents the film thickness loss rate.

[0037] According to this embodiment, the steps for establishing the Ohmic overpotential sub-model in step S4 are as follows: S421: Constructing an Ohmic overpotential dynamic model based on Ohm's law: , , in, The total resistance is... The bulk resistance and surface oxide resistance of the PTL; The contact resistance at the three-phase interface; The ion conduction resistance of the proton exchange membrane; For other constant resistors.

[0038] S422: Mapping the rate of change of resistance of the earth in S421 to expressions for microstructural parameters: (1) Under high potential and oxygen-rich conditions, a non-conductive TiO2 layer will rapidly form on the surface of titanium-based PTL. The growth rate of the oxide layer decreases with increasing thickness and eventually reaches saturation, that is, the resistance growth rate exhibits an exponential decay: ; (2) Due to mechanical creep, gas pressure accumulation, and catalyst layer dissolution during operation, the micro-gap (L) at the three-phase interface increases. gap The gap gradually increases, therefore it is assumed that the gap expansion is a linear process: ; (3) Due to free radical chemical erosion and mechanical wear, PEM gradually thins, due to R mem It is proportional to the thickness, assuming that the thinning of the film is a linear process. v thin ),have: ; S423: Combining S421 and S423, we get: .

[0039] Furthermore, the sub-model in step S4 also includes a mass transfer overpotential sub-model, which decomposes the rate of change of the mass transfer overpotential into an exponential decay term characterizing the increased bubble blockage due to passivation of the porous diffusion layer and an exponential decay term characterizing the break-in effect: , in, For mass transfer overpotential, For the resistivity of the passivation layer, The passivation coefficient of the porous diffusion layer. The bubble pinning coefficient is... The time constant for passivation of the porous diffusion layer. This is the improvement factor for the run-in period. The degree of integration in the mass transfer process. This is the time constant for the break-in effect.

[0040] According to this embodiment, the steps for establishing the mass transfer overpotential model in step S4 are as follows: S431: A mass transfer overpotential dynamic model is constructed based on a variation of the Nernst equation. The mass transfer overpotential is related to the effective active area of ​​the electrode surface. When bubbles cover the electrode surface, the effective area decreases, leading to an increase in overpotential. Therefore: , , in, j lim For limiting current density, θ gas This refers to the bubble coverage or the gas content in the flow channel.

[0041] S432: Will This can be broken down into two competing processes: the increase in gas density caused by PTL oxidation, and the decrease in gas density caused by the break-in effect, specifically including: (1) PTL oxidation-induced gas blockage: Ti metal exhibits a different gas contact angle compared to TiO2. As the oxide layer thickens, the hydrophilicity / hydrophobicity of the PTL surface changes, and bubbles are generally more prone to pinning after oxidation. It is known that the growth rate of the oxide layer exhibits exponential decay, therefore: , (2) Mechanical break-in causing gas clearance: In the initial operation stage, the physical action of the high-pressure water flow and the violent bubble bursting effect will remove residual processing debris, microburrs, or trapped bubbles in the PTL micropores. However, this break-in effect is irreversible and gradually weakens as the blockage decreases. .

[0042] S433: Combining S431 and S432, we get: .

[0043] Step S5 involves iterative calculations based on operational data and microstructural characteristics using a performance-structure coupling model to output the voltage variation trajectory of the water electrolysis hydrogen production device. For example... Figure 5 As shown in the figure, the voltage evolution curves under four operating conditions in this embodiment demonstrate that the method of the present invention can effectively distinguish the changes in decay rate under different operating conditions.

[0044] Step S5 includes the failure of the water electrolysis hydrogen production unit, employing a state-driven rigid switching mechanism to simulate the voltage surge during MEA physical structure failure. Specifically, S5 includes: S51: Preset physical failure threshold, which includes the cumulative film thickness reduction threshold, the cumulative interfacial gap increase threshold, and the cumulative catalyst dissolution threshold; S52: When the cumulative reduction in membrane thickness reaches the threshold, it is determined that membrane perforation has occurred, causing a short circuit in the device, and the activation, ohmic and mass transfer overpotentials are zero.

[0045] Among them, the cumulative reduction in film thickness is: In this embodiment, an N117 membrane is used, and the cumulative membrane thickness reduction threshold is set to 180 μm. Membrane perforation occurs when the membrane thickness is ≥ 180 μm, indicating a short circuit in the PEMWE. η kin = η ohm = η mass = 0.

[0046] When the threshold for the cumulative increase in interfacial gap or the threshold for the cumulative amount of catalyst dissolved are reached, it is determined that the interfacial contact has been broken or the catalyst has been depleted, causing the device to be disconnected, and the activation, ohmic and mass transfer overpotentials tend to infinity.

[0047] Among them, the cumulative increase in interface gaps is: In this embodiment, the threshold for the cumulative increase in interface gap is 20 μm. When the thickness is ≥ 20 μm, the three-phase interface is determined to be completely disconnected, and a PEMWE circuit is broken. η kin = η ohm = η mass = ∞.

[0048] Cumulative catalyst dissolution A diss,Ir / Pt In this embodiment, the cumulative catalyst dissolution threshold is 90%. A diss,Ir / Pt When the catalyst is ≥ 90%, it is considered depleted, and a PEMWE circuit breaks down. η kin = η ohm = η mass = ∞.

[0049] During the iterative calculation process, the device is determined to be faulty when any physical failure threshold is reached.

[0050] like Figure 6 As shown, the sub-model established in step S4 in this embodiment can predict the overpotential under different operating conditions. Figure 6 (a), (b), and (c) show the changes in activation, ohmic, and mass transfer overpotentials under different operating conditions, respectively.

[0051] like Figure 7 As shown, through the total voltage decomposition and dynamic model in step S3 and the sub-model in step S4, the total voltage change trajectory is output in the iterative calculation in step S5. The predicted data is compared with the actual measured data, and the result is obtained through R... 2 The accuracy of the predictions under different working conditions was evaluated, and the accuracy of the model was verified.

[0052] The method of this invention can also be used for long-term prediction of hydrogen production devices produced by water electrolysis, such as... Figure 8 and Figure 9 As shown, the device underwent a 5-hour degradation prediction to verify the effectiveness of the method of the present invention in full life cycle prediction and failure mode identification. It can be seen that (a) shows the evolution of activation overpotential under four operating conditions over 5 hours, with all four conditions exhibiting a trend of rapid increase followed by slow increase. Under the photovoltaic condition, the interfacial gap increased beyond the threshold, leading to an open circuit at 31750h and ultimately causing overall device failure; under the square wave condition, the interfacial gap increased beyond the threshold, leading to an open circuit at 35700h and ultimately causing overall device failure; under the triangular wave condition, the film thinning exceeded the threshold, leading to a short circuit at 38300h and ultimately causing overall device failure; and under the constant current condition, the catalyst dissolution exceeded the threshold, leading to an open circuit at 42600h and ultimately causing overall device failure. (b) shows the evolution of ohmic overpotential under four operating conditions over 5 hours. The constant current, square wave, and photovoltaic conditions all exhibit a trend of rapid increase followed by slow increase; while the triangular wave condition exhibits a trend of rapid increase followed by slow decrease, which is due to the decrease in ohmic impedance caused by the film thinning rate exceeding the interfacial expansion rate. The failure time and causes of failure for the four operating conditions are consistent with the activation overpotential in (a). (c) shows the evolution of mass transfer overpotential over 5 hours for the four operating conditions. The square wave, triangular wave, and photovoltaic operating conditions all show a trend of rapid increase followed by stabilization; while the constant current operating condition shows a trend of rapid increase followed by stabilization, which is due to the break-in effect caused by the high current density. The failure time and causes of failure for the four operating conditions are consistent with the activation overpotential in figure (a). Figure 9 The total voltage is Figure 8The sum of various overpotentials and reversible overpotentials, the failure time and cause of failure under four operating conditions, and... Figure 8 Consistent.

[0053] According to long-term forecast data, this invention not only significantly improves forecast accuracy, but also identifies sudden failures, providing reliable theoretical support for the design optimization and operation strategy formulation of electrolytic cells.

[0054] The above description is merely a preferred embodiment of this application and is not intended to limit this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the protection scope of this application.

[0055] While the specific embodiments of the present invention have been described above, they are not intended to limit the scope of protection of the present invention. Those skilled in the art should understand that various modifications or variations that can be made by those skilled in the art without creative effort based on the technical solutions of the present invention are still within the scope of protection of the present invention.

Claims

1. A method for predicting the performance degradation of a water electrolysis hydrogen production device, characterized in that, Includes the following steps: Step S1: Obtain the operating data and electrochemical indicators of the water electrolysis hydrogen production unit under the target operating strategy; Step S2: Obtain the microstructural features of the membrane electrode under the target operating strategy, wherein the microstructural features include physical structural features and chemical structural features; Step S3: Construct a performance-structure coupled dynamic model. The performance-structure coupled dynamic model decouples the total battery voltage into multiple overpotential components, including activation, ohmic, and mass transfer overpotentials. Step S4: Each overpotential is represented as a sub-model of multiple microstructural damage parameters, which are used to characterize the physical and chemical state evolution of the membrane electrode. The sub-model includes: Activation overpotential sub-model: ， in, To activate the overpotential, , These are the sensitivity coefficients of overpotential to the dissolution of the catalyst at the anode and cathode, respectively. , These are the solubility coefficients of the anode and cathode catalysts, respectively. To reconstruct sensitivity for the initial active site, The degree of crystallinity of the catalyst, This is the time constant for catalyst crystallization; Ohm overpotential model: ， in, For Ohm overpotential, For the resistivity of the passivation layer, The passivation coefficient of the porous diffusion layer. The time constant for passivation of the porous diffusion layer. The interfacial contact resistivity, This is the three-phase interface gap width coefficient. The reverse film conductivity, This refers to the film thickness loss rate. Mass transfer overpotential model: ， in, For mass transfer overpotential, For the resistivity of the passivation layer, The passivation degree of the porous diffusion layer, The bubble pinning coefficient is... The time constant for passivation of the porous diffusion layer. This is the improvement factor for the run-in period. The degree of integration in the mass transfer process. The time constant of the break-in effect; Step S5: Based on the operating data and microstructural characteristics, iterative calculations are performed using the performance-structure coupled dynamics model to output the voltage change trajectory of the water electrolysis hydrogen production device.

2. The method for predicting performance degradation of the water electrolysis hydrogen production device according to claim 1, characterized in that, The microstructural damage parameters include interfacial gap parameters characterizing the bonding state between the proton exchange membrane and the catalyst layer, membrane thickness loss parameters characterizing the degree of chemical degradation of the proton exchange membrane, anode catalyst solubility parameters characterizing the loss of active material from the anode catalyst, cathode catalyst solubility parameters characterizing the loss of active material from the cathode catalyst, crystallinity parameters characterizing changes in the micromorphology of the catalyst, porous diffusion layer passivation parameters characterizing the degree of oxidation on the surface of the porous diffusion layer, and mass transfer break-in parameters characterizing the break-in effect of the mass transfer channels.

3. The method for predicting performance degradation of the water electrolysis hydrogen production device according to claim 2, characterized in that, The performance-structure coupled dynamics model is used to describe the relationship between overpotential and microstructure damage parameters: ， ， in, For overpotential, S k For microstructural damage parameters, β k This represents the sensitivity of overpotential to physical damage. C k It is the modulation coefficient under operating conditions. α k Used for adjustment C k orders of magnitude f ( t ) is the physical or chemical stress term that drives structural damage.

4. The method for predicting performance degradation of the water electrolysis hydrogen production device according to claim 3, characterized in that, The operating condition modulation coefficient is used to distinguish the impact of different load modes on the microstructure damage rate. The load modes include at least constant current mode, square wave mode, triangular wave mode and photovoltaic fluctuation mode.

5. The method for predicting performance degradation of the water electrolysis hydrogen production device according to claim 1, characterized in that, Step S5 also includes the failure of the water electrolysis hydrogen production device, specifically including: A preset physical failure threshold is defined, which includes a cumulative film thickness reduction threshold, a cumulative interfacial gap increase threshold, and a cumulative catalyst dissolution threshold. During the iterative calculation process, the device is determined to be faulty when any physical failure threshold is reached.

6. The method for predicting performance degradation of the water electrolysis hydrogen production device according to claim 5, characterized in that, When the water electrolysis hydrogen production device fails, there is a sudden voltage change, specifically: When the cumulative reduction in membrane thickness reaches the threshold, it is determined that membrane perforation has occurred, causing a short circuit in the device, and the activation, ohmic, and mass transfer overpotentials are zero. When the threshold for the cumulative increase in interfacial gap or the threshold for the cumulative amount of catalyst dissolved are reached, it is determined that the interfacial contact has been broken or the catalyst has been depleted, causing the device to be disconnected, and the activation, ohmic and mass transfer overpotentials tend to infinity.

Images