Aging modeling method for proton exchange membrane fuel cell

Abstract

The invention discloses a proton exchange membrane fuel cell aging modeling method, and relates to the technical field of fuel cells, the method comprises a proton exchange membrane, a catalyst layer, a gas diffusion layer and a polar plate, the fuel cell overall mechanism aging model comprises a proton exchange membrane thickness and conductivity aging model based on a hydrogen permeation current change rate, a catalyst layer parameter aging model based on an oxidation and dissolution positive and negative reaction rate and an ECSA aging model based on platinum particle radius change. A simulation means and a simulation model probe interface are used, the problem that internal operation parameters of the fuel cell are difficult to monitor in an actual test is solved, parameter interaction between the two models is achieved through TCP / IP, and aging of the fuel cell can be simulated.

Description

Technical Field

[0001] The present invention belongs to the technical field of fuel cells, and in particular relates to a method for aging modeling of proton exchange membrane fuel cells. Background Art

[0002] Fuel cell technology has made significant progress in recent years, demonstrating enormous potential, particularly in energy, environmental protection, and sustainable development. Despite these significant breakthroughs, fuel cell development still faces challenges such as durability and lifespan. To address challenges such as the difficulty in obtaining fuel cell internal parameters during aging, the long duration of fuel cell aging tests, and the lack of open-source test datasets, a method for modeling proton exchange membrane fuel cell aging is urgently needed. Summary of the Invention

[0003] To solve the above technical problems, the present invention proposes a method for proton exchange membrane fuel cell aging modeling, which overcomes the difficulty in monitoring the internal operating parameters of the fuel cell in actual experiments and can simulate the aging of the fuel cell.

[0004] To achieve the above objectives, the present invention provides a method for proton exchange membrane fuel cell aging modeling, comprising: Establishing a three-dimensional computational fluid dynamics model of a proton exchange membrane fuel cell, the three-dimensional computational fluid dynamics model includes a proton exchange membrane, a catalyst layer, a gas diffusion layer, and a plate, and divides the anode and cathode reaction gas flow channels and a cooling circuit; distributing hydrogen fuel cell chemical fields, free and porous media flow fields, laminar fluid fields, and solid and fluid heat transfer fields in the three-dimensional computational fluid dynamics model; Deploying a monitoring probe in the three-dimensional computational fluid dynamics model to obtain internal operating parameters and external characteristic data of the fuel cell; Establishing a fuel cell multi-component aging mechanism model, the fuel cell multi-component aging mechanism model including a proton exchange membrane aging model based on the hydrogen permeation current change rate and an electrochemical active surface area aging model based on the change of platinum particle radius; Data interaction between the three-dimensional computational fluid dynamics model and the aging mechanism model is achieved through the TCP / IP protocol to form a closed-loop iterative system.

[0005] Optionally, the process of establishing the three-dimensional computational fluid dynamics model includes: constructing a geometric structure including a proton exchange membrane, a catalytic layer, a gas diffusion layer and an electrode plate according to actual fuel cell geometric parameters; dividing the anode and cathode reaction gas flow channels and the cooling circuit flow channels; and assigning corresponding physical fields to each component.

[0006] Optionally, the process of establishing the proton exchange membrane aging model includes: establishing a proton exchange membrane thickness and conductivity decay rate model based on the hydrogen permeation current change rate; calculating the current decay rate according to real-time stack status data and reference stack status data; and calculating the decay state of the proton exchange membrane thickness and conductivity in real time based on the decay rate.

[0007] Optionally, the process of establishing the electrochemically active surface area aging model includes: establishing forward and reverse reaction rate equations for platinum particle dissolution, deposition and oxidation reactions; calculating the rate of change of the platinum particle radius based on the forward and reverse reaction rate equations; and determining the aging state of the electrochemically active surface area based on the mathematical relationship between the platinum particle radius and the electrochemically active surface area.

[0008] Optionally, the data interaction process includes: the three-dimensional computational fluid dynamics model transmits the collected real-time parameters to the aging mechanism model; the aging mechanism model calculates the aging status of the component based on the received parameters and outputs it; and the calculated aging status parameters are substituted back into the three-dimensional computational fluid dynamics model for updating.

[0009] Optionally, the method further includes a model verification process: performing grid independence verification on the three-dimensional computational fluid dynamics model, using different grid densities for division and comparing calculation results; comparing the external characteristic data output by the model with actual test data to verify the model accuracy.

[0010] Optionally, the physical field distribution process includes: distributing the hydrogen fuel cell chemical field in areas other than the coolant flow channels; distributing the free and porous medium flow fields in the anode and cathode reaction gas flow channels and the gas diffusion layer; distributing the laminar fluid field in the cooling circuit flow channels; and distributing the solid and fluid heat transfer fields in all components.

[0011] Optionally, the internal operating parameters include proton exchange membrane temperature, relative humidity, and local potential of the anode and cathode; and the external characteristic data include polarization curves and electrochemical impedance spectroscopy.

[0012] Technical effect of the invention: The present invention discloses a method for aging modeling of proton exchange membrane fuel cells, including a proton exchange membrane, a catalyst layer, a gas diffusion layer and an electrode plate, and a set of fuel cell overall mechanism aging models that couple multiple component parameters: a proton exchange membrane thickness and conductivity aging model based on the hydrogen permeation current change rate, a catalyst layer parameter aging model based on the oxidation and dissolution forward and reverse reaction rates, and an ECSA aging model based on the change in platinum particle radius. By using simulation methods and a simulation model probe interface, the problem of difficulty in monitoring the internal operating parameters of the fuel cell in actual experiments is overcome, and parameter interaction between the above two models is achieved through TCP / IP, which can simulate the aging of the fuel cell. At the same time, the proposed coupling model has virtual sensing capabilities, which can realize the prediction of internal states such as local potential, current density, and gas concentration without invasive measurement. This method is not only suitable for fuel cell life modeling and health status assessment, but also provides reliable simulation support for system-level power allocation strategies, thermal management control and aging compensation algorithms, and has good engineering applicability and scalability. BRIEF DESCRIPTION OF THE DRAWINGS

[0013] The accompanying drawings, which constitute part of this application, are intended to provide a further understanding of this application. The exemplary embodiments and descriptions of this application are intended to explain this application and do not constitute an improper limitation on this application. In the accompanying drawings: Figure 1 A schematic flow chart of a method for modeling aging of a proton exchange membrane fuel cell according to an embodiment of the present invention; Figure 2 This is a diagram showing the principle of data transmission and iteration according to an embodiment of the present invention; Figure 3 This is a schematic diagram of the accuracy verification of the three-dimensional multi-physics field model according to an embodiment of the present invention; Figure 4 Schematic diagram of the 3D multi-physics field model and the output of each physics field at 0.6V voltage according to an embodiment of the present invention. DETAILED DESCRIPTION

[0014] It should be noted that, in the absence of conflict, the embodiments and features of the embodiments in this application can be combined with each other. The present application will be described in detail below with reference to the accompanying drawings and in combination with the embodiments.

[0015] It should be noted that the steps shown in the flowcharts of the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions, and that, although a logical order is shown in the flowcharts, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0016] like Figure 1As shown, this embodiment provides a method for modeling aging of a proton exchange membrane fuel cell, including: constructing a 3D computational fluid dynamics (CFD) model of a proton exchange membrane fuel cell cell based on the actual operating conditions and geometric parameters of the fuel cell to obtain external characteristic data of the fuel cell (such as real-time voltage, polarization curve, etc.) and internal data of the cell (such as membrane surface temperature, membrane relative humidity, etc.); Verify and analyze the model accuracy based on the external characteristic data of the test; Based on the aging models of multiple fuel cell components, an aging mechanism model of key fuel cell component parameters is established; Through TCP / IP communication, data iteration and interaction between the two models are completed, and a mechanism-based fuel cell aging model is constructed to simulate the aging of the fuel cell. It can also output the aging simulation data of the external characteristics and internal components of the fuel cell in real time.

[0017] In order to simulate the aging of fuel cell components, the mechanism aging equation is used to describe the aging of key fuel cell components.

[0018] The key components of fuel cells include proton exchange membrane (PEM), catalyst layer (CL), gas diffusion layer (GDL), etc. When considering aging, the battery is divided into two parts: proton exchange membrane and electrode (including catalyst layer, gas diffusion layer, current collector and plate). For proton exchange membranes (PEMs), thickness and conductivity are key parameters influencing fuel cell performance, directly determining their proton transport capacity and gas barrier properties. PEM aging is often accompanied by mechanical damage, chemical degradation, and material degradation in high-temperature environments. One of the most significant effects is an increase in hydrogen permeation current. When the PEM degrades or develops microcracks, its ability to block hydrogen permeation decreases, resulting in more hydrogen directly penetrating the membrane rather than generating electricity through catalytic reactions. The hydrogen permeation rate is closely related to the PEM's thickness, conductivity, and internal microstructure. Thinner PEMs typically have higher proton conductivity but are also less resistant to gas permeation. Consequently, they are more susceptible to chemical attack and mechanical stress during long-term operation, leading to increased hydrogen permeation rates. Furthermore, PEM conductivity is affected by its hydration state. Underhydration reduces the PEM's proton conductivity, and localized overheating can accelerate membrane degradation, further increasing the hydrogen permeation rate. Hydrogen permeation not only affects the energy conversion efficiency of the fuel cell, but may also cause mixing with the cathode oxygen, leading to side reactions, reducing the overall performance of the fuel cell and accelerating its aging. The hydrogen permeation rate is: ; in, is the hydrogen permeation rate, is the regression constant, , , , is the regression coefficient, T is the temperature, RH is the relative humidity, is the hydrogen pressure, t is the film thickness.

[0019] In one feasible embodiment, the fuel cell modeling module is further used to determine the aging rate of the proton exchange membrane portion of the fuel cell based on the hydrogen permeation rate model and the fuel cell external characteristic data. The fuel cell PEM thickness and conductivity attenuation rate model is as follows: ; in, is the decay rate, is the voltage, is the regression constant, , , , is the regression coefficient, T is the temperature, RH is the relative humidity, is the film thickness. Indicates real-time stack status data. Indicates the reference stack status data. The reference data needs to be calibrated according to the actual stack.

[0020] Based on the decay rate calculated by the above model, the decay state of PEM thickness and conductivity can be calculated in real time: ; ; in, for t PEM thickness at time, is the PEM thickness at the initial moment, for t PEM conductivity at time t, is the PEM conductivity at the initial moment, is the film thickness degradation rate function, representing the relative loss rate of film thickness per unit time, which is affected by the battery operating voltage The impact of t is the aging time.

[0021] The aging of the electrode is characterized by various microscopic manifestations: dissolution, precipitation, and agglomeration of platinum particles, reduced hydrophobicity of the catalytic layer, and corrosion of the carbon support. Macroscopically, this is usually manifested by a reduction in the electrochemically active surface area (ECSA), with the platinum particle radius being strongly correlated with the ECSA. Based on the relationship between the platinum particle radius and the electrochemically active surface area, the specific reaction formula is expressed by the following equation by modeling the change of the platinum particle radius and considering the dissolution, deposition and oxidation reactions of the platinum particles: ; .

[0022] In one feasible embodiment, based on the electrochemical reaction principle and taking into account the forward and reverse reaction rates, the forward and reverse reaction rates of the dissolution / precipitation of the platinum particle radius of the fuel cell electrode catalyst are determined according to the following equations: ; ; ; ; in, , is the forward and reverse reaction rate of the platinum particle dissolution reaction, , is the forward and reverse reaction rate of platinum particle oxidation, θ is the oxide coverage, , , , is the reaction rate constant, is the surface tension, is the molar volume of the platinum particles, and represent the equilibrium potentials of platinum oxidation and dissolution reactions, respectively, is the platinum particle radius, is the platinum ion concentration, is the Faraday constant, is the gas constant, is the temperature, is the current electrode potential, is the oxidation induction barrier coefficient, is the coverage of surface platinum oxide.

[0023] In one feasible embodiment, the rate of change of platinum ion concentration and platinum particle radius is determined according to the following equation: ; ; in, The number of platinum particles, is the electrode porosity.

[0024] In one feasible embodiment, the relationship between the radius of the platinum particle and the electrochemically active surface area is as follows: ; Will It is defined as the correlation coefficient between the platinum particle radius and ECSA, which is: ; in, For the i The radius of a platinum particle, is the equivalent density of platinum particles.

[0025] The physical fields of this model select hydrogen fuel cells, free and porous media flow, solid and fluid heat transfer, and laminar flow to describe the operating conditions and internal reactions of fuel cells. Furthermore, the above-mentioned aging mechanism model can be deployed in MATLAB software. At the same time, TCP / IP technology is applied to integrate the fuel cell 3D CFD model and the aging mechanism model to construct an aging mechanism model of multiple component parameters of a fuel cell. The model can estimate the aging rate of components based on the actual operating parameters of the fuel cell: temperature, relative humidity, pressure, voltage, etc. to simulate the one-dimensional time field of fuel cell aging, while recording the status of each component parameter in real time. The data transfer and iteration principles in the model are as follows: Figure 2 express.

[0026] In building a 3D multiphysics model of a fuel cell, the hydrogen fuel cell interface is used to describe charge and species balances, reactions, gas-phase thermodynamics, and electrochemical reactions, including water permeation through the membrane and electroosmotic drag.

[0027] The following equations describe the electrochemical reactions and the resulting current: ; ; ; ; in, The actual current density (A / m 2 ), is the current density source term generated by the electrochemical reaction in the battery (A / m 2 ), is the total current density source term (A / m 2 ), is the divergence of current density (A / m 3 ), is the effective conductivity (S / m), is the electrical conductivity of the solid phase (S / m), is the potential gradient of the electrolyte phase (V / m), is the solid phase potential gradient (V / m).

[0028] ; ; ; in, is the diffusion flux of substance i (mol / (m 2 ·s) describes the amount of material flowing per unit area per unit time; is the density of the substance (mol / m 3 ), is the fluid velocity (m / s), It is matter The reaction rate (mol / (m 3 ·s))、 It is matter The mass mole fraction of It is matter The effective diffusion coefficient (m 2 / s), is the concentration gradient of the substance (mol / m 3 ), is the concentration gradient of substance k (mol / m 4 ), is the gas pressure (Pa), is the molar concentration of substance k (mol / m 3 ), is the molar mass of substance k (kg / mol), is the molar mass of the reference substance (kg / mol).

[0029] The above equations describe the diffusion, convection and material transport processes of internal substances during the operation of the fuel cell. At the same time, the equations describe the electrochemical reactions of the fuel cell and are coupled to the overall mass transfer and fluid management equations in the form of influence terms.

[0030] During the actual operation of the fuel cell, there will be mass transfer and electrochemical reactions between different substances in the hydrogen and oxygen phases. In a feasible embodiment, the mass transfer between the two substances is determined according to the following formula ( and ) interaction: ; in, is the mole fraction of the substance (kg / mol), the concentration distribution of the reaction substance in the two gas phases, is the effective diffusion coefficient between the two substances (m 2 / s), describes the diffusion rate between substances, is the mass flow density (A / m2 ), is the system density (kg / m 3 ) In one feasible embodiment, the relationship between the mass flow density of the substance and the driving force such as the concentration gradient is determined by the following formula: ; ; ; in, is the water activity, is the reference pressure (Pa), is the acceleration due to gravity (m / s 2 ), Temperature Saturated vapor pressure of water (Pa), is the chemical formula of water (J / mol), R is the gas constant, T is the temperature (K), For material The partial pressure (Pa), substance The mole fraction of is the total pressure of the mixed gas (Pa).

[0031] In one feasible embodiment, the mass transport and electrochemical process of the proton exchange membrane are determined by the following formula: ; is the ion flux (mol / (m 2 ·s)), which means that the ion flux of PEM is 0, and the number of ions in PEM is conserved without accumulation or dissipation.

[0032] In one possible embodiment, the ion flux due to electric field driven or chemical potential gradient driven ion flow is determined by the following formula: ; in, is the conductivity of PEM (S / m), is the ion migration coefficient, dimensionless, is the Faraday constant (C / mol), is the potential gradient of the PEM (V / m), is the correlation coefficient of ion transport in PEM, dimensionless, is the chemical potential gradient (J / mol).

[0033] In one embodiment, the current density in the PEM ( ) is determined by the following equation: ; in, Determined by the Butler-Volmer equation: ; ; ; ; in, is the exchange current density (A / m 2 ), is the temperature (K), is the activation overpotential (V), is the reference activation overpotential (V), is the electrode potential (V), is the equilibrium potential (V), is the actual electric potential, is the number of electrons involved in the electrode reaction, is the gas constant, is the cathode charge transfer coefficient, is the anode charge transfer coefficient, is the Faraday constant, For the The partial pressure of each reactant (Pa), For the Reference partial pressure of each reactant (Pa), For the The stoichiometric number of reactants, is the reference exchange current density, which varies with temperature. is the reference equilibrium potential, which varies with temperature.

[0034] The flow of the reaction gases at the anode and cathode in the flow channel and porous media is expressed by the following equations: The Free and Porous Media Flow interface is used to define the convection and pressure of the positive and negative electrode reaction gases in their flow channels and gas diffusion layers. The free gas domain in the flow channel is described using the Navier-Stokes equations, and the porous medium in the gas diffusion layer is described using the Brinkman equations.

[0035] Runner (free flow): ; ; in, ρ is the fluid density (kg / m 3 ), u is the fluid velocity vector (m / s), is the convection term, is the liquid pressure (Pa), K is the permeability (m 2 )、 F is the external force term (N / m 3 ). is the continuity equation that describes the conservation of fluid mass.

[0036] ; in, μ is the dynamic viscosity (Pa·s), is the velocity gradient (1 / m), is the divergence of the velocity field (1 / s), l is the characteristic length (m).

[0037] For the flow in porous media, the following equation is given: ; ; ; Compared with the free flow in the flow channel, the equation describing the flow of fluid in porous media adds a term describing the flow of porous media, where is the porosity, dimensionless. is the permeability (m 2 ), is the inertia coefficient, The source of quality.

[0038] Convection in the cooling circuit is described using the Laminar Flow interface, and the coolant motion in this section is determined by solving the Navier-Stokes equations.

[0039] ; ; in, is the fluid density (kg / m3), is the cooling fluid velocity vector (m / s), is the gradient operator, is the convection term, is the liquid pressure (Pa), is the characteristic length (m), is the viscous stress term of the fluid (Pa), is the external force term (N / m3). is the continuity equation that describes the conservation of fluid mass.

[0040] The heat transfer and temperature field in the battery are defined using the Heat Transfer in Solids and Fluids interface.

[0041] For the heat transfer and flow of solid parts: ; in, is the density of the material (kg / m 3 ), is the specific heat capacity of the substance (J / (kg·K)), is the material flow rate (m / s), is the temperature gradient (K / m), is the divergence of heat flux (W / m 3 ), is the volume heat source (W / m 3 ), is the additional heat source (W / m 3 ).

[0042] According to Fourier's law of heat conduction: ; in, is the heat flux (W / m 2 ), is the thermal conductivity (W / (m·K)), is the temperature gradient (K / m) In the ideal gas domain, we have: ; in, is the gas density, is the gas pressure, is the gas constant, is the gas temperature.

[0043] Furthermore, considering that there are coupled calculation terms in the four physical fields mentioned above, it is necessary to apply the corresponding interfaces to define the above coupling: Apply reacting flow definitions to convective velocity, pressure, and mass transfer in hydrogen fuel cells and electrochemical reaction kinetics in free and porous media flows.

[0044] The mass transfer from this phase to the other phases is described by the following equation: ; in, is the total mass flow rate during the mass transfer process, which is equal to the sum of multiple different mass transfer channels. represents the mass flow rate of each channel.

[0045] The rate of transformation between the gas and liquid phases is described by the Stefan rate: ; in, is the density or conversion rate at the gas-liquid interface, is a constant, It is The flow of the components, It is The density of the component.

[0046] The ohmic heat generated by the electrochemical reaction is coupled to the Fluid and Solid Heat Transfer interface as a heat source through the Electrochemical Heat Interface.

[0047] The heat transfer caused by heat conduction and the heat caused by other sources such as electrochemical reactions in this part of the heat transfer process are: ; in, is the density of the material, is the specific heat capacity at constant pressure, is the velocity vector of the fluid, is the temperature field, is the thermal conductivity.

[0048] The total heat source for the reaction is: ; in, is the total heat source, is the ohmic heat from the electrochemical reaction, is the sum of the heat from other heat sources, is the volume correlation coefficient of each heat source, Represents the heat from a single reaction process.

[0049] The ohmic heat generated during the electrochemical reaction is: ; in, represents ohmic heat, i.e. heat generated by the flow of current and by the electric potential, is the current density of the solid phase, is the current density of the liquid phase, and is the electric potential of the solid and liquid phases.

[0050] For the heat generated by a single electrochemical reaction in the electrochemical process, there is an expression: ; in, is the potential difference between the solid and liquid phases, is the equilibrium potential of a single reaction, is the temperature, is the temperature dependence of the equilibrium potential, is the local current density of the electrochemical reaction.

[0051] For the heat flow and total heat source caused by heat conduction during the electrochemical reaction, we have: ; The total heat source during the electrochemical reaction is: ; The effects of thermal energy on the fluid described in free and porous media flow are described through the Non-Isothermal Flow interface.

[0052] In fluids and pressure Heat sources related to temperature changes The change relationship is described by the following formula: ; in, is the volume expansion coefficient, is the temperature of the fluid, is the rate of change of pressure with time, is the fluid velocity The dot product with the pressure gradient describes the pressure change caused by the flow.

[0053] For the volume expansion coefficient in the above equation, there is an equation describing it: ; in, is the density of the fluid, It is the rate of change of density with respect to temperature. The influence of temperature change on the density of the fluid affects the volume expansion behavior of the fluid.

[0054] The energy transfer process in fluid motion is caused by shear stress and heat sources in porous media The heat source of the components is described by the following equation: ; in, is the stress tensor, describing the shear forces within the fluid, is the velocity gradient, which represents the rate of change of flow, is an additional heat source generated by the porous medium.

[0055] The heat generation process in porous media, taking into account the viscosity, permeability and flow rate of the fluid, is expressed by the following equation: ; in, is the heat source caused by the viscosity of the fluid and the permeability of the porous medium, where is the dynamic viscosity of the fluid, It is a nonlinear heat source term related to fluid velocity and density, which describes the effect of flow velocity on the heat source.

[0056] like Figure 3 As shown in the figure, the accuracy of the three-dimensional multi-physics model is verified. After the model is established, calculations can only be performed after meshing. To ensure that the mesh density has a relatively small impact on the calculation results, the model should be mesh-independent verification and analysis. When dividing the mesh density, the predefined partitioning interface provided by COMSOL Multiphysics 6.2 is used to divide the mesh density into three types: fine, normal, and coarse. The polarization curves calculated using models with different mesh densities are compared and analyzed to verify the mesh independence of the model. Based on the mesh independence verification, the external characteristic results (polarization curves) output by the model are compared with the actual experimental results to verify the model accuracy. The simulation data divides the polarization curve with a current density step size of 0.01V, while the experimental data is divided into six discrete points.

[0057] After precision verification, it can be shown that the proposed fuel cell aging modeling method is accurate and has practical application value, such as Figure 4 As shown in FIG, a schematic diagram of the 3D multi-physics field model and the output results of each physical field at a voltage of 0.6V.

[0058] The above are merely preferred embodiments of the present application, but the scope of protection of the present application is not limited thereto. Any changes or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in this application should be included in the scope of protection of the present application. Therefore, the scope of protection of the present application should be based on the scope of protection of the claims.

Claims

1. A method for aging modeling of a proton exchange membrane fuel cell, characterized in that: include: Establishing a three-dimensional computational fluid dynamics model of a proton exchange membrane fuel cell, the three-dimensional computational fluid dynamics model includes a proton exchange membrane, a catalyst layer, a gas diffusion layer, and a plate, and divides the anode and cathode reaction gas flow channels and a cooling circuit; distributing hydrogen fuel cell chemical fields, free and porous media flow fields, laminar fluid fields, and solid and fluid heat transfer fields in the three-dimensional computational fluid dynamics model; Deploying a monitoring probe in the three-dimensional computational fluid dynamics model to obtain internal operating parameters and external characteristic data of the fuel cell; Establishing a fuel cell multi-component aging mechanism model, the fuel cell multi-component aging mechanism model including a proton exchange membrane aging model based on the hydrogen permeation current change rate and an electrochemical active surface area aging model based on the change of platinum particle radius; Data interaction between the three-dimensional computational fluid dynamics model and the aging mechanism model is achieved through the TCP / IP protocol to form a closed-loop iterative system.

2. The method for proton exchange membrane fuel cell aging modeling according to claim 1, characterized in that: The process of establishing the three-dimensional computational fluid dynamics model includes: constructing a geometric structure including a proton exchange membrane, a catalyst layer, a gas diffusion layer and a plate based on the actual geometric parameters of the fuel cell; dividing the anode and cathode reaction gas flow channels and the cooling circuit flow channels; and assigning corresponding physical fields to each component.

3. The method for proton exchange membrane fuel cell aging modeling according to claim 1, characterized in that: The process of establishing the proton exchange membrane aging model includes: establishing a proton exchange membrane thickness and conductivity decay rate model based on the hydrogen permeation current change rate; calculating the current decay rate based on real-time stack status data and reference stack status data; and calculating the decay state of the proton exchange membrane thickness and conductivity in real time based on the decay rate.

4. The method for proton exchange membrane fuel cell aging modeling according to claim 1, wherein: The process of establishing the electrochemically active surface area aging model includes: establishing forward and reverse reaction rate equations for platinum particle dissolution, deposition, and oxidation reactions; calculating the rate of change of the platinum particle radius based on the forward and reverse reaction rate equations; and determining the aging state of the electrochemically active surface area based on the mathematical relationship between the platinum particle radius and the electrochemically active surface area.

5. The method for aging modeling of a proton exchange membrane fuel cell according to claim 1, wherein: The data interaction process includes: the three-dimensional computational fluid dynamics model transmits the collected real-time parameters to the aging mechanism model; the aging mechanism model calculates the aging status of the component based on the received parameters and outputs it; and the calculated aging status parameters are substituted back into the three-dimensional computational fluid dynamics model for updating.

6. The method for proton exchange membrane fuel cell aging modeling according to claim 1, characterized in that: The method also includes a model verification process: grid independence verification of the three-dimensional computational fluid dynamics model, using different grid densities for division and comparing calculation results; and comparing external characteristic data output by the model with actual test data to verify model accuracy.

7. The method for proton exchange membrane fuel cell aging modeling according to claim 2, wherein: The physical field distribution process includes: distributing the hydrogen fuel cell chemical field in the area except the coolant flow channel; distributing the free and porous medium flow fields in the anode and cathode reaction gas flow channels and the gas diffusion layer; distributing the laminar fluid field in the cooling circuit flow channel; and distributing the solid and fluid heat transfer fields in all components.

8. The method for aging modeling of a proton exchange membrane fuel cell according to claim 1, wherein: The internal operating parameters include proton exchange membrane temperature, relative humidity, and local potential of the anode and cathode; the external characteristic data include polarization curves and electrochemical impedance spectroscopy.

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