A fuel cell numerical simulation method, device and medium

Abstract

The present invention discloses a fuel cell numerical simulation method, device and medium, the method comprising the following steps: establishing a macro-scale fuel cell model and a micro-scale local oxygen transfer model; coupling the macro-scale fuel cell model and the micro-scale local oxygen transfer model; setting boundary conditions of the calculation domain; verifying and solving the macro-scale fuel cell model and the micro-scale local oxygen transfer model; and evaluating the oxygen transfer characteristics of the ordered electrode. By establishing a macro-scale fuel cell model and a micro-scale local oxygen transfer model, the present invention can not only reflect the local oxygen transfer process, but also reflect the heat and mass transfer and electrochemical performance of the battery from a macroscopic perspective.

Description

Technical Field

[0001] The present invention belongs to the technical field of fuel cells, and in particular relates to a fuel cell numerical simulation method, equipment and medium. Background Art

[0002] Proton exchange membrane fuel (PEMFC) is regarded as the most promising clean energy conversion device due to its high power density, high energy efficiency, and zero emissions. Although the technology has made significant progress in the past few decades, its widespread commercialization is still hindered by high cost, insufficient power density, and low durability. The oxygen reduction reaction (ORR) kinetics on the cathode side of the proton exchange membrane fuel cell are slow, and the precious metal platinum (Pt) catalyst is still required in the short term, making it difficult to further reduce costs. Therefore, it is crucial to reduce the load of precious metal platinum while ensuring battery performance.

[0003] Research has found that when the platinum catalyst loading is reduced, the proton exchange membrane fuel cell will suffer a considerable performance loss due to the sharp increase in oxygen transport resistance (OTR). Constructing an ordered catalyst layer can effectively improve the mass transfer performance and platinum catalyst utilization, and can significantly reduce the platinum loading while maintaining fuel cell performance.

[0004] Numerical simulation methods can be used to study multi-physics problems in proton exchange membrane fuel cells, including electrochemistry and heat and mass transfer, to help further understand the electrochemical and heat and mass transfer processes in ordered electrodes, thereby helping to design fuel cell electrodes and optimize operating conditions.

[0005] Traditional numerical simulation studies usually use agglomeration models to calculate mass transfer and electrochemical reactions within the fuel cell catalyst layer. The agglomeration model is based on the use of porous carbon-supported platinum catalysts, assuming that ionomers and porous carbon-supported platinum catalysts are prone to form agglomerates. However, the ordered catalyst layer is different from the traditional porous carbon-supported platinum catalyst structure. The ordered catalyst layer is composed of carriers of characteristic shape units in an orderly manner, which can avoid the agglomeration phenomenon similar to porous carbon-supported platinum catalysts. Therefore, the use of conventional agglomeration models is not suitable for simulating and calculating the mass transfer and electrochemical reactions of ordered catalysts. On the other hand, the traditional agglomeration model only considers the interfacial mass transfer resistance of oxygen from the pores in the catalyst layer to the agglomerates, and does not fully consider the more detailed and accurate mass transfer processes within the agglomerates, such as the local oxygen transfer process from the ionomer to the catalyst surface. Therefore, it is crucial to establish a multi-scale model that includes a microscopic model of the local oxygen transfer process and a macroscopic fuel cell model for accurately simulating the multi-physics field process of the fuel cell.

[0006] In terms of oxygen transfer resistance evaluation methods, the mass transfer resistance of proton exchange membrane fuel cells is usually measured by the limiting current method, and the mass transfer resistance can be divided into bulk mass transfer resistance and local mass transfer resistance. The limiting current method has the following two limitations: 1. Fuel cells usually operate under non-limiting current conditions. The oxygen transfer resistance characteristics measured by the limiting current method cannot be directly used to represent the oxygen transfer characteristics of fuel cells. The focus of research should be on non-limiting current conditions; 2. The limiting current method can only measure the total local oxygen transfer resistance and cannot evaluate the impact of each local oxygen transfer process. Understanding the local oxygen transfer process is crucial and can help optimize the design of electrode structure and improve the performance of fuel cells with low platinum loading. Summary of the invention

[0007] In order to overcome the above technical defects, the present invention provides a fuel cell numerical simulation method.

[0008] In order to solve the above problems, the present invention is implemented according to the following technical solutions:

[0009] A fuel cell numerical simulation method, the method comprising the following steps:

[0010] Establish a macro-scale fuel cell model and a micro-scale local oxygen transport model;

[0011] Coupling a macroscale fuel cell model with a microscale local oxygen transport model;

[0012] Set the boundary conditions of the computational domain;

[0013] Validate and solve macroscale fuel cell models and microscale local oxygen transport models;

[0014] The oxygen transport properties of the ordered electrodes were evaluated.

[0015] Furthermore, a macro-scale fuel cell model is established using the mass conservation equation, momentum conservation equation, energy conservation equation, gas component conservation equation, electron conservation equation and proton conservation equation.

[0016] Furthermore, the macro-scale fuel cell model includes:

[0017] Secondary current distribution module for simulating electrochemical reactions;

[0018] Two-phase water transport module, used to simulate the two-phase changes of water evaporation and condensation;

[0019] Gas material transport module, used to simulate the diffusion of hydrogen and oxygen in porous media;

[0020] Ionomer Water Transport Module, for simulating transmembrane water transport and the corresponding proton conductivity of ionomers;

[0021] The heat transfer module is used to simulate and calculate the non-uniform temperature field caused by ohmic heat and reaction heat.

[0022] Further, the step of coupling the macro-scale fuel cell model and the micro-scale local oxygen transport model includes:

[0023] The current density, bulk oxygen concentration in the catalyst layer, ionomer thickness related to liquid water content, and water film thickness are used as inputs to couple to the micro-scale local oxygen transport model.

[0024] The oxygen concentration on the platinum surface from the microscale local oxygen transport model is coupled to the secondary current module to calculate the electrochemical reaction rates and exchange current density.

[0025] Furthermore, the boundary conditions include: specifying the oxygen initial value and Dirichlet boundary conditions for the cathode oxygen flow channel in the calculation domain, specifying the hydrogen initial value and Dirichlet boundary conditions for the anode hydrogen flow channel in the calculation domain, specifying the potential boundary conditions for the cathode plate in the calculation domain, and specifying the electrical grounding boundary conditions for the anode plate in the calculation domain.

[0026] Furthermore, the steps of verifying and solving the macro-scale fuel cell model and the micro-scale local oxygen transport model include:

[0027] The finite element mesh is set up to verify and solve the macro-scale fuel cell model and the micro-scale local oxygen transport model. The working conditions to be solved include: limiting current conditions and non-limiting current conditions.

[0028] Furthermore, the step of evaluating the oxygen transport characteristics of the ordered electrode comprises the steps of:

[0029] According to the microscopic local oxygen transport model, the oxygen concentration in the local oxygen mass transfer process of the ordered electrode is calculated;

[0030] The oxygen transport resistance of conventional electrodes and ordered electrodes was calculated according to the limiting current method and the logarithmic normalized oxygen transport resistance method.

[0031] Furthermore, the microscopic local oxygen transport model is used to describe the microscopic transport process of oxygen molecules, and the microscopic transport process of oxygen molecules includes:

[0032] The oxygen dissolves from the pores of the catalyst layer to the interface transport process of liquid water;

[0033] Diffusion transport of oxygen in liquid water;

[0034] The oxygen transport process from liquid water to ionomer interface;

[0035] Diffusion transport process of oxygen in ionomers;

[0036] Diffusion of oxygen in ionomers.

[0037] Compared with the prior art, the present invention has the following beneficial effects:

[0038] The present invention discloses a fuel cell numerical simulation method. By establishing a macro-scale fuel cell model and a micro-scale local oxygen transport model, it can not only reflect the local oxygen transport process, but also reflect the heat and mass transfer and electrochemical performance of the battery from a macroscopic perspective. It is more reasonable and accurate than traditional models.

[0039] The present invention also discloses a computer device, characterized in that it comprises:

[0040] processor;

[0041] a memory for storing instructions executable by the processor;

[0042] Wherein, the processor is configured to execute the instructions to implement the above-mentioned numerical simulation method.

[0043] The present invention also discloses a computer-readable storage medium, characterized in that it is a computer-readable storage medium on which a computer program is stored, and the computer program implements the above-mentioned numerical simulation method when executed. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] The specific embodiments of the present invention are further described in detail below in conjunction with the accompanying drawings, wherein:

[0045] Figure 1 Schematic diagram of the model of the numerical simulation method described in Example 1: (a) calculation domain, (b) three-dimensional ordered hierarchical porous electrode unit and local oxygen transport process;

[0046] Figure 2 A schematic diagram of model verification of the numerical simulation method described in Example 1;

[0047] Figure 3 Schematic diagram of oxygen transport resistance at different platinum loadings according to the numerical simulation method described in Example 1: (a) bulk and local oxygen transport resistance, (b) fitting relationship between local oxygen transport resistance and roughness;

[0048] Figure 4 Schematic diagram of comparison between the ordered electrode and the conventional electrode of the numerical simulation method described in Example 1: (a) bulk oxygen concentration comparison, (b) local oxygen concentration comparison, (c) local oxygen transport resistance comparison;

[0049] Figure 5 Schematic diagram of the local oxygen transport resistance analysis of the numerical simulation method described in Example 1: (a) logarithmic normalized oxygen concentration, (b) logarithmic normalized oxygen transport resistance;

[0050] Figure 6 This is a schematic diagram of the structure of the computer device described in Example 2;

[0051] Marking Description: Figure 1 b, r p is the pore radius, r c is the radius of the ordered electrode representative unit, δ w is the thickness of the liquid water film, δ ion is the ionomer film thickness, δ c is the average thickness of the carbon support. is the oxygen concentration in the pores of the cathode catalyst layer, is the oxygen concentration at the water film interface, is the oxygen concentration at the interface between liquid water and ionomer, is the oxygen concentration at the interface between liquid water and ionomer, is the oxygen concentration across the ionomer-water interface, is the oxygen concentration after passing through the ionomer membrane, is the oxygen concentration on the platinum surface. DETAILED DESCRIPTION

[0052] The preferred embodiments of the present invention are described below in conjunction with the accompanying drawings. It should be understood that the preferred embodiments described herein are only used to illustrate and explain the present invention, and are not used to limit the present invention.

[0053] Example 1

[0054] like Figure 1 As shown, this embodiment discloses a fuel cell numerical simulation method, the method comprising the following steps:

[0055] S1. Establish a macro-scale fuel cell model and a micro-scale local oxygen transport model; the macro-scale fuel cell model is used to simulate and calculate the heat and mass transfer processes in the porous electrode and the electrochemical reactions in the fuel cell, and the micro-scale local oxygen transport model is used to describe the microscopic transport process of oxygen molecules from the nanopores of the catalyst layer to the platinum surface of the catalyst, and solve the local oxygen concentration that depends on the adsorption on the platinum surface of the catalyst, thereby calculating the electrochemical reaction rate and exchange current density.

[0056] S2. Couple the macroscale fuel cell model and the microscale local oxygen transport model.

[0057] S3. Set the boundary conditions of the computational domain.

[0058] S4. Verify and solve the macro-scale fuel cell model and the micro-scale local oxygen transport model.

[0059] S5. Evaluate the oxygen transport properties of the ordered electrode.

[0060] Specifically, a macroscopic fuel cell model is established using the mass conservation equation, momentum conservation equation, energy conservation equation, gas component conservation equation, electron conservation equation and proton conservation equation.

[0061] The mass conservation equation:

[0062]

[0063] Momentum conservation equation:

[0064]

[0065] Energy conservation equation:

[0066]

[0067] Where ρ is the gas density, is the velocity field, S g is the gas component source, S l is the liquid water source. K is the permeability of the porous medium, k r is the relative permeability, μ is the gas viscosity, p is the pressure, ε is the porosity of the porous medium, C p is the heat capacity of porous media, T is the temperature, k eff is the effective thermal conductivity, S T As heat source.

[0068] Gas composition conservation equation:

[0069]

[0070] Among them, C i is the concentration of gas component i, is the effective diffusion rate of gas, S i is the source term of gas component i.

[0071] Electron conservation equation:

[0072]

[0073] Proton conservation equation:

[0074]

[0075] in, and are the electronic conductivity and proton conductivity, respectively, s and φ e are the electrode potential and electrolyte potential respectively, and j is the current density.

[0076] The electrochemical reaction rate on the cathode side is shown in (7-8), and the exchange current density depends on the local oxygen concentration adsorbed on the platinum surface of the catalyst.

[0077]

[0078] θ PtO =1 / (1+e 22.4(0.818-E) ) (8)

[0079] Among them, A c is the electrochemical effective specific surface area, and are the reference exchange current density and reference oxygen concentration, θ PtO is the PtO coverage, α c is the cathode oxygen reduction reaction transfer coefficient, ω is the Temkin isothermal energy parameter, η c is the cathode overpotential, R is the gas constant, F is the Faraday constant, and T is the temperature.

[0080] Specifically, the macro-scale fuel cell model includes: a secondary current distribution module for simulating electrochemical reactions, a two-phase water transport module for simulating the two-phase changes of water evaporation and condensation, a gas material transport module for simulating the diffusion of hydrogen and oxygen in porous media, an ionomer water transport module for simulating transmembrane water transport and the corresponding proton conductivity of ionomers, and a heat transfer module for simulating and calculating the uneven temperature field caused by ohmic heat and reaction heat.

[0081] Specifically, step S2 includes the following steps:

[0082] The current density, bulk oxygen concentration in the catalyst layer, ionomer thickness related to liquid water content, and water film thickness are used as inputs to couple to the micro-scale local oxygen transport model.

[0083] The oxygen concentration on the platinum surface from the microscale local oxygen transport model is coupled to the secondary current module to calculate the electrochemical reaction rates and exchange current density.

[0084] Specifically, step S3 includes the following steps:

[0085] In the calculation domain, the initial value of oxygen is specified in the cathode oxygen flow channel (such as Dirichlet boundary condition), the initial value of hydrogen is specified in the anode hydrogen flow channel (such as Dirichlet boundary condition), the potential boundary condition is specified in the calculation domain cathode plate, and the electrical grounding boundary condition is specified in the calculation domain anode plate.

[0086] In the above embodiment, the microscopic local oxygen transport model is used to describe the microscopic transport process of oxygen molecules from the nanopores of the catalyst layer to the surface of the platinum catalyst, and is used to solve the local oxygen concentration adsorbed on the surface of the platinum catalyst to calculate the electrochemical reaction rate and the exchange current density. The microscopic transport process of oxygen molecules from the nanopores of the catalyst layer to the surface of the platinum catalyst includes: (1) dissolution of oxygen from the pores of the catalyst layer into the liquid water film (2) diffusion of oxygen in liquid water (3) interfacial transport of oxygen from liquid water to the ionomer (4) diffusion of oxygen in the ionomer (5) interfacial transport of oxygen from the ionomer to the platinum catalyst.

[0087] The interfacial transport process of oxygen dissolving from the pores of the catalyst layer to liquid water:

[0088]

[0089] in, is the oxygen concentration at the water film interface, is the oxygen concentration in the pores of the cathode catalyst layer, is the Henry constant of oxygen in water.

[0090] Diffusion and transport process of oxygen in liquid water:

[0091]

[0092] in, is the oxygen flux of the microscopic oxygen transport process, is the oxygen concentration at the interface between liquid water and ionomer, is the diffusion coefficient of oxygen in water, δ w is the thickness of liquid water.

[0093] Oxygen transfer process from liquid water to ionomer interface:

[0094]

[0095] in, is the oxygen concentration at the interface between liquid water and ionomer, and are the Henry coefficients of oxygen in ionomer and water, respectively.

[0096] Diffusion transport process of oxygen in ionomers:

[0097]

[0098]

[0099] in, is the oxygen concentration across the ionomer-water interface, k w-ionis the equivalent mass transfer resistance, and δ ion are the diffusion coefficient of oxygen in ionomer and the thickness of ionomer, respectively.

[0100] Diffusion of oxygen in ionomers:

[0101]

[0102] in, is the oxygen concentration after passing through the ionomer membrane.

[0103] In this example, the Langmuir adsorption equation is proposed to describe the interfacial transport process of ionomer-platinum catalyst in a microscale local oxygen transport model:

[0104]

[0105]

[0106] in, is the oxygen concentration on the platinum surface, is the maximum adsorbed oxygen concentration on the platinum surface, K L is the Langmuir adsorption coefficient, θ max Number of oxygen molecules adsorbed on the platinum surface, N A is Avogadro's constant, A Pt is the effective specific surface area of ​​platinum, is the oxygen density.

[0107] Through the above equation, the concentration of oxygen adsorbed on the surface of the platinum catalyst participating in the oxygen reduction reaction can be accurately obtained, thereby calculating the electrochemical reaction rate and exchange current density.

[0108] The traditional limiting current method, such as the following equations (17)-(18), can only calculate the bulk transport resistance and the total local transport resistance, but lacks a more detailed description and understanding of the local oxygen transport process. Therefore, it is proposed to use a microscopic local oxygen transport model, such as equations (9)-(16), to calculate the impact of each local oxygen transport process.

[0109] Traditional limiting current method:

[0110]

[0111]

[0112] Among them, R CL,bulk , R MPL,bulk , R BL,bulk are the bulk oxygen mass transfer resistance of the catalyst layer, microporous layer, and gas diffusion layer, respectively, R CL,local is the local transport resistance of the catalyst layer, is the oxygen concentration in the flow channel, i lim is the limiting current.

[0113] In the above embodiment, a logarithmic normalization method is proposed to evaluate the influence of each local oxygen transfer process. The local oxygen mass transfer resistance is divided into the air-water interface resistance (r g-w,int ), diffusion resistance in water (r w,dif ), water-ionomer interfacial resistance (r w-ion,int ), the diffusion resistance in the ionomer (r ion,dif ), ionomer-platinum catalyst interfacial resistance (r ion-Pt,int ), by using a logarithmic normalization method, the effects of these five local oxygen transport steps can be directly compared and quantified.

[0114]

[0115]

[0116] r total,local =r g-w,int +r w,dif +r w-ion,int +r ion,dif +r ion-Pt,int (twenty one)

[0117] in, is the oxygen concentration in the local mass transfer process, r i is the logarithmically normalized local oxygen transmission resistance, r total,local is the total log-normalized local oxygen transfer resistance.

[0118] A multiscale model was developed to study the mass transfer characteristics of ordered catalyst layers in proton exchange membrane fuel cells.

[0119] In the microscale local oxygen transport model, the Langmuir adsorption equation is proposed to describe the interfacial transport process of the ionomer-platinum catalyst in the microscale local oxygen transport model. The microscale local oxygen transport model is coupled to a two-dimensional, two-phase flow macroscale fuel cell model. The macroscale fuel cell model is used to simulate and calculate the heat and mass transfer processes in the porous electrode and the electrochemical reactions in the fuel cell.

[0120] In the macro-scale fuel cell model, the secondary current distribution module is used to simulate electrochemical reactions and calculate the potential and current density. The two-phase water transport module is used to simulate the two-phase changes of water evaporation and condensation. The gas material transport module is used to simulate the diffusion of hydrogen and oxygen in porous media. The ionomer water transport module is used to simulate transmembrane water transport and the corresponding proton conductivity of the ionomer. The heat transfer module is used to simulate the non-uniform temperature field caused by ohmic heat and reaction heat.

[0121] In order to couple the macroscale fuel cell model with the microscale local oxygen transport model, parameters such as current density, bulk oxygen concentration in the catalyst layer, ionomer thickness related to liquid water content, and water film thickness are used as inputs and coupled to the microscale local oxygen transport model. The oxygen concentration on the platinum surface in the microscale local oxygen transport model is coupled to the electrochemical reaction equation in the secondary current module in the macroscale fuel cell model to calculate the electrochemical reaction rate and exchange current density, thereby achieving full coupling of the macroscale fuel cell model and the microscale local oxygen transport model.

[0122] like Figure 2 As shown, the model was verified. For conventional electrodes and ordered electrodes, the temperature, relative humidity, platinum loading, and research voltage were set to 1.00-0V, respectively. The corresponding battery current results were calculated and the polarization curve results were obtained. The results showed that the proposed model can accurately simulate the performance of ordered fuel cells, and the simulation results can well reproduce the experimental results.

[0123] Based on the macroscale fuel cell model and the microscale local oxygen transport model, the limiting current under each platinum loading condition is obtained. According to the limiting current method (i.e., equations (17)-(18)), the oxygen transport characteristics of the ordered electrode can be calculated; Figure 3 As shown, Figure 3 The bulk oxygen transport resistance of the fuel cell gas diffusion layer, microporous layer, and catalyst layer and the local oxygen transport resistance of the catalyst layer are shown. The local oxygen transport resistance is a linear function of 1 / roughness, which is consistent with theoretical derivation and once again demonstrates the reliability of the model.

[0124] The oxygen concentrations in the mass transfer process of the ordered electrode and the conventional electrode were calculated and compared using the macroscale fuel cell model and the microscale local oxygen transport model. The oxygen transport resistances of the conventional electrode and the ordered electrode were calculated according to the limiting current method (i.e., equations (17)-(18)) and the logarithmic normalized oxygen transport resistance method (i.e., equations (19)-(21)), as follows: Figure 4 As shown, Figure 4 Comparison between the ordered electrode and conventional electrode showed that the ordered electrode has better performance in both bulk oxygen transport and local oxygen transport.

[0125] The macroscale fuel cell model and the microscale local oxygen transport model are used to calculate the oxygen concentration in the local oxygen mass transfer process of the ordered electrode. The various oxygen transport resistances in the local oxygen transport process are calculated according to equations (19)-(21). The influence of various oxygen transport resistances is shown in the following figure: Figure 5 As shown in Figure 5, at the limiting current, the ionomer to platinum interface transport resistance increases significantly with decreasing platinum loading, limiting the performance of the fuel cell at further low platinum loadings.

[0126] The macro-scale fuel cell model and micro-scale local oxygen transfer model established in the present invention can accurately describe the macro- and micro-scale oxygen transfer characteristics and electrochemical reaction characteristics of the proton exchange membrane fuel cell. They can not only reflect the micro-local oxygen transfer process, but also reflect the heat and mass transfer and electrochemical performance of the battery from a macro-electrode scale. They are more reasonable and accurate than traditional models.

[0127] The microscopic-scale local oxygen transport model established in the present invention can accurately describe the local oxygen transport process from the pores in the catalyst layer to the surface of the platinum catalyst, and evaluate the contribution of each process to the resistance of oxygen transport, providing a theoretical basis and guidance for the design of proton exchange membrane fuel cell electrodes.

[0128] The logarithmic normalization method proposed in the present invention is applicable to limiting current conditions and non-limiting current conditions, can evaluate and analyze the oxygen transfer performance under various conditions, and can be used to optimize the operating conditions of fuel cells.

[0129] Example 2

[0130] like Figure 6 This embodiment discloses a computer device, including: a processor and a memory for storing instructions executable by the processor; the processor is configured to execute instructions to implement the above-mentioned numerical simulation method.

[0131] Those skilled in the art should be aware that in one or more of the above examples, the functions described in the embodiments of the present application can be implemented with hardware, software, firmware, or any combination thereof. When implemented using software, these functions can be stored in a computer-readable medium or transmitted as one or more instructions or codes on a computer-readable medium. Computer-readable media include computer storage media and communication media, wherein the communication media include any media that facilitates the transmission of a computer program from one place to another. The storage medium can be any available medium that a general or special-purpose computer can access.

[0132] Example 3

[0133] This embodiment discloses a computer-readable storage medium, which is a computer-readable storage medium on which a computer program is stored. When the computer program is executed, the numerical simulation method in Embodiment 1 is implemented.

[0134] Optionally, the computer readable storage medium may include: a read-only memory (ROM), a random access memory (RAM), a solid state drive (SSD) or an optical disk, etc. Among them, the random access memory may include a resistance random access memory (ReRAM) and a dynamic random access memory (DRAM).

[0135] The above description is only a preferred embodiment of the present invention and does not limit the present invention in any form. Therefore, any modification, equivalent change and modification made to the above embodiment based on the technical essence of the present invention without departing from the technical solution of the present invention shall still fall within the scope of the technical solution of the present invention.

Claims

1. A fuel cell numerical simulation method, It is characterized in that The method comprises the following steps: A macroscale fuel cell model and a microscale local oxygen transport model are established; the macroscale fuel cell model includes: Secondary current distribution module for simulating electrochemical reactions; Two-phase water transport module, used to simulate the two-phase changes of water evaporation and condensation; Gas material transport module, used to simulate the diffusion of hydrogen and oxygen in porous media; Ionomer Water Transport Module, for simulating transmembrane water transport and the corresponding proton conductivity of ionomers; Heat transfer module, used to simulate and calculate the non-uniform temperature field caused by ohmic heat and reaction heat; Coupling a macroscale fuel cell model with a microscale local oxygen transport model; Set boundary conditions of the computational domain; Validate and solve macroscale fuel cell models and microscale local oxygen transport models; The oxygen transport properties of the ordered electrode were evaluated, including the following steps: Based on the macro-scale fuel cell model and the micro-scale local oxygen transport model, the oxygen concentration in the local oxygen mass transfer process of the ordered electrode is calculated; The oxygen transport resistance of conventional electrodes and ordered electrodes was calculated according to the limiting current method and the logarithmic normalized oxygen transport resistance method. The steps for coupling the macroscale fuel cell model and the microscale local oxygen transport model include: The current density, bulk oxygen concentration in the catalyst layer, ionomer thickness related to liquid water content, and water film thickness are used as inputs to couple to the micro-scale local oxygen transport model. The oxygen concentration on the platinum surface in the microscopic local oxygen transport model is coupled to the secondary current module to calculate the electrochemical reaction rate and exchange current density. The steps of verifying and solving the macro-scale fuel cell model and the micro-scale local oxygen transport model include: Finite element meshes are set up to verify and solve the macro-scale fuel cell model and micro-scale local oxygen transport model. The working conditions to be solved include: limiting current conditions and non-limiting current conditions; The microscopic local oxygen transport model is used to describe the microscopic transport process of oxygen molecules, and the microscopic transport process of oxygen molecules includes: The oxygen dissolves from the pores of the catalyst layer to the interface transport process of liquid water; Diffusion transport of oxygen in liquid water; The oxygen transport process from liquid water to ionomer interface; Diffusion transport process of oxygen in ionomers; Diffusion of oxygen in ionomers.

2. The fuel cell numerical simulation method according to claim 1, It is characterized in that A macro-scale fuel cell model is established using the mass conservation equation, momentum conservation equation, energy conservation equation, gas component conservation equation, electron conservation equation and proton conservation equation.

3. The fuel cell numerical simulation method according to claim 1, It is characterized in that The boundary conditions include: specifying the oxygen initial value and Dirichlet boundary conditions for the cathode oxygen flow channel in the calculation domain, specifying the hydrogen initial value and Dirichlet boundary conditions for the anode hydrogen flow channel in the calculation domain, specifying the potential boundary conditions for the cathode plate in the calculation domain, and specifying the electrical grounding boundary conditions for the anode plate in the calculation domain.

4. A computer device, It is characterized in that include: processor; a memory for storing instructions executable by the processor; The processor is configured to execute the instructions to implement the numerical simulation method according to any one of claims 1 to 3.

5. A computer-readable storage medium, It is characterized in that It is a computer-readable storage medium on which a computer program is stored. When the computer program is executed, the numerical simulation method according to any one of claims 1 to 3 is implemented.

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