Analog calculation method and device for mass transfer coefficient of oxygen in catalytic layer of fuel cell

Abstract

The invention mainly relates to the technical field of hydrogen energy application, in particular to a simulation calculation method and device for an oxygen mass transfer coefficient in a fuel cell catalyst layer. The invention provides a simulation calculation method for an oxygen mass transfer coefficient in a fuel cell catalytic layer. The simulation calculation method comprises the following steps: establishing a fuel cell numerical model based on an aggregate sub-model; based on the numerical model, constructing an iterative algorithm based on a fuel cell polarization curve; setting an oxygen mass transfer coefficient in the catalyst layer as an initial value within a preset value range, calculating based on an iterative algorithm to obtain a corresponding simulated polarization curve, and comparing the simulated polarization curve with a pre-obtained experimental polarization curve to obtain an error value; and based on the obtained error value as a convergence criterion, updating the value of the oxygen mass transfer coefficient through the iterative algorithm until the error value meets a preset threshold value, and outputting the corresponding oxygen mass transfer coefficient. And efficient and accurate calculation of the mass transfer coefficient of oxygen in the ionomer is realized.

Description

Technical Field

[0001] This application mainly relates to the field of hydrogen energy application technology, specifically to a method and apparatus for simulating and calculating the oxygen mass transfer coefficient in the catalyst layer of a fuel cell. Background Technology

[0002] The cathode catalyst layer of a proton exchange membrane fuel cell (PEMFC) is the core site for the oxygen reduction reaction, typically composed of a catalyst, ionomers, and pore spaces. The catalyst generally consists of carbon particles loaded with the noble metal platinum (Pt). Pt exhibits excellent catalytic performance but is expensive; therefore, reducing the amount of costly Pt is crucial for the commercial application of PEMFCs. However, with the reduction of Pt loading in the catalyst layer, the voltage loss caused by increased local oxygen mass transfer resistance becomes increasingly significant at high current densities (typically ≥1.0 A / cm²), hindering further performance improvements in low-platinum fuel cells. It is generally believed that oxygen penetration through the ionomer film covering the catalyst surface is the main factor generating this local mass transfer resistance. Therefore, the oxygen mass transfer coefficient in the ionomer is a key parameter for characterizing and quantifying this resistance, and its value is directly influenced by the properties of the ionomer itself and its distribution morphology on the catalyst surface.

[0003] Currently, the main method for obtaining the oxygen mass transfer coefficient in ionomers of catalyst layers is experimental, such as the limiting current method. However, these methods typically suffer from problems such as cumbersome and complex measurement processes, long measurement times, high costs, and limited accuracy, thus creating a technical bottleneck in studying the local mass transfer performance of low-platinum catalyst layers.

[0004] On the other hand, computational fluid dynamics (CFD) techniques, such as establishing PEMFC numerical models using commercial software like COMSOL, provide powerful tools for studying the internal mass transfer behavior of batteries. However, existing conventional numerical models face two major challenges in practical applications: First, the models themselves cannot directly provide the oxygen mass transfer coefficient of the ionomers, and their simulation calculations still rely on input parameters obtained through the aforementioned experimental methods, failing to escape the constraints of experimental determination; second, with the continuous emergence of low-platinum catalyst layers with novel microstructures (such as carbon supports with different pore characteristics), conventional numerical models struggle to describe the complex mass transfer processes brought about by these new structures in a timely and accurate manner, leading to discrepancies between simulation prediction performance and actual conditions.

[0005] In summary, there is an urgent need for a method to simulate and calculate the oxygen mass transfer coefficient of ionomers that can be freed from dependence on experimental measurements and can accurately adapt to the structural characteristics of novel catalyst layers. Summary of the Invention

[0006] This application provides a method, apparatus, and computer-readable medium for simulating the oxygen mass transfer coefficient in the catalyst layer of a fuel cell. It can solve the technical problem that traditional experiments or existing general models are difficult to accurately and efficiently obtain the oxygen mass transfer coefficient in ionomers due to the complex and variable structure of the catalyst layer.

[0007] The technical solution adopted in this application to solve the above-mentioned technical problems is a simulation calculation method for the oxygen mass transfer coefficient in the catalyst layer of a fuel cell, which includes the following steps: A numerical model of a fuel cell based on an aggregate sub-model is established, wherein the aggregate sub-model is used to describe the oxygen mass transfer behavior of the catalyst at different locations in the catalyst layer of the fuel cell. Based on the numerical model, an iterative algorithm based on the polarization curve of a fuel cell is constructed. The iterative algorithm is implemented by a numerical calculation program and is used to set input parameters, including the oxygen mass transfer coefficient, to the numerical model, drive the numerical model to solve, and obtain simulation results. The oxygen mass transfer coefficient in the catalyst layer is set as the initial value within a preset range. The corresponding simulated polarization curve is calculated based on the iterative algorithm. The simulated polarization curve is compared with the experimental polarization curve obtained in advance to obtain the error value. Based on the obtained error value, as a convergence criterion, the value of the oxygen mass transfer coefficient is updated through the iterative algorithm until the error value meets the preset threshold, and the corresponding oxygen mass transfer coefficient is output.

[0008] In one embodiment of this application, in the agglomerate sub-model, the distribution of catalyst active metal particles in the carbon support is divided into a first type of active site and a second type of active site; The local mass transfer resistance of oxygen is composed of the first mass transfer resistance corresponding to the mass transfer of oxygen to the first type of active site and the second mass transfer resistance corresponding to the mass transfer to the second type of active site, which are connected in parallel.

[0009] In one embodiment of this application, in the aggregate sub-model, the total oxygen mass transfer resistance is a function of the oxygen mass transfer coefficient; The total oxygen mass transfer resistance is obtained by weighting the first mass transfer resistance and the second mass transfer resistance in parallel according to the mass fraction of the active metal particles in the catalyst.

[0010] In one embodiment of this application, the first mass transfer resistance includes: the diffusion resistance of oxygen in liquid water within the catalytic hierarchical pores and the mass transfer resistance of oxygen through an ionomer film covering the surface of the catalyst composed of active metal particles and carbon support particles.

[0011] In one embodiment of this application, the second mass transfer resistance includes: the diffusion resistance of oxygen in liquid water in the catalytic hierarchical pores, the mass transfer resistance of oxygen through the ionomer film covering the catalyst surface, the diffusion resistance of oxygen through the primary pore openings, and the one-dimensional diffusion resistance of oxygen in the primary pores.

[0012] In one embodiment of this application, the iterative calculation uses a bisection method to update the oxygen mass transfer coefficient.

[0013] In one embodiment of this application, establishing the fuel cell numerical model based on the aggregate sub-model further includes: Establish a geometric model of the fuel cell and divide each geometric region into meshes; A sub-model of the aggregates was established, and the current density was calculated based on the electrochemical kinetic equations. Configure simulation parameters, add and couple multiple physical field interfaces, and set the corresponding physical property parameters, transmission coefficients, source terms and boundary conditions in each physical field interface; Set the research content and solver to solve the coupled multiphysics model.

[0014] In one embodiment of this application, the error value is the average relative error between the current density corresponding to the simulated polarization curve and the current density corresponding to the experimental polarization curve within a preset voltage range.

[0015] To address the aforementioned technical problems, this application also proposes a simulation calculation device for the oxygen mass transfer coefficient in a fuel cell catalyst layer, comprising: a memory for storing instructions executable by a processor; and a processor for executing the instructions to implement the simulation calculation method for the oxygen mass transfer coefficient in the fuel cell catalyst layer as described above.

[0016] To address the aforementioned technical problems, this application also proposes a computer-readable medium storing computer program code, which, when executed by a processor, implements the above-described method for simulating the oxygen mass transfer coefficient in the fuel cell catalyst layer.

[0017] This application provides a simulation method for calculating the oxygen mass transfer coefficient in the catalyst layer of a fuel cell. By constructing a catalyst layer agglomerate sub-model based on the simulation software COMSOL, a numerical model of a proton exchange membrane fuel cell (PEMFC) is established, achieving efficient and accurate calculation of the oxygen mass transfer coefficient within the ionomer. Furthermore, the agglomerate sub-model overcomes the limitations of describing resistance at a single location, fully considering the differences in oxygen mass transfer resistance at different microscopic locations of active Pt particles. This allows for a more comprehensive and realistic reflection of the spatial distribution characteristics of local oxygen mass transfer resistance, significantly improving the reliability and practicality of the simulation results. Attached Figure Description

[0018] To make the above-mentioned objectives, features and advantages of this application more apparent and understandable, the specific embodiments of this application will be described in detail below with reference to the accompanying drawings, wherein: Figure 1 The diagram illustrates the steps of a method for simulating the oxygen mass transfer coefficient in the catalyst layer of a fuel cell according to an embodiment of this application. Figure 2 This application discloses a schematic diagram of the physical model of the carbon carrier particles and the local oxygen mass transfer resistance used in the agglomerate sub-model of an embodiment of the present application; Figure 3 A flowchart illustrating a method for calculating the oxygen mass transfer coefficient in the catalyst layer of a fuel cell according to an embodiment of this application is provided. Figure 4 A comparison diagram of experimental data and simulation results of the PEMFC polarization curve of a 40wt% Pt / VC catalyst layer according to an embodiment of this application is shown. Figure 5 A comparison chart showing the experimental data and simulation results of the PEMFC polarization curve of a 40wt%Pt / KB catalyst layer according to another embodiment of this application is presented. Figure 6 A system block diagram of a simulation calculation system for the oxygen mass transfer coefficient in the catalyst layer of a fuel cell according to an embodiment of this application is disclosed. Detailed Implementation

[0019] To make the above-mentioned objectives, features and advantages of this application more apparent and understandable, the specific embodiments of this application will be described in detail below with reference to the accompanying drawings.

[0020] Many specific details are set forth in the following description in order to provide a full understanding of this application. However, this application may also be implemented in other ways different from those described herein, and therefore this application is not limited to the specific embodiments disclosed below.

[0021] As illustrated in this application, unless the context clearly indicates otherwise, the words "a," "an," "an," and / or "the" do not specifically refer to the singular and may also include the plural. Generally speaking, the terms "comprising" and "including" only indicate the inclusion of explicitly identified steps and elements, which do not constitute an exclusive list, and the method or apparatus may also include other steps or elements.

[0022] Flowcharts are used in this application to illustrate the operations performed by the system according to embodiments of this application. It should be understood that the preceding or following operations are not necessarily performed in exact order. Instead, various steps can be processed in reverse order or simultaneously. Furthermore, other operations may be added to these processes, or one or more steps may be removed from these processes.

[0023] Figure 1A step-by-step diagram illustrating a method for simulating the oxygen mass transfer coefficient in a fuel cell catalyst layer according to an embodiment of this application is provided. (Refer to...) Figure 1 As shown, the simulation calculation method for the oxygen mass transfer coefficient in the fuel cell catalyst layer of this embodiment includes the following steps: Step S110: Establish a numerical model of the fuel cell based on the agglomerate sub-model, which is used to describe the oxygen mass transfer behavior of the catalyst at different locations in the fuel cell catalyst layer.

[0024] Step S120: Based on the numerical model, construct an iterative algorithm based on the polarization curve of the fuel cell. The iterative algorithm is implemented by a numerical calculation program and is used to set input parameters, including the oxygen mass transfer coefficient, to the numerical model, drive the numerical model to solve, and obtain simulation results.

[0025] Step S130: Set the oxygen mass transfer coefficient in the catalyst layer as an initial value within a preset range, calculate the corresponding simulated polarization curve through iterative algorithm, and compare the simulated polarization curve with the pre-obtained experimental polarization curve to obtain the error value.

[0026] Step S140: Based on the obtained error value as a convergence criterion, update the value of the oxygen mass transfer coefficient through the iterative algorithm until the error value meets the preset threshold, and output the corresponding oxygen mass transfer coefficient.

[0027] This application provides a simulation calculation method for the oxygen mass transfer coefficient in the catalyst layer of a fuel cell. The method involves establishing a numerical model of a proton exchange membrane fuel cell (PEMFC) based on an aggregate sub-model in simulation software, combining experimental polarization curve data of a specific catalyst layer membrane electrode, and using an iterative algorithm (such as the bisection method) to automatically adjust and input the assumed oxygen mass transfer coefficient for simulation calculation. By continuously comparing the simulation and experimental curves until the error is minimized, the oxygen mass transfer coefficient closest to the actual value is finally determined.

[0028] The method provided in this application has the advantages of being fast, accurate, and low-cost, and can be precisely adapted to catalyst layers with different structures, providing key quantitative analysis tools and theoretical guidance for the design and optimization of catalyst layers in low-platinum, high-performance fuel cells.

[0029] The following details steps S110 to S140 described above: In step S110, a numerical model of the fuel cell based on the agglomerate sub-model is established. The agglomerate sub-model is used to describe the oxygen mass transfer behavior of the catalyst at different locations in the fuel cell catalyst layer.

[0030] A proton exchange membrane fuel cell (PEMFC) is an energy device that generates electricity through the electrochemical reaction of hydrogen and oxygen. It uses a proton exchange membrane as the electrolyte core and is also known as a polymer electrolyte fuel cell (PEFC) or a solid polymer fuel cell (SPFC).

[0031] A numerical model of a proton exchange membrane fuel cell based on an aggregate submodel was established in COMSOL software. COMSOL software is a multiphysics simulation software that uses numerical simulation to simulate the design, equipment and processes in various fields such as engineering, manufacturing and scientific research.

[0032] In some embodiments, establishing a fuel cell numerical model based on an aggregate sub-model further includes: Establish a geometric model of the fuel cell and divide each geometric region into a mesh.

[0033] A geometric model of the fuel cell was built in COMSOL software, and meshing was performed. Specifically, in COMSOL software, geometric components were selected, and seven rectangular regions were created sequentially to represent the anode flow channel (aCH), anode diffusion layer (aGDL), anode catalyst layer (aCL), proton exchange membrane, cathode catalyst layer (cCL), cathode diffusion layer (cGDL), and cathode flow channel (cCH). The length dimension of each rectangular region represents the flow channel length, and the width dimension represents the actual thickness of the corresponding component. Subsequently, the length and width directions of each region were divided into elements, and a mapping method was used to generate a computational mesh to complete the geometric discretization of the model.

[0034] An aggregate sub-model was established, and the current density was calculated based on the electrochemical kinetic equations.

[0035] An agglomerate sub-model was established to describe the microstructure of the catalyst layer and the current density was calculated based on the electrochemical kinetic equations.

[0036] The agglomerate submodel is a physical model used to characterize the complex mass transfer processes at the three-phase reaction interface (gas-electrolyte (e.g., ionomer)-catalyst) within a catalyst layer. In this model, the catalyst layer is considered to be composed of many "agglomerates," each consisting of carbon support particles, catalyst active metal particles (e.g., platinum) supported on them, an ionomer film encapsulating the carbon particles, and primary pores and secondary pores between the agglomerates.

[0037] Figure 2 This application discloses a schematic diagram of the physical model of the carbon carrier particles used in the agglomerate sub-model of an embodiment of the present application and a schematic diagram of the local oxygen mass transfer resistance, as shown in the figure. Figure 2As shown in the agglomerate sub-model, the agglomerates in the fuel cell catalyst layer are described as being composed of the following key parts: carbon support particles (black part), catalyst active metal (Pt) particles (orange part), ionomers (gray ring part), and internal pore spaces (white part).

[0038] like Figure 2 As shown, (mol / cm) 3 () represents the oxygen concentration within the secondary pores. (mol / cm) 3 ( ) represents the oxygen concentration in the ionomer membrane. This represents the concentration of oxygen at the Pt particles on the outer surface of the carbon particles. (mol / cm) 3 The concentration of Pt particles in the primary pores inside the carbon particles is denoted as . (m) represents the effective diffusion length of oxygen in the primary pore. The thickness of the liquid water film. (m) is the thickness of the ionomer film. r carbon (m) is the radius of the carbon particle, where, (m) represents carbon r carbon Half of (m).

[0039] Specifically, the catalyst active metal / carbon support particles form aggregates, and secondary pores are formed between the aggregates. These secondary pores are occupied by the electrolyte or a mixture of reactants and liquid-phase products. Within the aggregates, there are primary pores, which are occupied by a mixture of reactants and gaseous products.

[0040] The mass transfer process of oxygen molecules (represented by red dots in the figure) is as follows: It first diffuses through the secondary pores between aggregates, then dissolves and enters the aqueous phase (blue area) and ionomer electrolyte surrounding the aggregates. Next, it diffuses to the outer surface of the aggregates, and finally reaches the catalyst active reaction sites on the outer surface of the carbon support particles and inside the primary pores. Therefore, the aggregate sub-model can effectively describe and quantify the mass transfer resistance experienced by the reactant gas from the gas phase to the three-phase reaction interface within the catalyst layer.

[0041] In some embodiments, in the aggregate sub-model, the distribution of catalyst active metal particles in the carbon support is divided into a first type of active site and a second type of active site. The local mass transfer resistance of oxygen is composed of the first mass transfer resistance corresponding to the mass transfer of oxygen to the first type of active site and the second mass transfer resistance corresponding to the mass transfer to the second type of active site, which are connected in parallel.

[0042] In this embodiment, in the agglomerate sub-model, the distribution of active Pt particles in the carbon support includes distribution at first-type and second-type active sites. The first-type active sites are where Pt particles are distributed on the outer surface of the carbon support particles, and the second-type active sites are where Pt particles are distributed inside the primary pores of the carbon support particles. The local oxygen mass transfer resistance is formed by the parallel combination of the mass transfer resistances corresponding to oxygen diffusion to the first-type and second-type active sites. The total oxygen mass transfer resistance is obtained by weighting the local oxygen mass transfer resistances according to the catalyst mass fraction in parallel.

[0043] In one embodiment, in the aggregate sub-model, the total oxygen mass transfer resistance R total It is a function of the oxygen mass transfer coefficient; The total oxygen mass transfer resistance The first mass transfer resistance With the second mass transfer resistance The results were obtained by weighting and paralleling the active metal particles of the catalyst according to their mass fraction.

[0044] In this embodiment, the oxygen mass transfer coefficient is a key parameter for measuring the magnitude of local oxygen mass transfer resistance; the larger the oxygen mass transfer coefficient, the smaller the local oxygen mass transfer resistance. Total oxygen mass transfer resistance oxygen mass transfer coefficient The function of total oxygen mass transfer resistance Calculate using the following formula: , f This can be defined as the mass fraction of Pt inside the primary pores of carbon support particles relative to the total Pt.

[0045] In one embodiment, the first mass transfer resistance (oxygen mass transfer resistance on the outer surface of the carbon support particles) may include: the diffusion resistance of oxygen in liquid water within the catalytic hierarchical pores and the mass transfer resistance generated by oxygen through an ionomer film covering the catalyst surface composed of active metal particles and carbon support particles. The properties of the ionomer film and its distribution morphology on the catalyst particles are decisive factors affecting the magnitude of the oxygen mass transfer coefficient.

[0046] The mass transfer resistance generated by the ionomer membrane includes the resistance to the dissolution and diffusion of oxygen in the ionomer; The diffusion resistance is related to the oxygen mass transfer coefficient.

[0047] In this embodiment, the diffusion resistance of oxygen in liquid water within the catalytic hierarchical pores ( (s / m), can be represented by Fick diffusion: ;in, The thickness of the liquid water film. This represents the oxygen diffusivity in water.

[0048] Assume the equivalent water film thickness is: Where s is the saturation of liquid water, The porosity of the cathode catalyst layer (cCL) and (m) -1 ( ) represents the specific surface area of ​​the ionomer film.

[0049] The mass transfer resistance caused by oxygen passing through the ionomer film covering the catalyst surface ( sm -1 ), including dissolution resistance and diffusion resistance : ; , ; in, (m) is the thickness of the ionomer film. (m) 2 / s) is the oxygen mass transfer coefficient in the ionomer. (m) -1 ) represents the specific surface area of ​​Pt particles. The value is 0.15, and the dissolution resistance is usually coupled with the diffusion resistance through a coefficient.

[0050] Therefore, the first mass transfer resistance (The oxygen mass transfer resistance on the outer surface of the carbon support) is: .

[0051] In one embodiment, the second mass transfer resistance (oxygen mass transfer resistance within the carbon support) may include: the diffusion resistance of oxygen in liquid water within the catalytic hierarchical pores ( The mass transfer resistance generated by oxygen through the ionomer film covering the catalyst surface ( The diffusion resistance of oxygen through the primary pore opening ( ) and the one-dimensional diffusion resistance of oxygen in the primary pores ( ).

[0052] In this embodiment, the diffusion resistance of oxygen through the primary pore opening ( This can be equivalent to oxygen passing through an infinitely large ionomer membrane to a point with a radius of... The resistance of the circular primary pore is such that, since the diameter of the primary pore is a few nanometers, the diffusion of oxygen in the primary pore is Knudsen diffusion.

[0053] Diffusion resistance of oxygen through the primary pore opening One-dimensional diffusion resistance of oxygen in primary pores , , ,in, (m) 2 s -1 () is the Knudsen diffusion coefficient. R (J mol) -1 K -1 ) is the universal gas constant. T (K) is the local temperature. M (kg mol) -1 ) is the molar mass of oxygen. Pore radius ( r p (m) is based on the average value of all primary holes being cylindrical. It is the porosity of the catalyst layer. Let be the porosity of the primary pores of the carbon particles, and assume the effective diffusion length ( (m) is the radius of the carbon particle ( r carbon Half of (m).

[0054] Therefore, the local oxygen transport resistance from the secondary pore to the Pt surface inside the primary pore R int for: + .

[0055] Configure simulation parameters, add and couple multiple physical field interfaces, and set the corresponding physical property parameters, transmission coefficients, source terms and boundary conditions in each physical field interface.

[0056] In COMSOL software, variables are added to define parameters such as electrochemical reaction kinetics mechanism and aggregate sub-model.

[0057] The anode current of the fuel cell is calculated using the Butler–Volmer equation: .

[0058] in, The current density at the anode is... The anode reference exchange current density is expressed in A m. -2 ), The specific surface area of ​​the Pt particles at the anode is expressed in m². -1 ), This is the anode temperature correction factor. This represents the actual molar concentration of hydrogen, in units of (mol m). -3 ), This is the reference molar concentration of hydrogen, in units of (mol m). -3F is the Faraday constant (96485, C / mol), and R is the universal gas constant (8.314, J / mol). -1 K -1 T represents thermodynamic temperature, and its unit is (K). This is the anode transfer coefficient. This is the anodic overpotential, measured in V.

[0059] The cathode current of a fuel cell can include an external current generated by Pt distributed outside the carbon particles. and the corresponding internal current of the carbon particles cathode current The calculation is performed using the following formula: = ; = ; ; in, = ; ; ); The specific surface area of ​​the polymer film is expressed in m². -1 ); This is the reference molar concentration of oxygen. This represents the actual concentration of oxygen, expressed in mol / m³. -3 ); It is the Henry's law constant for oxygen in water (mol m). -3 Pa -1 ); As the first mass transfer resistance, The second mass transfer resistance is expressed in (s / m). (J mol) -1 ) represents the energy parameter; E (V) is the cathode potential relative to the reversible hydrogen electrode (RHE); The cathode reference exchange current density; For the coverage dependence correction coefficient of Pt-oxides; This is the Pt-ionomer coverage dependence correction coefficient; The mass ratio of Pt to C; The mass ratio of ionomer to C; f The mass fraction of Pt inside the primary pores of carbon particles relative to the total Pt. This is the cathode transfer coefficient; This is the cathode overpotential, measured in V.

[0060] Adding multiple physics interfaces within the COMSOL component can include adding a secondary current distribution interface, multiple concentrated mass transfer interfaces, multiple free and porous medium flow interfaces, porous medium dilute mass transfer interfaces, and coefficient form partial differential equation interfaces.

[0061] For example, the added multiphysics fields include one "secondary current distribution", two "concentrated mass transport", two "free and porous medium flow (brinkman interface)", one "dilute mass transport in porous medium" and one "coefficient form partial differential equation".

[0062] In the "Secondary Current Distribution" step, the conductivity of the electrolyte is determined, the anode is marked as 0 potential, the conductivity and volume fraction of the anode and cathode electrode materials are determined, the conductivity and volume fraction of the diffusion layer are determined, and the initial potential values ​​of the anode and cathode are set.

[0063] In the "concentrated mass transfer" process, the molar mass of gas molecules is determined; the density of the gas mixture in the flow channel, diffusion layer, and catalyst layer is determined using binary diffusion coefficients; the molar fraction of water in the feed gas is determined, as well as the phase change source terms of water in the catalyst layer and diffusion layer.

[0064] In "Free and Porous Media Flow, Brinkman", determine the dynamic viscosity of the gas; determine the porosity and permeability of the diffusion layer and catalyst layer; and determine the feed rate and pressure.

[0065] In the section on "Dilute Mass Transfer in Porous Media," the selected transfer mechanism is electric field migration and mass transfer within the porous media. The water diffusion coefficient, potential, and mobility within the membrane and catalyst layers are determined, with the volume fraction of ionomers in the catalyst layer requiring additional determination. The source term and initial concentration of membrane water are also determined.

[0066] The distribution of liquid water is calculated using "coefficient form partial differential equations" to determine the diffusion coefficient, damping or mass coefficient, and conserved flux source within the catalytic and diffusion layers.

[0067] Set the research content and solver to solve the coupled multiphysics model.

[0068] Add a study within the COMSOL component. In the study steps, select "Current Distribution Initialization" and "Steady-State Study". In the solver settings, select an appropriate convergence method: in "Steady-State Solver", select "Direct", select PARDISO as the solver, and select "Separate Solver". Select tolerance as the termination technique, and select pseudo-time step in the "Stabilization and Acceleration" column.

[0069] The above steps complete the preparation work within the COSMOL software.

[0070] In step S120, based on the numerical model, an iterative algorithm based on the fuel cell polarization curve is constructed. The iterative algorithm is implemented by a numerical calculation program and is used to set input parameters, including the oxygen mass transfer coefficient, to the numerical model, drive the numerical model to solve, and obtain simulation results.

[0071] Write bisection code in MATLAB and combine it with COMSOL to iteratively calculate the oxygen mass transfer coefficient in the ionomer of the catalyst layer.

[0072] First, obtain the membrane electrode polarization curve data of the fuel cell based on a specific catalyst layer. Then, set the oxygen mass transfer coefficient in the ionomer of the catalyst layer, start iterative calculation and data processing and analysis, and finally find the optimal oxygen mass transfer coefficient in the ionomer within the catalyst layer.

[0073] In step S130, the oxygen mass transfer coefficient in the catalyst layer is set as an initial value within a preset range. The corresponding simulated polarization curve is calculated based on an iterative algorithm. The simulated polarization curve is compared with the pre-obtained experimental polarization curve to obtain the error value.

[0074] In some embodiments, the iterative calculation uses a bisection method to update the oxygen mass transfer coefficient.

[0075] The bisection method refers to a method for obtaining an approximate value of a function y=f(x) that is continuous on the interval [a, b] and f(a)·f(b)<0. This is achieved by repeatedly dividing the interval containing the zero of the function f(x) into two parts, so that the two endpoints of the interval gradually approach the zero. In practice, it is also the golden section method, which divides the whole into two parts, and the ratio of the larger part to the whole is equal to the ratio of the smaller part to the larger part. The ratio is approximately 0.618.

[0076] In this embodiment, the bisection method follows the rule of the golden ratio, by dividing the assumed oxygen mass transfer coefficient range [ D a , D b The range is halved by approximately 0.618, and the simulation error is iterated until the accuracy requirement is met. m Or D n This is the final determined mass transfer coefficient.

[0077] Experimental polarization curves are data obtained experimentally from the polarization curves of fuel cell membrane electrodes based on a specific catalyst layer, i.e., current density to voltage value.

[0078] Create an EXCEL table, input the experimentally measured membrane electrode polarization curve data of the fuel cell based on a specific catalyst layer, as well as the physical property parameters and structural parameters of the specific catalyst layer required for simulation calculations using the PEMFC numerical model based on the above-mentioned agglomerate sub-model.

[0079] In some embodiments, the error is the average relative error between the current density corresponding to the simulated polarization curve and the current density corresponding to the experimental polarization curve within a preset voltage range.

[0080] The preset voltage range is the operating range where the fuel cell output voltage is below 0.65V.

[0081] Figure 3 A flowchart illustrating a method for calculating the oxygen mass transfer coefficient in the catalyst layer of a fuel cell according to an embodiment of this application is shown, as follows: Figure 3 As shown, a PEMFC numerical model based on the aggregate sub-model is established in COMSOL. The assumed oxygen mass transfer coefficient range is entered in the command line window. D a , D b and relative error threshold ε min and defining the average relative error of current density ε avg It reads data from an Excel file and performs simulation calculations of polarization curves.

[0082] Within a preset range (10e-6~10e-12), the oxygen mass transfer coefficient in the catalyst layer ionomer is assumed to be (i.e., m 2 s -1 ) is within a certain interval [ D a , D b Within the [file], combining the parameters in the EXCEL file, the oxygen mass transfer coefficient was simulated and calculated as follows: D a D b The polarization curve values ​​at that time.

[0083] Preferably, since the oxygen mass transfer performance is more significantly affected at high polarization current densities (voltage output below 0.65V), the average relative error between the simulated and experimental values ​​of the current density for the polarization curve at 0.65V is calculated. ).

[0084] like Less than the relative error threshold The calculation ends when the mass transfer coefficient is assumed.D a or D b This is the oxygen mass transfer coefficient in the most likely ionomer within the catalyst layer.

[0085] relative error threshold The relative error threshold is a key index used to evaluate the accuracy of current density simulation results. It is defined as the upper limit that the average relative error between the simulated and actual values ​​must meet. The relative error threshold reflects the required accuracy of the calculation; the smaller the threshold, the higher the requirement for simulation precision, i.e., the higher the required computational accuracy. When the average relative error obtained from the actual calculation is lower than the relative error threshold, it indicates that the simulation results have reached the preset accuracy standard, the calculation task is completed, and the process ends.

[0086] relative error threshold The average relative error between simulated and experimental current density values. The upper limit value to be achieved is used to measure the accuracy of the simulation calculation. The smaller the relative error threshold, the higher the calculation accuracy. When the average relative error is lower than this relative error threshold, the calculation accuracy is considered to have met the requirements, and the calculation can be terminated.

[0087] Optionally, the relative error threshold is used in this application. Set to 2%.

[0088] The average relative error between the simulated and experimental current density values ​​is calculated using the following formula: =[(Experimental current density value - Simulated current density value) / Experimental current density value] / N· 100%, where N is the number of data points.

[0089] In step S140, based on the obtained error value as a convergence criterion, the value of the oxygen mass transfer coefficient is updated through the iterative algorithm until the error value meets the preset threshold, and the corresponding oxygen mass transfer coefficient is output.

[0090] In this step, the preset threshold is the relative error threshold. .

[0091] like > Then proceed to iterative calculation: comparison ,like < By using a dichotomy method, the oxygen mass transfer coefficient range is narrowed down to [ D a , D n The new polarization curve values ​​were obtained through resimulation, and the results were derived. value; like The new interval is then reduced to [ D m , D b The simulation was re-performed, and the results were obtained. value.

[0092] Repeated comparisons and iterations until... Less than the allowable relative error threshold The calculation is complete, and the result is obtained. value.

[0093] Therefore, the currently assumed mass transfer coefficient D m or D n This is the oxygen mass transfer coefficient in the ionomer within the optimal catalyst layer.

[0094] This application provides a simulation method for calculating the oxygen mass transfer coefficient in the catalyst layer of a fuel cell. By constructing a catalyst layer agglomerate sub-model based on the fluid simulation software COMSOL, a PEMFC numerical model is established, enabling rapid and accurate calculation of the oxygen mass transfer coefficient within the ionomer. This method not only avoids the cumbersome operational steps of traditional experimental measurements, significantly saving time and experimental costs, but also fully considers the oxygen mass transfer resistance of active Pt particles at different locations, overcoming the limitations of single resistance models. It can more comprehensively and realistically reflect the actual composition of local oxygen mass transfer resistance, thus significantly improving the reliability and practicality of the simulation results.

[0095] The following specific examples illustrate the simulation calculation method for the oxygen mass transfer coefficient in the fuel cell catalyst layer proposed in this application.

[0096] Example 1, taking a 40wt% Pt / VC catalyst layer as an example (VC is a solid carbon support).

[0097] Step S1: Establish a numerical model of the 40wt%Pt / VC catalyst layer PEMFC based on the aggregate sub-model in COMSOL.

[0098] Step S11: Create the geometric model of PEMFC in COMSOL and create the mesh.

[0099] Using COMSOL software, select geometry within the component and draw seven rectangles sequentially. The height of each rectangle represents the channel length, and the width of each rectangle represents the height / thickness of the anode channel (aCH), anode diffusion layer (aGDL), anode catalyst layer (aCL), membrane, cathode catalyst layer (cCL), cathode diffusion layer (cGDL), and cathode channel (cCH), respectively. Divide the channel length and the width of each component (aCH, aGDL, aCL, membrane, cCL, cGDL, cCH) into elements, and finally select mapping to create the mesh.

[0100] Step S12: Establish the aggregate sub-model, see [link / reference] Figure 2 As shown, the distribution of active Pt particles in the carbon particle support in the agglomerate sub-model is divided into two cases: Pt on the outer surface of the carbon particles and Pt inside the primary pores of the carbon particles. The local oxygen mass transfer resistance is composed of the parallel mass transfer resistance of oxygen diffusion to these two sites.

[0101] The physical properties and structural parameters of a specific catalyst layer required for simulation calculations of the 40wt%Pt / VC catalyst layer PEMFC numerical model of the aggregate sub-model are shown in Table 1.

[0102] Table 1

[0103] Step S13: Add multiphysics within the component, specifically one "secondary current distribution", two "concentrated mass transport", two "free and porous medium flow, brinkman", one "dilute mass transport in porous medium" and one "coefficient form partial differential equation".

[0104] Step S14: Add research content. In the research steps, select "current distribution initialization" and "steady-state study", and select an appropriate convergence method in the solver settings.

[0105] In the "Steady-State Solver" section, select "Direct". The solver can be PARDISO. Also select "Separate Solver". Select tolerance as the termination technique. The number of iterations should be no less than 20. Select the tolerance factor as 10. In the "Stabilization and Acceleration" section, select pseudo-time stepping.

[0106] Step S15: In the secondary current distribution interface, set the conductivity and corresponding volume fraction of the electrolyte, electrode material and diffusion layer, and mark the potential of the anode catalyst layer (CL) and gas diffusion layer (GDL) as 0, set the cathode potential to be the same as the battery voltage, and keep the initial potential values ​​of the anode and cathode consistent with the above input potential.

[0107] In the concentrated mass transfer interface, the molar masses of the gas components involved in the anode (hydrogen, nitrogen) and cathode (oxygen, nitrogen, water vapor) are defined respectively. The diffusion type is selected from the Maxwell-Stefan model, and the effective diffusion coefficient is corrected using the Bruggeman model. The density and binary diffusion coefficient of the gas mixture in the flow channel, diffusion layer and catalyst layer are set, and the molar fraction of water in the inlet gas is given. The phase change source term of water in the catalyst layer and diffusion layer is set according to the mass conservation equation.

[0108] The mass conservation equation is calculated using the following formula: = ; The momentum conservation equation is calculated using the following formula: ( ) ; The gas conservation equation is calculated using the following formula: = ; in, (kg mol) -1 ) represents the density of the gas mixture. u g (ms) -1 ) is the apparent velocity vector. Pg (Pa) represents the pressure of the gas mixture. (kg m) -1 s -1 ( ) represents dynamic viscosity. Where is the porosity and s is the liquid water saturation. Yi For matter " i "quality score" i (Represents hydrogen, oxygen, or water vapor) (m) 2 s -1 () is the effective diffusion coefficient. S m (kg) m -3 s -1 ) represents the quality source item. S u (m) s -1 ) represents the rate source term. S i (kg) m -3 s -1 () is the mass source term for the reaction and phase change of hydrogen, oxygen and gaseous water.

[0109] In the "Free and Porous Media Flow, Brinkman" interface, set the dynamic viscosity of the gas, the porosity and permeability of the diffusion layer and catalyst layer, and specify the gas flow rate and pressure at the feed inlet.

[0110] In the "Dilute Mass Transfer in Porous Media" interface, the transfer mechanism is set as electric field migration and mass transfer within the porous medium. The diffusion coefficient, potential, and mobility of water in the membrane and catalyst layer regions are defined, and the volume fraction of ionomers in the catalyst layer is additionally specified. The source term of the substance and the initial concentration of membrane water are also set. This process follows the following conservation equation: + = + .

[0111] in, n d This is the electroosmotic resistance (EOD) coefficient. I ion (A m) -2 ) represents the ion current density. D mw eff (m) 2 s -1 ) is the effective diffusion coefficient of film water. The porosity of the cathode catalyst layer, The water content of the membrane. im (kg) m -3 ) represents the density of the proton exchange membrane, EW (kg). mol -1 ( ) represents the membrane equivalent mass. The volume fraction of ionomers in the catalyst layer. (mol) m -3 s -1 ) represents the source term of membrane water. is Faraday's constant.

[0112] Since the membrane volume expands with increasing water content, the porosity of the cathode catalyst layer is affected. Revised to: .

[0113] in, The mass ratio of Pt to C. The mass ratio of ionomer to C. The thickness of the catalyst layer is expressed in meters (m). The Pt loading per unit membrane area, expressed in mg. m -2 ), and The molar mass (g) of liquid water are respectively mol -1 ) and density (g m -3 ).

[0114] The distribution of liquid water is calculated using the "coefficient form partial differential equation" interface, defining the diffusion coefficient, damping (or mass) coefficient, and conserved flux source term of liquid water within the catalytic and diffusion layers. The governing equations are: + .

[0115] To characterize the abrupt change in liquid water saturation at the interface inside the porous electrode, the liquid pressure conservation equation is solved here. K (m) 2 )and These represent the intrinsic permeability and relative permeability of liquid water in a porous medium, respectively. The viscosity of liquid water is expressed in Pa·s. It is the vapor pressure of liquid water (Pa). (kg) m -3 s -1 () represents the mass source term of liquid water, and the liquid water saturation. s It can be obtained using the Leverett-J function.

[0116] The Leverett J function is an important tool for describing the relationship between physical properties such as porosity and permeability.

[0117] Step S2: Write bisection code in MATLAB and combine it with COMSOL to iteratively calculate the oxygen mass transfer coefficient in the ionomer of the catalyst layer.

[0118] In MATLAB, iterative code based on the bisection method was written and coupled with COMSOL to achieve efficient calculation of the oxygen mass transfer coefficient within the ionomer of the catalyst layer. The code logic is as follows: Figure 3 As shown.

[0119] The specific process is as follows: First, create an Excel file and enter the polarization curve data (current density-voltage correspondence) of the membrane electrode of a fuel cell with a specific catalyst layer (such as 40wt%Pt / VC) obtained from experiments, as well as all the physical and structural parameters required for PEMFC numerical simulation based on the agglomerate submodel.

[0120] Next, enter the initial assumed interval [D] for the oxygen mass transfer coefficient in the MATLAB command line. a D b [and relative error threshold] The program reads data from Excel, calls the COMSOL model to simulate polarization curves, and calculates the average relative error between the simulated current density and the experimental value at 0.65V. The mass transfer coefficient estimate is updated using the bisection method, the simulation is re-enhanced and the error is recalculated, and this process is iterated until the latest estimate is reached. Smaller than the set value The corresponding mass transfer coefficient at this time This is the desired result.

[0121] Step S3: Set the iterative range of the mass transfer coefficient of oxygen in the ionomer, perform the calculation and analyze the results.

[0122] Step 31: Assume the oxygen mass transfer coefficient range is... 10e-6m 2 / s, 10e-12m 2 The simulated polarization curves are obtained by solving the established aggregate sub-model in COMSOL.

[0123] Step 32: Create an Excel spreadsheet and input the experimental polarization curve data corresponding to the 40wt%Pt / VC catalyst layer (see Table 1) as well as the physical and structural parameters required for the model simulation (see Table 1).

[0124] Step 33: Run the MATLAB program, input the initial intervals a=10e-6m² / s and b=10e-12m² / s, and calculate the corresponding average relative errors as follows: =10.7%, ==8.98%; because Greater than Based on the bisection method, the update intervals are a = 3.82e-10m² / s and b = 10e-12m² / s, and the calculated errors are as follows: =6.77%, =8.98%.

[0125] As shown in Table 2, after 21 iterations of comparison, the mass transfer coefficient of oxygen in the ionomer was finally obtained. =4.90e-10m² / s, at which point the average relative error is 0.8% (below the set threshold of 2%), and the iteration terminates.

[0126] Table 2. Polarization curves of the electrode of 40wt% Pt / VC catalyst film

[0127] Figure 4 A comparison chart showing the experimental data and simulation results of the PEMFC polarization curve of a 40wt% Pt / VC catalyst layer according to an embodiment of this application is provided, as follows: Figure 4 As shown, the effectiveness of the fuel cell numerical model based on the aggregate sub-model is verified, indicating that the simulation results are in good agreement with the experimental data, thus confirming the reliability of the method of iteratively obtaining the mass transfer coefficient by coupling COMSOL and MATLAB.

[0128] Example 2, taking a 40wt% Pt / KB catalyst layer as an example (KB is a porous carbon support).

[0129] Step 1: Establish a PEMFC numerical model based on the aggregate sub-model in COMSOL software.

[0130] Step 2: Write bisection iterative code in MATLAB and couple it with COMSOL to calculate the oxygen mass transfer coefficient in the ionomer of the catalyst layer.

[0131] Step 3: Obtain the polarization curve experimental data of the 40wt%Pt / KB catalytic membrane electrode, set the initial range of the oxygen mass transfer coefficient, and perform iterative calculations and data analysis.

[0132] The main difference between Example 2 and Example 1 is that the catalyst support material used and its corresponding structural parameters and polarization curve experimental data (Table 3) are different.

[0133] As shown in Table 3, after 23 iterations, the mass transfer coefficient of oxygen in the ionomer within the catalyst layer was finally obtained. The speed is 6.12e-10m² / s, and the average relative error is 1.8%, which is less than 2%, meeting the requirements. The iteration ends.

[0134] Table 3. Polarization curves of the electrode of 40wt%Pt / KB catalyst film

[0135] Figure 5 A comparison chart of experimental data and simulation results of the PEMFC polarization curve of a 40wt% Pt / KB catalyst layer according to another embodiment of this application is shown, as follows: Figure 5 As shown, the effectiveness of the fuel cell numerical model based on the aggregate sub-model is verified, indicating that the simulation results are in good agreement with the experimental data, thus confirming the reliability of the method of iteratively obtaining the mass transfer coefficient by coupling COMSOL and MATLAB.

[0136] In the two embodiments described above, 40wt% Pt catalysts were used with VC (solid carbon) and KB (porous carbon) as supports, respectively. Because the KB support has more pores and a larger specific surface area, the catalytic layer formed by it differs significantly in pore structure from the VC-based catalytic layer, resulting in different oxygen mass transfer performance and consequently affecting the battery voltage output performance at high current densities.

[0137] This application also includes a simulation calculation device for the oxygen mass transfer coefficient in a fuel cell catalyst layer, comprising a memory and a processor. The memory stores instructions executable by the processor; the processor executes these instructions to implement the aforementioned simulation calculation method for the oxygen mass transfer coefficient in a fuel cell catalyst layer.

[0138] Figure 6 A system block diagram of a simulation calculation system for the oxygen mass transfer coefficient in the catalyst layer of a fuel cell according to an embodiment of this application is shown. (Reference) Figure 6 As shown, the simulation calculation system 600 for the oxygen mass transfer coefficient in the fuel cell catalyst layer may include an internal communication bus 601, a processor 602, a read-only memory (ROM) 603, a random access memory (RAM) 604, and a communication port 605. When applied to a personal computer, the simulation calculation system 600 may also include a hard disk 606. The internal communication bus 601 enables data communication between the components of the simulation calculation system 600. The processor 602 can perform judgments and issue prompts. In some embodiments, the processor 602 may consist of one or more processors. The communication port 605 enables data communication between the simulation calculation system 600 and external sources. In some embodiments, the simulation calculation system 600 can send and receive information and data from a network via the communication port 605. The simulation calculation system 600 for the oxygen mass transfer coefficient in the fuel cell catalyst layer may also include different types of program storage units and data storage units, such as a hard disk 606, a read-only memory (ROM) 603, and a random access memory (RAM) 604, capable of storing various data files used for computer processing and / or communication, as well as possible program instructions executed by the processor 602. The processor executes these instructions to implement the main part of the method. The results of the processor processing are transmitted to the user equipment via a communication port and displayed on the user interface.

[0139] The above-mentioned simulation calculation method for the oxygen mass transfer coefficient in the fuel cell catalyst layer can be implemented as a computer program, stored in the hard disk 606, and loaded into the processor 602 for execution, so as to implement the simulation calculation method for the oxygen mass transfer coefficient in the fuel cell catalyst layer of this application.

[0140] This application also includes a computer-readable medium storing computer program code that, when executed by a processor, implements the aforementioned method for simulating the oxygen mass transfer coefficient in the fuel cell catalyst layer.

[0141] When the method for simulating the oxygen mass transfer coefficient in the fuel cell catalyst layer is implemented as a computer program, it can also be stored as an article of manufacture in a computer-readable storage medium. For example, computer-readable storage media can include, but are not limited to, magnetic storage devices (e.g., hard disks, floppy disks, magnetic stripes), optical discs (e.g., compact discs (CDs), digital multifunction discs (DVDs)), smart cards, and flash memory devices (e.g., electrically erasable programmable read-only memory (EPROM), cards, sticks, key drives). Furthermore, the various storage media described herein can represent one or more devices and / or other machine-readable media for storing information. The term "machine-readable medium" can include, but is not limited to, wireless channels and various other media (and / or storage media) capable of storing, containing, and / or carrying code and / or instructions and / or data.

[0142] It should be understood that the embodiments described above are merely illustrative. The embodiments described herein may be implemented in hardware, software, firmware, middleware, microcode, or any combination thereof. For hardware implementation, the processor may be implemented within one or more application-specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field-programmable gate arrays (FPGAs), processors, controllers, microcontrollers, microprocessors, and / or other electronic units designed to perform the functions described herein, or combinations thereof.

[0143] Some aspects of this application can be executed entirely by hardware, entirely by software (including firmware, resident software, microcode, etc.), or by a combination of hardware and software. The aforementioned hardware or software may be referred to as a "data block," "module," "engine," "unit," "component," or "system." The processor may be one or more application-specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DAPDs), programmable logic devices (PLDs), field-programmable gate arrays (FPGAs), processors, controllers, microcontrollers, microprocessors, or combinations thereof. Furthermore, aspects of this application may manifest as computer products residing in one or more computer-readable media, including computer-readable program code. For example, computer-readable media may include, but are not limited to, magnetic storage devices (e.g., hard disks, floppy disks, magnetic tapes, etc.), optical discs (e.g., compressed CDs, digital multifunction DVDs, etc.), smart cards, and flash memory devices (e.g., cards, sticks, key drives, etc.).

[0144] A computer-readable medium may contain a propagated data signal containing computer program code, for example, on baseband or as part of a carrier wave. This propagated signal may take various forms, including electromagnetic, optical, and so on, or suitable combinations thereof. A computer-readable medium can be any computer-readable medium other than a computer-readable storage medium, which can be connected to an instruction execution system, apparatus, or device to enable communication, propagation, or transmission of a program for use. The program code located on the computer-readable medium can be propagated through any suitable medium, including radio, cable, fiber optic cable, radio frequency signals, or similar media, or any combination of the above media.

[0145] The basic concepts have been described above. Obviously, for those skilled in the art, the above disclosure is merely illustrative and does not constitute a limitation of this application. Although not explicitly stated herein, those skilled in the art may make various modifications, improvements, and corrections to this application. Such modifications, improvements, and corrections are suggested in this application, and therefore remain within the spirit and scope of the exemplary embodiments of this application.

[0146] Furthermore, this application uses specific terms to describe embodiments of the application. For example, "an embodiment," "one embodiment," and / or "some embodiments" refer to a particular feature, structure, or characteristic related to at least one embodiment of the application. Therefore, it should be emphasized and noted that "an embodiment," "one embodiment," or "an alternative embodiment" mentioned twice or more in different locations in this specification do not necessarily refer to the same embodiment. In addition, certain features, structures, or characteristics in one or more embodiments of the application can be appropriately combined.

[0147] In some embodiments, numbers describing the quantity of components and attributes are used. It should be understood that such numbers used to describe embodiments are sometimes modified by the terms "approximately," "approximately," or "generally." Unless otherwise stated, "approximately," "approximately," or "generally" indicates that the numbers are allowed to vary by ±20%. Accordingly, in some embodiments, the numerical parameters used in this application are approximate values, which may be changed depending on the characteristics required by individual embodiments. In some embodiments, numerical parameters should take into account specified significant digits and employ a general method of digit reservation. Although the numerical ranges and parameters used to confirm their breadth of range in some embodiments of this application are approximate values, in specific embodiments, such values ​​are set as precisely as feasible.

Claims

1. A method for simulating and calculating the oxygen mass transfer coefficient in a fuel cell catalyst layer, characterized in that, Includes the following steps: A numerical model of a fuel cell based on an aggregate sub-model is established, wherein the aggregate sub-model is used to describe the oxygen mass transfer behavior of the catalyst at different locations in the catalyst layer of the fuel cell. Based on the numerical model, an iterative algorithm based on the polarization curve of a fuel cell is constructed. The iterative algorithm is implemented by a numerical calculation program and is used to set input parameters, including the oxygen mass transfer coefficient, to the numerical model, drive the numerical model to solve, and obtain simulation results. The oxygen mass transfer coefficient in the catalyst layer is set as the initial value within a preset range. The corresponding simulated polarization curve is calculated based on the iterative algorithm. The simulated polarization curve is compared with the experimental polarization curve obtained in advance to obtain the error value. Based on the obtained error value, as a convergence criterion, the value of the oxygen mass transfer coefficient is updated through the iterative algorithm until the error value meets the preset threshold, and the corresponding oxygen mass transfer coefficient is output.

2. The method for simulating and calculating the oxygen mass transfer coefficient in the fuel cell catalyst layer according to claim 1, characterized in that, In the agglomeration sub-model, the distribution of catalyst active metal particles in the carbon support is divided into a first type of active site and a second type of active site. The local mass transfer resistance of oxygen is composed of the first mass transfer resistance corresponding to the mass transfer of oxygen to the first type of active site and the second mass transfer resistance corresponding to the mass transfer to the second type of active site, which are connected in parallel.

3. The method for simulating and calculating the oxygen mass transfer coefficient in the fuel cell catalyst layer according to claim 2, characterized in that, In the aggregate sub-model, the total oxygen mass transfer resistance is a function of the oxygen mass transfer coefficient; The total oxygen mass transfer resistance is obtained by weighting the first mass transfer resistance and the second mass transfer resistance in parallel according to the mass fraction of the active metal particles in the catalyst.

4. The method for simulating and calculating the oxygen mass transfer coefficient in the fuel cell catalyst layer according to claim 2, characterized in that, The first mass transfer resistance includes: the diffusion resistance of oxygen in liquid water in the catalytic hierarchical pores and the mass transfer resistance of oxygen through the ionomer film covering the catalyst surface composed of active metal particles and carbon support particles.

5. The method for simulating and calculating the oxygen mass transfer coefficient in the catalyst layer of a fuel cell according to claim 2, characterized in that, The second mass transfer resistance includes: the diffusion resistance of oxygen in liquid water in the catalytic hierarchical pores, the mass transfer resistance of oxygen through the ionomer film covering the catalyst surface, the diffusion resistance of oxygen through the primary pore openings, and the one-dimensional diffusion resistance of oxygen in the primary pores.

6. The method for simulating and calculating the oxygen mass transfer coefficient in the catalyst layer of a fuel cell according to claim 1, characterized in that, The iterative calculation uses a bisection method to update the oxygen mass transfer coefficient.

7. The method for simulating and calculating the oxygen mass transfer coefficient in the fuel cell catalyst layer according to claim 1, characterized in that, The establishment of the fuel cell numerical model based on the aggregate sub-model further includes: Establish a geometric model of the fuel cell and divide each geometric region into meshes; A sub-model of the aggregates was established, and the current density was calculated based on the electrochemical kinetic equations. Configure simulation parameters, add and couple multiple physical field interfaces, and set the corresponding physical property parameters, transmission coefficients, source terms and boundary conditions in each physical field interface; Set the research content and solver to solve the coupled multiphysics model.

8. The method for simulating and calculating the oxygen mass transfer coefficient in the catalyst layer of a fuel cell according to claim 1, characterized in that, The error value is the average relative error between the current density corresponding to the simulated polarization curve and the current density corresponding to the experimental polarization curve within a preset voltage range.

9. A device for simulating and calculating the oxygen mass transfer coefficient in a fuel cell catalyst layer, characterized in that, include: Memory is used to store instructions executed by the processor; A processor for executing the instructions to implement the method for simulating the oxygen mass transfer coefficient in the catalyst layer of a fuel cell as described in any one of claims 1-8.

10. A computer-readable medium storing computer program code, characterized in that, The computer program code, when executed by a processor, implements the method for simulating the oxygen mass transfer coefficient in the fuel cell catalyst layer as described in any one of claims 1-8.

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