Collaborative matching optimization method for flow channel and diffusion layer structure of fuel cell based on orthogonal test method

Abstract

The invention discloses a collaborative matching optimization method for a gradient diffusion layer and a flow field structure of a fuel cell based on an orthogonal test method. The collaborative matching optimization method comprises the following steps: determining parameters and optimization targets of collaborative optimization; establishing a CFD analysis model; orthogonal test design is carried out on all the structure parameters, and results are obtained; and analysis is carried out based on the structure parameters after orthogonal test optimization. Through an orthogonal test design mode, the experiment cost is greatly saved, the selected multi-level values can better reflect the influence rule of collaborative matching of the gradient diffusion layer and the flow channel structure on the performance of the fuel cell, and an optimal design scheme is obtained according to an orthogonal test structure.

Description

Technical Field

[0001] The present invention relates to the technical field of proton exchange membrane fuel cells, and particularly to a method for optimizing the collaborative matching of a fuel cell flow channel and a diffusion layer structure based on the orthogonal test method. Background Art

[0002] A proton exchange membrane fuel cell (PEMFC) is a clean energy conversion device that directly converts the chemical energy in hydrogen and oxygen into electrical energy. It has the characteristics of high energy conversion efficiency, extremely low pollution emissions, fast startup speed, high reliability, and strong flexibility, and is considered to be one of the optimal solutions to solve human environmental pollution and energy crisis. There are still some key problems in realizing the commercialization of fuel cells, such as revealing the multi-field coupling transmission mechanism of water, heat, and electricity inside the battery, developing new battery materials to reduce production costs, and carrying out the research and design of membrane electrode and bipolar plate flow field structures, so as to further improve the overall output performance of fuel cells, extend the service life, and reduce costs.

[0003] A proton exchange membrane fuel cell usually consists of a bipolar plate, a gas diffusion layer, a catalytic layer, and a proton exchange membrane. Among them, the bipolar plate and the gas diffusion layer play an important role in transporting gases and liquids, conducting electrons, and supporting the battery. In actual operation, a proton exchange membrane fuel cell needs to operate at a relatively high current density, which can improve its energy conversion efficiency and power density and reduce voltage loss. However, as the current density increases, a large amount of water will be generated. If the water management is not proper, a water imbalance phenomenon will occur inside the battery. Excessive water in the fuel cell will block the gas transmission channels of the flow channel, the diffusion layer, and the catalytic layer, resulting in flooding, hindering gas diffusion, and affecting the performance of the fuel cell. Therefore, the reasonable design of the bipolar plate and the diffusion layer can promote the discharge of liquid water and the diffusion of reactant gases, thereby improving the overall performance of the fuel cell.

[0004] In recent years, most researchers have studied the bipolar plate and the diffusion layer structure separately. Most of them studied the influence of the optimization of the bipolar plate structure on the drainage ability and battery performance, and the influence of the mass transfer performance of the diffusion layer structure on the battery performance. Only a few scholars studied the matching of parameters such as the rib width ratio of the flow channel, the thickness of the diffusion layer, and the porosity. Summary of the Invention

[0005] The purpose of the present invention is to overcome the deficiencies and defects of the prior art, and provide a method for optimizing the collaborative matching of a fuel cell flow channel and a diffusion layer structure based on the orthogonal test method, aiming to study the synergistic effect of the matching of the bipolar plate and the gradient diffusion layer structure parameters on the performance of the fuel cell, so as to optimize the collaborative matching of the fuel cell gradient diffusion layer and the flow channel structure to the greatest extent.

[0006] The present invention is implemented as follows:

[0007] An optimization method for collaborative matching of fuel cell flow channels and diffusion layer structures based on the orthogonal test method, comprising the steps of:

[0008] S1. Determine the structural parameters and optimization objectives for collaborative optimization;

[0009] The structural parameters include the flow channel width, flow channel length, diffusion layer thickness, average porosity, and the degree of gradient of porosity on the diffusion layer; the optimization objectives include the maximum power density of the battery, the uniformity of oxygen distribution and current density distribution in the cathode catalyst layer, and the pressure drop in the flow channel;

[0010] S2. Conduct an orthogonal test design for all structural parameters;

[0011] S3. According to the test scheme of the orthogonal test design, establish a CFD analysis model and obtain the results;

[0012] S4. Analyze based on the structural parameters optimized by the orthogonal test.

[0013] In step S1, the degree of gradient of porosity on the diffusion layer is related to the following distribution function;

[0014] ε GDL =(a - 2b)X + b;

[0015] where ε GDL is the porosity on the diffusion layer, a and b are correction coefficients, X is the normalized length, the value of a corresponds to the level value of the average porosity, and the value of b corresponds to the level value of the degree of gradient of porosity on the diffusion layer.

[0016] In step S3, according to the test scheme of the orthogonal test design, establish a CFD analysis model and obtain the results, including:

[0017] S31. Use modeling software to construct a three-dimensional model of the fuel cell according to the size of the fuel cell model determined by the test scheme;

[0018] S32. Import the constructed three-dimensional fuel cell model into ANSYS workbench for mesh generation;

[0019] S33. Import the fuel cell model with good mesh division into ANSYS fluent, and set the relevant physical parameters and boundary conditions during the actual operation of the fuel cell model;

[0020] S34. Initialize and assign values to the temperature, reactant gas content, and current density. After assigning the initial values, start the simulation calculation. During the simulation calculation, observe the monitoring curves of parameters including the residual, current density, liquid water saturation, and reactant gas to determine whether the calculation converges. After reaching the convergence standard, read the volumetric current density in the cathode catalyst layer, the uniformity of the current density distribution in the cathode catalyst layer, the uniformity of the oxygen concentration distribution, and the pressure drop in the cathode flow channel as the evaluation indicators for the subsequent performance of the fuel cell.

[0021] In step S33, it includes loading the user-defined function UDF on the ANSYS fluent platform and embedding the relevant distribution function of the average pore and gradient level value into the gas diffusion layer component of the fuel cell model in the form of code.

[0022] The modeling of the fuel cell model follows the partial differential conservation equations of mass, momentum, species, liquid water transport, dissolved water transport, electron-proton transport, and energy, and uses the Butler-Volmer equation to handle the electrochemical solution of the current density in the catalyst layer, including the charge transfer and energy conservation equations. At the same time, the Leverett J function and the Wyllie model are applied to describe the liquid water transport in the GDL and CLs.

[0023] The scope of application of the mass conservation equation includes the catalyst layers, gas diffusion layers, and flow channels of the cathode and anode. All processes including flow, diffusion, phase change, and electrochemical reactions in the fuel cell follow the law of mass conservation. The mass conservation equation is as follows:

[0024]

[0025] Among them, represents the Hamiltonian operator, which refers to the vector sum of the partial derivatives of a physical quantity in all directions; ρ g represents the density, kg / m 3 ; represents the velocity vector, m / s; ε represents the porosity, which is the ratio of the pore volume to the total volume. The porosity of the porous media including the catalyst layer and gas diffusion layer is related to the material properties itself, and the default porosity in the flow channel is 1; s is the liquid water saturation, that is, the ratio of the volume of liquid water in the porous media to the total pore volume; S m represents the mass source term, kg / (m 3 ·s), and the mass source term is 0 in the gas diffusion layer and flow channel;

[0026] The scope of application of the momentum conservation equation is the catalyst layers, gas diffusion layers, and flow channels of the cathode and anode. The momentum conservation equation is as follows:

[0027]

[0028] Among them, p g represents the gas pressure, in Pa, which is the solution variable of the equation; μ g represents the dynamic viscosity, in kg / (m·s); S u represents the momentum source term, in kg / (m 3 ·s);

[0029] The scope of the energy conservation equation includes all computational domains, namely the catalyst layer, gas diffusion layer, and flow channel; the energy conservation equation is as follows:

[0030]

[0031] Among them, ρ l and ρ g represent the densities of the liquid phase and gas phase, in kg / m 3 ; T represents the temperature, in K, which is the solution variable of the equation; C p,l represents the specific heat capacity at constant pressure of the liquid phase, in J / (kg·K); C p,g represents the specific heat capacity at constant pressure of the gas phase; u l and u g represent the velocities of the liquid phase and gas phase; k eff represents the effective thermal conductivity, in W / (m·K), and the superscript eff represents the effective value: S T represents the energy source term;

[0032] The diffusion equation of species in the catalyst layer, gas diffusion layer, and flow channel is:

[0033]

[0034] In the formula, X i is the mass fraction of the gas phase, and i represents hydrogen, oxygen, water vapor, and nitrogen in the gas components; D i eff is the effective diffusion coefficient of the gas phase; S i is the diffusion source phase;

[0035] The transport equation of liquid water in the catalyst layer, gas diffusion layer, and flow channel is:

[0036]

[0037] In the formula, k p is the permeability of the porous medium; k kl = s 3 is the relative permeability of liquid water in the porous medium; ρ l is the pressure of liquid water; μ l is the viscosity coefficient of liquid water, D c is the diffusion coefficient of liquid water, S l is the evaporation source term of liquid water.

[0038] The transport equations of dissolved water in the catalyst layer and the membrane are as follows:

[0039]

[0040] λ represents the content of water in the membrane state; is the current density in the membrane; F is the Faraday constant, ρ MEM is the density of the proton exchange membrane; EW is the membrane of equal mass; is the effective diffusion coefficient of water in the membrane state; S d Source term of the water conservation equation in the membrane state;

[0041] The transport equations of electrons in the end plate, catalyst layer, and bulk diffusion layer are as follows:

[0042]

[0043] represents the electron conductivity; φ e represents the electron potential; S e represents the source term of the electron potential;

[0044] The transport equations of protons in the catalyst layer and the membrane are as follows:

[0045]

[0046] represents the proton conductivity; φ ion represents the proton potential; S ion represents the source term of the proton potential.

[0047] In step S4, analysis is carried out based on the structure parameters optimized by the orthogonal experiment, including obtaining the optimal scheme of each performance parameter under different factor level combinations by comparing the experimental results.

[0048] In step S4, analysis is carried out based on the structure parameters optimized by the orthogonal experiment, including performing range analysis on the orthogonal table to obtain the influence law of factor level values on each performance parameter.

[0049] In step S4, analysis is carried out based on the structure parameters optimized by the orthogonal experiment, including performing variance analysis on the orthogonal table to obtain the sensitivity degree of the influence of each factor on the performance parameter.

[0050] The present invention can optimize the cooperative matching of the fuel cell gradient diffusion layer and the flow channel structure to the greatest extent, which plays an important role in improving the overall performance of the final fuel cell. At the same time, this method can also be applied to other scenarios, which has important practical significance for the subsequent research of fuel cell systems. Description of the Drawings

[0051] Figure 1 is a schematic diagram of the fuel cell model with structured grids obtained in the embodiment of the present invention.

[0052] Figure 2 It is a schematic diagram of the porosity distribution of the model obtained in the embodiment of the present invention along the length direction.

[0053] Figure 3 It is the average value of the maximum power density obtained after the calculation of the test scheme in the embodiment of the present invention converges.

[0054] Figure 4 It is the average value of the standard deviation of the current density obtained after the calculation of the test scheme in the embodiment of the present invention converges.

[0055] Figure 5 It is the average value of the standard deviation of the oxygen distribution obtained after the calculation of the test scheme in the embodiment of the present invention converges.

[0056] Figure 6 It is the average value of the pressure drop obtained after the calculation of the test scheme in the embodiment of the present invention converges. Detailed implementation manners

[0057] The present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0058] A method for collaborative matching optimization of a fuel cell flow channel and diffusion layer structure based on the orthogonal test method in an embodiment of the present invention includes the steps of:

[0059] S1. Determine the structural parameters and optimization objectives for collaborative optimization;

[0060] S11: Take five variables, namely the flow channel width, flow channel length, diffusion layer thickness, average porosity, and the degree of gradient of the porosity on the diffusion layer, as the optimization factors. The selected variables are the key factors affecting the performance of the fuel cell in the flow channel and diffusion layer respectively. Combining with the subsequent result analysis, it can comprehensively reflect the optimization design of the gradient diffusion layer and flow channel and the improvement of the fuel cell performance.

[0061] Specifically, a conventional parallel flow field is selected for illustration in the example. To reduce the number of grids and improve the calculation efficiency, according to the symmetry of the parallel flow field, the model is calculated in the form of a half flow channel and half rib. The fuel cell model consists of a flow channel (CH), an end plate (BP), a gas diffusion layer (GDL), a catalyst layer (CL), and a proton exchange membrane (MEM). Except for the proton exchange membrane, other components are divided into two parts, the anode and the cathode. The fuel cell model is as Figure 1 shown. The degree of gradient of the porosity on the diffusion layer is related to the following distribution function.

[0062] ε GDL =(a - 2b)X + b

[0063] where ε GDLis the porosity on the diffusion layer, a and b are correction factors, and X is the normalized length.

[0064] By constructing a mathematical formula, the distribution formula of the average porosity with respect to the length change is obtained, realizing the gradient distribution of the pores in the diffusion layer. Here, to ensure that the formula can adapt to fuel cell models of different lengths, the length is normalized, that is, the length at the inlet of the flow channel is 0 and the length at the outlet is 1. The gradient level value is different from the other four factors. The level values of the other factors are fixed values, while the gradient represents the degree of change. The value of a corresponds to the level value of the average porosity, and the value of b corresponds to the level value of the gradient. The selection of the level value should ensure that it is within the reasonable value range of the factor.

[0065] The specific operations are as follows:

[0066] To ensure that the average porosity is 0.4, in the gradient distribution formula in S1, the value of a is 0.8, and b are 0.2, 0.3, 0.35, 0.5, 0.6 respectively. The values of b correspond to the gradient levels 1 - 5 respectively, and there are five linear distribution cases of the porosity in the length direction. It should be noted that when the average porosity is 0.5, a is 1, and b are 0.3, 0.4, 0.45, 0.6, 0.7 respectively. When the average porosity is 0.6, 0.7, 0.8, a are 1.2, 1.4, 1.65 respectively, and b are 0.4 - 0.8, 0.5 - 0.9, 0.6 - 1.0 respectively. This section should determine the selected factors and their level ranges. The different factors and their level values are shown in the table:

[0067]

[0068] The selection of the level value should ensure that it is within the reasonable value range of the factor. When selecting factors, the variables in the diffusion layer and the flow channel structure that have a greater impact on the results are selected, and the collaborative matching of the gradient diffusion layer structure and the flow channel structure is considered more comprehensively.

[0069] S12: In the research on the matching of the gradient diffusion layer and the flow field structure, reasonable optimization objectives should be selected to maximize the effect of collaborative matching. Here, the maximum power density of the battery, the uniformity of the oxygen distribution and the current density distribution in the cathode catalyst layer, and the pressure drop in the flow channel are selected as the optimization objectives.

[0070] S2. Conduct an orthogonal experiment design for all structural parameters;

[0071] In S1, the number of factors and levels related to the gradient diffusion layer and flow channel structure are determined, and based on this, an orthogonal experiment analysis is carried out to establish an orthogonal table. Using Minitab software, select the Taguchi design in the experimental design section. To improve the accuracy of the orthogonal experiment, add a blank column as the error column. Therefore, when designing the parameters, set it as an experimental design with six factors and five levels, and select the matching orthogonal table as the subsequent simulation scheme table. The number of groups of experiments selected in this table is 25 groups, as shown in the following table:

[0072]

[0073] It should be noted that if no orthogonal experiment design is carried out and all combination cases are listed, there are 3125 cases in total, and it is unreasonable to conduct experimental analysis one by one.

[0074] S3. According to the experimental scheme of the orthogonal experiment design, establish a CFD analysis model and obtain the results;

[0075] S31: Three-dimensional model construction;

[0076] According to the experimental scheme provided by the orthogonal table in the second step, the size of the model is determined. Use the modeling software SolidWorks to construct the model according to the size, select the plane consistent with the subsequent program direction to construct a sketch, and perform stretching on the basis of the sketch. Repeat the operations of constructing the sketch and stretching to complete the construction of each component of the model.

[0077] The specific operation is as follows:

[0078] Select Scheme 1 for example. In the modeling software SolidWorks, create a new part, select the ZY plane to create a rectangular plane, with a length of 0.3 mm in the Z direction and a length of 60 mm in the Y direction. Then, select the boss to stretch 0.6 mm along the X direction. At this time, the construction of the anode end plate in the model is completed; perform the same operation, using the anode end plate as a reference, draw the anode flow channel on its left side. Then, on the basis of the flow channel and the end plate, stretch out the anode diffusion layer, anode catalyst layer, and proton exchange membrane components. Mirror the cathode components to complete the construction of the three-dimensional fuel cell model.

[0079] S32: Model mesh generation;

[0080] Import the model entity established in S31 into the commercial software ANSYS workbench for mesh generation. First, perform pre-processing operations such as splitting the model to ensure the quality of subsequent mesh generation; second, since the battery model is a regular cube, use the sweep method to complete the global structured mesh generation; finally, since the complex electrochemical reactions in the fuel cell are concentrated in the membrane electrode, the key components of the fuel cell: the membrane electrode (diffusion layer, catalyst layer, and proton exchange membrane) are respectively encrypted in mesh. The specific operation is as follows:

[0081] First, select the common surface of the flow channel and the end plate, and choose the splitting tool to divide the whole model into two parts, the left and the right. This is to avoid the situation where the mesh is deformed and the quality deteriorates due to software recognition problems when encrypting the mesh later. Secondly, select the whole model and choose the swept mesh generation method, and set the global size to 0.1; finally, insert the size mesh generation method for the cathode and anode catalyst layers, the cathode and anode diffusion layers, and the proton exchange membrane respectively, and encrypt them to five layers of mesh along the X direction respectively.

[0082] It should be noted that in this section, by adjusting the global size and the encryption degree, it is necessary to ensure that the average quality of the mesh is above 0.6 and the number of mesh nodes of each component is at least 3.

[0083] S33: Set boundary conditions;

[0084] Import the fuel cell model with the mesh divided in S32 into ANSYS fluent, and set the relevant physical parameters and boundary conditions when the fuel cell model actually works. It should be noted that on the fluent platform, load the user-defined function (UDF), and embed the pore gradient function in S1 into the gas diffusion layer component of the model in the form of code. So far, the preparation of the fuel cell model is completed. The specific operations are as follows:

[0085] In fluent, set the species diffusion module, turn on the energy term, change the fluid flow mode to laminar flow, set the physical property parameters such as the porosity of each component, set the inlet as the mass flow inlet, set the outlet as the pressure outlet, and set the operating voltage for the cathode and anode end plates. Among them, the porosity on the gas diffusion layer is loaded with the gradient-related C language program to the diffusion layer of the model through the user-defined module. In the formula, the value of a is taken as 0.8 and the value of b is taken as 0.2.

[0086] S34: After completing the settings of the fuel cell model in S33, initialize and assign values to the temperature, the content of the reactant gas, and the current density. After assigning the initial values, start the simulation calculation. During the process, observe the monitoring curves of parameters such as the residual, the current density, the liquid water saturation, and the reactant gas, and determine whether the calculation converges. After reaching the convergence standard, read the volume current density in the cathode catalyst layer, the uniformity of the current density distribution in the cathode catalyst layer, the uniformity of the oxygen concentration distribution, and the pressure drop in the cathode flow channel as the evaluation indicators for the subsequent performance of the fuel cell. The simulation results after the calculation converges for all test schemes are shown in the table:

[0087]

[0088]

[0089] S4. Analyze based on the structural parameters optimized by the orthogonal experiment;

[0090] In S3, the simulated values of each performance parameter are obtained and organized into an orthogonal array. First, it can be obtained by comparing the test results that the optimal solutions of each performance parameter under different factor level combinations can be obtained. The orthogonal array can also be subjected to range analysis and variance analysis to respectively obtain the influence law of the factor level values on each performance parameter and the sensitivity degree of each factor to the performance parameter. It includes:

[0091] S41: Obtain the optimal solution by comparing the orthogonal test results obtained in S3. Since the orthogonal array has the characteristic of evenly sampling points, it can effectively replace all experimental schemes. By comparing the results of the orthogonal array (the values of each performance parameter) obtained in S34, that is, the evaluation indexes, the optimal combination corresponding to each evaluation index can be obtained to complete the optimization design. By observing the data in the table, it can be seen that each evaluation index of case18 has obvious advantages in the orthogonal array, and its scheme is: average porosity: 0.7, diffusion layer thickness: 0.2 mm, flow channel width 0.6 mm, degree of gradient: 4, flow channel length: 80 mm.

[0092] S42: Conduct range analysis on the orthogonal test results obtained in S34. Range analysis, also known as the intuitive analysis method, is the most commonly used analysis method for orthogonal test analysis. It calculates the mean value of the level value corresponding to a single performance parameter value, obtains the relationship between different levels and the performance index, and uses the difference between the maximum and minimum values of the mean to describe the dispersion degree of the data, and determines the influence degree of this factor on the performance index. In this section, according to the mean value of a single performance parameter value corresponding to each group of levels, the influence law of different level value settings on the performance parameter can be determined, and one or more groups of optimized combination schemes can be manually generated. The optimal combination scheme can be updated by comparing the results. The range analysis diagram is shown in Figures 3 - 6 . Through the range analysis diagram, it can be intuitively obtained at which level value the performance of the performance parameter is the best.

[0093] S43: Conduct variance analysis on the orthogonal test results obtained in S34. As a statistical test method, variance analysis can be used to test the significance of the influence of relevant factors on the test results during the test. Through variance analysis, the influence degree of the factor on the performance parameter can be obtained. Different evaluation indexes correspond to different significant influence factors. When a factor has a significant influence on multiple evaluation indexes, it can be determined accordingly that this factor has an important influence on the performance of the fuel cell. Use the minitab software to conduct variance analysis on the results of the orthogonal array to obtain the significant influence factors of each performance parameter

[0094]

[0095] It should be noted that if variance analysis is required, a blank column must be added as the error column to estimate the test error and improve the accuracy of variance analysis.

[0096] Through the established three-dimensional two-phase fuel cell model, the present invention conducts a study on the influence characteristics of the overall performance of the fuel cell under the synergistic effect of the gradient diffusion layer and the flow channel structure parameters based on the orthogonal test method. The present invention is not limited to the mentioned parallel flow field structure. When the optimization variables and their value ranges can be determined, the method of the present invention can be used for orthogonal test analysis.

[0097] The present invention studies the collaborative matching optimization of the gradient diffusion layer and the flow channel structure. Considering many factors and a wide coverage of the horizontal range, more experimental times are required to comprehensively study the collaborative matching law.

[0098] Taking the present invention as an example, five factors are selected, and each factor has five levels. To count all the experiments, there are 3125 combination schemes. Through the orthogonal test design, the number of schemes is reduced to 25 groups, greatly saving the experimental cost. In addition, through the orthogonal test design, not only can the design optimization scheme of the structural matching be obtained, but also the influence law and degree of the parameter variables on the performance parameters can be obtained.

[0099] The above shows and describes the basic principles, main features and advantages of the present invention. For those skilled in the art, it is obvious that the present invention is not limited to the details of the above exemplary embodiments, and can be implemented in other specific forms without departing from the spirit or basic features of the present invention.

[0100] Therefore, from any point of view, the embodiments should be regarded as exemplary and non-restrictive. The scope of the present invention is defined by the appended claims rather than the above description. Therefore, all changes falling within the meaning and scope of the equivalent elements of the claims are intended to be included in the present invention.

[0101] In addition, it should be understood that although this specification is described according to the embodiments, not every embodiment only contains an independent technical solution. This narrative way of the specification is only for clarity. Those skilled in the art should regard the specification as a whole, and the technical solutions in each embodiment can also be appropriately combined to form other embodiments that can be understood by those skilled in the art.

Claims

1. A fuel cell flow channel and diffusion layer structure coordinated matching optimization method based on orthogonal test method, characterized in that: Includes steps: S1. Determine the structural parameters and optimization objectives of collaborative optimization; The structural parameters include flow channel width, flow channel length, diffusion layer thickness, average porosity and the gradient degree of porosity on the diffusion layer; the optimization objectives include the maximum power density of the battery, the uniformity of oxygen distribution and current density distribution in the cathode catalyst layer and the pressure drop in the flow channel; S2. Conduct orthogonal experimental design for all structural parameters; S3. Establish a CFD analysis model and obtain results based on the experimental plan of orthogonal experimental design; S4. Analysis of structural parameters after orthogonal test optimization.

2. The method for optimizing the coordinated matching of fuel cell flow channel and diffusion layer structure based on orthogonal test method according to claim 1 is characterized in that: In step S1, the gradient degree of porosity on the diffusion layer is related to the following distribution function; ε GDL =(a-2b)X+b; where ε GDL is the porosity of the diffusion layer, a and b are correction coefficients, X is the normalized length, the value of a corresponds to the level of the average porosity, and the value of b corresponds to the level of the gradient degree of the porosity of the diffusion layer.

3. The method for optimizing the coordinated matching of the fuel cell flow channel and the diffusion layer structure based on the orthogonal test method according to claim 2 is characterized in that: In step S3, according to the experimental scheme of orthogonal experimental design, a CFD analysis model is established and results are obtained, including: S31. Using modeling software, construct a three-dimensional battery model according to the size of the fuel cell model determined by the test plan; S32. Import the constructed fuel cell three-dimensional model into ANSYS workbench for meshing; S33. Import the fuel cell model with network division into ANSYS fluent, and set the relevant physical parameters and boundary conditions of the fuel cell model when it is actually working; S34. Initialize the temperature, reactant gas content and current density, and start the simulation calculation after assigning the initial values; observe the monitoring curves of the residual, current density, liquid water saturation and reactant gas content during the simulation calculation to determine whether the calculation has converged. After reaching the convergence standard, read the volume current density in the cathode catalyst layer, the current density distribution uniformity and oxygen concentration distribution uniformity in the cathode catalyst layer, and the pressure drop in the cathode flow channel as evaluation indicators for the subsequent fuel cell performance.

4. The method for optimizing the coordinated matching of the fuel cell flow channel and the diffusion layer structure based on the orthogonal test method according to claim 3 is characterized in that: Step S33 includes loading a user-defined function (UDF) on the ANSYS fluent platform, and embedding the average pore and gradient level value related distribution functions into the gas diffusion layer component of the fuel cell model in the form of code.

5. The method for optimizing the coordinated matching of fuel cell flow channel and diffusion layer structure based on orthogonal test method according to claim 1 is characterized in that: The modeling of the fuel cell model follows the partial differential conservation equations of mass, momentum, matter, liquid water transport, dissolved water transport, electron proton transport and energy, and uses the Butler-Volmer equation to deal with the electrochemistry in the catalyst layer to solve the current density, including charge transfer and energy conservation equations; at the same time, the Leverett J function and Wyllie model are applied to describe the liquid water transport in the GDL and CLs.

6. The method for optimizing the coordinated matching of fuel cell flow channel and diffusion layer structure based on orthogonal test method according to claim 5 is characterized in that: The scope of the mass conservation equation includes the catalyst layer, gas diffusion layer and flow channel of the cathode and anode; all processes in the fuel cell, including flow, diffusion, phase change and electrochemical reaction, follow the law of mass conservation. The mass conservation equation is as follows: in, represents the Hamiltonian operator, which refers to the vector sum of partial derivatives of a physical quantity in all directions; ρ g Indicates density, kg / m 3 ; represents the velocity vector, m / s; ε represents the porosity, which is the ratio of the pore volume to the total volume. The porosity of porous media including the catalyst layer and the gas diffusion layer is related to the properties of the material itself. The default porosity in the flow channel is 1; s is the liquid water saturation, that is, the ratio of the volume of liquid water in the porous medium to the total pore volume; S m Represents the mass source term, kg / (m 3 ·s), the mass source term in the gas diffusion layer and the flow channel is 0; The momentum conservation equation is applied to the cathode and anode catalyst layers, gas diffusion layers, and flow channels; the momentum conservation equation is as follows: Among them, p g represents the gas pressure, pa, which is the variable to be solved in the equation; μ g Indicates dynamic viscosity, kg / (m·s); S u represents the momentum source term, kg / (m 3 s); The scope of the energy conservation equation includes all computational domains, namely the catalyst layer, gas diffusion layer, and flow channel; the energy conservation equation is as follows: Among them, ρ l and ρ g Indicates the relative density of liquid and gas phases, kg / m 3 ; T represents temperature, K, which is the variable to be solved in the equation; C p,l Indicates the specific heat capacity of liquid at constant pressure, J / (kg·K); C p,g Indicates the specific heat capacity of gas phase at constant pressure; u l and u g Indicates the velocity of liquid and gas phase; k eff Represents effective thermal conductivity, W / (m·K), the superscript eff represents the effective value: S T represents the energy source term; The diffusion equation of species in the catalyst layer, gas diffusion layer and flow channel is: Where, X i is the mass fraction of the gas phase, i represents hydrogen, oxygen, water vapor and nitrogen in the gas components; D i eff is the gas phase effective diffusion coefficient; S i is the diffusion source phase; The transport equation of liquid water in the catalyst layer, gas diffusion layer and flow channel is: In the formula, k p is the permeability of the porous medium; k kl =s 3 is the relative permeability of liquid water in porous media; ρ l is the pressure of liquid water; μ l is the viscosity coefficient of liquid water, D c is the liquid water diffusion coefficient, S l is the source term of liquid water evaporation. The transport equation of dissolved water in the catalyst layer and membrane is: λ represents the content of membrane water; is the current density in the membrane; F is the Faraday constant; ρ MEM is the density of proton exchange membrane; EW is the equal mass membrane; is the effective diffusion coefficient of membrane water; S d Source term of membrane water conservation equation; The electron transport equation in the end plate, catalyst layer and bulk diffusion layer is: Represents electronic conductivity; φ e represents the electron potential; S e represents the electron potential source term; The transport equation of protons in the catalyst layer and membrane is: represents proton conductivity; φ ion represents the proton potential; S ion represents the proton potential source term.

7. The method for optimizing the coordinated matching of fuel cell flow channel and diffusion layer structure based on orthogonal test method according to claim 1 is characterized in that: In step S4, the structural parameters optimized based on the orthogonal test are analyzed, including obtaining the optimal solution of each performance parameter under different factor level combinations by comparing the test results.

8. The method for optimizing the coordinated matching of fuel cell flow channel and diffusion layer structure based on orthogonal test method according to claim 7 is characterized in that: In step S4, the structural parameters optimized based on the orthogonal test are analyzed, including performing range analysis on the orthogonal table to obtain the influence of the factor level value on each performance parameter.

9. The method for optimizing the coordinated matching of fuel cell flow channel and diffusion layer structure based on orthogonal test method according to claim 8, characterized in that: In step S4, the structural parameters optimized by the orthogonal test are analyzed, including variance analysis of the orthogonal table, to obtain the sensitivity of each factor to the performance parameters.

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