A method for determining an optimal value of compression of a fuel cell gas diffusion layer

Abstract

The present invention discloses a method for determining the optimal value of the compression of a fuel cell gas diffusion layer. The method includes obtaining characteristic parameters of a gas diffusion backing (GDB) and a microporous layer (MPL), performing three-dimensional reconstruction on a gas diffusion layer (GDL) sample in an uncompressed state, generating a reconstruction model, and continuously adjusting other parameters in the algorithm until the reconstruction model is consistent with the characteristics of the real sample; determining the compressive modulus ratio of GDB and MPL, and calculating the thickness and porosity of each of the two layers under different compressions; using other parameters, reconstructing the three-dimensional model under compression with porosity as a constraint; extracting the key mass transfer parameters of GDB and MPL through mesoscopic scale simulation, and superimposing and calculating the key mass transfer parameters of GDL under different compressions; inputting the key mass transfer parameters of GDL into a three-dimensional numerical simulation model of a fuel cell for simulation, and optimizing the compression of GDL through performance comparison. The present scheme provides a method for optimizing the compression of a fuel cell gas diffusion layer that takes both cost and scalability into consideration.

Description

Technical Field

[0001] The present invention relates to the technical field of fuel cells, and in particular to a method for determining an optimal value of a fuel cell gas diffusion layer compression amount. Background Art

[0002] As a hydrogen-electricity direct conversion device, the proton exchange membrane fuel cell (PEMFC) is regarded as one of the development directions of the next generation of automotive power due to its many advantages such as high efficiency, high power density, zero carbon emissions, and low-temperature start-up.

[0003] The gas diffusion layer (GDL) is a key component to ensure the internal reaction gas transmission, water and heat management, and current conduction of fuel cells. The current mainstream technical solution is to combine two porous sheets with different pore sizes and hydrophobicity, namely, the gas diffusion backing (GDB) and the microporous layer (MPL). GDB is usually stacked with carbon fibers and is the main part of GDL. Its macroporous structure ensures good gas and water transmission capacity; MPL is a thin layer formed by the accumulation of smaller carbon black, which is mainly used to reduce the contact resistance between GDL and the catalyst layer and improve the internal water and heat management capacity of the fuel cell. As a macroscopic porous layered material, GDL will be significantly compressed in the thickness direction during the fuel cell packaging process. Its compression has a significant impact on the output performance of the battery and is an important control parameter when assembling the stack. However, for the many different models of commercial GDL products on the market, there is still a lack of a universal method for selecting the optimal value of compression, which leads to difficult and tedious optimization problems for fuel cell manufacturers in the process of GDL selection and stack packaging parameter matching.

[0004] Previously, there have been many studies on the changes in the physical properties of GDL under compression, in order to guide the optimization of GDL compression through mechanism exploration. These studies can be divided into two categories. The first category is experimental-based research, such as measuring the mass transfer parameters of GDL under different compression states, measuring the liquid water distribution characteristics of GDL during battery operation, etc. For the former, since most traditional characterization equipment is difficult to apply compressive stress to GDL samples at the same time during the test, new special experimental devices must be developed, which have problems such as long cycle and high cost. The latter can only be achieved through advanced characterization technologies such as neutron imaging and X-ray micro-tomography, and also faces the problem of high cost. The second category is simulation-based research, which aims to lower the threshold of research through numerical simulation. This type of research is often carried out based on numerical model reconstruction, that is, constructing a GDL microstructure model under different compression states through three-dimensional reconstruction, and on this basis, calculating its mass transfer parameters through mesoscopic scale simulation (such as lattice Boltzmann method, pore scale model, etc.). However, there are at least two orders of magnitude differences between the stacking element sizes and pore sizes of the two layers (i.e., GDB and MPL) in the GDL, and this scale difference is reflected in the resolution difference of the numerical reconstruction model. Therefore, in order to avoid problems such as data volume expansion, huge computing resources, and convergence difficulties, most simulation work only studies one layer (GDB or MPL) in the GDL, and cannot reproduce the structure of the two layers of GDB and MPL in the real sample.

[0005] In addition, since the selection of the compression amount of the gas diffusion layer (GDL) must be guided by improving the performance of the fuel cell, and the above studies have not established the relationship between the intrinsic properties of the GDL and the output performance of the battery, they cannot provide a direct basis for optimizing the GDL compression amount. Although macroscopic simulation models (such as one-dimensional and three-dimensional models) can simulate the output performance of the fuel cell, the values ​​of important parameters in the GDL submodule (such as mass transfer parameters, etc.) often rely on engineering experience or fitting functions, and overly ignore the influence of the complex microstructure of the GDL. Therefore, the currently commonly used compression optimization method is to conduct performance control experiments on multiple groups of actual batteries under different GDL compression amounts. However, this type of solution has a long implementation cycle and limited scalability. When the GDL model or battery fixture is changed, the data is often no longer applicable and the experiment needs to be repeated.

[0006] In summary, the following problems exist in the research on the changes in the physical properties of GDL under compression: experimental research has problems such as long cycle and high cost; most of the research based on three-dimensional reconstruction and mesoscopic simulation does not consider the superposition of the two layers (i.e., GDB and MPL) in GDL, and the degree of simulation of real samples is limited. At the same time, due to the lack of correlation between the measured / simulated results of the above research and the actual fuel cell performance, it is impossible to provide a direct basis for optimizing the GDL compression amount.

[0007] The currently commonly used GDL compression optimization scheme has the following problems: the fuel cell output performance simulation based on macro-scale simulation has problems such as over-simplification of the model and lack of accuracy in the values ​​of GDL key mass transfer parameters; the series of control experiments on actual battery performance have a long implementation cycle and limited generalizability, and the conclusions are only applicable to specific GDL models and battery fixtures. Summary of the invention

[0008] In order to solve the technical problem that there is a lack of low-cost and universal solution for selecting the optimal value of the compression amount of the gas diffusion layer of the existing fuel cell, an embodiment of the present invention provides a method for determining the optimal value of the compression amount of the gas diffusion layer of the fuel cell.

[0009] The technical solution of the embodiment of the present invention is achieved as follows:

[0010] An embodiment of the present invention provides a method for determining an optimal value of compression of a fuel cell gas diffusion layer, the method comprising: obtaining a gas diffusion layer sample, performing basic experimental characterization on the gas diffusion layer sample in an uncompressed state, obtaining characteristic parameters of a gas diffusion backing and characteristic parameters of a microporous layer in the gas diffusion layer sample, the characteristic parameters comprising stacking element size, porosity, polytetrafluoroethylene content and initial thickness; performing three-dimensional reconstruction on the gas diffusion layer sample in an uncompressed state based on the characteristic parameters of the gas diffusion backing and the characteristic parameters of the microporous layer to generate a reconstructed model in an uncompressed state; and obtaining a microscopic morphology and A first control parameter whose pore size distribution is consistent with the actual microscopic morphology and pore size distribution of the gas diffusion layer sample; performing a macro-scale compression load-displacement test and a micro-scale compression load-displacement test on the gas diffusion layer sample in an uncompressed state to determine the compressive modulus of the gas diffusion backing and the compressive modulus of the microporous layer in the gas diffusion layer sample; and calculating the thickness and porosity of the gas diffusion backing and the thickness and porosity of the microporous layer at different compression amounts based on the compressive modulus and initial thickness of the gas diffusion backing and the compressive modulus and initial thickness of the microporous layer; and calculating the porosity of the gas diffusion backing and the porosity of the microporous layer at different compression amounts. The porosity is a constraint condition, and the first control parameter is used to perform three-dimensional reconstruction of the gas diffusion layer samples under different compression amounts to generate reconstruction models under different compression amounts; for the reconstruction models under different compression amounts, the reconstruction models under different compression amounts are used as calculation domains to perform mesoscopic scale computational fluid dynamics simulations to extract the key mass transfer parameters of the gas diffusion backing and the key mass transfer parameters of the microporous layer under different compression amounts; and based on the key mass transfer parameters and thickness of the gas diffusion backing and the key mass transfer parameters and thickness of the microporous layer under different compression amounts, according to the physical definition of the key mass transfer parameters and the property superposition formula of the series transport element, the calculation of the uncompressed gas diffusion layer is performed. The key mass transfer parameters of the gas diffusion layer sample under the same compression amount; the key mass transfer parameters include tortuosity, diffusivity and permeability; the fuel cell unit test fixture parameters are obtained, and a fuel cell three-dimensional numerical simulation model is established based on the fuel cell unit test fixture parameters; the key mass transfer parameters of the gas diffusion layer sample under different compression amounts are input into the gas diffusion layer submodule in the fuel cell three-dimensional numerical simulation model for simulation, and the output performance of the fuel cell output by the fuel cell three-dimensional numerical simulation model under different gas diffusion layer compression amounts is obtained, and the gas diffusion layer compression amount corresponding to the optimal output performance in the output performance is determined as the preferred compression amount value.

[0011] In one embodiment, a basic experimental characterization is performed on the gas diffusion layer sample in an uncompressed state to obtain characteristic parameters of the gas diffusion backing and the microporous layer in the gas diffusion layer sample, including: photographing the gas diffusion layer sample using a scanning electron microscope to determine the stacking element size and initial thickness of the gas diffusion backing and the stacking element size and initial thickness of the microporous layer in the gas diffusion layer sample; testing the gas diffusion layer sample using a mercury injection method to determine the overall porosity of the gas diffusion layer sample; testing the gas diffusion layer sample using an inductively coupled plasma emission spectroscopy to determine the total content of polytetrafluoroethylene in the gas diffusion layer sample; and calculating the initial porosity and polytetrafluoroethylene content of the microporous layer based on the overall porosity of the gas diffusion layer sample, the total content of polytetrafluoroethylene in the gas diffusion layer sample, the initial thickness of the gas diffusion backing and the initial thickness of the microporous layer in the gas diffusion layer sample, and the known initial porosity and polytetrafluoroethylene content of the gas diffusion backing using the following calculation formula:

[0012] Formula (1)

[0013] Formula (2)

[0014] in, represents the overall porosity of the GDL sample, represents the initial porosity of the gas diffusion backing, represents the initial thickness of the gas diffusion backing, represents the initial porosity of the microporous layer, represents the initial thickness of the microporous layer, Indicates the total content of polytetrafluoroethylene in the gas diffusion layer sample, Indicates the PTFE content of the gas diffusion backing, Indicates the polytetrafluoroethylene content of the microporous layer.

[0015] In one embodiment, a macro-scale compression load-displacement test and a micro-scale compression load-displacement test are performed on the gas diffusion layer sample in an uncompressed state to determine the compressive modulus of the gas diffusion backing and the compressive modulus of the microporous layer in the gas diffusion layer sample, including: testing the gas diffusion layer sample using a universal testing machine to obtain a load-displacement curve of the overall compression of the gas diffusion layer sample; determining the compressive modulus of the gas diffusion layer sample based on the load-displacement curve of the overall compression of the gas diffusion layer sample; testing the gas diffusion layer sample using a micron indenter to obtain a load-displacement curve of the compression of the microporous layer in the gas diffusion layer sample; determining the compressive modulus of the microporous layer based on the load-displacement curve of the compression of the microporous layer; and determining the compressive modulus of the gas diffusion backing according to the compressive modulus of the gas diffusion layer sample and the compressive modulus of the microporous layer.

[0016] In one embodiment, the compressive modulus of the gas diffusion layer sample is determined based on the load-displacement curve of the overall compression of the gas diffusion layer sample, including: dividing the load-displacement curve of the overall compression of the gas diffusion layer sample into three intervals, and determining the starting point coordinates and the end point coordinates of the microstructure compression interval in the three intervals according to the division results; and calculating the compressive modulus of the gas diffusion layer sample according to the starting point coordinates and the end point coordinates of the microstructure compression interval in the three intervals using the following calculation formula:

[0017] N-type (3)

[0018] in, represents the compressive modulus of the gas diffusion layer sample, represents the compressive modulus of the gas diffusion backing, represents the compressive modulus of the microporous layer, and are the starting and ending coordinates of the microstructure compression interval in the three intervals, and the units of the horizontal and vertical coordinates are mm and kN respectively. is the cut area of ​​the gas diffusion layer sample, is the thickness of a single gas diffusion layer sample, and N is the number of single gas diffusion layer samples.

[0019] In one embodiment, the compressive modulus of the microporous layer is determined based on the load-displacement curve of the microporous layer compression, including: dividing the load-displacement curve of the microporous layer compression into two intervals, and determining the starting point coordinates and the end point coordinates of the microstructure compression interval in the two intervals according to the division result; according to the starting point coordinates and the end point coordinates of the microstructure compression interval in the two intervals, the compressive modulus of the microporous layer is calculated using the following calculation formula:

[0020] Formula (4)

[0021] in, represents the compressive modulus of the microporous layer, and are the starting point coordinates and the end point coordinates of the microstructure compression interval in the two intervals, and the horizontal and vertical coordinate units are μm and mN respectively. is the contact surface area of ​​the load indenter of the micro-indenter, is the thickness of the microporous layer.

[0022] In one embodiment, based on the compressive modulus and initial thickness of the gas diffusion backing and the compressive modulus and initial thickness of the microporous layer, the porosity and thickness of the gas diffusion backing and the porosity and thickness of the microporous layer under different compression amounts are calculated, including: based on the compressive modulus and initial thickness of the gas diffusion backing and the compressive modulus and initial thickness of the microporous layer, the compression amount of the gas diffusion backing and the compression amount of the microporous layer under different compression amounts are calculated using the following calculation formula:

[0023] Formula (5)

[0024] in, represents the compression of the gas diffusion backing, represents the compression amount of the microporous layer, and The sum is the overall compression of the gas diffusion layer, represents the initial thickness of the gas diffusion backing, represents the initial thickness of the microporous layer, represents the compressive modulus of the gas diffusion backing, represents the compressive modulus of the microporous layer;

[0025] According to the compression amount of the gas diffusion backing and the compression amount of the microporous layer, the porosity and thickness of the gas diffusion backing and the porosity and thickness of the microporous layer at different compression amounts are calculated using the following calculation formula:

[0026] Formula (6)

[0027] Formula (7)

[0028] Formula (8)

[0029] Formula (9)

[0030] in, represents the porosity of the gas diffusion backing at different compression amounts, represents the initial thickness of the gas diffusion backing, represents the porosity of the gas diffusion backing in the uncompressed state, represents the compression of the gas diffusion backing, represents the porosity of the microporous layer under different compression amounts, represents the initial thickness of the microporous layer, represents the porosity of the microporous layer in the uncompressed state, represents the compression amount of the microporous layer, represents the thickness of the gas diffusion backing at different compression amounts, Represents the thickness of the microporous layer under different compression amounts.

[0031] In one embodiment, the reconstructed model includes a representative volume unit model of the gas diffusion backing and a representative volume unit model of the microporous layer. The reconstructed model under different compression amounts is used as a calculation domain to perform a mesoscopic scale computational fluid dynamics simulation to extract the key mass transfer parameters of the gas diffusion backing and the key mass transfer parameters of the microporous layer under different compression amounts, including: extracting the key mass transfer parameters of the gas diffusion backing and the key mass transfer parameters of the microporous layer under different compression amounts using the following calculation formula:

[0032] Formula (10)

[0033] Formula (11)

[0034] Formula (12)

[0035] in, is the tortuosity of the gas diffusion backing or microporous layer, is the representative volume element model of the gas diffusion backing or the representative volume element model of the microporous layer, is the coordinate indicator of the computational domain, The representative volume unit model of the gas diffusion backing or the representative volume unit model of the microporous layer are the velocity components in the three directions of the calculation domain, is the diffusivity of the gas diffusion backing or microporous layer, is the porosity of the gas diffusion backing or microporous layer, is the permeability of the gas diffusion backing or microporous layer, is the representative volume cell model of the gas diffusion backing or the representative volume cell model of the microporous layer, is the average flow velocity in the computational domain, is the representative volume cell model of the gas diffusion backing or the representative volume cell model of the microporous layer is the fluid dynamic viscosity of the computational domain, is the representative volume unit model of the gas diffusion backing or the representative volume unit model of the microporous layer, is the length of the computational domain in the flow direction, The representative volume cell model of the gas diffusion backing or the representative volume cell model of the microporous layer is the pressure difference between the outlet and the inlet of the computational domain.

[0036] In one embodiment, based on the key mass transfer parameters and thickness of the gas diffusion backing and the key mass transfer parameters and thickness of the microporous layer at the different compression amounts, according to the physical definition of the key mass transfer parameters and the property superposition formula of the series transport element, the key mass transfer parameters of the gas diffusion layer samples at different compression amounts are calculated, including: using the following calculation formula to calculate the key mass transfer parameters of the gas diffusion layer samples at different compression amounts:

[0037] Formula (13)

[0038] Formula (14)

[0039] Formula (15)

[0040] in, represents the tortuosity of the gas diffusion layer sample, Indicates the tortuosity of the gas diffusion backing, Represents the tortuosity of the microporous layer, represents the thickness of the gas diffusion backing, represents the thickness of the microporous layer, represents the diffusion rate of the gas diffusion layer sample, represents the porosity of the gas diffusion backing, represents the porosity of the microporous layer, represents the permeability of the gas diffusion layer sample, represents the permeability of the gas diffusion backing, Represents the permeability of the microporous layer.

[0041] In one embodiment, based on the fuel cell monomer test fixture parameters, a three-dimensional numerical simulation model of a fuel cell is established, including: the assumptions of the three-dimensional numerical simulation model of the fuel cell are: the battery operates stably; the gas flow in the flow channel is incompressible and laminar; only the gas phase components in the flow channel are considered; the gas diffusion layer sample is uniform and isotropic; the diffusion of gas in the porous medium is described by Fick's law; the influence of gravity is ignored; the electrochemical reaction in the catalyst layer is solved by the Butler-Volmer equation; the control equation of the three-dimensional numerical simulation model of the fuel cell is as follows:

[0042] The conservation equations for mass and momentum are:

[0043] Formula (16)

[0044] Formula (17)

[0045] in, is the porosity of the porous medium, is the fluid density, is the velocity vector of the fluid, is the fluid pressure, is the dynamic viscosity of the fluid, and are the mass source term and the momentum source term respectively;

[0046] Energy conservation equation:

[0047] Formula (18)

[0048] in, is the temperature, is the specific heat capacity, is the energy source term, is the effective thermal conductivity, which can be expressed as: ,in, and are the thermal conductivity of fluid and solid respectively;

[0049] Species transport equation:

[0050] Formula (19)

[0051] in, For species The volume fraction of is the species source term, For species The effective diffusion coefficient can be expressed as: ,in, is the water saturation, and are the local pressure and local temperature, respectively, For species In and The mass diffusion coefficient under

[0052] Liquid water saturation equation:

[0053] Formula (20)

[0054] in, and are the density and viscosity of liquid water, is the condensation rate, is the capillary pressure, which can be expressed as:

[0055] ,in, is the surface tension, is the contact angle, is the absolute permeability;

[0056] Butler-Volmer equation:

[0057] Formula (21)

[0058] Formula (22)

[0059] Among them, the subscript and represent the anode and cathode respectively, is the volume transfer current, is the reference exchange current density, is the transmission coefficient, is the concentration dependence, is the effective specific surface area, is the local activation overpotential, which can be expressed as: , where the subscript and represent the solid phase and membrane, respectively. is the electric potential, is the open circuit voltage;

[0060] Current conservation equation:

[0061] Formula (23)

[0062] Formula (24)

[0063] in, is the conductivity, is the volume transport current calculated by the Butler-Volmer equation, and the membrane conductivity It is expressed as: ,in, is the moisture content of the membrane;

[0064] The series of source terms that appear in the above equation are as follows:

[0065] Quality source term:

[0066]

[0067] Momentum source term:

[0068]

[0069] Energy source term:

[0070]

[0071] Species source term:

[0072]

[0073]

[0074]

[0075]

[0076] The boundary conditions include: all external surfaces are set to no-slip and zero-flux boundary types, the flow channel outlet is set to pressure outlet boundary type, and the flow channel inlet is set to mass flow inlet boundary type.

[0077] In one embodiment, the output performance is a series of output current density data of the fuel cell at different voltages; and determining the compression amount of the gas diffusion layer corresponding to the best output performance among the output performance as the preferred compression amount value comprises:

[0078] Plotting a series of output current density data of the fuel cell at different voltages into a polarization curve diagram;

[0079] The data of the polarization curve diagram within the specified working voltage range are compared, and the compression amount of the gas diffusion layer corresponding to the optimal data is determined as the preferred compression amount value.

[0080] The method for determining the optimal value of the fuel cell gas diffusion layer compression provided by the embodiment of the present invention provides a fuel cell GDL compression optimization method that takes both cost and scalability into consideration. BRIEF DESCRIPTION OF THE DRAWINGS

[0081] Figure 1 It is a flow chart of a method for determining an optimal value of a gas diffusion layer compression amount for a fuel cell according to an embodiment of the present invention;

[0082] Figure 2 This is a schematic diagram of key parameters of basic experimental characterization of an embodiment of the present invention;

[0083] Figure 3 Schematic diagram of the microscopic morphology comparison between the actual sample and the reconstructed model in the embodiment of the present invention; wherein, Figure 3 (a) is a schematic diagram of the microscopic morphology of the actual sample. Figure 3 (b) Schematic diagram of the microstructure of the reconstructed model;

[0084] Figure 4 Schematic diagram of the pore size distribution comparison between the actual sample and the reconstructed model in the embodiment of the present invention; wherein, Figure 4 (a) is a schematic diagram of the pore size distribution of the actual sample. Figure 4(b) Schematic diagram of pore size distribution of the reconstructed model;

[0085] Figure 5 A schematic diagram of calculating the compressive load-displacement test data at the macroscale and microscale of an embodiment of the present invention;

[0086] Figure 6 It is a schematic diagram of the load-displacement curve results of the overall compression test of the GDL according to an embodiment of the present invention;

[0087] Figure 7 This is a schematic diagram of the load-displacement curve results of the MPL micro-indentation test according to an embodiment of the present invention;

[0088] Figure 8 Schematic diagram of the simulation results of the fuel cell polarization curve under different GDL compression amounts according to an embodiment of the present invention. DETAILED DESCRIPTION

[0089] The present invention will be described in further detail below with reference to the accompanying drawings and embodiments.

[0090] The embodiment of the present invention provides a method for determining an optimal value of a fuel cell gas diffusion layer compression amount, such as Figure 1 As shown, the method includes:

[0091] Step 101: obtaining a gas diffusion layer sample, performing basic experimental characterization on the gas diffusion layer sample in an uncompressed state, and obtaining characteristic parameters of a gas diffusion backing and a microporous layer in the gas diffusion layer sample, wherein the characteristic parameters include stacking element size, porosity, polytetrafluoroethylene content, and initial thickness;

[0092] Step 102: Based on the characteristic parameters of the gas diffusion backing and the characteristic parameters of the microporous layer, three-dimensionally reconstruct the gas diffusion layer sample in an uncompressed state to generate a reconstructed model in an uncompressed state; and obtain a first control parameter that makes the microscopic morphology and pore size distribution of the reconstructed model in an uncompressed state consistent with the actual microscopic morphology and pore size distribution of the gas diffusion layer sample;

[0093] Step 103: performing a macro-scale compression load-displacement test and a micro-scale compression load-displacement test on the gas diffusion layer sample in an uncompressed state to determine the compressive modulus of the gas diffusion backing and the compressive modulus of the microporous layer in the gas diffusion layer sample; and calculating the thickness and porosity of the gas diffusion backing and the thickness and porosity of the microporous layer under different compression amounts based on the compressive modulus and initial thickness of the gas diffusion backing and the compressive modulus and initial thickness of the microporous layer;

[0094] Step 104: using the porosity of the gas diffusion backing and the porosity of the microporous layer at different compression amounts as constraint conditions and utilizing the first control parameter, three-dimensionally reconstruct the gas diffusion layer samples at different compression amounts to generate reconstructed models at different compression amounts;

[0095] Step 105: for the reconstructed model under different compression amounts, taking the reconstructed model under different compression amounts as the calculation domain, performing mesoscopic scale computational fluid dynamics simulation, extracting the key mass transfer parameters of the gas diffusion backing and the key mass transfer parameters of the microporous layer under different compression amounts; and based on the key mass transfer parameters and thickness of the gas diffusion backing and the key mass transfer parameters and thickness of the microporous layer under different compression amounts, according to the physical definition of the key mass transfer parameters and the property superposition formula of the series transport element, calculating the key mass transfer parameters of the gas diffusion layer sample under different compression amounts; the key mass transfer parameters include tortuosity, diffusivity and permeability;

[0096] Step 106: Obtain fuel cell cell test fixture parameters, and establish a fuel cell three-dimensional numerical simulation model based on the fuel cell cell test fixture parameters; input key mass transfer parameters of the gas diffusion layer samples under different compression amounts into the gas diffusion layer submodule in the fuel cell three-dimensional numerical simulation model for simulation, and obtain the output performance of the fuel cell under different gas diffusion layer compression amounts output by the fuel cell three-dimensional numerical simulation model, and determine the gas diffusion layer compression amount corresponding to the optimal output performance among the output performance as the preferred compression amount value.

[0097] The method of this embodiment combines traditional test characterization, three-dimensional numerical reconstruction, mesoscopic computational fluid dynamics simulation and three-dimensional fuel cell model simulation. Without relying on high-end characterization equipment and actual fuel cell performance experiments, it constructs the corresponding relationship of "GDL compression state-GDL mass transfer parameters-single fuel cell performance", and then optimizes the GDL compression amount through performance comparison.

[0098] The present embodiment will be described in detail below with reference to a specific embodiment.

[0099] This embodiment first obtains the characteristic parameters of the GDL sample in the uncompressed state through a series of basic experimental characterizations, which are used as the key parameters of the numerical reconstruction algorithm, and the model reconstruction is implemented to verify the reconstruction quality, and other control parameters in the algorithm are continuously adjusted until the reconstructed model is consistent with the characteristics of the real sample. Afterwards, the GDB and MPL models under different compression amounts are reconstructed respectively with porosity as a constraint condition. Considering that for the actual GDL sample, although the overall compression amount is a controllable amount, it is difficult to directly measure the compression amount of GDB and MPL respectively, and it is also difficult to determine their respective porosities. Therefore, the compression modulus of each layer is determined by macro-scale and micro-scale compression tests, and then the porosity and thickness of GDB and MPL respectively under different GDL compression amounts are calculated based on this parameter, and the model reconstruction is implemented. Then, with the reconstructed model as the calculation domain, the mass transfer parameters of GDB and MPL under different compression amounts are obtained through mesoscopic scale simulation, and then the property superposition formula of GDB and MPL is derived based on the physical definition of each mass transfer parameter, and the various mass transfer parameters of GDL under different compression amounts are calculated. Finally, a three-dimensional simulation model is established according to the actual fuel cell fixture parameters, and the mass transfer parameters of the GDL are input into the corresponding sub-modules in the simulation model to simulate the fuel cell output performance under different GDL compression amounts. The GDL compression amount corresponding to the group with the best performance is determined by comparison, thereby achieving the optimization of the GDL compression amount.

[0100] Next, the specific implementation of the method is described.

[0101] In the first step, a series of basic experimental characterizations are carried out on the GDL sample in the uncompressed state, including: ① Using a scanning electron microscope to photograph the gas diffusion backing (GDB), microporous layer (MPL) and cross-section morphology of the sample, and determining the stacking element size and initial thickness of GDB and MPL, where the stacking element size will be used as the key parameter 1 in the three-dimensional reconstruction algorithm; ② Using the mercury intrusion method to measure the overall porosity and pore size distribution of the sample, and using formula 1 to calculate the porosity of GDB and MPL, as the key parameter 2 in the three-dimensional reconstruction algorithm, and the pore size distribution will be used as the basis for verifying the quality of model reconstruction; ③ Using inductively coupled plasma-optical emission spectrometry (ICP-OES) to measure the total content of polytetrafluoroethylene (PTFE) in the sample, and using formula 2 to calculate the PTFE content of GDB and MPL, as the key parameter 3 in the three-dimensional reconstruction method. The process of this step is as follows: Figure 2 shown.

[0102] Calculation formula 1

[0103] Calculation 2

[0104] Among them, the subscript and Represent GDB and MPL respectively, subscript Represents the parameters in the uncompressed state, is the porosity, is the PTFE content (this parameter is not affected by the compression state), For thickness. and They are the porosity and PTFE content of GDB respectively. The parameters can be provided by the manufacturer (i.e. the corresponding parameters of the product when it is not covered with MPL).

[0105] In the second step, based on the obtained series of characteristic parameters, the sample in the uncompressed state is reconstructed to generate representative volume element (RVE) models of GDB and MPL for verification. The typical reconstruction algorithm for GDB is the random reconstruction algorithm proposed by Schladitz. The typical reconstruction algorithm for MPL is the quartet structure generation set (QSGS). For the generation of PTFE in the model, the typical method is the morphological operator in AVIZO software. Subsequently, based on the reconstructed RVE model in the uncompressed state, the rationality of the micromorphology and pore size distribution is verified respectively, and other control parameters in the reconstruction algorithm (such as the probability distribution function describing the fiber orientation) are continuously adjusted until the reconstructed model is consistent with the characteristics of the real sample. The final parameters will be used in the subsequent model reconstruction algorithm under different compression amounts.

[0106] Here, this embodiment takes GDB as an example to illustrate the process of verifying the rationality of the microscopic morphology and pore size distribution.

[0107] First, verify the micromorphology of the reconstructed model. Figure 3 The following is an example of the microscopic morphology comparison between the GDB side of the actual sample and the GDB reconstruction model. Verification point 1: The fiber arrangement should not show obvious regularity, that is, it should be close to random arrangement. Verification point 2: PTFE should be distributed in the gaps between different fibers.

[0108] The pore size distribution of the reconstructed model was then verified. Figure 4 This is an example of the comparison of the pore size distribution of the actual sample and the reconstructed model. (Note: The reconstructed model is a binary three-dimensional array, and its pore size distribution is discrete data. The ordinate represents the volume percentage of pores of different sizes, which is essentially the same as the differential pore volume of the actual sample)

[0109] Verification point 1: Overall distribution range of pore size ( Figure 4 Verification point 2: The pore size range with a relatively high proportion ( Figure 4 There are two types of pores with a relatively high proportion, namely pores below 10μm and pores around 30μm, which are marked as A and B in the figure).

[0110] In the third step, the GDL samples were subjected to macro-scale and micro-scale compression load-displacement tests using a universal testing machine and a micro-indenter, respectively. The process of this step is as follows: Figure 5 shown.

[0111] Before the test, all GDL samples were cut into the same shape and size (e.g., 5 cm × 5 cm square) using a special die cutter. Then, multiple GDL samples were overlapped to reduce the measurement error. The compression test was performed using a universal testing machine to obtain the compression load-displacement curve of the GDL as a whole (i.e., the superposition of the two layers of GDB and MPL), as shown in Figure 2. Figure 6 The curve is divided into three intervals, and the starting and ending coordinates of the microstructure compression interval are selected. The compressive modulus of GDL is calculated by formula 3 (this value is the sum of the compressive moduli of GDB and MPL).

[0112] Calculation 3

[0113] in, and Represent the compressive modulus of GDB and MPL, is the thickness of a single GDL sample, and are the starting point and end point of the GDL microstructure compression interval (i.e. Figure 6 The coordinates of point A and point B in is the cut area of ​​the GDL sample, and N is the number of gas diffusion layer samples.

[0114] Afterwards, a GDL sample was taken and the compression load-displacement curve of the MPL single layer was measured by a microindenter, as shown in Figure 7 The curve is divided into two intervals, the microstructure compression interval is taken, and the compressive modulus of the MPL single layer is calculated by calculation formula 4.

[0115] Calculation 4

[0116] in, and are the starting point and end point of the MPL microstructure compression interval (i.e. Figure 7 The origin and the first mutation point C) coordinates, is the thickness of the MPL layer, is the contact area of ​​the load indenter of the micro-indenter. Based on this, the compressive modulus of the GDB monolayer is further calculated.

[0117] The fourth step is to calculate the compression of GDB and MPL respectively according to Formula 5, based on the known compressive modulus, initial thickness and overall compression of GDL.

[0118] Calculation 5

[0119] Among them, the subscript and Represent GDB and MPL respectively, subscript Represents the parameters in the uncompressed state, is the compression of a single layer, and The sum is the overall compression of the GDL. Further, the porosity of the two layers under different overall compression of the GDL is calculated by formula 6.

[0120] Calculation 6

[0121] in, is the compression of a single layer (GDB or MPL) calculated by equation 5, is the thickness of a single layer in an uncompressed state, is the porosity of a single layer in an uncompressed state. The calculated porosity of a single layer (GDB or MPL) under different compression amounts is used as a constraint of the reconstruction algorithm, and other determined control parameters are used to reconstruct the models of GDB and MPL, respectively, to generate a series of RVE models of GDB and MPL under different compression amounts.

[0122] In the fifth step, the series of RVE models are used as the computational domain to implement mesoscopic computational fluid dynamics simulation (typically the lattice Boltzmann method (LBM)) to extract the key mass transfer parameters of GDB and MPL under different compression amounts, including tortuosity, diffusivity and permeability. The calculation formula is as follows:

[0123] Calculation 7

[0124] in, is the tortuosity, is the coordinate indicator of the computational domain, are the velocity components in three directions respectively.

[0125] Calculation 8

[0126] in, is the diffusion rate, is the porosity, For tortuosity.

[0127] Formula 9

[0128] in, is the permeability, is the average flow velocity in the calculation domain obtained by LBM simulation, is the fluid dynamic viscosity, is the length of the computational domain in the flow direction, is the pressure difference between the outlet and the inlet.

[0129] Subsequently, the property superposition formulas of the series transport elements are derived based on the physical definitions of various mass transfer parameters.

[0130] According to the physical definition of tortuosity:

[0131] Formula 10

[0132] in, is the actual flow path length, is the macroscopic flow length, i.e. the thickness of the porous medium. GDL is composed of GDB and MPL overlapping each other. Its actual flow path length and thickness are the sum of GDB and MPL. Therefore, the tortuosity of GDL can be expressed as:

[0133] Formula 11

[0134] in, and are the actual flow path lengths in GDB and MPL, respectively. and To express it, we can get the calculation formula of GDL tortuosity:

[0135] Calculation 12

[0136] Will and Substituting the expression of into equation 8, we can get the calculation formula of GDL diffusion rate:

[0137] Formula 13

[0138] In equations 12 and 13, and is the tortuosity of GDB and MPL obtained by LBM simulation.

[0139] Then, the permeability calculation formula is derived based on Darcy's law. GDB and MPL are regarded as transport elements connected in series, and they satisfy the following two formulas respectively:

[0140] Calculation 14

[0141] Calculation 15

[0142] in, , are the permeabilities of GDB and MPL, , and , are the outlet pressure and inlet pressure of the two, respectively. , , , are the flow rate and cross-sectional area of ​​the two respectively. For the GDL as a whole, which is composed of GDB and MPL, its cross-sectional area is and thickness The following conditions are met:

[0143] Calculation 16

[0144] Formula 17

[0145] The pressure at the interface between GDB and MPL satisfies the following conditions:

[0146] Calculation 18

[0147] At the same time, the flow of GDL meets the following conditions:

[0148] Formula 19

[0149] From this, we can get the Darcy's law relationship for the entire GDL:

[0150] Calculation 20

[0151] Combining equations 14 and 15 with equation 20, we can get the equation for GDL permeability:

[0152] Formula 21

[0153] Based on calculation formulas 12, 13, and 21, the tortuosity, diffusivity, and permeability of the GDL can be calculated, respectively. Table 1 is a summary table of the superposition calculation results of mass transfer parameters.

[0154] Table 1

[0155]

[0156] The sixth step is to establish a three-dimensional numerical simulation model of the fuel cell based on the specific parameters of the actual fuel cell unit test fixture. The model is three-dimensional, multiphase, non-isothermal, and based on the following assumptions: the battery operates stably; the gas flow in the flow channel is incompressible and laminar; only the gas phase components in the flow channel are considered; the GDL is uniform and isotropic; the diffusion of gas in porous media is described by Fick's law; the influence of gravity is ignored; the electrochemical reaction in the catalyst layer is solved by the Butler-Volmer equation. The control equations are as follows:

[0157] The conservation equations for mass and momentum are:

[0158] Calculation 22

[0159] Calculation 23

[0160] in, is the porosity of the porous medium, is the fluid density, is the velocity vector of the fluid, is the fluid pressure, is the dynamic viscosity of the fluid, and are the mass source term and the momentum source term respectively.

[0161] Energy conservation equation:

[0162] Calculation 24

[0163] in, is the temperature, is the specific heat capacity, is the energy source term, is the effective thermal conductivity, which can be expressed as:

[0164] Calculation 25

[0165] in, and are the thermal conductivities of fluid and solid, respectively.

[0166] Species transport equation:

[0167] Calculation 26

[0168] in, For species The volume fraction of is the species source term, For species The effective diffusion coefficient can be expressed as: Calculation 27

[0169] in, is the water saturation, and are the local pressure and local temperature, respectively, For species In and The mass diffusion coefficient under .

[0170] Liquid water saturation equation:

[0171] Calculation 28

[0172] in, and are the density and viscosity of liquid water, is the condensation rate, is the capillary pressure, which can be expressed as:

[0173] Formula 29

[0174] in, is the surface tension, is the contact angle, is the absolute permeability.

[0175] Butler-Volmer equation:

[0176] Calculation 30

[0177] Calculation 31

[0178] Among them, the subscript and represent the anode and cathode respectively, is the volume transfer current, is the reference exchange current density, is the transmission coefficient, is the concentration dependence, is the effective specific surface area, is the local activation overpotential, which can be expressed as:

[0179] Calculation 32

[0180] Calculation 33

[0181] Among them, the subscript and represent the solid phase and membrane, respectively. is the electric potential, is the open circuit voltage.

[0182] Current conservation equation:

[0183] Calculation 34

[0184] Calculation 35

[0185] in, is the conductivity, is the volume transport current calculated by the Butler-Volmer equation, and the membrane conductivity It is expressed as:

[0186] Calculation 36

[0187] in, is the moisture content of the membrane.

[0188] The series of source terms that appear in the above equation are as follows:

[0189] Quality source term:

[0190] Calculation 37

[0191] Momentum source term:

[0192] Calculation 38

[0193] Energy source term:

[0194] Calculation 39

[0195] Species source term:

[0196] Calculation 40

[0197] Calculation 41

[0198] Calculation 42

[0199] Calculation 43

[0200] The boundary conditions include: all external surfaces are set to no-slip and zero-flux boundary types, the flow channel outlet is set to the pressure outlet boundary type, and the flow channel inlet is set to the mass flow inlet boundary type. The operating conditions of the simulation model can refer to the operating conditions set in the actual test. Subsequently, the various mass transfer parameters of the GDL calculated in the fourth step are input into the GDL submodule of the simulation model to simulate the output performance of the fuel cell under different GDL compression amounts. The solution of the model can be based on commercial computing software (such as ANSYS Fluent). The simulation results are a series of output current density data of the battery at different voltages, which can be plotted as a polarization curve. By comparing the battery output performance within the specified operating voltage range, the GDL compression corresponding to the optimal performance group can be intuitively determined, thereby achieving the optimization of the GDL compression amount. Table 2 and Figure 8 They are a summary table of simulation data and an example of a polarization curve diagram (for example, in the operating voltage range of 0.5V~0.6V, the preferred value of GDL compression is 60μm).

[0201] Table 2

[0202]

[0203] In addition, it should be noted that other methods can be used for the reconstruction of GDB, the reconstruction of MPL, and the generation of PTFE, as long as the numerical reconstruction method that can reproduce the sample morphology and pore characteristics well can be used. In addition, the method for extracting the mass transfer parameters of the diffusion layer is not necessarily based on the lattice Boltzmann method (LBM), but can be based on pore scale models, AVIZO software's built-in solver, and other computational fluid dynamics solution methods / software suitable for mesoscopic scale flows. The method for simulating the three-dimensional model of the fuel cell is not necessarily based on ANSYS Fluent software, but can be based on COMSOL software, OpenFOAM computing platform, etc.

[0204] This embodiment firstly realizes the extraction of key mass transfer parameters of GDL under compression state by three-dimensional reconstruction based on the characteristics of real samples and computational fluid dynamics simulation at mesoscopic scale, without relying on high-end characterization equipment and without developing new experimental equipment, thus significantly reducing the research cost (low cost). Furthermore, the mesoscopic simulation results are input into the three-dimensional numerical simulation model of the fuel cell, which not only ensures the reliability of the battery performance simulation data, but also constructs the corresponding relationship of "GDL compression state-GDL mass transfer parameters-monomer fuel cell performance", thereby realizing the rapid optimization of GDL compression amount (accurate, reliable, and efficient). In addition, this method can also be relatively easily extended to different models of GDL products without the need for long-term and high-cost actual battery performance testing (extensibility).

[0205] It should also be noted that the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, commodity or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, commodity or device. In the absence of more restrictions, the elements defined by the sentence "comprises a ..." do not exclude the existence of other identical elements in the process, method, commodity or device including the elements.

[0206] The above are only embodiments of the present application and are not intended to limit the present application. For those skilled in the art, the present application may have various changes and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included within the scope of the claims of the present application.

Claims

1. A method for determining an optimal value of compression of a fuel cell gas diffusion layer, characterized in that: The method comprises: Obtaining a gas diffusion layer sample, performing basic experimental characterization on the gas diffusion layer sample in an uncompressed state, and obtaining characteristic parameters of a gas diffusion backing and a microporous layer in the gas diffusion layer sample, wherein the characteristic parameters include stacking element size, porosity, polytetrafluoroethylene content, and initial thickness; Based on the characteristic parameters of the gas diffusion backing and the characteristic parameters of the microporous layer, the gas diffusion layer sample in an uncompressed state is three-dimensionally reconstructed to generate a reconstructed model in an uncompressed state; and a first control parameter is obtained to make the microscopic morphology and pore size distribution of the reconstructed model in an uncompressed state consistent with the actual microscopic morphology and pore size distribution of the gas diffusion layer sample; Performing a macro-scale compression load-displacement test and a micro-scale compression load-displacement test on the gas diffusion layer sample in an uncompressed state to determine the compressive modulus of the gas diffusion backing and the compressive modulus of the microporous layer in the gas diffusion layer sample; and calculating the thickness and porosity of the gas diffusion backing and the thickness and porosity of the microporous layer under different compression amounts based on the compressive modulus and initial thickness of the gas diffusion backing and the compressive modulus and initial thickness of the microporous layer; Taking the porosity of the gas diffusion backing and the porosity of the microporous layer at different compression amounts as constraints, and utilizing the first control parameter, three-dimensionally reconstructing the gas diffusion layer samples at different compression amounts to generate reconstructed models at different compression amounts; The reconstructed model under different compression amounts is used as the calculation domain to perform mesoscopic computational fluid dynamics simulation to extract the key mass transfer parameters of the gas diffusion backing and the key mass transfer parameters of the microporous layer under different compression amounts; and based on the key mass transfer parameters and thickness of the gas diffusion backing and the key mass transfer parameters and thickness of the microporous layer under different compression amounts, according to the physical definition of the key mass transfer parameters and the property superposition formula of the series transport element, the key mass transfer parameters of the gas diffusion layer sample under different compression amounts are calculated; the key mass transfer parameters include tortuosity, diffusivity and permeability; Obtain fuel cell cell test fixture parameters, and establish a fuel cell three-dimensional numerical simulation model based on the fuel cell cell test fixture parameters; input key mass transfer parameters of the gas diffusion layer samples under different compression amounts into the gas diffusion layer submodule in the fuel cell three-dimensional numerical simulation model for simulation, obtain the output performance of the fuel cell under different gas diffusion layer compression amounts output by the fuel cell three-dimensional numerical simulation model, and determine the gas diffusion layer compression amount corresponding to the optimal output performance among the output performance as the preferred compression amount value.

2. The method for determining the optimal value of the compression amount of the fuel cell gas diffusion layer according to claim 1, characterized in that: The gas diffusion layer sample in an uncompressed state is subjected to basic experimental characterization to obtain characteristic parameters of the gas diffusion backing and the microporous layer in the gas diffusion layer sample, including: photographing the gas diffusion layer sample using a scanning electron microscope to determine the stacking element size and initial thickness of the gas diffusion backing and the stacking element size and initial thickness of the microporous layer in the gas diffusion layer sample; Testing the gas diffusion layer sample using a mercury intrusion method to determine the overall porosity of the gas diffusion layer sample; Testing the gas diffusion layer sample using inductively coupled plasma emission spectroscopy to determine the total content of polytetrafluoroethylene in the gas diffusion layer sample; Based on the overall porosity of the gas diffusion layer sample, the total polytetrafluoroethylene content in the gas diffusion layer sample, the initial thickness of the gas diffusion backing and the initial thickness of the microporous layer in the gas diffusion layer sample, and the known initial porosity and polytetrafluoroethylene content of the gas diffusion backing, the initial porosity and polytetrafluoroethylene content of the microporous layer are calculated using the following calculation formula: Formula (1) Formula (2) in, represents the overall porosity of the GDL sample, represents the initial porosity of the gas diffusion backing, represents the initial thickness of the gas diffusion backing, represents the initial porosity of the microporous layer, represents the initial thickness of the microporous layer, Indicates the total content of polytetrafluoroethylene in the gas diffusion layer sample, Indicates the PTFE content of the gas diffusion backing, Indicates the polytetrafluoroethylene content of the microporous layer.

3. The method for determining the optimal value of the compression amount of the fuel cell gas diffusion layer according to claim 1, characterized in that: Performing a macro-scale compression load-displacement test and a micro-scale compression load-displacement test on the gas diffusion layer sample in an uncompressed state to determine the compressive modulus of the gas diffusion backing and the compressive modulus of the microporous layer in the gas diffusion layer sample, comprising: Using a universal testing machine to test the gas diffusion layer sample, to obtain a load-displacement curve of the overall compression of the gas diffusion layer sample; based on the load-displacement curve of the overall compression of the gas diffusion layer sample, to determine the compressive modulus of the gas diffusion layer sample; Testing the gas diffusion layer sample using a microindenter to obtain a load-displacement curve of compression of the microporous layer in the gas diffusion layer sample; determining the compressive modulus of the microporous layer based on the load-displacement curve of compression of the microporous layer; The compressive modulus of the gas diffusion backing is determined based on the compressive modulus of the gas diffusion layer sample and the compressive modulus of the microporous layer.

4. The method for determining the optimal value of the compression amount of the fuel cell gas diffusion layer according to claim 3, characterized in that: Determining the compressive modulus of the gas diffusion layer sample based on a load-displacement curve of the overall compression of the gas diffusion layer sample comprises: Dividing the load-displacement curve of the overall compression of the gas diffusion layer sample into three intervals, and determining the starting point coordinates and the end point coordinates of the microstructure compression interval in the three intervals according to the division results; According to the starting point coordinates and the end point coordinates of the microstructure compression interval in the three intervals, the compressive modulus of the gas diffusion layer sample is calculated using the following calculation formula: N-type (3) in, represents the compressive modulus of the gas diffusion layer sample, represents the compressive modulus of the gas diffusion backing, represents the compressive modulus of the microporous layer, and are the starting and ending coordinates of the microstructure compression interval in the three intervals, and the units of the horizontal and vertical coordinates are mm and kN respectively. is the cut area of ​​the gas diffusion layer sample, is the thickness of a single gas diffusion layer sample, and N is the number of single gas diffusion layer samples.

5. The method for determining the optimal value of the fuel cell gas diffusion layer compression according to claim 3, characterized in that: Determining the compressive modulus of the microporous layer based on a load-displacement curve of compression of the microporous layer comprises: Dividing the load-displacement curve of the microporous layer compression into two intervals, and determining the starting point coordinates and the ending point coordinates of the microstructure compression interval in the two intervals according to the division result; According to the starting point coordinates and the end point coordinates of the microstructure compression interval in the two intervals, the compressive modulus of the microporous layer is calculated using the following calculation formula: Formula (4) in, represents the compressive modulus of the microporous layer, and are the starting point coordinates and the end point coordinates of the microstructure compression interval in the two intervals, and the horizontal and vertical coordinate units are μm and mN respectively. is the contact surface area of ​​the load indenter of the micro-indenter, is the thickness of the microporous layer.

6. The method for determining the optimal value of the fuel cell gas diffusion layer compression according to claim 1, characterized in that: Based on the compressive modulus and initial thickness of the gas diffusion backing and the compressive modulus and initial thickness of the microporous layer, the porosity and thickness of the gas diffusion backing and the porosity and thickness of the microporous layer at different compression amounts are calculated, including: Based on the compressive modulus and initial thickness of the gas diffusion backing and the compressive modulus and initial thickness of the microporous layer, the compression amount of the gas diffusion backing and the compression amount of the microporous layer at different compression amounts are calculated using the following calculation formula: Formula (5) in, represents the compression of the gas diffusion backing, represents the compression amount of the microporous layer, and The sum is the overall compression of the gas diffusion layer, represents the initial thickness of the gas diffusion backing, represents the initial thickness of the microporous layer, represents the compressive modulus of the gas diffusion backing, represents the compressive modulus of the microporous layer; According to the compression amount of the gas diffusion backing and the compression amount of the microporous layer, the porosity and thickness of the gas diffusion backing and the porosity and thickness of the microporous layer at different compression amounts are calculated using the following calculation formula: Formula (6) Formula (7) Formula (8) Formula (9) in, represents the porosity of the gas diffusion backing at different compression amounts, represents the initial thickness of the gas diffusion backing, represents the porosity of the gas diffusion backing in the uncompressed state, represents the compression of the gas diffusion backing, represents the porosity of the microporous layer under different compression amounts, represents the initial thickness of the microporous layer, represents the porosity of the microporous layer in the uncompressed state, represents the compression amount of the microporous layer, represents the thickness of the gas diffusion backing at different compression amounts, Represents the thickness of the microporous layer under different compression amounts.

7. The method for determining the optimal value of the fuel cell gas diffusion layer compression according to claim 1, characterized in that: The reconstructed model includes a representative volume unit model of the gas diffusion backing and a representative volume unit model of the microporous layer. The reconstructed model under different compression amounts is used as a calculation domain to perform mesoscopic scale computational fluid dynamics simulation to extract key mass transfer parameters of the gas diffusion backing and the microporous layer under different compression amounts, including: The key mass transfer parameters of the gas diffusion backing and the key mass transfer parameters of the microporous layer under different compression amounts are extracted using the following calculation formula: Formula (10) Formula (11) Formula (12) in, is the tortuosity of the gas diffusion backing or microporous layer, is the representative volume element model of the gas diffusion backing or the representative volume element model of the microporous layer, is the coordinate indicator of the computational domain, The representative volume unit model of the gas diffusion backing or the representative volume unit model of the microporous layer are the velocity components in the three directions of the calculation domain, is the diffusivity of the gas diffusion backing or microporous layer, is the porosity of the gas diffusion backing or microporous layer, is the permeability of the gas diffusion backing or microporous layer, is the representative volume cell model of the gas diffusion backing or the representative volume cell model of the microporous layer, is the average flow velocity in the computational domain, is the representative volume cell model of the gas diffusion backing or the representative volume cell model of the microporous layer is the fluid dynamic viscosity of the computational domain, is the representative volume unit model of the gas diffusion backing or the representative volume unit model of the microporous layer, is the length of the computational domain in the flow direction, The representative volume cell model of the gas diffusion backing or the representative volume cell model of the microporous layer is the pressure difference between the outlet and the inlet of the computational domain.

8. The method for determining the optimal value of the compression amount of the fuel cell gas diffusion layer according to claim 1, characterized in that: Based on the key mass transfer parameters and thickness of the gas diffusion backing and the key mass transfer parameters and thickness of the microporous layer at different compression amounts, according to the physical definition of the key mass transfer parameters and the property superposition formula of the series transport element, the key mass transfer parameters of the gas diffusion layer samples at different compression amounts are calculated, including: The key mass transfer parameters of the gas diffusion layer samples under different compression amounts were calculated using the following formula: Formula (13) Formula (14) Formula (15) in, represents the tortuosity of the gas diffusion layer sample, Indicates the tortuosity of the gas diffusion backing, Represents the tortuosity of the microporous layer, represents the thickness of the gas diffusion backing, represents the thickness of the microporous layer, represents the diffusion rate of the gas diffusion layer sample, represents the porosity of the gas diffusion backing, represents the porosity of the microporous layer, represents the permeability of the gas diffusion layer sample, represents the permeability of the gas diffusion backing, Represents the permeability of the microporous layer.

9. The method for determining the optimal value of the compression amount of the fuel cell gas diffusion layer according to claim 1, characterized in that: Based on the fuel cell monomer test fixture parameters, a fuel cell three-dimensional numerical simulation model is established, including: The assumptions of the three-dimensional numerical simulation model of the fuel cell are: The battery operates stably; the gas flow in the flow channel is incompressible and laminar; only the gas phase components in the flow channel are considered; the gas diffusion layer sample is uniform and isotropic; the diffusion of gas in porous media is described by Fick's law; the influence of gravity is ignored; the electrochemical reaction in the catalyst layer is solved by the Butler-Volmer equation; The control equation of the three-dimensional numerical simulation model of the fuel cell is as follows: The conservation equations for mass and momentum are: Formula (16) Formula (17) in, is the porosity of the porous medium, is the fluid density, is the velocity vector of the fluid, is the fluid pressure, is the dynamic viscosity of the fluid, and are the mass source term and the momentum source term respectively; Energy conservation equation: Formula (18) in, is the temperature, is the specific heat capacity, is the energy source term, is the effective thermal conductivity, expressed as: ,in, and are the thermal conductivity of fluid and solid respectively; Species transport equation: Formula (19) in, For species The volume fraction of is the species source term, For species The effective diffusion coefficient is expressed as: ,in, is the water saturation, and are the local pressure and local temperature, respectively, For species In and The mass diffusion coefficient under Liquid water saturation equation: Formula (20) in, and are the density and viscosity of liquid water, is the condensation rate, is the capillary pressure, expressed as: ,in, is the surface tension, is the contact angle, is the absolute permeability; Butler-Volmer equation: Formula (21) Formula (22) Among them, the subscript and represent the anode and cathode respectively, is the volume transfer current, is the reference exchange current density, is the transmission coefficient, is the concentration dependence, is the effective specific surface area, is the local activation overpotential, expressed as: , where the subscript and represent the solid phase and membrane, respectively. is the electric potential, is the open circuit voltage; Current conservation equation: Formula (23) Formula (24) in, is the conductivity, is the volume transport current calculated by the Butler-Volmer equation, and the membrane conductivity It is expressed as: ,in, is the moisture content of the membrane; The series of source terms that appear in the above equation are as follows: Quality source term: Momentum source term: Energy source term: Species source term: The boundary conditions include: all external surfaces are set to no-slip and zero-flux boundary types, the flow channel outlet is set to pressure outlet boundary type, and the flow channel inlet is set to mass flow inlet boundary type.

10. The method for determining the optimal value of the fuel cell gas diffusion layer compression according to claim 1, characterized in that: The output performance is a series of output current density data of the fuel cell at different voltages; and the compression amount of the gas diffusion layer corresponding to the best output performance among the output performance is determined as the preferred value of the compression amount, including: Plotting a series of output current density data of the fuel cell at different voltages into a polarization curve diagram; The data of the polarization curve diagram within the specified working voltage range are compared, and the compression amount of the gas diffusion layer corresponding to the optimal data is determined as the preferred compression amount value.

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