Flow field structure optimization method based on vibration characteristics of proton exchange membrane fuel cell and proton exchange membrane fuel cell

Abstract

The invention discloses a proton exchange membrane fuel cell vibration characteristic-based cell flow field structure optimization method and a cell thereof. The method comprises the following steps: S1, establishing a three-dimensional finite element model of the cell; s2, determining each order vibration mode and inherent frequency of the fuel cell; s3, the acceleration power spectral density of the random vibration load is loaded, and the structural response characteristics of the fuel cell assembly under three-axis random vibration excitation are obtained; s4, designing a pre-optimization scheme of each flow field structure, and establishing a geometric model of the flow field structure; s5, obtaining a maximum stress and a stress distribution coefficient; s6, obtaining a final optimization scheme; the battery has the flow field structure obtained by any optimization method; according to the invention, modal analysis is carried out by establishing a three-dimensional finite element model and a modal superposition method, the width ratio of the flow channel to the ridge is improved, the stress concentration and deformation of the assembly are obviously reduced in a dynamic load environment, and the stability of the fuel cell is improved; through simulation and optimization design, the stress distribution coefficient is reduced, so that the stress distribution of the fuel cell is more uniform, the performance of the cell is remarkably improved, and the service life of the cell is remarkably prolonged.

Description

Technical Field

[0001] The invention relates to the technical field of proton exchange membrane fuel cells, and in particular to a battery flow field structure optimization method based on the vibration characteristics of a proton exchange membrane fuel cell and a battery thereof. Background Art

[0002] A fuel cell is a device that converts the chemical energy of a fuel directly into electrical energy through an electrochemical reaction. It has the advantages of low noise, zero pollution, and high efficiency. Among them, proton exchange membrane fuel cells can be widely used in transportation, aerospace, portable power sources, and other fields because of their low operating temperature. Proton exchange membrane fuel cells are mainly composed of flow field plates, gas diffusion layers, catalyst layers, and proton exchange membranes. The flow field structure inside the flow field plate plays an important role in the temperature distribution, current density distribution, water distribution, etc. inside the battery, and significantly affects the local and overall performance of the battery. Therefore, designing the battery flow field structure can not only improve the overall performance of the proton exchange membrane fuel cell, but also help to extend its service life.

[0003] At present, the performance optimization of proton exchange membrane fuel cells is mainly focused on the optimization of conventional flow field structure and bionic flow field design. Conventional flow fields include serpentine flow fields, parallel flow fields and interdigital flow field structures. By adding blocks and changing their shapes in conventional flow fields, the mass transfer and water management capabilities in the battery can be improved, and the gas distribution in the flow field can be promoted, thereby improving the water distribution, temperature distribution and electrochemical reaction rate in the battery, improving the uniformity of the distribution of its key parameters, and effectively improving the performance of the battery. The bionic flow field uses the bionic similarity theory to design the bionic flow field structure of the fuel cell, so that it has the characteristics of low fluid flow resistance and uniform distribution in the biological structure, ensuring excellent heat and mass transfer channels in the battery, and effectively improving the heat dissipation performance and power density of the battery. However, the service environment of fuel cells is complex, and the internal components of the battery will experience stress concentration and warping deformation under the action of environmental loads, resulting in a significant reduction in the porosity of the components, cracks and delamination, causing the battery performance to drop sharply or even fail.

[0004] Chinese patent CN115621484B discloses a method for optimizing the flow field of a polymer membrane fuel cell, which relies on three-dimensional numerical simulation. By constructing a numerical model of the fuel cell, based on the actual boundary conditions of the battery during operation, such as temperature, pressure, and gas concentration, the gas flow, reaction rate, and heat distribution in the battery are simulated and calculated, and the key simulation parameters that affect the performance of the fuel cell are extracted. The flow field structure is designed accordingly, such as by embedding pins to form a honeycomb structure to promote uniform gas distribution and improve battery performance. However, this method only analyzes the effect of the flow field structure on the reaction rate of the gas in the battery, lacks quantitative analysis of its stress deformation response in the battery, and cannot link the layout of the battery stack and the effect of the generated stress value on the life of the battery material.

[0005] In his master's thesis on the design and thermal management theory of PEM fuel cell variable diameter flow channels based on enhanced mass transfer, Ye Zhijie emphasized the effectiveness of using enhanced mass transfer and pressure difference principles in PEMFC flow channel design. By integrating complex fluid dynamics and geometric changes (such as bosses and variable diameters), the optimized flow channels are significantly superior to traditional designs in terms of power density, uniformity of reactant distribution, and operational stability. However, this study focused on a single flow channel design, without comprehensively considering the optimization scheme from the perspective of the interaction between components within the cell, which means that it is unable to reflect the stress of the components and the impact of the uniformity of stress distribution on fuel cell performance.

[0006] Liang Fengli, Wen Ranran, et al., in the article "Research Status and Development Trends of PEMFC Flow Channel Structure" published in the Journal of Nanjing University of Aeronautics and Astronautics (2021, 53(04): 477-503), summarized the types of bipolar plates and the specific parameters that need to be considered when optimizing the design of bipolar plate flow channel structures. By selecting appropriate and effective bipolar plate geometry designs, problems such as poor water management, uneven distribution of reactants, and uneven current distribution can be solved. The modifications to the flow channel design in this study may cause significant changes in the fluid dynamics characteristics, including but not limited to the distribution of internal pressure and fluid velocity. The disadvantages of this study are that some flow channel designs are too complex, which increases manufacturing costs and difficulty, high pressure drop problems reduce system efficiency, and insufficient water and thermal management may also lead to performance and life problems. Summary of the invention

[0007] In order to overcome the shortcomings of the above-mentioned prior art, the purpose of the present invention is to provide a flow field structure optimization method based on the vibration characteristics of proton exchange membrane fuel cells. Through a three-dimensional finite element model and a modal superposition method, the stress distribution and distribution coefficient of the random vibration response of the battery assembly are obtained, the performance of the battery under different dynamic loads is simulated and analyzed in detail, and the battery structure response performance under different flow field structures is compared and analyzed to achieve battery flow field structure optimization. This can significantly reduce the stress concentration and warping deformation of the battery assembly, and ensure the long-term stable operation of the battery in harsh environmental conditions.

[0008] To achieve the above object, the present invention provides the following technical solutions:

[0009] A method for optimizing the flow field structure of a proton exchange membrane fuel cell based on its vibration characteristics comprises the following steps:

[0010] Step S1, establishing a three-dimensional finite element model of the proton exchange membrane fuel cell based on the physical size and structure of the battery; the three-dimensional finite element model of the battery is specifically as follows: end plates are respectively arranged on both sides, and current collecting plates, flow field plates, sealing gaskets, gas diffusion layers (GDL), catalyst coating membranes (CCM) and proton exchange membranes (PEM) are respectively arranged in sequence from the end plates on both sides to the middle;

[0011] Step S2, using the three-dimensional finite element model obtained in step S1 to perform modal analysis on the proton exchange membrane fuel cell using a modal superposition method to determine each vibration mode and natural frequency of the fuel cell;

[0012] Step S3, applying the acceleration power spectrum density of the random vibration load of each vibration mode and natural frequency of the proton exchange membrane fuel cell obtained in step S2 to the three-dimensional finite element model of the proton exchange membrane fuel cell to obtain the structural response characteristics of the proton exchange membrane fuel cell assembly under triaxial random vibration excitation;

[0013] Step S4, combining the structural response characteristics of the proton exchange membrane fuel cell under triaxial random vibration excitation obtained in step S3, dividing the flow channel of the proton exchange membrane fuel cell into n×m regions according to the flow direction of the gas from the inlet to the outlet and numbering them, performing a pre-optimization scheme design on the flow field structure of each region, and establishing flow field structure geometric models with different flow channels and ridge width ratios, respectively, wherein n is greater than or equal to 3, and m is greater than or equal to 3;

[0014] Step S5, combining the structural response characteristics obtained in step S3 and the flow field structure geometric models with different flow channels and ridge width ratios established in step S4, evaluating and calculating the vibration response of the key components of the proton exchange membrane fuel cell under various flow field structures, and obtaining the maximum stress and stress distribution coefficient of the vibration response of the key components of the proton exchange membrane fuel cell under the flow field structures with different flow channels and ridge width ratios;

[0015] Step S6: Compare the flow field structure optimization schemes to determine the flow field structure with the smallest proton exchange membrane fuel cell component response stress and stress distribution coefficient, and select the final optimization scheme.

[0016] The step S1 of establishing a three-dimensional finite element model includes the following process:

[0017] The key components of the proton exchange membrane fuel cell and their specific dimensions are determined, and based on these data, the end plate, collector plate, flow field plate, sealing gasket, gas diffusion layer (GDL), catalyst coating membrane (CCM) and proton exchange membrane (PEM) of the proton exchange membrane fuel cell are constructed, and the material properties of the key components of the proton exchange membrane fuel cell, including density, elastic modulus and Poisson's ratio, are defined; a three-dimensional finite element model of the proton exchange membrane fuel cell is constructed according to the above key components and their specific dimensions and material properties, and meshing is performed using second-order hexahedral units, and the meshes of the catalyst coating membrane (CCM) and the gas diffusion layer (GDL) are refined; loading boundary conditions, including several groups of bolt-nut fasteners with a tightening torque of 5-9 N·m, an applied assembly load, and a temperature load of a constant power heat source loaded on the cathode side of the proton exchange membrane (PEM) at different ambient temperatures to simulate the redox reaction on the cathode side and reflect the thermal stress on the catalyst coating membrane (CCM).

[0018] In step S2, the vibration modes and natural frequencies of the proton exchange membrane fuel cell are determined, and the mass matrix M and stiffness matrix K of the system are generated using finite element analysis software; by solving the eigenvalue problem (K-ω 2 M)Φ=0, and the natural frequency ω of the proton exchange membrane fuel cell is obtained i and the corresponding vibration mode Φ i , extract the natural frequencies and vibration modes of each order from the solution.

[0019] The acceleration power spectral density of the random vibration load in step S3 refers to applying three-axis random vibration excitation to the proton exchange membrane fuel cell. In the random vibration analysis module, the acceleration power spectral density (PSD) function in the three-axis directions of X, Y, and Z is defined, and then applied as a random vibration load to the relevant parts of the three-dimensional finite element model of the proton exchange membrane fuel cell, ensuring that the acceleration power spectral density (PSD) function is applied in the three directions of X, Y, and Z, so as to obtain the displacement, velocity and acceleration response of the proton exchange membrane fuel cell, as well as the stress and strain of the structure under different operating conditions.

[0020] The pre-optimization scheme for each flow field structure in step S4 is designed as follows: a geometric model of a flow field structure with different channel and ridge width ratios is established, and different channel widths and ridge widths, as well as different channel and ridge width ratios, are obtained by changing the channel and ridge widths, with the channel and ridge widths being between 1 and 2 mm.

[0021] In step S5, the maximum stress and stress distribution coefficient of the vibration response of the key components of the proton exchange membrane fuel cell under the flow field structures with different flow channels and ridge width ratios are obtained, specifically: based on the modal analysis results of the prestressed force, the mechanical response law of the fuel cell under the random vibration excitation of the three axes of Z axis, Y axis and Z axis is calculated. For the transient dynamic analysis, the explicit central difference method is used at 0, ..., t n When the time step solution is known, solve t n+1 The solution of the time step rewrites the dynamic equilibrium differential equation as:

[0022]

[0023] In the formula, n represents the nth time point; For external forces, Arrange for inner strength;

[0024] The central difference method is used to integrate the acceleration over time to obtain the velocity at the midpoint of the current incremental step:

[0025]

[0026] In the formula, Indicates at time The velocity vector, Indicates at time The velocity vector, At time t n The acceleration vector of

[0027] This is then added to the displacement at the start of the increment to determine the displacement at the end of the increment:

[0028]

[0029] In the formula, {u(t n+1 )} means at time t n+1 The displacement vector, {u(t n )} means at time t n The displacement vector of

[0030] The above integral formula can be used to calculate the displacement, velocity and acceleration of each discrete time point in the entire time domain, and then the unit stress can be determined through the material constitutive relationship;

[0031] After superimposing the unit stiffness of the unit in the calculation domain and the temperature-variable equivalent node load, the overall stiffness equation of the temperature load is obtained:

[0032]

[0033] In the formula, is the unit temperature equivalent node load matrix [K] is the overall stiffness matrix;

[0034] The thermal deformation {q} of the computational domain node can be obtained by solving the problem. The thermal deformation of any point in the computational domain can be calculated by interpolation function. Then it is substituted into the physical equation of the unit:

[0035] {σ} e =[D]({ε} e -{ε} t )=[D][B]q e -[D]·α·ΔT[1 1 1 0 0 0] T

[0036] Where, {σ} e represents the effective stress vector, [D] represents the material stiffness matrix, {ε} e represents the total strain vector, {ε} t represents the thermal strain vector caused by temperature change, α represents the thermal expansion coefficient of the material, and ΔT represents the temperature change;

[0037] Thus the corresponding thermal stress can be obtained;

[0038] By observing the results of frequency domain analysis, the vibration response analysis of each area of ​​each flow field structure design is carried out, and its maximum stress and average stress are collected. By calculating the ratio of maximum stress to average stress, its stress distribution coefficient is calculated.

[0039] The step S6 is to obtain the optimal flow channel structure by comparing the average stress and stress distribution coefficient of each ridge under three-dimensional random vibration at different flow channel ridge width ratios.

[0040] A battery has a flow field structure obtained by any of the above-mentioned battery flow field structure optimization methods.

[0041] Compared with the prior art, the present invention has the following beneficial effects:

[0042] 1. The present invention establishes a three-dimensional finite element model and adopts the modal superposition method to perform modal analysis, applies random vibration loads in the three directions of Z, Y, and Z, and significantly reduces the stress concentration and deformation of the components under dynamic load conditions by improving the width ratio of the flow channel and the ridge, thereby improving the mechanical stability and reliability of the fuel cell.

[0043] 2. The present invention introduces the concept of stress distribution coefficient to characterize the uniformity and stability of component structural response under dynamic load. Through simulation analysis and optimized design, the stress distribution coefficient is reduced, making the stress distribution of the fuel cell more uniform, thereby significantly improving the performance and service life of the battery.

[0044] In summary, the present invention establishes a three-dimensional finite element model and adopts the modal superposition method to perform modal analysis, and introduces the concept of stress distribution coefficient. Through simulation analysis and optimization design, the response performance of the battery structure under different flow field structures is compared and analyzed, and the battery flow field structure is optimized. It can significantly reduce the stress concentration and warping deformation of the battery assembly, and ensure the long-term stable operation of the battery in harsh environmental conditions. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] Figure 1 The present invention is a flow chart of the method.

[0046] Figure 2 It is a schematic diagram of the battery structure of the present invention.

[0047] Figure 3 This is a diagram of the battery local area block division of the present invention.

[0048] Figure 4 is the deformation distribution of the proton exchange membrane (PEM) under different bolt torques of the present invention; wherein, Figure 4 (a) is the deformation distribution diagram of proton exchange membrane (PEM) under the bolt torque of 5N·m. Figure 4 (b) is the deformation distribution diagram of the proton exchange membrane (PEM) under the bolt torque of 7N·m. Figure 4 (c) is the deformation distribution diagram of the proton exchange membrane (PEM) under the bolt torque of 9 N·m.

[0049] Figure 5 Graph showing the von-Mises stress under the ridge under different bolt preloads of the present invention.

[0050] Figure 6 3σ average von-Mises stress diagram of the gas diffusion layer of the present invention under random vibration at different temperatures.

[0051] Figure 7 It is a stress distribution diagram of the local area under different flow field structures of the present invention.

[0052] Figure 8 It is a dimensionless stress distribution diagram under different flow channel structures of the present invention. DETAILED DESCRIPTION

[0053] In order to more clearly illustrate the technical solution and advantages of the present invention, the present invention is described in detail below in combination with specific implementation steps. It should be noted that the implementation steps described below are only used to explain the present invention, rather than to limit the present invention.

[0054] Reference Figure 1 The specific steps of the present invention are as follows:

[0055] A method for optimizing a battery flow field structure comprises the following steps:

[0056] Step S1: Establish a three-dimensional finite element model of the proton exchange membrane fuel cell based on its physical size and structure. The specific steps are as follows:

[0057] Step S1.1: Create the basic geometry of each component in Unigraphics (UG) software according to the actual size of the fuel cell, and define the basic size and thickness of each component. Figure 2 As shown: Among them, the size of the gas diffusion layer (GDL) is 40mm×40mm, the thickness is 0.3mm, the size of the flow field plate is 76mm×76mm, the thickness is 13mm, the size of the sealing gasket is 76mm×76mm, the thickness is 0.362mm, the size of the current collector is 76mm×108mm, the thickness is 1.3mm, the size of the end plate is 108mm×108mm, the thickness is 19mm, and finally all the components are assembled: when starting the assembly process, the gas diffusion layer is placed on the flow field plate first, followed by the sealing gasket to prevent gas leakage; then, the catalyst layer and the proton exchange membrane are installed, the current collector is placed on both sides of the flow field plate, and finally covered with the end plate and the entire structure is fixed with bolts and nuts according to the specified torque;

[0058] Step S1.2, importing the geometric structure obtained in step S1.1 into the finite element analysis software, and determining the material properties of various parts of the fuel cell, including density, elastic modulus, Poisson's ratio, thermal conductivity, thermal expansion coefficient and specific heat capacity;

[0059] Step S1.3, simplifying the model, simplifying the meshing, and reducing the computational cost; deleting fillets and chamfers in the model to avoid generating smaller and more complex mesh units, reducing the mesh generation time, and reducing the demand for computing resources; meshing the three-dimensional model, with the global mesh being mainly a second-order hexahedral unit mesh, and meshing the gas diffusion layer (GDL) to obtain multiple model regions with different mesh densities;

[0060] Step S1.4, boundary condition loading is performed on the three-dimensional model, the assembly load is that the fuel cell is assembled by 8 sets of bolt-nut fasteners on its end plate, and each set of bolt-nut has a tightening torque of 7 N·m for the fuel cell, and the temperature load refers to the operation of the proton exchange membrane fuel cell as a whole at room temperature and the electrochemical reaction heat at the cathode catalyst layer;

[0061] Step S2: determine the vibration modes and natural frequencies of the fuel cell, and use the modal superposition method to perform modal analysis on the fuel cell. The specific steps are as follows:

[0062] Step S2.1, select modal analysis as the analysis type in the ANSYS working environment, and obtain the natural frequency and corresponding vibration mode of the system through software calculation;

[0063] Step S2.2, select the main mode that has the greatest impact on the system response, and use the modal superposition method to perform weighted superposition to calculate the total response. The obtained dynamic response results are post-processed and analyzed in the finite element software, and the total displacement, stress distribution, strain analysis, safety factor, and frequency response formed by the superposition of each main mode are viewed in the Post-Processing tool of ANSYS;

[0064] Step S3, combining the vibration modes and natural frequencies of the fuel cell obtained in step S2, loading the acceleration power spectrum density of the random vibration load, and obtaining the structural response characteristics of the fuel cell assembly under triaxial random vibration excitation, the specific steps are as follows:

[0065] Step S3.1, define the acceleration power spectral density (PSD) function in ANSYS and apply it in the Z, Y, and Z directions;

[0066] Define the power spectral density function S of the stationary random process Z(t) x (ω) is the autocorrelation function R x The Fourier transform of (τ) is:

[0067]

[0068] Due to S x (ω) is an even function, which can convert the spectral density in the negative frequency range into the positive frequency range to obtain the unilateral power spectral density G x (ω), that is:

[0069]

[0070] Step S3.2, using the frequency domain analysis method, calculate the structural response of each point of the component within a predefined frequency range, such as displacement, velocity, acceleration, and stress; view it through a post-processing tool, and generate a response spectrum and a stress distribution diagram;

[0071] Step S4: designing pre-optimization schemes for each flow field structure, and establishing flow field structure geometric models with different flow channels and ridge width ratios. The specific steps are as follows:

[0072] Step S4.1, refer to Figure 3, the flow channel of the fuel cell is divided into 3×3 areas according to the flow direction of the gas from the inlet to the outlet and numbered, the average stress value of the gas diffusion layer at each area is measured, and the stress difference between different areas is compared to evaluate the reliability of different areas under normal working conditions. The area with small and uniform stress distribution has a longer life, avoiding local stress concentration and enhancing the durability and reliability of the overall structure; the area with large and uneven stress distribution has a shorter life, and stress concentration will accelerate the fatigue, crack formation and expansion of local materials, increase the possibility of early failure, and lead to a decrease in the durability and reliability of the overall structure;

[0073] Step S4.2, changing the width of the flow channel and the ridge to obtain different flow field structures, adjusting the width ratio of the flow channel and the ridge from the original 2:2 (unit: mm) to three new ratios: 1:1, 1:2 and 2:1;

[0074] Step S5: combining the response spectrum and stress distribution diagram of step (3) and the different flow field structures of step (4), obtaining the maximum stress and stress distribution coefficient of the vibration response of the key components of the battery under different flow field structures, the specific steps are as follows:

[0075] Step S5.1, based on the modal analysis results of prestress, calculate the mechanical response law of the fuel cell under the random vibration excitation of the Z axis, Y axis, and Z axis. For transient dynamic analysis, use the explicit central difference method at 0, ..., t n When the time step solution is known, solve t n+1 The solution of the time step rewrites the dynamic equilibrium differential equation as:

[0076]

[0077] In the formula, n represents the nth time point; For external forces, Arrange for inner strength;

[0078] The central difference method is used to integrate the acceleration over time to obtain the velocity at the midpoint of the current incremental step:

[0079]

[0080] In the formula, Indicates at time The velocity vector, Indicates at time The velocity vector, At time t n The acceleration vector

[0081] This is then added to the displacement at the start of the increment to determine the displacement at the end of the increment:

[0082]

[0083] In the formula, {u(t n+1 )} means at time t n+1 The displacement vector, {u(t n )} means at time t n The displacement vector of

[0084] The above integral formula can be used to calculate the displacement, velocity and acceleration of each discrete time point in the entire time domain, and then the unit stress can be determined through the material constitutive relationship;

[0085] Step S5.2: After superimposing the unit stiffness and temperature equivalent node load of the unit in the calculation domain, the overall stiffness equation is obtained:

[0086]

[0087] In the formula, is the unit temperature equivalent node load matrix, [K] is the overall stiffness matrix;

[0088] The thermal deformation {q} of the computational domain node can be obtained by solving the problem. The thermal deformation of any point in the computational domain can be calculated by interpolation function. Then it is substituted into the physical equation of the unit:

[0089] {σ} e =[D]({ε} e -{ε} t )=[D][B]q e -[D]·α·ΔT[1 1 1 0 0 0] T

[0090] Where, {σ} e represents the effective stress vector, [D] represents the material stiffness matrix, {ε} e represents the total strain vector, {ε} t represents the thermal strain vector caused by temperature change, α represents the thermal expansion coefficient of the material, and ΔT represents the temperature change;

[0091] Thus the corresponding thermal stress can be obtained;

[0092] Step S5.3: In step S3, the vibration response analysis is performed on each region of each flow field structure design by observing the frequency domain analysis results, such as Figure 4 , collect its maximum stress and average stress, so as to determine the stress concentration area on the gas diffusion layer;

[0093] Step S5.4, reference Figure 5According to the average stress value and stress distribution obtained in step S5.3, the stress distribution coefficient of the component under different flow field structures is calculated according to the different flow field structure geometric models established in step S4, where the calculation formula of the average stress is as follows:

[0094]

[0095] In the formula, is the mean stress, n is the total number of data points, σ i is the stress value of the ith data point

[0096] Calculate the stress average value of each region from region 1 to region 9 on the gas diffusion layer; determine the maximum stress value in different regions, and calculate the stress distribution coefficient of different regions. The calculation formula is as follows:

[0097]

[0098] Where K is the stress distribution coefficient, σ max is the maximum stress, is the mean stress;

[0099] Step S6, compare the flow field structure optimization schemes among the flow field structure optimization schemes, and find the flow field structure with the smallest battery component response stress and stress distribution coefficient as the final optimization scheme. By comparing the average stress and stress distribution coefficient of each ridge under three-dimensional random vibration at different flow channel ridge width ratios, the optimal flow channel structure optimization method is obtained.

[0100] A battery has a flow field structure obtained by any of the above-mentioned battery flow field structure optimization methods.

[0101] The above description is only a specific embodiment of the present invention and does not constitute any limitation to the present invention. Obviously, for professionals in this field, after understanding the content and design principles of the present invention, they may make various modifications and changes in form and details based on the principles and structures of the present invention. However, these modifications and changes based on the ideas of the present invention are still within the scope of protection of the claims of the present invention.

[0102] Example 1

[0103] In the initialization design step, the proton exchange membrane battery is initialized and designed to obtain an initialization model of the proton exchange membrane battery. The initialization model includes: gas diffusion layer (GDL), flow field plate, sealing gasket, current collector plate, end plate, M6 bolts and nuts. When the assembly process begins, the gas diffusion layer is first placed on the flow field plate, followed by the sealing gasket to prevent gas leakage. Subsequently, the catalyst layer and proton exchange membrane are installed, the current collector plates are placed on both sides of the flow field plate, and finally covered with end plates and the entire structure is fixed with bolts and nuts according to the specified torque.

[0104] In this embodiment, the stress distribution of the gas diffusion layer (GDL) is measured under different bolt torque settings, with particular attention paid to the stress changes under the ridge and under the flow channel. By changing the bolt preload from 5N·m to 9N·m, the changes in the stress of the gas diffusion layer (GDL) are observed and recorded. It is determined that under all bolt preload settings, the stress of the gas diffusion layer (GDL) presents a "concave" shape, that is, the stress at the center is the smallest and the stress at the edge is relatively high.

[0105] For Example 1, the study on the preload of fuel cell bolts is intended to understand the stress response of the gas diffusion layer (GDL) in an actual working environment, especially the performance under different mechanical loading conditions, so as to optimize the design and improve the overall reliability and efficiency of the fuel cell. Through this detailed stress analysis, potential structural failures caused by stress concentration or uneven distribution can be better predicted and prevented, thereby extending the life of the fuel cell and improving its performance.

[0106] Figure 4 This is a deformation distribution diagram of the gas diffusion layer (GDL) under different bolt torques in Example 1 of the present invention. The trend of stress distribution can be observed: as the bolt torque increases, the stress in the gas diffusion layer increases as a whole, especially in the area close to the flow channel.

[0107] Figure 5 The von-Mises stress curve under the ridge under different bolt preloads in Example 1 of the present invention describes the curve of the von-Mises stress of the gas diffusion layer (GDL) changing with radial position under different bolt torques. Each curve corresponds to a different torque setting (5N·m to 9N·m). It can be seen that due to the stress concentration phenomenon at the flow channel corner, the stress of the gas diffusion layer under the ridge is the largest at x=0, then decreases sharply and gradually decreases, reaches the minimum at the center of the ridge, and then gradually increases along the X direction. With the increase of the bolt preload, the stress of the gas diffusion layer under the ridge increases, especially at the flow channel corner, the stress overshoot value increases from 0.58MPa at 5N·m to 1.15MPa at 9N·m, an increase of about 98.28%.

[0108] Example 2

[0109] In the initialization design step, when the proton exchange membrane fuel cell is initialized, the temperature change has a significant effect on the stress response of the gas diffusion layer. Under different temperature conditions (such as -30°C, 20°C, 70°C), simulation analysis is run to collect the stress, strain, displacement data and von-Mises stress of the gas diffusion layer (GDL) under various temperature and load conditions.

[0110] Figure 6is the 3σ average von-Mises stress of the gas diffusion layer under random vibration at different temperatures in Example 2 of the present invention. The average 3σ von-Mises stress under random vibration at different temperatures is as follows: Figure 8 As shown. Under X-axis random vibration excitation, the increase in temperature will cause a small decrease in the average stress of the gas diffusion layer. Under Y-axis random vibration excitation, the gas diffusion layer (GDL) has the maximum stress level at room temperature. The increase or decrease in temperature will reduce the maximum stress and average stress of the gas diffusion layer (GDL). Under Z-axis random vibration excitation, both the maximum stress and the average stress show a negative correlation with temperature, that is, they decrease with increasing temperature. Under XYZ triaxial random vibration excitation, temperature has a greater influence on the stress response of the gas diffusion layer. Compared with the temperature conditions of 20℃ and 70℃, the maximum stress decreases from 6.33MPa to 4.60MPa, with a reduction amplitude of 1.73MPa, and the average stress decreases by 0.73MPa. The reduction amplitudes of the maximum stress and average stress under this condition are greater than those under the other three uniaxial vibration conditions.

[0111] Example 3

[0112] In the initial design step, when the proton exchange membrane fuel cell flow channel is initialized, the width of the flow channel and the ridge is changed to obtain different flow field structures, and the width ratio of the flow channel and the ridge is adjusted from the original 2:2 (Unit: mm) to three new ratios: 1:1, 1:2 and 2:1. Finite element analysis is used to predict the stress response of the gas diffusion layer (GDL) under different flow channel and ridge configurations.

[0113] like Figure 7 The stress levels of the gas diffusion layer (GDL) under different configurations of the local area stress distribution under different flow field structures in Example 3 of the present invention are different, among which the 1:1 configuration shows a moderate stress level, which is relatively stable, provides a balanced stress distribution, and is suitable for general applications. The 1:2 configuration and the 2:1 configuration have higher stress levels most of the time, indicating that this configuration may produce greater local stress on the gas diffusion layer (GDL), which is suitable for scenarios requiring high fluid dynamic performance, but may require additional structural reinforcement measures. The 2:2 configuration shows the lowest stress level, indicating good structural support and uniform distribution of force, and may be the most suitable configuration for long-term stable operation.

[0114] Figure 8The dimensionless stress distribution under different flow channel structures in Example 3 of the present invention is extracted, and the average stress ratio of each ridge position under different flow channel ridge width ratios to the average stress ratio of the ridge position in the entire flow field area is extracted. The average stress ratio under the four flow channel ridge width ratios is divided into three parts, the top, the middle and the bottom. The average stress ratio of the gas diffusion layer (GDL) in contact with the middle ridge is close to 1, indicating that the stress at the middle ridge position is relatively uniform relative to the entire flow basin. The average stress ratio at the top ridge position is above 1.1, and the average stress ratio at the bottom ridge position is approximately in the range of 0.85 to 0.9, indicating that the stress uniformity of the gas diffusion layer (GDL) at the bottom ridge position is better than that at the top. Comparing the stress of the lateral blocks at the top, middle and bottom, the lateral stress uniformity is the worst when the flow channel width is 1mm and the ridge width is 1mm. When the flow channel width is 2mm and the ridge width is 2mm, the lateral stress uniformity of the top and middle of the gas diffusion layer (GDL) is the best.

[0115] It can be seen from the above embodiments and accompanying drawings that, compared with the prior art, the present invention obtains the stress distribution and distribution coefficient of the random vibration response of the battery assembly through a three-dimensional finite element model and a modal superposition method, simulates and analyzes the performance of the battery under different dynamic loads in detail, compares and analyzes the battery structure response performance under different flow field structures, and optimizes the battery flow field structure, which can better predict and prevent potential structural failures caused by stress concentration or uneven distribution, thereby extending the life of the fuel cell and improving its performance; in Example 1, as the bolt preload increases, the stress at each location of the gas diffusion layer under the ridge increases accordingly, especially at the flow channel corner position, the stress overshoot value decreases from 1.15MPa at 9N·m to 0.58MPa at 5N·m, and as the stress overshoot value decreases, the affected area in the battery The material life of the affected area is significantly increased. Specifically, the life may increase by 681.2%, that is, by about 7.8 times; in Example 2, under XYZ three-axis random vibration excitation, the temperature has a greater influence on the stress response of the gas diffusion layer. Compared with the temperature conditions of 20°C and 70°C, the maximum stress value is reduced from 6.33MPa to 4.60MPa, the reduction amplitude is 1.73MPa, and the average stress is reduced by 0.73MPa. As the temperature rises to 70°C, the reduction in the maximum stress value will increase the fatigue life of the material by about 92%; in Example 3, when the flow channel width is 2mm and the ridge width is 2mm, the lateral stress uniformity of the top and middle of the gas diffusion layer (GDL) is the best, and the stress difference caused by the different flow channel width and ridge width ratio may cause a significant increase in life. The flow channel ridge width ratio is from 2:1 to 2:2, and the life may increase by about 196%. It can be seen that the present invention has the innovative points and advantages of being able to optimize the battery flow field structure, significantly reduce the stress concentration and warping deformation of the battery assembly, and ensure the long-term stable operation of the battery in harsh environmental conditions.

Claims

1. A battery flow field structure optimization method based on the vibration characteristics of a proton exchange membrane fuel cell, characterized in that: The following steps are involved: Step S1, establishing a three-dimensional finite element model of the proton exchange membrane fuel cell based on the physical size and structure of the battery; the three-dimensional finite element model of the battery is specifically as follows: end plates are respectively arranged on both sides, and current collecting plates, flow field plates, sealing gaskets, gas diffusion layers (GDL), catalyst coating membranes (CCM) and proton exchange membranes (PEM) are respectively arranged in sequence from the end plates on both sides to the middle; Step S2, using the three-dimensional finite element model obtained in step S1 to perform modal analysis on the proton exchange membrane fuel cell using a modal superposition method to determine each vibration mode and natural frequency of the proton exchange membrane fuel cell; Step S3, applying the acceleration power spectrum density of the random vibration load of each vibration mode and natural frequency of the proton exchange membrane fuel cell obtained in step S2 to the three-dimensional finite element model of the proton exchange membrane fuel cell to obtain the structural response characteristics of the proton exchange membrane fuel cell assembly under triaxial random vibration excitation; Step S4, combining the structural response characteristics of the proton exchange membrane fuel cell under triaxial random vibration excitation obtained in step S3, dividing the flow channel of the proton exchange membrane fuel cell into n×m regions according to the flow direction of the gas from the inlet to the outlet and numbering them, performing a pre-optimization scheme design on the flow field structure of each region, and establishing flow field structure geometric models with different flow channels and ridge width ratios, respectively, wherein n is greater than or equal to 3, and m is greater than or equal to 3; Step S5, combining the structural response characteristics obtained in step S3 and the flow field structure geometric model with different flow channel and ridge width ratios established in step S4, evaluating and calculating the vibration response of the key components of the proton exchange membrane battery under various flow field structures, and obtaining the maximum stress and stress distribution coefficient of the vibration response of the key components of the battery under the flow field structures with different flow channel and ridge width ratios; Step S6: Compare the flow field structure optimization schemes and determine the flow field structure with the smallest proton exchange membrane battery module response stress and stress distribution coefficient as the final optimization scheme.

2. A battery flow field structure optimization method according to claim 1, characterized in that: The step S1 of establishing a three-dimensional finite element model includes the following process: The key components of the proton exchange membrane fuel cell and their specific dimensions are determined, and based on these data, the end plate, collector plate, flow field plate, sealing gasket, gas diffusion layer (GDL), catalyst coating membrane (CCM) and proton exchange membrane (PEM) of the proton exchange membrane fuel cell are constructed, and the material properties of the key components of the proton exchange membrane fuel cell, including density, elastic modulus and Poisson's ratio, are defined; a three-dimensional finite element model of the proton exchange membrane fuel cell is constructed according to the above key components and their specific dimensions and material properties, and meshing is performed using second-order hexahedral units, and the meshes of the catalyst coating membrane (CCM) and the gas diffusion layer (GDL) are refined; loading boundary conditions, including several groups of bolt-nut fasteners with a tightening torque of 5-9 N·m, an applied assembly load, and a temperature load of a constant power heat source loaded on the cathode side of the proton exchange membrane (PEM) at different ambient temperatures to simulate the redox reaction on the cathode side and reflect the thermal stress on the catalyst coating membrane (CCM).

3. A battery flow field structure optimization method according to claim 1, characterized in that: In step S2, the vibration modes and natural frequencies of the proton exchange membrane fuel cell are determined, and the mass matrix M and stiffness matrix K of the system are generated using finite element analysis software; by solving the eigenvalue problem (K-ω 2 M)Φ=0, and the natural frequency ω of the proton exchange membrane fuel cell is obtained i and the corresponding vibration mode Φ i , extract the natural frequencies and vibration modes of each order from the solution.

4. A battery flow field structure optimization method according to claim 1, characterized in that: The acceleration power spectral density of the random vibration load in step S3 refers to applying three-axis random vibration excitation to the fuel cell. In the random vibration analysis module, the acceleration power spectral density (PSD) function in the three-axis directions of X, Y, and Z is defined, and then applied as a random vibration load to the relevant parts of the three-dimensional finite element model of the proton exchange membrane fuel cell, ensuring that the acceleration power spectral density (PSD) function is applied in the three directions of X, Y, and Z, thereby obtaining the displacement, velocity and acceleration response of the proton exchange membrane fuel cell, as well as the stress and strain of the structure under different operating conditions.

5. A battery flow field structure optimization method according to claim 1, characterized in that: The pre-optimization scheme for each flow field structure in step S4 is designed as follows: a geometric model of a flow field structure with different channel and ridge width ratios is established, and different channel widths and ridge widths, as well as different channel and ridge width ratios, are obtained by changing the channel and ridge widths, with the channel and ridge widths being between 1 and 2 mm.

6. A battery flow field structure optimization method according to claim 1, characterized in that: In step S5, the maximum stress and stress distribution coefficient of the vibration response of the key components of the proton exchange membrane battery under the flow field structures with different flow channels and ridge width ratios are obtained, specifically: based on the modal analysis results of prestress, the mechanical response law of the proton exchange membrane fuel cell under the random vibration excitation of the three axes of X-axis, Y-axis and Z-axis is calculated. For transient dynamic analysis, the explicit central difference method is used at 0, ..., t n When the time step solution is known, solve t n+1 The solution of the time step rewrites the dynamic equilibrium differential equation as: In the formula, n represents the nth time point; For external forces, Arrange for inner strength; The central difference method is used to integrate the acceleration over time to obtain the velocity at the midpoint of the current incremental step: In the formula, Indicates at time The velocity vector, Indicates at time The velocity vector, At time t n The acceleration vector of This is then added to the displacement at the start of the increment to determine the displacement at the end of the increment: In the formula, {u(t n+1 )} means at time t n+1 The displacement vector, {u(t n )} means at time t n The displacement vector of The above integral formula can be used to calculate the displacement, velocity and acceleration of each discrete time point in the entire time domain, and then the unit stress can be determined through the material constitutive relationship; After superimposing the unit stiffness of the unit in the calculation domain and the temperature-variable equivalent node load, the overall stiffness equation of the temperature load is obtained: In the formula, is the unit temperature equivalent node load matrix, K is the overall stiffness matrix; The thermal deformation q of the nodes in the computational domain can be obtained by solving the problem. The thermal deformation at any point in the computational domain can be calculated by interpolation function. Then it is substituted into the physical equation of the unit: {s} e =[D]({e} e -{e} t )=[D][B]q e -[D]·α·ΔT[1 1 1 0 0 0] T Where, {σ} e represents the effective stress vector, [D] represents the material stiffness matrix, {ε} e represents the total strain vector, {ε} t represents the thermal strain vector caused by temperature change, α represents the thermal expansion coefficient of the material, and ΔT represents the temperature change; Thus the corresponding thermal stress can be obtained; By observing the results of frequency domain analysis, the vibration response analysis of each area of ​​each flow field structure design is carried out, and its maximum stress and average stress are collected. By calculating the ratio of maximum stress to average stress, its stress distribution coefficient is calculated.

7. A battery flow field structure optimization method according to claim 1, characterized in that: The step S6 is to obtain the best flow channel optimization structure by comparing the average stress and stress distribution coefficient of each ridge under three-dimensional random vibration at different flow channel ridge width ratios.

8. A battery, characterized in that: The battery has a flow field structure obtained by a battery flow field structure optimization method as described in any one of claims 1 to 7.

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