A multi-physics field prediction method and related device in a fuel cell stack

Abstract

The present invention belongs to the field of new energy technology, and discloses a multi-physical field prediction method and related devices in a fuel cell stack, including: obtaining the geometric parameters of the fuel cell air cavity, the physical property parameters of the fuel cell gas and the operating parameters of the fuel cell air cavity; according to the geometric parameters of the fuel cell air cavity, the physical property parameters of the fuel cell gas and the operating parameters of the fuel cell air cavity, simulation calculation is performed to obtain the inlet parameters of the air side of each single cell in the fuel cell and the operating temperature of each single cell in the fuel cell, and the results are output to a pre-constructed multi-physical field rapid prediction model for each single cell in the fuel cell, and the multi-physical field prediction results of each single cell in the fuel cell are output; the present invention effectively reduces the time for performing a complete multi-physical field simulation analysis on a commercial-scale stack, and can accurately obtain the detailed distribution characteristics of the multi-physical quantities of the entire commercial-scale fuel cell stack under the premise of less simplification.

Description

Technical Field

[0001] The present invention belongs to the field of new energy technology, and in particular relates to a multi-physical field prediction method and related devices in a fuel cell stack. Background Art

[0002] A proton exchange membrane fuel cell stack, consisting of several single cells stacked in series, is an important hydrogen energy utilization device. As the commercialization of proton exchange membrane fuel cell stacks continues to accelerate, a comprehensive evaluation of the performance of the entire stack is of great significance in guiding the design and operation and maintenance of commercial fuel cell stacks.

[0003] Due to the limitation of computing resources, existing three-dimensional multi-physics simulation research on proton exchange membrane fuel cells is mostly concentrated on the scale of typical units to single cells. Although some researchers have proposed simulation research at the stack scale, they mostly focus on the analysis process of manifold flow distribution phenomena. Secondly, most of them directly extend the full three-dimensional fine field simulation model to the stack, which can only be used for micro stacks. As a result, the simulation model of existing commercial stacks is greatly simplified, making it difficult to balance simulation accuracy and calculation time, and thus unable to accurately obtain the complete multi-physics distribution characteristics of the entire commercial fuel cell stack. Summary of the Invention

[0004] In response to the technical problems existing in the prior art, the present invention provides a multi-physical field prediction method and related devices within a fuel cell stack to solve the technical problem that simulation research at the existing stack scale cannot accurately obtain the complete multi-physical field distribution characteristics of the entire commercial fuel cell stack.

[0005] In order to achieve the above object, the technical solution adopted by the present invention is:

[0006] The present invention provides a method for predicting multiple physical fields in a fuel cell stack, comprising:

[0007] Obtaining geometric parameters of the fuel cell air cavity, physical property parameters of the fuel cell gas, and operating parameters of the fuel cell air cavity;

[0008] According to the geometric parameters of the fuel cell air cavity, the physical property parameters of the fuel cell gas and the operating parameters of the fuel cell air cavity, the inlet parameters of the air side of each single cell in the fuel cell are obtained by simulation calculation;

[0009] Calculating the operating temperature of each cell in the fuel cell based on the inlet parameters of the air side of each cell in the fuel cell and the predetermined inlet parameters of the coolant flow field plates in the fuel cell;

[0010] The inlet parameters of the air side of each single cell in the fuel cell and the operating temperature of each single cell in the fuel cell are output to a pre-built multi-physical field rapid prediction model of each single cell in the fuel cell, and the multi-physical field prediction results of each single cell in the fuel cell are output.

[0011] Furthermore, the prediction method specifically includes the following steps:

[0012] Obtaining geometric parameters of the fuel cell air cavity, physical property parameters of the fuel cell gas, and operating parameters of the fuel cell air cavity;

[0013] The geometric parameters of the fuel cell air cavity, the physical property parameters of the fuel cell gas, the operating parameters of the fuel cell air cavity, and the predetermined air resistance coefficient of each single cell in the fuel cell are input into a pre-built simulation model of the distribution characteristics of each cell in the fuel cell to obtain the inlet parameters of the air side of each single cell in the fuel cell;

[0014] Inputting the air side inlet parameters of each single cell in the fuel cell and the predetermined inlet parameters of each coolant flow field plate in the fuel cell into a pre-built fuel cell operating temperature calculation model to obtain the operating temperature of each single cell in the fuel cell;

[0015] The inlet parameters of the air side of each single cell in the fuel cell and the operating temperature of each single cell in the fuel cell are output to a pre-built multi-physical field rapid prediction model of each single cell in the fuel cell, and the multi-physical field prediction results of each single cell in the fuel cell are output.

[0016] Furthermore, the geometric parameters of the fuel cell air cavity include the type of the air cavity single cell flow field plate, the size of the air cavity single cell flow field plate, the type of the manifold, the size of the manifold, the type of the end head and the size of the end head; the physical property parameters of the fuel cell gas include the density and viscosity of the cathode gas; the operating parameters of the fuel cell air cavity include the stoichiometric ratio of the entire stack inlet, the operating temperature of the entire stack inlet, the relative humidity of the entire stack inlet and the pressure of the entire stack outlet.

[0017] Furthermore, the pre-built simulation model of the distribution characteristics of each piece in the fuel cell is a computational fluid dynamics model based on the finite volume method;

[0018] The inlet parameters of the air side of each single cell in the fuel cell are the inlet velocity, inlet volume flow, inlet mass flow or inlet stoichiometric ratio of the air side of each single cell in the fuel cell.

[0019] Furthermore, the process of determining the predetermined inlet parameters of each coolant flow field plate in the fuel cell is as follows:

[0020] Obtaining geometric parameters of the fuel cell coolant cavity, physical property parameters of the fuel cell coolant, and operating condition parameters of the fuel cell coolant cavity;

[0021] Obtaining a flow-pressure drop characteristic curve of a coolant cavity flow field plate according to geometric parameters of the fuel cell coolant cavity and physical property parameters of the fuel cell coolant;

[0022] The resistance coefficient of the coolant cavity flow field plate under the simplified porous medium model is calibrated according to the flow-pressure drop characteristic curve of the coolant cavity flow field plate to obtain the resistance coefficient of the coolant cavity flow field plate under the simplified porous medium model; wherein the simplified porous medium model is obtained by simplifying the coolant flow field of each single cell of the fuel cell using a volume averaging method;

[0023] The geometric parameters of the fuel cell coolant cavity, the resistance coefficient of the coolant cavity flow field plate under the simplified porous medium model, the physical property parameters of the fuel cell coolant, and the operating parameters of the fuel cell coolant cavity are input into a pre-built simulation model of the distribution characteristics of each piece in the fuel cell, and the inlet parameters of each coolant flow field plate in the fuel cell are obtained as output.

[0024] Furthermore, the pre-built calculation model for the operating temperature of each single cell of the fuel cell is constructed based on a heat balance method.

[0025] Furthermore, the construction process of the pre-built multi-physics field rapid prediction model of each single cell in the fuel cell is as follows:

[0026] From the pre-determined simulation model parameters of each fuel cell, variable parameters of different cells in the fuel cell are selected, and variable input parameters of several snapshot operating conditions are determined through experimental design methods;

[0027] Input variable input parameters of several snapshot operating conditions into a pre-built fuel cell multi-physics field coupling model, and obtain multi-physics field simulation results under several snapshot operating conditions through simulation;

[0028] generating snapshot matrices under the plurality of snapshot working conditions according to the multi-physics field simulation results under the plurality of snapshot working conditions; performing matrix decomposition and transformation on the snapshot matrices under the plurality of snapshot working conditions to obtain a plurality of basis functions;

[0029] Sort the basis functions from large to small according to the amount of information, to obtain a basis function sorting result;

[0030] According to a preset truncation order, a plurality of basis functions with the highest information content are selected from the basis function sorting results to obtain the first plurality of basis functions;

[0031] Based on the first several basis functions, obtaining weight coefficients corresponding to each basis function under the extrapolation working condition, and obtaining weight coefficients of the first several basis functions;

[0032] According to the first several basis functions and the weight coefficients of the first several basis functions, a multi-physical field rapid prediction model of each single cell in the pre-constructed fuel cell is constructed.

[0033] Furthermore, the method further includes inputting the air-side inlet parameters of each single cell in the fuel cell and the operating temperature of each single cell in the fuel cell into a pre-built multi-physics field rapid prediction model for each single cell in the fuel cell, and outputting the multi-physics field prediction results for each single cell in the fuel cell, and further includes a convergence judgment step;

[0034] The convergence judgment step is specifically as follows:

[0035] Inputting the inlet parameters of the air side of each single cell in the fuel cell and the operating temperature of each single cell in the fuel cell into a pre-built multi-physics field rapid prediction model for each single cell in the fuel cell, and outputting a predicted pressure drop value for each single cell in the fuel cell;

[0036] The geometric parameters of the fuel cell air cavity, the physical property parameters of the fuel cell gas, the operating parameters of the fuel cell air cavity, and the predetermined air resistance coefficient of each single cell in the fuel cell are input into a pre-built simulation model of the distribution characteristics of each cell in the fuel cell to obtain the pressure field of the fuel cell air cavity;

[0037] Counting the pressure drop of each single cell in the air cavity of the fuel cell to obtain a statistical value of the pressure drop of each single cell of the fuel cell;

[0038] Comparing the predicted pressure drop value of each single cell of the fuel cell with the statistical pressure drop value of each single cell of the fuel cell to obtain the calculated convergence of the inlet parameters of the air side of each single cell in the fuel cell;

[0039] According to the calculation convergence of the inlet parameters of the air side of each single cell in the fuel cell, it is determined whether it is necessary to re-iterate the calculation of the inlet parameter ratio of the air side of each single cell in the fuel cell.

[0040] The present invention also provides a multi-physics field prediction system in a fuel cell stack, comprising:

[0041] A parameter acquisition module, used to obtain geometric parameters of the fuel cell air cavity, physical property parameters of the fuel cell gas, and operating parameters of the fuel cell air cavity;

[0042] an inlet parameter simulation calculation module, configured to obtain, by simulation and calculation, inlet parameters of the air side of each single cell in the fuel cell based on geometric parameters of the fuel cell air cavity, physical property parameters of the fuel cell gas, and operating condition parameters of the fuel cell air cavity;

[0043] An operating temperature calculation module is used to calculate the operating temperature of each single cell in the fuel cell based on the inlet parameters of the air side of each single cell in the fuel cell and the predetermined inlet parameters of each coolant flow field plate in the fuel cell;

[0044] The physical field prediction module is used to output the inlet parameters of the air side of each single cell in the fuel cell and the operating temperature of each single cell in the fuel cell to a pre-built multi-physical field rapid prediction model of each single cell in the fuel cell, and output the multi-physical field prediction results of each single cell in the fuel cell.

[0045] The present invention also provides a multi-physics field prediction device in a fuel cell stack, comprising:

[0046] memory for storing computer programs;

[0047] A processor is used to implement the steps of the multi-physical field prediction method in a fuel cell stack when executing the computer program.

[0048] Compared with the prior art, the present invention has the following beneficial effects:

[0049] The present invention provides a method for predicting multi-physical fields in a fuel cell stack. Based on the multi-scale idea of ​​"step-by-step calculation and scale improvement", it effectively reduces the time for performing complete multi-physical field simulation analysis on commercial-scale fuel cell stacks; it achieves accurate acquisition of the distribution characteristics of detailed multi-physical quantities of the entire commercial-scale fuel cell stack under the premise of less simplification; the prediction method described in the present invention can evaluate the unevenness of the temperature distribution of each battery cell in the stack, and consider the mutual influence of the transmission process in each battery cell and the distribution characteristics of the stack, and at the same time obtain the detailed three-dimensional multi-physical field of each battery cell in the stack, which is of great significance to the design and operation and maintenance of commercial fuel cell stacks.

[0050] Furthermore, by constructing a multi-scale model for multi-physics field simulation of a commercial fuel cell stack, using a pre-built simulation model of the distribution characteristics of each piece in the fuel cell and a pre-built single cell operating temperature calculation model of the fuel cell, the inlet parameters of each single cell side in the fuel cell and the operating temperature of each single cell in the fuel cell are obtained respectively. Then, based on the pre-built multi-physics field rapid prediction model of each single cell in the fuel cell, the multi-physics field prediction results of each single cell in the fuel cell are obtained, thereby realizing accurate prediction of the three-dimensional multi-physics field in the fuel cell stack. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] Figure 1 A flowchart of a method for predicting multiple physical fields in a fuel cell stack provided in an embodiment;

[0052] Figure 2 is the inlet stoichiometric ratio diagram of the air side of each single cell solved in the embodiment;

[0053] Figure 3 Graph showing the drag coefficient distribution on the air side of each cell in the embodiment;

[0054] Figure 4 is a distribution curve diagram of the operating temperature of each single battery in the embodiment;

[0055] Figure 5 The distribution curve of the average temperature and the maximum temperature of each single cell membrane in the embodiment;

[0056] Figure 6 is a distribution curve diagram of the output voltage of each single battery in the embodiment;

[0057] Figure 7 This is a distribution curve of the membrane water content in each single cell membrane in the embodiment;

[0058] Figure 8 The distribution curve of the average value, maximum value and average value below the rib of the liquid water saturation of the cathode gas diffusion layer of each single cell in the embodiment;

[0059] Figure 9 The temperature distribution cloud diagram of the central cross section of each typical cell in the battery stack in the embodiment is perpendicular to the membrane plane and the inlet plane;

[0060] Figure 10 The temperature distribution cloud diagram of the central cross section of a typical unit membrane of each single cell in the battery stack in the embodiment;

[0061] Figure 11 The cloud diagram of the distribution of liquid water saturation in the central cross section of the gas diffusion layer of a typical unit of each single cell in the fuel cell stack in the embodiment;

[0062] Figure 12 This is a cloud diagram of the distribution of membrane water content in the central cross section of a typical unit membrane of each single cell in the battery stack in the embodiment. DETAILED DESCRIPTION

[0063] In order to make the technical problems, technical solutions and beneficial effects solved by the present invention more clearly understood, the present invention is further described in detail in the following specific embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0064] The present invention provides a method for predicting multiple physical fields in a fuel cell, comprising the following steps:

[0065] Step 1: Establish a multi-physics field rapid prediction model for each single cell in the fuel cell, and obtain a pre-built multi-physics field rapid prediction model for each single cell in the fuel cell; wherein the pre-built multi-physics field rapid prediction model for each single cell in the fuel cell is specifically:

[0066]

[0067] Where f is the multi-physics field prediction result of each single cell in the fuel cell; ψ j is the jth basis function; b j is the weight coefficient corresponding to the jth basis function; l is the truncation order, that is, the number of basis functions; preferably, the multi-physical field and basis function are three-dimensional multi-physical layers and three-dimensional basis functions, respectively.

[0068] Specifically, the pre-built multi-physics field rapid prediction model for each single cell in the fuel cell is as follows:

[0069] Step 101: construct a multi-physics field simulation model of a fuel cell; wherein the multi-physics field simulation model of the fuel cell is a multi-physics field coupling model based on computational fluid dynamics.

[0070] Step 102: Determine the simulation model parameters of each single cell in the fuel cell based on the multi-physics field simulation model of the fuel cell; wherein the simulation model parameters of each single cell in the fuel cell include the geometric parameters of each single cell in the fuel cell, the physical property parameters of each single cell in the fuel cell, the electrochemical parameters of each single cell in the fuel cell, and the operating parameters of each single cell in the fuel cell.

[0071] Specifically, the geometric parameters of each single cell of the fuel cell include the type of flow field plate, the size of the flow field plate and the size of each layer in the membrane electrode; the physical property parameters of each single cell of the fuel cell include hydrogen density, air density, water vapor density, nitrogen density, viscosity coefficient, specific heat capacity, thermal conductivity, diffusion coefficient, plate equivalent heat transfer coefficient, membrane water diffusion coefficient, phase change rate and the correlation between liquid water pressure and liquid water saturation in porous media; wherein, the phase change rate is the phase change rate between membrane water, liquid water and gaseous water; the electrochemical parameters of each single cell of the fuel cell include the reference exchange current density, specific surface area, reference concentration, reaction order, conversion coefficient and current generation source term calculation method of the cathode and anode; the operating parameters of each single cell of the fuel cell include the flow channel inlet temperature, flow channel inlet relative humidity, flow channel outlet pressure, flow channel inlet stoichiometric ratio and plate coolant temperature of the cathode and anode.

[0072] Step 103: Select variable parameters of different cells in the fuel cell from the simulation model parameters of each cell in the fuel cell; adopt an experimental design method to determine variable input parameters of several snapshot operating conditions when the variable parameters of different cells in the selected fuel cell change; preferably, the variable input parameters of the snapshot operating conditions include the cathode stoichiometric ratio and the operating temperature.

[0073] Step 104: Input the variable input parameters of the plurality of snapshot working conditions into the multi-physics field simulation model of the fuel cell, and obtain the multi-physics field simulation results under the plurality of snapshot working conditions through simulation; wherein the multi-physics field simulation results under the plurality of snapshot working conditions are the plurality of snapshots.

[0074] Step 105: Generate snapshot matrices under several snapshot working conditions based on several snapshots; perform matrix decomposition or matrix transformation on the snapshot matrices under the several snapshot working conditions to obtain several basis functions; sort the several basis functions from large to small according to the amount of information to obtain a basis function sorting result; select several basis functions with the highest amount of information from the basis function sorting result according to a preset truncation order to obtain the first several basis functions; it should be noted that the amount of information contained in the basis function is the information dimension it contains, and the basis functions ranked at the end of the basis function sorting result contain dimensions with very little information; when selecting several basis functions with the highest amount of information from the basis function sorting result, the dimensions with very little information can be ignored, and only the first several basis functions are selected to describe the original problem.

[0075] Step 106: Based on the first several basis functions in step 105, obtain the weight coefficients corresponding to each basis function under the extrapolated working condition, and obtain the weight coefficients of the first several basis functions; according to the first several basis functions and the weight coefficients of the first several basis functions, construct a multi-physical field rapid prediction model for each single cell in the pre-constructed fuel cell.

[0076] Step 2: Determine the inlet parameters of each coolant flow field plate in the fuel cell to obtain predetermined inlet parameters of each coolant flow field plate in the fuel cell; preferably, the inlet parameters of each coolant flow field plate in the fuel cell are the inlet flow rates of each coolant flow field plate in the fuel cell.

[0077] Specifically, the process of determining the inlet parameters of each coolant flow field plate in the fuel cell is as follows:

[0078] Step 201: Acquire geometric parameters of the fuel cell coolant cavity, physical property parameters of the fuel cell coolant, and operating parameters of the fuel cell coolant cavity. Specifically, the geometric parameters of the fuel cell coolant cavity include the type of the coolant flow field plate, the geometric dimensions of the coolant flow field plate, the type of the manifold, the geometric dimensions of the manifold, and the type and geometric dimensions of the terminal. The physical property parameters of the fuel cell coolant include the density and viscosity of the coolant. The operating parameters of the fuel cell coolant cavity include the flow rate and temperature of the coolant.

[0079] Step 202: Draw a grid model of a fuel cell coolant cavity flow field plate based on the geometric parameters of the fuel cell coolant.

[0080] Step 203: Input the grid model of the fuel cell coolant flow field plate, the physical property parameters of the fuel cell coolant, and the operating parameters of the fuel cell coolant chamber into a pre-constructed resistance characteristic simulation model to obtain a flow-pressure drop characteristic curve of the coolant flow field plate; wherein the pre-constructed resistance characteristic simulation model is a simulation model based on computational fluid dynamics; it should be noted that the flow-pressure drop characteristic curve of the coolant flow field plate can also be obtained through experiments.

[0081] Step 204: Calibrate the resistance coefficient of the coolant cavity flow field plate under the simplified porous medium model according to the flow-pressure drop characteristic curve of the coolant flow field plate to obtain the resistance coefficient of the coolant cavity flow field plate under the simplified porous medium model; wherein the simplified porous medium model is obtained by simplifying the coolant flow field of each single cell of the fuel cell using a volume averaging method.

[0082] Specifically, the process of calibrating the resistance coefficient of the cooling liquid cavity flow field plate under the simplified porous medium model comprises the following steps: first, assuming a set of permeation resistance coefficients and inertial resistance coefficients for trial calculation, a flow-pressure drop characteristic curve of the trial-calculated simplified porous medium model is obtained; the flow-pressure drop characteristic curve of the trial-calculated simplified porous medium model is compared with the flow-pressure drop characteristic curve of the cooling liquid flow field plate in step 203; if the deviation of the comparison result is greater than a preset threshold, the permeation resistance coefficient and the inertial resistance coefficient are adjusted and recalculated until the flow-pressure drop characteristic curve of the trial-calculated simplified porous medium model is consistent with the flow-pressure drop characteristic curve of the cooling liquid flow field plate in step 203, and the resistance coefficient of the cooling liquid cavity flow field plate under the simplified porous medium model is output.

[0083] Step 205 : Draw a grid model of the fuel cell stack coolant cavity according to the geometric parameters of the fuel cell coolant cavity; wherein the grid model of the fuel cell stack coolant cavity includes an end cap, a distribution manifold, and a simplified flow field plate of a porous medium.

[0084] Step 206: Input the grid model of the fuel cell stack coolant cavity, the resistance coefficient of the coolant cavity flow field plate under the simplified porous medium model, the physical property parameters of the fuel cell coolant, and the operating parameters of the fuel cell coolant cavity into a pre-constructed simulation model of the distribution characteristics of each piece in the fuel cell, output the preset physical field information of the fuel cell coolant, and obtain the inlet parameters of each coolant flow field plate in the fuel cell; wherein the preset physical field information of the fuel cell coolant includes the fuel cell coolant velocity field and the fuel cell coolant pressure field; the pre-constructed simulation model of the distribution characteristics of each piece in the fuel cell is a computational fluid dynamics model based on the finite volume method.

[0085] Step 3: Multi-physics field prediction; specifically, it includes the following steps:

[0086] Step 301, obtaining the geometric parameters of the fuel cell air cavity, the physical property parameters of the fuel cell gas and the operating parameters of the fuel cell air cavity; wherein, the geometric parameters of the fuel cell air cavity include the type of the air cavity single cell flow field plate, the size of the air cavity single cell flow field plate, the type of the manifold, the size of the manifold, the type of the end head and the size of the end head; the physical property parameters of the fuel cell gas include the density and viscosity of the cathode gas; the operating parameters of the fuel cell air cavity include the whole stack inlet stoichiometric ratio, the whole stack inlet operating temperature, the whole stack inlet relative humidity and the whole stack outlet pressure of the fuel cell.

[0087] Step 302: Determine the air cavity resistance coefficient of each single cell in the fuel cell; specifically, simplify the cathode flow channel of each single cell in the fuel cell into a porous medium region, and assume the air cavity resistance coefficient of each single cell in the fuel cell based on the simplified cathode flow channel in the porous medium region; wherein the air cavity resistance coefficient of each single cell in the fuel cell is the air cavity viscous resistance coefficient of each single cell in the fuel cell.

[0088] Step 303: Draw a grid model of the fuel cell stack air cavity according to the geometric parameters of the fuel cell air cavity.

[0089] Step 304: input the grid model of the fuel cell stack air cavity, the physical property parameters of the fuel cell gas, the operating parameters of the fuel cell air cavity, and the air cavity resistance coefficient of each single cell in the fuel cell determined in step 302 into a pre-built simulation model of the distribution characteristics of each piece in the fuel cell, iteratively obtain the velocity field of the fuel cell air cavity and the pressure field of the fuel cell air cavity, and extract the inlet parameters of the air side of each single cell in the fuel cell; wherein, the inlet parameters of each single cell in the fuel cell are the inlet velocity, inlet volume flow, inlet mass flow or inlet stoichiometric ratio of the air side of each single cell in the fuel cell; in this embodiment 1, the inlet parameters of each single cell in the fuel cell are the inlet stoichiometric ratio of the air side of each single cell in the fuel cell as an example for specific description.

[0090] Step 305: Input the inlet stoichiometric ratio of the air side of each single cell in the fuel cell, the inlet parameters of each coolant flow field plate in the fuel cell pre-determined in step 206, and the multi-physics field rapid prediction model of each single cell in the fuel cell pre-constructed in step 1 into the pre-constructed operating temperature calculation model of each single cell in the fuel cell to obtain the operating temperature of each single cell in the fuel cell.

[0091] Step 306: Input the inlet stoichiometric ratio of the air side of each single cell in the fuel cell and the operating temperature of each single cell in the fuel cell in step 305 into the multi-physics field rapid prediction model of each single cell in the fuel cell pre-built in step 1, and output the pressure drop prediction value of each single cell in the fuel cell.

[0092] Step 307: Calculate the pressure drop of each single cell in the pressure field of the fuel cell air cavity in step 304 to obtain the pressure drop statistical value Δp of each single cell of the fuel cell; and calculate the pressure drop prediction value of each single cell of the fuel cell. The calculated convergence of the inlet stoichiometric ratio of the air side of each single cell in the fuel cell is obtained by comparing it with the statistical value Δp of the pressure drop of each single cell in the fuel cell; based on the calculated convergence of the inlet stoichiometric ratio of the air side of each single cell in the fuel cell, it is determined whether it is necessary to re-iterate the calculation of the inlet stoichiometric ratio of the air side of each single cell in the fuel cell.

[0093] Specifically, if the predicted pressure drop value of each single cell of the fuel cell is If the calculated inlet stoichiometric ratio of the air side of each single cell in the fuel cell is not equal to the statistical value Δp of the pressure drop of each single cell in the fuel cell, the calculation convergence of the inlet stoichiometric ratio of the air side of each single cell in the fuel cell is non-convergence. At this time, after correcting the air cavity resistance coefficient of each single cell in the fuel cell, return to step 304 and re-iterate the calculation; otherwise, the calculation converges and jumps to step 308.

[0094] Step 308: Input the inlet stoichiometric ratio of the air side of each single cell in the fuel cell and the operating temperature of each single cell in the fuel cell in step 305 into the multi-physical field rapid prediction model of each single cell in the fuel cell pre-built in step 1, and output the multi-physical field prediction result of each single cell in the fuel cell, that is, the prediction result of the multi-physical field in the fuel cell stack is obtained.

[0095] The multi-physics field prediction method in the fuel cell stack described in the present invention constructs a multi-scale model for multi-physics field simulation of commercial fuel cell stacks and adopts the multi-scale idea of ​​"step-by-step calculation and scale improvement". It effectively reduces the time for complete multi-physics field simulation analysis of commercial-scale fuel cell stacks. Under the premise of less simplification, it obtains the detailed distribution characteristics of multi-physics quantities of the entire commercial-scale fuel cell stack, effectively solving the technical problem of balancing simulation accuracy and calculation time in the existing commercial fuel cell stack simulation process.

[0096] Compared with traditional multi-scale research, the present invention applies the multi-scale method to the multi-physical field simulation of fuel cell stacks. Through the multi-scale simulation idea of ​​step-by-step calculation and scale enhancement, it aims to use the model of the largest scale studied in the actual simulation process. The key issues such as sub-models, empirical correlations or parameters contained in the model are given by the results of detailed micro-scale research, which can accurately obtain the simulation results of the multi-physical quantity distribution of commercial fuel cell stacks containing hundreds of single cells.

[0097] The present invention also provides a multi-physical field prediction system in a fuel cell stack, comprising: a parameter acquisition module for acquiring the geometric parameters of the fuel cell air cavity, the physical property parameters of the fuel cell gas and the operating condition parameters of the fuel cell air cavity; an inlet parameter simulation calculation module for simulating and calculating the inlet parameters of the air side of each single cell in the fuel cell based on the geometric parameters of the fuel cell air cavity, the physical property parameters of the fuel cell gas and the operating condition parameters of the fuel cell air cavity; an operating temperature calculation module for calculating the operating temperature of each single cell in the fuel cell based on the inlet parameters of the air side of each single cell in the fuel cell and the inlet parameters of the predetermined coolant flow field plates in the fuel cell; a physical field prediction module for outputting the inlet parameters of the air side of each single cell in the fuel cell and the operating temperature of each single cell in the fuel cell to a pre-constructed multi-physical field rapid prediction model for each single cell in the fuel cell, and outputting the multi-physical field prediction results for each single cell in the fuel cell.

[0098] The present invention also provides a device for predicting multiple physical fields in a fuel cell stack, comprising: a memory for storing a computer program; and a processor for implementing the steps of a method for predicting multiple physical fields in a fuel cell stack when executing the computer program.

[0099] When the processor executes the computer program, the steps of the above-mentioned method for predicting multiple physical fields in a fuel cell stack are implemented; or, when the processor executes the computer program, the functions of each module in the above-mentioned system are implemented.

[0100] Exemplarily, the computer program may be divided into one or more modules / units, which are stored in the memory and executed by the processor to implement the present invention. The one or more modules / units may be a series of computer program instruction segments capable of completing preset functions, and the instruction segments are used to describe the execution process of the computer program in the multi-physics field prediction device within the fuel cell stack.

[0101] The multi-physics field prediction device in the fuel cell stack can be a computing device such as a desktop computer, a notebook, a PDA, and a cloud server. The multi-physics field prediction device in the fuel cell stack may include, but is not limited to, a processor and a memory. Those skilled in the art will understand that the above is an example of a multi-physics field prediction device in a fuel cell stack and does not constitute a limitation on the multi-physics field prediction device in a fuel cell stack. It may include more components than the above, or a combination of certain components, or different components. For example, the multi-physics field prediction device in the fuel cell stack may also include input and output devices, network access devices, buses, etc.

[0102] The processor may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or any conventional processor, etc. The processor is the control center of the multi-physics field prediction device in the fuel cell stack, and utilizes various interfaces and lines to connect various parts of the multi-physics field prediction device in the entire fuel cell stack.

[0103] The memory can be used to store the computer program and / or module, and the processor realizes various functions of the multi-physics field prediction device in the fuel cell stack by running or executing the computer program and / or module stored in the memory, and calling the data stored in the memory.

[0104] The memory may mainly include a program storage area and a data storage area. The program storage area may store an operating system and at least one application required for a function (such as a sound playback function, an image playback function, etc.); the data storage area may store data created based on the use of the mobile phone (such as audio data, a phone book, etc.). In addition, the memory may include a high-speed random access memory and may also include a non-volatile memory, such as a hard disk, internal memory, a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) card, a Flash Card, at least one disk storage device, a flash memory device, or other volatile solid-state storage device.

[0105] Example

[0106] Take the three-dimensional multi-physical layer prediction process of a hydrogen fuel proton exchange membrane fuel cell stack as an example; wherein the hydrogen fuel proton exchange membrane fuel cell stack is a parallel flow channel.

[0107] As attached Figure 1 As shown, this embodiment provides a method for predicting multiple physical fields in a fuel cell stack, comprising the following steps:

[0108] Step 1: Establish a multi-physics field rapid prediction model for each single cell in the fuel cell, and obtain a pre-built multi-physics field rapid prediction model for each single cell in the fuel cell; wherein, the prediction model construction process is as follows:

[0109] Step 101, constructing a three-dimensional multi-physics field simulation model of a fuel cell; wherein, the three-dimensional multi-physics field simulation model of the fuel cell is a multi-physics field coupling model based on computational fluid dynamics; preferably, the multi-physics field coupling model based on computational fluid dynamics includes a mass conservation equation, three momentum conservation equations, an energy conservation equation, three component conservation equations, an electron potential conservation equation, a proton potential conservation equation, a membrane water content conservation equation, and a liquid water pressure conservation equation.

[0110] Step 102: Determine simulation model parameters of each cell in the fuel cell according to the three-dimensional multi-physics field simulation model of the fuel cell.

[0111] The simulation model parameters of each cell in the fuel cell include geometric parameters of each cell in the fuel cell, physical property parameters of each cell in the fuel cell, electrochemical parameters of each cell in the fuel cell, and operating parameters of each cell in the fuel cell.

[0112] Specifically, the geometric parameters of each single cell of the fuel cell include the type of flow field plate, the size of the flow field plate and the size of each layer in the membrane electrode; the physical property parameters of each single cell of the fuel cell include hydrogen density, air density, water vapor density, nitrogen density, viscosity coefficient, specific heat capacity, thermal conductivity, diffusion coefficient, plate equivalent heat transfer coefficient, membrane water diffusion coefficient, phase change rate and the correlation between liquid water pressure and liquid water saturation in porous media; wherein, the phase change rate is the phase change rate between membrane water, liquid water and gaseous water; the electrochemical parameters of each single cell of the fuel cell include the reference exchange current density, specific surface area, reference concentration, reaction order, conversion coefficient and current generation source term calculation method of the cathode and anode; the operating parameters of each single cell of the fuel cell include the flow channel inlet temperature, flow channel inlet relative humidity, flow channel outlet pressure, flow channel inlet stoichiometric ratio and plate coolant temperature of the cathode and anode.

[0113] It should be noted that the flow field plate is in the form of a straight flow channel including a distribution area, and the simulation model of each single cell in the fuel cell adopts a three-dimensional two-phase non-isothermal model based on hydraulic continuity.

[0114] Step 103: Select variable parameters of different cells in the fuel cell from the simulation model parameters of each cell in the fuel cell; use a full factorial experimental design method to determine variable input parameters for a plurality of snapshot operating conditions when the variable parameters of different cells in the selected fuel cell change; wherein the variable input parameters of the snapshot operating conditions include the cathode stoichiometric ratio and the operating temperature.

[0115] Step 104 : Input the variable input parameters of the plurality of snapshot working conditions into the three-dimensional multi-physics field simulation model of the fuel cell, and obtain the three-dimensional multi-physics field simulation results under the plurality of snapshot working conditions through simulation, that is, obtain a plurality of snapshots.

[0116] Step 105: Generate snapshot matrices under several snapshot working conditions based on the several snapshots; perform matrix decomposition or matrix transformation on the snapshot matrices under the several snapshot working conditions to obtain several three-dimensional basis functions; sort the several three-dimensional basis functions from large to small according to the amount of information to obtain a basis function sorting result; select several three-dimensional basis functions with the highest amount of information from the basis function sorting result according to a preset cutoff order to obtain the first several three-dimensional basis functions.

[0117] In this embodiment, to capture more than 99.99% of the information, the truncation orders of the voltage, temperature, proton potential, membrane water, hydraulic pressure, and liquid water saturation fields are set to 3, 19, 16, 27, 25, and 25, respectively. The truncation order is the number of selected three-dimensional basis functions. The POD basis function can be obtained by solving the eigenvalue problem of the dot product equation as follows:

[0118]

[0119] in, is the three-dimensional basis function; f is the snapshot; is the conjugate transpose of f; three-dimensional basis function The first subscript of the snapshot f represents the number of the grid node, and the second subscript represents the k-th snapshot. In this embodiment, a singular value decomposition (SVD) method based on the Jacobi algorithm is used for solving.

[0120] Step 106: Based on the first several three-dimensional basis functions in step 105, obtain the weight coefficients corresponding to each three-dimensional basis function under the extrapolated working condition, and obtain the weight coefficients of the first several three-dimensional basis functions; based on the first several three-dimensional basis functions and the weight coefficients of the first several three-dimensional basis functions, construct a multi-physics field rapid prediction model for each single cell in the pre-constructed fuel cell; wherein, the pre-constructed multi-physics field rapid prediction model for each single cell in the fuel cell is specifically:

[0121]

[0122] Where f is the multi-physics field prediction result of each single cell in the fuel cell; ψ j is the jth three-dimensional basis function; b j is the weight coefficient corresponding to the jth three-dimensional basis function; l is the truncation order, that is, the number of three-dimensional basis functions.

[0123] Step 2: Determine the inlet parameters of each coolant flow field plate in the fuel cell, and obtain predetermined inlet parameters of each coolant flow field plate in the fuel cell.

[0124] Specifically, the process of determining the inlet parameters of each coolant flow field plate in the fuel cell is as follows:

[0125] Step 201: Acquire geometric parameters of the fuel cell coolant cavity, physical property parameters of the fuel cell coolant, and operating parameters of the fuel cell coolant cavity; wherein, the geometric parameters of the fuel cell coolant cavity include the type of the coolant flow field plate, the geometric dimensions of the coolant flow field plate, the type of the manifold, the geometric dimensions of the manifold, and the type and geometric dimensions of the terminal; the physical property parameters of the fuel cell coolant include the density of the coolant and the viscosity of the coolant; the operating parameters of the fuel cell coolant cavity include the flow rate of the coolant and the temperature of the coolant; specifically, the type of the coolant flow field plate is a parallel straight channel including a distribution area; the type of the distribution manifold is a square cross-section U-shaped configuration; the terminal and the manifold are the same type; and the coolant is deionized water.

[0126] Step 202: Based on the geometric parameters of the fuel cell coolant, a structured grid form is used to draw a grid model of the fuel cell coolant cavity flow field plate.

[0127] Step 203: Input the grid model of the fuel cell coolant flow field plate, the physical property parameters of the fuel cell coolant, and the operating parameters of the fuel cell coolant chamber into a pre-built resistance characteristic simulation model to obtain a flow-pressure drop characteristic curve of the coolant flow field plate; wherein the pre-built resistance characteristic simulation model is a simulation model based on computational fluid dynamics.

[0128] Step 204: Calibrate the resistance coefficient of the coolant cavity flow field plate under the simplified porous medium model according to the flow-pressure drop characteristic curve of the coolant flow field plate to obtain the resistance coefficient of the coolant cavity flow field plate under the simplified porous medium model; wherein the simplified porous medium model is obtained by simplifying the coolant flow field of each single cell of the fuel cell using a volume averaging method.

[0129] Specifically, the process of calibrating the resistance coefficient of the cooling liquid cavity flow field plate under the simplified porous medium model comprises the following steps: first, assuming a set of permeation resistance coefficients and inertial resistance coefficients for trial calculation, the flow-pressure drop characteristic curve of the trial-calculated simplified porous medium model is obtained; the flow-pressure drop characteristic curve of the trial-calculated simplified porous medium model is compared with the flow-pressure drop characteristic curve of the cooling liquid flow field plate in step 203; if the deviation of the comparison result is greater than the preset threshold, the permeation resistance coefficient and the inertial resistance coefficient are adjusted and recalculated using the negative feedback adjustment idea until the flow-pressure drop characteristic curve of the trial-calculated simplified porous medium model is consistent with the flow-pressure drop characteristic curve of the cooling liquid flow field plate in step 203, and the resistance coefficient of the cooling liquid cavity flow field plate under the simplified porous medium model is output.

[0130] Step 205: Draw a grid model of the fuel cell stack coolant cavity based on the geometric parameters of the fuel cell coolant cavity; wherein the grid model of the fuel cell stack coolant cavity includes an end head, a distribution manifold, and a simplified porous medium flow field plate; it should be noted that the simplified porous medium flow field plate is drawn using a geometric dimension simplification method, but does not restore the overall dimensions of the original flow field plate.

[0131] Step 206: Input the grid model of the fuel cell stack coolant cavity, the resistance coefficient of the coolant cavity flow field plate under the simplified porous medium model, the physical property parameters of the fuel cell coolant, and the operating parameters of the fuel cell coolant cavity into a pre-constructed simulation model of the distribution characteristics of each piece in the fuel cell, output the preset physical field information of the fuel cell coolant, and obtain the inlet parameters of each coolant flow field plate in the fuel cell; in this embodiment, the preset physical field information of the fuel cell coolant includes the fuel cell coolant velocity field and the fuel cell coolant pressure field; the pre-constructed simulation model of the distribution characteristics of each piece in the fuel cell is a computational fluid dynamics model based on the finite volume method; the inlet parameters of each coolant flow field plate in the fuel cell include the inlet flow rate of each coolant flow field plate.

[0132] Step 3: Multi-physics field prediction; specifically, it includes the following steps:

[0133] Step 301, obtaining the geometric parameters of the fuel cell air cavity, the physical property parameters of the fuel cell gas and the operating parameters of the fuel cell air cavity; wherein, the geometric parameters of the fuel cell air cavity include the type of the air cavity single cell flow field plate, the size of the air cavity single cell flow field plate, the type of the manifold, the size of the manifold, the type of the end head and the size of the end head; the physical property parameters of the fuel cell gas include the density and viscosity of the cathode gas; the operating parameters of the fuel cell air cavity include the whole stack inlet stoichiometric ratio, the whole stack inlet operating temperature, the whole stack inlet relative humidity and the whole stack outlet pressure; in this embodiment, the type of the air cavity single cell flow field plate is a parallel straight channel including a distribution area; the type of the manifold is a square interface U-shaped configuration; the end head is the same as the manifold type.

[0134] Step 302: Simplify the cathode flow channel of each single cell of the fuel cell into a porous medium region, and assume the air cavity resistance coefficient of each single cell of the fuel cell based on the simplified cathode flow channel of the porous medium region; wherein the air cavity resistance coefficient of each single cell of the fuel cell is the air cavity viscous resistance coefficient of each single cell of the fuel cell.

[0135] Step 303: Draw a grid model of the fuel cell stack air cavity according to the geometric parameters of the fuel cell air cavity.

[0136] Step 304: Input the grid model of the fuel cell stack air cavity, the physical property parameters of the fuel cell gas, the operating parameters of the fuel cell air cavity, and the air cavity resistance coefficient of each single cell in the fuel cell determined in step 302 into the pre-built simulation model of the distribution characteristics of each piece in the fuel cell, iteratively obtain the velocity field of the fuel cell air cavity and the pressure field of the fuel cell air cavity, and calculate the inlet stoichiometric ratio of the air side of each single cell in the fuel cell.

[0137] In this embodiment, the pre-constructed simulation model of the distribution characteristics of each piece in the fuel cell is a computational fluid dynamics model based on the finite volume method; in the process of iteratively obtaining the velocity field of the fuel cell air cavity and the pressure field of the fuel cell air cavity, the number of iterations is one or a preset number of iterations, and the flow field convergence may not be reached; the inlet parameter of the air side of each single cell in the fuel cell is the inlet flow rate of the air side of each single cell in the fuel cell.

[0138] Step 305: Input the inlet stoichiometric ratio of the air side of each single cell in the fuel cell in step 304, the inlet parameters of each coolant flow field plate in the fuel cell pre-determined in step 206, and the multi-physics field rapid prediction model of each single cell in the fuel cell pre-constructed in step 1 into the pre-constructed operating temperature calculation model of each single cell in the fuel cell to obtain the operating temperature of each single cell in the fuel cell.

[0139] In this embodiment, the pre-built calculation model for the operating temperature of each single cell of the fuel cell is constructed based on the heat balance method. The principle of obtaining the operating temperature of each single cell in the fuel cell is as follows:

[0140] Assuming that all physical fields in each cell of the fuel cell are known, the heat generation power density in each cell is integrated by volume to obtain the heat transfer of the coolant flow channels on both sides of each cell of the fuel cell; based on the heat transfer and the flow rate of cooling water on each coolant flow field plate, according to the principle of conservation of energy, the coolant temperature difference ΔT at the inlet and outlet of the i-th coolant flow field plate is calculated. i ; Among them, the inlet and outlet coolant temperature difference ΔT of the i-th coolant flow field plate i , specifically:

[0141]

[0142] Where: C is the specific heat capacity of the coolant; Q i 、 and They respectively represent the total heat transfer heat flow of the i-th coolant flow field plate, the heat flow taken away from the single cell on the left, and the heat flow taken away from the single cell on the right. From left to right, they represent the direction away from the total inlet of the battery stack.

[0143] Therefore, the average temperature of the coolant of the i-th coolant flow field plate is the temperature of the convective heat transfer fluid of the coolant flow field plate; wherein the average temperature of the coolant of the i-th coolant flow field plate is specifically:

[0144]

[0145] in, is the average temperature of the coolant on the i-th coolant flow field plate.

[0146] Since the i-th cell is sandwiched between the i-th coolant flow channel and the i+1-th coolant flow channel, assuming that the operating temperature of the i-th cell is the average of the two temperatures, the operating temperature of the i-th cell is Specifically:

[0147]

[0148] in, is the operating temperature of the i-th single cell.

[0149] In summary, the operating temperature of each single cell T cell,f,i With heat production Q i and the flow rate q of each coolant channel m,cool,i The change relationship is expressed as follows:

[0150] T cell,f =f′(Q,q m,cool )

[0151] The heat generated by each single cell is Q i It is also affected by the operating temperature T of each cell. cell,f,i The impact is shown in the following formula:

[0152] Q i =f(T cell,f,i )

[0153] The unique function expression of the stack operating temperature on the coolant flow rate can be obtained:

[0154] T cell,f =f(q m,cool )

[0155] Finally, the Newton iteration method is used to obtain the operating temperature of each single cell in the fuel cell.

[0156] Step 306: Input the inlet stoichiometric ratio of the air side of each single cell in the fuel cell in step 304 and the operating temperature of each single cell in the fuel cell in step 305 into the multi-physics field rapid prediction model of each single cell in the fuel cell pre-built in step 1, and output the pressure drop prediction value of each single cell in the fuel cell.

[0157] In this embodiment, the geometric structure of the single cell is divided into three parts: the inlet section, the outlet section, and the parallel flow channel section. The pressure drop characteristic within the single cell is expressed as:

[0158]

[0159] in: Represents the predicted value based on the digital twin result; for the parallel flow channel section inside the single battery, its pressure drop Obtained from POD prediction results at the preset single cell operating temperature and cathode stoichiometric ratio;

[0160] The pressure drop at the inlet and outlet sections is calculated by the following formula:

[0161]

[0162] Among them, a1 and a2 are coefficients, and are affected by fluid viscosity and density respectively. Coefficients a1 and a2 are obtained by regression of simulation results.

[0163] Step 307: Calculate the pressure drop of each single cell in the pressure field of the fuel cell air cavity in step 304 to obtain the pressure drop statistical value Δp of each single cell of the fuel cell; and calculate the pressure drop prediction value of each single cell of the fuel cell. The calculated convergence of the inlet stoichiometric ratio of the air side of each single cell in the fuel cell is obtained by comparing it with the statistical value Δp of the pressure drop of each single cell in the fuel cell; based on the calculated convergence of the inlet stoichiometric ratio of the air side of each single cell in the fuel cell, it is determined whether it is necessary to re-iterate the calculation of the inlet stoichiometric ratio of the air side of each single cell in the fuel cell.

[0164] Specifically, if the predicted pressure drop value of each single cell of the fuel cell is If the calculated inlet stoichiometric ratio of the air side of each single cell in the fuel cell is not equal to the statistical value Δp of the pressure drop of each single cell in the fuel cell, the calculation convergence of the inlet stoichiometric ratio of the air side of each single cell in the fuel cell is non-convergence. At this time, after correcting the air cavity resistance coefficient of each single cell in the fuel cell, return to step 304 and re-iterate the calculation; otherwise, the calculation converges and jumps to step 308.

[0165] In this embodiment, when correcting the air cavity resistance coefficient of each single cell in the fuel cell, a negative feedback method is used for correction; the details are as follows:

[0166]

[0167] Where C is the viscous drag coefficient, i is the number of the single cell, k is the number of iterations, and α is the sub-relaxation factor.

[0168] Step 308: Input the inlet stoichiometric ratio of the air side of each single cell in the fuel cell in step 304 and the operating temperature of each single cell in the fuel cell in step 305 into the multi-physical field rapid prediction model of each single cell in the fuel cell pre-constructed in step 1, and output the multi-physical field prediction results of each single cell in the fuel cell, that is, obtain the prediction results of the three-dimensional multi-physical field in the fuel cell stack; wherein, the multi-physical field prediction results of each single cell in the fuel cell specifically include the temperature field, membrane water content field, and liquid water saturation field of each single cell in the fuel cell.

[0169] Test results description:

[0170] The following is a detailed description of the prediction effect of the three-dimensional multi-physics field measurement method described in this embodiment.

[0171] First, statistics of local physical quantities in each single cell are collected, including cathode stoichiometric ratio, temperature, output voltage, membrane water content, and liquid water saturation, as follows:

[0172] As attached Figure 2 、 3 As shown, Figure 2 The inlet stoichiometric ratio diagram of the air side of each single cell is given in the figure. Figure 3 The drag coefficient distribution curve of each cell on the air side is given in the attached figure. Figure 2 As can be seen from the figure, as the cell moves away from the stack inlet, the stoichiometric ratio gradually decreases, and the stoichiometric ratio of the tail cell increases slightly. The viscous drag coefficient of each cell at the final convergence is shown in the attached figure. Figure 3 shown.

[0173] As attached Figure 4 As shown, attached Figure 4 The distribution curve of the operating temperature of each single cell is given in the attached figure. Figure 4 It can be seen that as the single cell moves away from the total inlet of the battery stack, the operating temperature gradually increases, and the temperature decreases slightly near the end of the curve. The operating temperature of the single cell at the outermost edge is lower than that of the single cell on the inner side.

[0174] As attached Figure 5 As shown, attached Figure 5 The distribution curves of the average and maximum temperatures of each cell membrane are given in the attached figure. Figure 5 As can be seen in the figure, the temperature field at other locations within the stack follows the same trend as the operating temperature. The average temperature of the proton exchange membrane and the maximum temperature within the single cell are approximately 5.2°C and 10.1°C higher than the operating temperature, respectively, reflecting the presence of temperature differences within the single cell.

[0175] As attached Figure 6 As shown, attached Figure 6The distribution curve of the output voltage of each single cell is given in the attached figure. Figure 6 It can be seen from the figure that as the single cell moves away from the stack entrance, the battery output voltage first decreases approximately linearly, and then the decreasing trend gradually slows down. The voltage non-uniformity of each single cell is 7.2%.

[0176] As attached Figure 7 As shown, attached Figure 7 The distribution curve of membrane water content in each single cell membrane is given in the attached figure. Figure 7 As can be seen from the graph, the minimum and maximum membrane water content within the entire stack are 12.08 and 19.62, respectively, with a 47% nonuniformity. In general, the membrane water content gradually increases as the cells move away from the stack inlet. The membrane water content suddenly drops in one cell at the end of the stack. The first cell has a higher average membrane water content and a lower minimum membrane water content.

[0177] As attached Figure 8 As shown, attached Figure 8 The distribution curves of the average value, maximum value and average value below the rib of the liquid water saturation of the cathode gas diffusion layer of each single cell are given in the attached figure. Figure 8 As can be seen in the figure, the maximum liquid water saturation within the gas diffusion layer is 0.181. In general, the liquid water saturation decreases slowly as the cell moves away from the stack inlet. Near the end of the stack, the liquid water saturation below the ribs increases slightly. Liquid water saturation increases in the first and last cells. Furthermore, the liquid water saturation is higher within the stack's gas diffusion layer.

[0178] The prediction results of this embodiment are analyzed in detail as follows:

[0179] As attached Figure 9 、 10 As shown, attached Figure 9 The temperature distribution cloud of the central section of each typical cell in the stack perpendicular to the membrane plane and the inlet plane is given in the appendix. Figure 10 The temperature distribution cloud of the typical unit membrane center section of each single cell in the battery stack is given in the figure. Figure 9 It can be seen that the temperature on the inlet side of the battery is lower than that on the outlet side, and the temperature gradually rises along the direction of fluid flow. At the center of the membrane near the outlet of the single cell far away from the total inlet of the fuel cell stack, the local temperature reaches nearly 93°C. The temperature on the cathode side of the battery is lower than that on the anode side. Figure 9 (a), (b) and (c) show that the temperature change from the first battery to the second battery is greater than the temperature change from the second battery to the 20th battery; Figure 9 (k) and (l) show that the temperature of the last battery is lower than that of the second to last battery; Figure 9(h) to (k) show that, except for the last single cell, the temperature distribution of the 43 cells near the end of the stack is relatively uniform. Figure 10 It can be seen that the temperature on the lower side of the flow channel is higher due to its longer heat dissipation path, and the temperature at the center of the rib is the lowest.

[0180] As attached Figure 11 As shown, attached Figure 11 The distribution cloud diagram of liquid water saturation in the central section of the gas diffusion layer of each typical cell in the stack is given in the figure. Figure 11 It can be seen from the figure that due to the longer drainage distance under the ribs, the liquid water saturation under the ribs of each single cell is higher than that under the flow channel. The liquid water saturation under the flow channel of the penultimate cell near the outlet area is the lowest, while the liquid water saturation under the ribs of the first cell near the inlet area is the highest. Therefore, water accumulation is more likely to occur at this location, which has a negative impact on the mass transfer characteristics of this location.

[0181] As attached Figure 12 As shown, attached Figure 12 The distribution cloud diagram of the water content in the central cross section of each typical unit membrane of each single cell in the stack is given in the figure. Figure 12 and attached Figure 11 It can be seen that the distribution pattern of film water content is roughly the same as that of liquid water content. Drainage is better at the lower side of the flow channel, resulting in lower film water content. Drainage is poorer below the ribs, resulting in higher film water content. Furthermore, the film water content is lower near the center of the inlet rib.

[0182] As can be seen from this example, in the hydrogen-fueled proton exchange membrane fuel cell stack described in this example, the membrane water content distribution in the first 120 cells changes dramatically, while the distribution contours for cells 100 to 163 remain virtually unchanged. The variation in membrane water content between individual cells is less affected by temperature, while the distribution trend within the same cell is more significantly affected by temperature. In summary, the prediction method described in this example can effectively achieve comprehensive multi-physics field distribution prediction for commercial-scale fuel cell stacks.

[0183] The description of the relevant parts of the multi-physical field prediction system, device and computer-readable storage medium in a fuel cell stack provided in this embodiment can be found in the detailed description of the corresponding parts of the multi-physical field prediction method in a fuel cell stack described in this embodiment, and will not be repeated here.

[0184] The above embodiment is only one of the implementation methods that can realize the technical solution of the present invention. The scope of protection claimed by the present invention is not limited only to this embodiment, but also includes changes, replacements and other implementation methods that can be easily thought of by any technician familiar with this technical field within the technical scope disclosed by the present invention.

Claims

1. A method for predicting multi-physical fields in a fuel cell stack, characterized in that: include: Obtaining geometric parameters of the fuel cell air cavity, physical property parameters of the fuel cell gas, and operating parameters of the fuel cell air cavity; According to the geometric parameters of the fuel cell air cavity, the physical property parameters of the fuel cell gas and the operating parameters of the fuel cell air cavity, the inlet parameters of the air side of each single cell in the fuel cell are obtained by simulation calculation; Calculating the operating temperature of each cell in the fuel cell based on the inlet parameters of the air side of each cell in the fuel cell and the predetermined inlet parameters of the coolant flow field plates in the fuel cell; Outputting the inlet parameters of the air side of each single cell in the fuel cell and the operating temperature of each single cell in the fuel cell to a pre-built multi-physics field rapid prediction model for each single cell in the fuel cell, and outputting a multi-physics field prediction result for each single cell in the fuel cell; The prediction method specifically comprises the following steps: Obtaining geometric parameters of the fuel cell air cavity, physical property parameters of the fuel cell gas, and operating parameters of the fuel cell air cavity; The geometric parameters of the fuel cell air cavity, the physical property parameters of the fuel cell gas, the operating parameters of the fuel cell air cavity, and the predetermined air resistance coefficient of each single cell in the fuel cell are input into a pre-built simulation model of the distribution characteristics of each cell in the fuel cell to obtain the inlet parameters of the air side of each single cell in the fuel cell; Inputting the air side inlet parameters of each single cell in the fuel cell and the predetermined inlet parameters of each coolant flow field plate in the fuel cell into a pre-built fuel cell operating temperature calculation model to obtain the operating temperature of each single cell in the fuel cell; Outputting the inlet parameters of the air side of each single cell in the fuel cell and the operating temperature of each single cell in the fuel cell to a pre-built multi-physics field rapid prediction model for each single cell in the fuel cell, and outputting a multi-physics field prediction result for each single cell in the fuel cell; The pre-built simulation model of the distribution characteristics of each piece in the fuel cell is a computational fluid dynamics model based on the finite volume method; The inlet parameters of the air side of each single cell in the fuel cell are the inlet velocity, inlet volume flow, inlet mass flow or inlet stoichiometric ratio of the air side of each single cell in the fuel cell; The process of determining the predetermined inlet parameters of each coolant flow field plate in the fuel cell is as follows: Obtaining geometric parameters of the fuel cell coolant cavity, physical property parameters of the fuel cell coolant, and operating condition parameters of the fuel cell coolant cavity; Obtaining a flow-pressure drop characteristic curve of a coolant cavity flow field plate according to geometric parameters of the fuel cell coolant cavity and physical property parameters of the fuel cell coolant; The resistance coefficient of the coolant cavity flow field plate under the simplified porous medium model is calibrated according to the flow-pressure drop characteristic curve of the coolant cavity flow field plate to obtain the resistance coefficient of the coolant cavity flow field plate under the simplified porous medium model; wherein the simplified porous medium model is obtained by simplifying the coolant flow field of each single cell of the fuel cell using a volume averaging method; The geometric parameters of the fuel cell coolant cavity, the resistance coefficient of the coolant cavity flow field plate under the simplified porous medium model, the physical property parameters of the fuel cell coolant, and the operating condition parameters of the fuel cell coolant cavity are input into a pre-built simulation model of the distribution characteristics of each piece in the fuel cell, and the inlet parameters of each coolant flow field plate in the fuel cell are obtained as output; The pre-built calculation model for the operating temperature of each single cell of the fuel cell is constructed based on a heat balance method.

2. A method for predicting multi-physical fields in a fuel cell stack according to claim 1, characterized in that: The geometric parameters of the fuel cell air cavity include the type of the air cavity single cell flow field plate, the size of the air cavity single cell flow field plate, the type of the manifold, the size of the manifold, the type of the end head and the size of the end head; the physical property parameters of the fuel cell gas include the density and viscosity of the cathode gas; the operating parameters of the fuel cell air cavity include the stoichiometric ratio of the entire stack inlet, the operating temperature of the entire stack inlet, the relative humidity of the entire stack inlet and the pressure of the entire stack outlet.

3. The method for predicting multi-physical fields in a fuel cell stack according to claim 1, characterized in that: The construction process of the pre-built multi-physics field rapid prediction model for each single cell in the fuel cell is as follows: From the pre-determined simulation model parameters of each fuel cell, variable parameters of different cells in the fuel cell are selected, and variable input parameters of several snapshot operating conditions are determined through experimental design methods; Input variable input parameters of several snapshot operating conditions into a pre-built fuel cell multi-physics field coupling model, and obtain multi-physics field simulation results under several snapshot operating conditions through simulation; generating snapshot matrices under the plurality of snapshot working conditions according to the multi-physics field simulation results under the plurality of snapshot working conditions; performing matrix decomposition and transformation on the snapshot matrices under the plurality of snapshot working conditions to obtain a plurality of basis functions; Sort the basis functions from large to small according to the amount of information, to obtain a basis function sorting result; According to a preset truncation order, a plurality of basis functions with the highest information content are selected from the basis function sorting results to obtain the first plurality of basis functions; Based on the first several basis functions, obtaining weight coefficients corresponding to each basis function under the extrapolation working condition, and obtaining weight coefficients of the first several basis functions; According to the first several basis functions and the weight coefficients of the first several basis functions, a multi-physical field rapid prediction model of each single cell in the pre-constructed fuel cell is constructed.

4. The method for predicting multi-physical fields in a fuel cell stack according to claim 1, wherein: The method further includes inputting the inlet parameters of the air side of each single cell in the fuel cell and the operating temperature of each single cell in the fuel cell into a pre-built multi-physics field rapid prediction model for each single cell in the fuel cell, and outputting the multi-physics field prediction results for each single cell in the fuel cell, and further includes a convergence judgment step; The convergence judgment step is specifically as follows: Inputting the inlet parameters of the air side of each single cell in the fuel cell and the operating temperature of each single cell in the fuel cell into a pre-built multi-physics field rapid prediction model for each single cell in the fuel cell, and outputting a predicted pressure drop value for each single cell in the fuel cell; The geometric parameters of the fuel cell air cavity, the physical property parameters of the fuel cell gas, the operating parameters of the fuel cell air cavity, and the predetermined air resistance coefficient of each single cell in the fuel cell are input into a pre-built simulation model of the distribution characteristics of each cell in the fuel cell to obtain the pressure field of the fuel cell air cavity; Counting the pressure drop of each single cell in the air cavity of the fuel cell to obtain a statistical value of the pressure drop of each single cell of the fuel cell; Comparing the predicted pressure drop value of each single cell of the fuel cell with the statistical pressure drop value of each single cell of the fuel cell to obtain the calculated convergence of the inlet parameters of the air side of each single cell in the fuel cell; According to the calculation convergence of the inlet parameters of the air side of each single cell in the fuel cell, it is determined whether it is necessary to re-iterate the calculation of the inlet parameter ratio of the air side of each single cell in the fuel cell.

5. A multi-physics field prediction system in a fuel cell stack, characterized in that: include: A parameter acquisition module, used to obtain geometric parameters of the fuel cell air cavity, physical property parameters of the fuel cell gas, and operating parameters of the fuel cell air cavity; an inlet parameter simulation calculation module, configured to obtain, by simulation and calculation, inlet parameters of the air side of each single cell in the fuel cell based on geometric parameters of the fuel cell air cavity, physical property parameters of the fuel cell gas, and operating condition parameters of the fuel cell air cavity; An operating temperature calculation module is used to calculate the operating temperature of each single cell in the fuel cell based on the inlet parameters of the air side of each single cell in the fuel cell and the predetermined inlet parameters of each coolant flow field plate in the fuel cell; a physical field prediction module, configured to output the inlet parameters of the air side of each single cell in the fuel cell and the operating temperature of each single cell in the fuel cell to a pre-built multi-physical field rapid prediction model for each single cell in the fuel cell, and output a multi-physical field prediction result for each single cell in the fuel cell; The prediction system specifically includes: Obtaining geometric parameters of the fuel cell air cavity, physical property parameters of the fuel cell gas, and operating parameters of the fuel cell air cavity; The geometric parameters of the fuel cell air cavity, the physical property parameters of the fuel cell gas, the operating parameters of the fuel cell air cavity, and the predetermined air resistance coefficient of each single cell in the fuel cell are input into a pre-built simulation model of the distribution characteristics of each cell in the fuel cell to obtain the inlet parameters of the air side of each single cell in the fuel cell; Inputting the air side inlet parameters of each single cell in the fuel cell and the predetermined inlet parameters of each coolant flow field plate in the fuel cell into a pre-built fuel cell operating temperature calculation model to obtain the operating temperature of each single cell in the fuel cell; Outputting the inlet parameters of the air side of each single cell in the fuel cell and the operating temperature of each single cell in the fuel cell to a pre-built multi-physics field rapid prediction model for each single cell in the fuel cell, and outputting a multi-physics field prediction result for each single cell in the fuel cell; The pre-built simulation model of the distribution characteristics of each piece in the fuel cell is a computational fluid dynamics model based on the finite volume method; The inlet parameters of the air side of each single cell in the fuel cell are the inlet velocity, inlet volume flow, inlet mass flow or inlet stoichiometric ratio of the air side of each single cell in the fuel cell; The process of determining the predetermined inlet parameters of each coolant flow field plate in the fuel cell is as follows: Obtaining geometric parameters of the fuel cell coolant cavity, physical property parameters of the fuel cell coolant, and operating condition parameters of the fuel cell coolant cavity; Obtaining a flow-pressure drop characteristic curve of a coolant cavity flow field plate according to geometric parameters of the fuel cell coolant cavity and physical property parameters of the fuel cell coolant; The resistance coefficient of the coolant cavity flow field plate under the simplified porous medium model is calibrated according to the flow-pressure drop characteristic curve of the coolant cavity flow field plate to obtain the resistance coefficient of the coolant cavity flow field plate under the simplified porous medium model; wherein the simplified porous medium model is obtained by simplifying the coolant flow field of each single cell of the fuel cell using a volume averaging method; The geometric parameters of the fuel cell coolant cavity, the resistance coefficient of the coolant cavity flow field plate under the simplified porous medium model, the physical property parameters of the fuel cell coolant, and the operating condition parameters of the fuel cell coolant cavity are input into a pre-built simulation model of the distribution characteristics of each piece in the fuel cell, and the inlet parameters of each coolant flow field plate in the fuel cell are obtained as output; The pre-built calculation model for the operating temperature of each single cell of the fuel cell is constructed based on a heat balance method.

6. A multi-physics field prediction device in a fuel cell stack, characterized in that: include: memory for storing computer programs; A processor is configured to implement the steps of the method for predicting multiple physical fields in a fuel cell stack as described in any one of claims 1 to 4 when executing the computer program.

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