A method and system for predicting response of an integrated regenerative fuel cell mode switching

Abstract

The application provides a method and system for predicting response of integrated regenerative fuel cell mode switching, and belongs to the field of fuel cells, which comprises the following steps: establishing a multi-physical field control equation group; solving the multi-physical field control equation group to obtain physical field distribution data under a steady state before mode switching; keeping the hydrogen side physical field distribution data under the steady state before mode switching continuous before and after mode switching; converting the oxygen mass fraction and oxygen partial pressure in the oxygen side physical field distribution data, and converting the liquid water saturation and gas saturation in the oxygen side physical field distribution data to obtain physical field distribution data under an initial state after mode switching; and based on an updated electrochemical reaction source term, substituting the physical field distribution data under the initial state into the multi-physical field control equation group to obtain a response prediction result of the integrated regenerative fuel cell. The method can provide reliable simulation support for switching strategy optimization and structure improvement.

Description

Technical Field

[0001] This invention belongs to the field of fuel cells, and specifically relates to a response prediction method and system for mode switching of an integrated regenerative fuel cell. Background Technology

[0002] Integrated regenerative fuel cells can achieve both fuel cell power generation and water electrolysis hydrogen production in the same electrochemical device, offering advantages such as compact structure, high energy conversion efficiency, and suitability for renewable energy storage. However, in actual operation, integrated regenerative fuel cells need to frequently switch between fuel cell mode and electrolysis mode. Due to significant differences in reaction direction, water and gas supply methods, gas-liquid two-phase distribution, thermal effects, electrochemical source terms, and boundary conditions between the two modes, problems such as residual liquid water, obstructed gas transport, sudden changes in membrane water content, voltage overshoot or undershoot, prolonged response time, and reduced cycle efficiency can easily occur during mode switching.

[0003] To address the aforementioned issues, existing research establishes steady-state or quasi-steady-state models in fuel cell mode and electrolysis mode, respectively. After solving the steady-state model, the physical quantities are assigned to the other mode as initial conditions, and the transient solver obtains the dynamic response process after the switch.

[0004] However, the fuel cell mode is dominated by gas transport, while the electrolysis mode is dominated by liquid water transport. The two modes differ in terms of solution variables and control equations. The multiphysics state at the initial moment of mode switching is physically inconsistent, and transient calculations are prone to numerical oscillations or divergence, resulting in low accuracy of response prediction. Summary of the Invention

[0005] To address the issue of low accuracy in existing response prediction methods, this invention provides a response prediction method and system for mode switching in an integrated regenerative fuel cell.

[0006] To achieve the above objectives, the present invention provides the following technical solution: A response prediction method for mode switching of an integrated regenerative fuel cell includes: A set of multiphysics control equations was established based on the operating characteristics of the integrated regenerative fuel cell in fuel cell mode and electrolysis mode. Solve the multiphysics control equations to obtain the physical field distribution data under steady state before mode switching. The physical field distribution data includes the physical field distribution data on the hydrogen side and the physical field distribution data on the oxygen side. The hydrogen-side physical field distribution data under steady state before mode switching is used as the hydrogen-side physical field distribution data under the initial state after mode switching. With the gas-liquid saturation complementarity relationship and capillary pressure consistency relationship as constraints, the oxygen mass fraction and oxygen partial pressure in the hydrogen-side physical field distribution data under steady state before mode switching are converted, and the liquid water saturation and gas saturation in the hydrogen-side physical field distribution data under steady state before mode switching are converted to obtain the oxygen-side physical field distribution data under the initial state after mode switching. The electrochemical reaction source terms of the multiphysics control equations are updated based on the switched mode to obtain the updated multiphysics control equations. The physical field distribution data under the initial state are substituted into the updated multiphysics control equations to obtain the response prediction results of the integrated regenerative fuel cell.

[0007] Optionally, the response prediction method for mode switching of an integrated regenerative fuel cell provided by the present invention further includes: Based on the geometry of an integrated regenerative fuel cell, a three-dimensional computational domain shared by the fuel cell mode and the electrolysis mode is established. Based on the operating characteristics of fuel cell mode and electrolysis mode, gas phase conservation control equation, liquid phase conservation control equation, membrane water transport control equation, charge conservation control equation, and energy conservation control equation are constructed in the three-dimensional computational domain, and capillary pressure control equation and electrochemical reaction source term are introduced. By coupling the gas phase conservation control equation, liquid phase conservation control equation, membrane water transport control equation, charge conservation control equation, energy conservation control equation with the capillary pressure control equation and the electrochemical reaction source term, a multiphysics field control equation set is obtained.

[0008] Optionally, the hydrogen-side physical field distribution data includes gas composition and liquid water saturation. The integrated regenerative fuel cell mode switching response prediction method provided by this invention further includes: The gas composition and liquid water saturation under steady state before mode switching are respectively used as the gas composition and liquid water saturation under the initial state after mode switching.

[0009] Optionally, the response prediction method for mode switching of an integrated regenerative fuel cell provided by the present invention further includes: When the mode switching direction of the integrated regenerative fuel cell is from fuel cell mode to electrolysis mode, the oxygen mass fraction is converted into oxygen partial pressure and the liquid water saturation is converted into gas saturation, with the gas-liquid saturation complementarity relationship and capillary pressure consistency relationship as constraints. The temperature distribution data, membrane water content distribution data and electric potential field distribution data of the fuel cell mode are mapped to the electrolysis mode. When the mode switching direction of the integrated regenerative fuel cell is from electrolysis mode to fuel cell mode, the oxygen partial pressure is converted into oxygen mass fraction and the gas saturation is converted into liquid water saturation, with the gas-liquid saturation complementarity relationship and capillary pressure consistency relationship as constraints. The temperature distribution data, membrane water content distribution data and electric potential field distribution data of the electrolysis mode are mapped into the fuel cell.

[0010] Optionally, the response prediction method for mode switching of an integrated regenerative fuel cell provided by the present invention further includes: When the mode switching direction of the integrated regenerative fuel cell is from fuel cell mode to electrolysis mode, the hydrogen component source term and oxygen component source term in the electrochemical reaction source term are switched from consumption term to generation term, the water-related source term in the electrochemical reaction source term is switched from generation term to consumption term, the charge source term sign is reversed, and the energy source term in the electrochemical reaction source term is switched from exothermic source term to endothermic source term. When the mode switching direction of the integrated regenerative fuel cell is from electrolysis mode to fuel cell mode, the hydrogen component source term and oxygen component source term in the electrochemical reaction source term are switched from generation term to consumption term, the water-related source term in the electrochemical reaction source term is switched from consumption term to generation term, the charge source term sign is reversed, and the energy source term in the electrochemical reaction source term is switched from endothermic source term to exothermic source term. An evaporation / condensation phase change source term is introduced into the water-containing region of an integrated regenerative fuel cell.

[0011] Optionally, the response prediction method for mode switching of an integrated regenerative fuel cell provided by the present invention further includes: When the mode switching mode of the integrated regenerative fuel cell is purge switching, the physical field distribution data under the initial state is substituted into the multi-physics field control equation set of the electrochemical reaction source term to obtain the response prediction result. When the mode switching method of the integrated regenerative fuel cell is direct switching, the physical field distribution data under the initial state is substituted into the multiphysics control equation set after the electrochemical reaction source term is updated to obtain the response prediction result.

[0012] Optionally, in the response prediction method for mode switching of an integrated regenerative fuel cell provided by the present invention, the response prediction results include one or more of the following: voltage overshoot peak value, voltage undershoot amplitude, voltage response time, gas saturation transition time, liquid water saturation transition time, cycle efficiency, and local mass transfer loss.

[0013] The present invention also provides a response prediction system for mode switching of an integrated regenerative fuel cell, comprising: The control equations construction module is used to establish a multiphysics control equation set based on the operating characteristics of the integrated regenerative fuel cell in fuel cell mode and electrolysis mode. The steady-state distribution calculation module is used to solve the multiphysics control equations and obtain the physical field distribution data under steady state before mode switching. The physical field distribution data includes the physical field distribution data on the hydrogen side and the physical field distribution data on the oxygen side. The physics field conversion module is used to convert the hydrogen-side physics field distribution data under steady-state conditions before mode switching into the hydrogen-side physics field distribution data under the initial state after mode switching. With the gas-liquid saturation complementarity relationship and capillary pressure consistency relationship as constraints, it converts the oxygen mass fraction and oxygen partial pressure in the hydrogen-side physics field distribution data under steady-state conditions before mode switching, and converts the liquid water saturation and gas saturation in the hydrogen-side physics field distribution data under steady-state conditions before mode switching, to obtain the oxygen-side physics field distribution data under the initial state after mode switching. The response prediction module is used to update the electrochemical reaction source terms of the multiphysics control equations based on the switched mode, so as to obtain the updated multiphysics control equations. The physical field distribution data of the initial state are substituted into the updated multiphysics control equations to obtain the response prediction results of the integrated regenerative fuel cell.

[0014] The present invention also provides a computer device, including a memory, a processor, and a computer program stored in the memory, wherein the processor executes the computer program to implement any of the steps in an integrated regenerative fuel cell mode switching response prediction method.

[0015] The present invention also provides a computer-readable storage medium storing a computer program that, when loaded by a processor, is capable of executing any step of an integrated regenerative fuel cell mode switching response prediction method.

[0016] The response prediction method for mode switching of an integrated regenerative fuel cell provided by this invention has the following beneficial effects: This invention establishes a reasonable bridging rule through differentiated processing on each side. For the hydrogen side, the invention directly uses the steady-state hydrogen side physical field distribution data before switching as the initial hydrogen side physical field distribution data after switching. This ensures the continuity of physical quantities while avoiding the introduction of unnecessary numerical disturbances, providing a stable initial boundary for the hydrogen side in transient calculations. For the oxygen side, considering that the oxygen side is mainly gaseous in fuel cell mode, the solution variables are oxygen mass fraction and liquid water saturation. In electrolysis mode, the oxygen side is mainly liquid water, and the solution variables are oxygen partial pressure and gas saturation. By converting oxygen mass fraction and oxygen partial pressure, and liquid water saturation and gas saturation, the initial field of the oxygen side after bridging is made consistent with the control equations of the target mode in terms of gas-liquid two-phase ratio and capillary transport characteristics. This avoids the problem of initial field physical inconsistency caused by direct assignment of incompatible dependent variable types, fundamentally eliminating the risk of numerical oscillation and divergence in the early stage of transient calculations, improving the stability and convergence efficiency of switching transient calculations, and ensuring the accuracy of response prediction. Attached Figure Description

[0017] To more clearly illustrate the embodiments and design schemes of the present invention, the accompanying drawings required for this embodiment will be briefly described below. The drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0018] Figure 1 A schematic diagram of a response prediction method for mode switching of an integrated regenerative fuel cell provided in an embodiment of the present invention; Figure 2 This is a general flowchart example of the dynamic response prediction method for dual-mode switching of a bulk regenerative fuel cell provided in this embodiment of the invention; Figure 3 This is an example of an integrated regenerative fuel cell computational domain structure provided in an embodiment of the present invention; Figure 4 This is an example of a dual-mode polarization curve for a fuel cell provided in an embodiment of the present invention; Figure 5 This is an example of the physical field bridging relationship between the fuel cell mode and the electrolysis mode provided in an embodiment of the present invention; Figure 6 This is an example of the dynamic response characteristic diagram for switching from fuel cell mode to electrolysis mode provided in an embodiment of the present invention; Figure 7 This is an example of a dynamic response cloud diagram of CL_O gas saturation during the switching from fuel cell mode to electrolysis mode provided in an embodiment of the present invention; Figure 8 This is a dynamic response characteristic diagram of switching from an example electrolysis mode to a fuel cell mode provided in an embodiment of the present invention; Figure 9 This is an example of a dynamic response cloud diagram of CL_O liquid saturation during the purging process of switching from electrolysis mode to fuel cell mode, provided in an embodiment of the present invention. Detailed Implementation

[0019] To enable those skilled in the art to better understand and implement the technical solutions of the present invention, the present invention will be described in detail below with reference to the accompanying drawings and specific embodiments. The following embodiments are only used to more clearly illustrate the technical solutions of the present invention and should not be construed as limiting the scope of protection of the present invention.

[0020] To address the need for response prediction during mode switching in integrated regenerative fuel cells, existing technologies mostly model the fuel cell mode or electrolysis mode separately, making it difficult to uniformly describe the multiphysics evolution of the two modes and their switching process. For the mode switching process, existing methods typically employ simple initial condition replacement or empirical correction, failing to fully consider the bridging relationships of pressure, temperature, gas composition, liquid water saturation, gas saturation, potential, and source terms before and after the switch. This results in models struggling to accurately predict the transient response during switching. Furthermore, the gas-liquid states on the oxygen side differ significantly between fuel cell and electrolysis modes. Failure to properly handle the coupling relationships between oxygen-side gas pressure, liquid water saturation, gas composition, and capillary transport will affect the accuracy of predicting mass transfer losses and voltage fluctuations during the switching process.

[0021] To address this, the present invention constructs a unified multiphysics model for fuel cell mode and electrolysis mode, establishes the physical field bridging relationship and source term switching rules during mode switching, predicts voltage response, water vapor distribution, temperature change and mass transfer state during direct switching or purging switching, and optimizes the gas diffusion layer structure and operation strategy based on the prediction results, thereby improving the switching stability, response speed and cycle efficiency of the integrated regenerative fuel cell.

[0022] Example 1 This invention provides a response prediction method for mode switching in an integrated regenerative fuel cell, specifically as follows: Figure 1 As shown, it includes the following steps: Step 1: Establish a set of multiphysics control equations based on the operating characteristics of the integrated regenerative fuel cell in fuel cell mode and electrolysis mode.

[0023] The multiphysics governing equations can be constructed through the following steps: Step 11: Based on the geometry of the integrated regenerative fuel cell, establish a three-dimensional computational domain shared by the fuel cell mode and the electrolysis mode.

[0024] Step 12: Based on the operating characteristics of fuel cell mode and electrolysis mode, construct gas phase conservation control equation, liquid phase conservation control equation, membrane water transport control equation, charge conservation control equation, and energy conservation control equation in the three-dimensional computational domain, and introduce capillary pressure control equation and electrochemical reaction source term.

[0025] Step 13: Couple the gas phase conservation control equation, liquid phase conservation control equation, membrane water transport control equation, charge conservation control equation, energy conservation control equation with the capillary pressure control equation and the electrochemical reaction source term to obtain a multi-physics field control equation set.

[0026] Specifically, a three-dimensional computational domain for an integrated regenerative fuel cell is first constructed, and a set of multiphysics control equations shared by both fuel cell and electrolysis modes is established. The computational domain includes the hydrogen-side electrode (BP_H), hydrogen-side flow channel (CH_H), hydrogen-side gas diffusion layer (GDL_H), hydrogen-side catalyst layer (CL_H), electrolyte membrane (Mem), oxygen-side catalyst layer (CL_O), oxygen-side gas diffusion layer (GDL_O), oxygen-side flow channel (CH_O), and oxygen-side electrode (BP_O). Based on the operating characteristics of the fuel cell and electrolysis modes, a unified set of multiphysics control equations for both modes is established. The multiphysics model includes a gas-liquid two-phase flow model, a gas component transport model, a liquid water transport model, a capillary pressure model, a membrane water transport model, a charge conservation model, an energy conservation model, and an electrochemical reaction source term model. Among them, the fuel cell mode is characterized by aeration and drainage, while the electrolysis mode is characterized by water flow and venting. The two modes share some transport equations and switch the corresponding source terms and boundary conditions according to the reaction direction and operating boundary.

[0027] Secondly, a multiphysics steady-state solution is performed to obtain the final field physical quantities of the mode before the current mode switch. These final field physical quantities include one or more of the following: pressure, temperature, gas composition, liquid water saturation, gas saturation, film water content, electron potential, ion potential, and local current density.

[0028] Step 2: Solve the multiphysics control equations to obtain the physical field distribution data under steady state before mode switching. The physical field distribution data includes the physical field distribution data on the hydrogen side and the physical field distribution data on the oxygen side.

[0029] Step 3: Use the hydrogen-side physical field distribution data under steady state before mode switching as the hydrogen-side physical field distribution data under the initial state after mode switching; using the complementary relationship of gas-liquid saturation and the consistent relationship of capillary pressure as constraints, convert the oxygen mass fraction and oxygen partial pressure in the hydrogen-side physical field distribution data under steady state before mode switching, and convert the liquid water saturation and gas saturation in the hydrogen-side physical field distribution data under steady state before mode switching to obtain the oxygen-side physical field distribution data under the initial state after mode switching.

[0030] The physical field distribution data for the hydrogen side includes gas composition and liquid water saturation. The physical field distribution data for both the hydrogen and oxygen sides can be bridged to the switched mode in the following manner: Step 31: Take the gas composition and liquid water saturation under steady state before mode switching as the gas composition and liquid water saturation under initial state after mode switching.

[0031] Step 32: When the mode switching direction of the integrated regenerative fuel cell is from fuel cell mode to electrolysis mode, the oxygen mass fraction is converted into oxygen partial pressure and the liquid water saturation is converted into gas saturation, using the complementary relationship of gas and liquid saturation and the consistent relationship of capillary pressure as constraints. The temperature distribution data, membrane water content distribution data and electric potential field distribution data of the fuel cell mode are mapped to the electrolysis mode.

[0032] Step 33: When the mode switching direction of the integrated regenerative fuel cell is from electrolysis mode to fuel cell mode, the oxygen partial pressure is converted into oxygen mass fraction and the gas saturation is converted into liquid water saturation, with the gas-liquid saturation complementarity relationship and capillary pressure consistency relationship as constraints. The temperature distribution data, membrane water content distribution data and electric potential field distribution data of the electrolysis mode are mapped into the fuel cell.

[0033] Specifically, after solving the physical quantities of the final field before mode switching, a physical field bridging relationship is established according to the switching direction. For example, when switching from fuel cell mode to electrolysis mode, the pressure, temperature, gas composition, liquid water saturation, gas saturation, and potential distribution in the final field of fuel cell mode are converted into the initial field of electrolysis mode; when switching from electrolysis mode to fuel cell mode, the hydraulic pressure, gas pressure, oxygen content, liquid water distribution, and temperature distribution in the final field of electrolysis mode are converted into the initial field of fuel cell mode. For the hydrogen side, the gas composition and liquid water transport variables are continuously bridged between the two modes; for the oxygen side, a mapping relationship between oxygen content, gas pressure, liquid water saturation, gas saturation, and total pressure is established based on the differences in gas-liquid state processing methods between the two modes.

[0034] Step 4: Update the electrochemical reaction source terms of the multiphysics control equation set based on the switched mode to obtain the updated multiphysics control equation set; substitute the physical field distribution data of the initial state into the updated multiphysics control equation set to obtain the response prediction results of the integrated regenerative fuel cell.

[0035] The source item update can be achieved through the following steps: Step 41: When the mode switching direction of the integrated regenerative fuel cell is from fuel cell mode to electrolysis mode, switch the hydrogen component source item and oxygen component source item in the electrochemical reaction source item from consumption items to generation items, switch the water-related source item in the electrochemical reaction source item from generation items to consumption items, reverse the sign of the charge source item, and switch the energy source item in the electrochemical reaction source item from exothermic source item to endothermic source item.

[0036] Step 42: When the mode switching direction of the integrated regenerative fuel cell is from electrolysis mode to fuel cell mode, the hydrogen component source term and oxygen component source term in the electrochemical reaction source term are switched from generation term to consumption term, the water-related source term in the electrochemical reaction source term is switched from consumption term to generation term, the charge source term sign is reversed, and the energy source term in the electrochemical reaction source term is switched from endothermic source term to exothermic source term.

[0037] Step 43: Introduce an evaporation / condensation phase change source term in the water-containing region of the integrated regenerative fuel cell.

[0038] Specifically, after the physical field bridging relationship is established, the source terms for mode switching are updated. For example, based on the current operating mode and current direction, the source terms for hydrogen, oxygen, water, charge, and heat are updated. In fuel cell mode, hydrogen and oxygen are consumed as reactants, while water and heat are generated. In electrolysis mode, water is consumed as a reactant, while hydrogen and oxygen are generated, and the energy source terms are updated based on the reaction heat effect. For regions where the conversion of water vapor to liquid water needs to be considered, evaporation / condensation phase change source terms are introduced to maintain coupling between gaseous water content, liquid water saturation, and local temperature.

[0039] The response prediction results include one or more of the following: voltage overshoot peak value, voltage undershoot amplitude, voltage response time, gas saturation transition time, liquid water saturation transition time, cycle efficiency, and local mass transfer loss. These response prediction results can be determined through the following steps: Step 44: When the mode switching mode of the integrated regenerative fuel cell is purge switching, substitute the physical field distribution data of the initial state into the multiphysics control equation set of the electrochemical reaction source term to obtain the response prediction result.

[0040] Step 45: When the mode switching mode of the integrated regenerative fuel cell is direct switching, substitute the physical field distribution data of the initial state into the updated multiphysics control equation set of the electrochemical reaction source term to obtain the response prediction result.

[0041] Specifically, after the source term update is completed, transient calculations are performed based on different switching strategies. When direct switching is used, the transient calculation of the target mode is directly entered after the physical field bridging and source term update are completed. When purge switching is used, a purge stage is set between the two modes. During the purge stage, the electrochemical reaction source term is turned off or the current density is made zero, while the flow, mass transfer, heat transfer and phase change processes are retained. The water vapor distribution inside the battery is adjusted by the purge gas before entering the transient calculation of the target mode.

[0042] Finally, output the switching dynamic response indicators. These dynamic response indicators include one or more of the following during the switching process: voltage overshoot peak value, voltage undershoot amplitude, response time, cycle efficiency, liquid water saturation change, gas saturation change, oxygen concentration change, hydrogen concentration change, temperature change, and local mass transfer loss.

[0043] In summary, this invention establishes a unified multiphysics model for fuel cell mode and electrolysis mode, which can simultaneously describe the two operating modes of an integrated regenerative fuel cell and their switching process, avoiding the problem that single-mode models are difficult to use directly for dual-mode switching analysis.

[0044] Furthermore, this invention proposes a physical field bridging method during mode switching, which can reasonably convert variables such as final field pressure, temperature, composition, saturation and potential of the pre-switching mode into the initial field of the post-switching mode based on the physical characteristics of the dual modes, and reconstruct key cache variables and update source terms to improve the continuity and stability of the switching transient calculation.

[0045] Furthermore, this invention addresses the issue of different gas-liquid states on the oxygen side in fuel cell mode and electrolysis mode by establishing a mapping relationship between oxygen content, gas pressure, liquid water saturation, gas saturation, and total pressure. This allows for a more accurate description of the impact of changes in water-gas transport on the oxygen side on voltage response and mass transfer loss.

[0046] Furthermore, this invention can simultaneously simulate the dynamic response process of mode switching under different switching strategies, analyze the impact of different switching strategies on liquid water discharge, reactant replenishment, voltage downsampling and response time, and provide a basis for the design of switching control strategies.

[0047] Example 2 Based on Example 1, an integrated regenerative fuel cell with an active area of ​​50 cm² and a parallel flow channel structure was used as the simulation object. The ANSYS Fluent 2022 R1 simulation platform was used, which is also compatible with mainstream multiphysics simulation software such as STAR-CCM+ and COMSOL Multiphysics. User-defined functions (UDFs) were combined to compile custom physical fields such as capillary pressure equation, hydraulic equation, gas pressure equation, and electronic potential equation, switch boundary conditions, calculate reaction source terms, bridge physical fields, and reconstruct key variables, so as to complete the transient simulation prediction of the entire process of direct switching from fuel cell mode to electrolysis mode.

[0048] like Figure 2 As shown, firstly, a three-dimensional computational domain for an integrated regenerative fuel cell is established. Based on the actual geometry of the integrated regenerative fuel cell, a three-dimensional single-channel computational model is constructed using ANSYS SpaceClaim. The computational domain sequentially includes the hydrogen-side electrode plate, hydrogen-side channel, hydrogen-side gas diffusion layer, hydrogen-side catalyst layer, proton exchange membrane, oxygen-side catalyst layer, oxygen-side gas diffusion layer, oxygen-side channel, and oxygen-side electrode plate. The key dimensions of each component in the model are shown in the table below, ensuring a high degree of fit between the model and the actual battery structure. Examples of component dimensions are shown in Table 1.

[0049] Table 1. Dimensions of Components in the Integrated Regenerative Fuel Cell Model Example Hydrogen side plate height 0.6 mm Hydrogen side channel height 0.4 mm Hydrogen side channel length 50 cm Hydrogen side diffusion layer 300 μm Hydrogen-side catalyst layer 10 μm Proton exchange membrane 127 μm oxygen-side catalyst layer 10 μm Oxygen side diffusion layer 300 μm Oxygen side channel length 50 cm oxygen side channel height 0.4 mm Oxygen side plate height 0.6 mm Specifically, this embodiment uses ANSYS Mesh to generate a fully hexahedral structured mesh, such as... Figure 3 As shown, the three-dimensional computational domain of the integrated regenerative fuel cell established in this embodiment is divided according to the actual layered structure of the membrane electrode assembly. In the thickness direction, it sequentially includes a hydrogen-side gas diffusion layer, a hydrogen-side catalyst layer, a proton exchange membrane, an oxygen-side catalyst layer, and an oxygen-side gas diffusion layer, connected to the flow channels and electrode regions on both sides. For regions with smaller thicknesses and larger physical field gradients, such as the catalyst layer, proton exchange membrane, and gas diffusion layer, the mesh is refined to improve the spatial resolution of local physical quantities such as gas components, liquid water, membrane water, potential, and electrochemical reactions. Figure 3 It can be seen that different functional regions remain continuously connected within a unified three-dimensional computational domain, providing a computational basis for solving multiphysics problems using the same geometric carrier in both fuel cell and electrolysis modes, as well as for the spatial correspondence and continuous bridging of physical fields before and after mode switching. Mesh independence verification shows that when the total number of grids is approximately 180,000, the relative changes in key physical quantities such as current density and liquid water saturation are less than 1%. This embodiment uses this grid for subsequent calculations.

[0050] The basic setup of materials and battery regions includes setting material properties for the gas diffusion layer (GDL), catalyst layer (CL), proton exchange membrane (PEM), flow channel (CH), and electrode plate (BP) according to conventional methods, including electrical conductivity, thermal conductivity, diffusion coefficient, etc., to ensure that the basic physical characteristics of each region are consistent with the actual battery components.

[0051] Subsequently, steady-state calculations for the fuel cell were performed. Inlet and outlet conditions or flow boundary conditions were set as follows: Hydrogen was introduced into the anode at a stoichiometric ratio of 1.5, relative humidity of 30%, and temperature of 80°C; oxygen was introduced into the cathode at a stoichiometric ratio of 2.0, relative humidity of 3%, and temperature of 80°C; the operating pressure was 1 atm absolute pressure, and the outlet boundary was set to atmospheric pressure; the temperature boundary condition was a constant temperature of 80°C on the outer wall of the battery; the electrical boundary condition was a constant current boundary condition of 0.1 A / cm at the outer boundary of the hydrogen-side electrode. 2 .

[0052] Then, based on the operating characteristics of the fuel cell mode and the electrolysis mode, one or more governing equations were established from the following: gas mass conservation, gas momentum conservation, gas mass transfer, liquid water mass conservation, membrane water transport, electronic potential, proton potential, energy conservation, gas pressure, hydraulic pressure, gas-liquid conservation, capillary transport, and electrochemical reaction source terms. These equations were then coupled to obtain a dual-mode multiphysics model equation set. In the fuel cell mode, due to the large amount of gas and the small amount of water, the gas conservation equation was completely solved to ensure the accuracy and stability of the numerical calculation. The liquid water in the flow channel was solved indirectly with a velocity ratio of 1 or was negligible. The capillary pressure of the porous electrode was solved by the liquid water saturation. The gas mass conservation equation, gas momentum conservation equation, and gas component equation are shown in Equations (1), (2), and (3), respectively.

[0053] (1) (2) (3) in, For time, The density of the mixed gas, For liquid water saturation, For the velocity of the mixed gas, Porosity For the source term of the mass conservation equation for a mixed gas, For gas pressure, For gas dynamic viscosity, This is the source term of the momentum conservation equation. The mass fractions of hydrogen, oxygen, and water vapor. The effective gas diffusion coefficient, It is the source of the gas composition equation.

[0054] The liquid mass conservation equation and the hydraulic equation are shown in formula (4) and formula (5), respectively: (4) (5) in, The density of liquid water, Porosity For the liquid velocity, For inherent penetration rate, This refers to relative penetration rate. For hydraulic, For the dynamic viscosity of the liquid, This is the source term of the hydraulic equation. The capillary transport equation is shown in equations (6) and (7):

[0055] (6) (7) in, For capillary pressure, and These are pneumatic and hydraulic pressure, respectively. The surface tension coefficient, Contact angle, Let be the inherent permeability. The membrane water equation is shown in formula (8):

[0056] (8) in, The dry film density, For time, The content of membrane water, This represents the electrolyte volume fraction. This is the electroosmotic drag coefficient. The ion current density, Where F is the Faraday constant and EW is the equivalent mass of the membrane. For the effective film water diffusion coefficient, This is the source term of the membrane water equation. The electron potential equations are shown in equations (9) and (10):

[0057] (9) (10) in, and These are the electronic potential and the ionic potential, respectively. and These are effective electronic conductivity and effective ionic conductivity, respectively. and These are the source terms of the electron potential equation and the ion potential equation, respectively. The energy equation is shown in formula (11):

[0058] (11) in, and The velocities of the mixed gas and liquid water are respectively. For specific heat capacity, For temperature, For effective thermal conductivity, The term represents the source term of the energy conservation equation, with subscripts g, l, and s indicating the mixture of gas, liquid water, and solid, respectively.

[0059] The governing equations for the hydrogen side in the electrolysis mode are the same as those in the fuel cell mode. However, for the oxygen side, due to the abundance of water and scarcity of gas, the liquid conservation equations are solved completely to ensure accuracy and stability in numerical calculations. The gas flow in the channel is solved indirectly with a velocity ratio of 1, or can be ignored. The capillary pressure of the porous electrode is solved using gas saturation. The membrane water equation, electronic potential equation, and energy equation are the same as those in the fuel cell mode. The liquid conservation equations are shown in equations (12) and (13), respectively:

[0060] (12) (13) in, For time, For the density of the liquid, For gas saturation, For the liquid velocity, Porosity For the source term of the liquid mass conservation equation, For gas pressure, For gas dynamic viscosity, This is the source term of the momentum conservation equation. The gas mass conservation equation is shown in equation (14):

[0061] (14) Since the liquid water is in excess, it is assumed that the gaseous water is in a saturated state at the current temperature. Therefore, the gas composition equation is omitted, and the gas pressure equation is as shown in formula (15): (15) in, For gas density, For gas velocity, For inherent penetration rate, This refers to relative penetration rate. For air pressure, For gas dynamic viscosity, This is the source term of the pressure equation. The capillary transport equations are shown in equations (16) and (17):

[0062] (16) (17) in, For capillary pressure, and These are pneumatic and hydraulic pressure, respectively. The surface tension coefficient, Contact angle, This represents the inherent penetration rate.

[0063] The control parameters were then set for the solution. The SIMPLE algorithm was used for pressure-velocity coupling, and a second-order upwind spatial discretization scheme was selected. The energy equation residual convergence criterion was 1 × 10⁻⁻⁻⁶. 6 The convergence criterion for the residuals of other equations is 1×10⁻ 4 After completing the above steps, the model is initialized, and then iterative calculations are initiated, with real-time monitoring of the residual curves and the core physical quantities corresponding to the monitoring points. After approximately 8000 iterative calculations, all residuals decrease below the convergence criterion, and the monitored core physical quantities remain stable, indicating that a steady state has been reached. The pressure, temperature, hydrogen mass fraction, oxygen mass fraction, water vapor mass fraction, liquid water saturation, membrane water content, electronic potential, ion potential, and local current density distribution under this steady state are extracted as the final field physical quantities of the fuel cell model.

[0064] To verify the effectiveness of the established dual-mode multiphysics model, the steady-state voltages corresponding to different current densities in both fuel cell and electrolysis modes were calculated, and the simulation results were compared with experimental results. The results are as follows: Figure 4 As shown, in fuel cell mode, the average output voltage of the battery gradually decreases as the current density increases; in electrolysis mode, the electrolysis voltage generally increases as the current density increases. The simulation results reflect the basic law of voltage variation with current density in both operating modes. Figure 4 It can be seen that, within the investigated current density range, the simulation results and experimental results show consistent trends in both modes, and the voltage calculation results under each typical operating condition generally match the experimental data. This indicates that the established multiphysics model can reasonably describe the steady-state electrochemical performance of the integrated regenerative fuel cell in both fuel cell mode and electrolysis mode. Therefore, the validated dual-mode steady-state physical field can be used as the final field and target steady-state reference for subsequent transient calculations during mode switching, thus providing a model basis for predicting voltage, water vapor distribution, and other dynamic response parameters during mode switching.

[0065] Then, according to the preset physical field bridging rules, the final field of the fuel cell mode is converted into the initial field of the electrolysis mode. For example... Figure 5 As shown, for the hydrogen side, the gas composition and liquid water transport variables are continuously bridged, and the hydrogen mass fraction, water vapor mass fraction, liquid water saturation, membrane water content, and potential distribution in the final field are directly mapped to the corresponding region on the hydrogen side in the electrolysis mode. The hydrogen-side variables remain continuous between the two modes, ensuring a smooth transition of the hydrogen-side physical field at the initial switching moment. For the oxygen side, since the oxygen side is predominantly gaseous in the fuel cell mode and predominantly liquid water in the electrolysis mode, there is a fundamental difference in the gas-liquid dominance state between the two modes. This embodiment establishes a mapping relationship between oxygen content, gas pressure, liquid water saturation, gas saturation, and total pressure: the oxygen mass fraction in the final field is converted to the initial oxygen partial pressure in the electrolysis mode, and the liquid water saturation in the final field is converted to the initial gas saturation in the electrolysis mode, ensuring that the capillary pressure calculated by the LJ equation remains consistent before and after the bridging. Simultaneously, the temperature distribution, membrane water content, and potential distribution in the final field are bridged to the corresponding region in the electrolysis mode, ensuring that the initial field of the target mode meets the continuity requirements of each physical quantity at the moment of switching.

[0066] Subsequently, the gas, water, charge, and heat source terms in the target mode are updated. In fuel cell mode, hydrogen and oxygen are consumed as reactants, while water and heat are generated; after switching to electrolysis mode, water becomes the reactant and is consumed, while hydrogen and oxygen become products. Specifically, the source terms of the hydrogen component equation are switched from negative values ​​indicating consumption to positive values ​​indicating generation, the source terms of the oxygen component equation are also switched from negative to positive, and the source terms of the water transport equation are switched from positive values ​​indicating generation to negative values ​​indicating consumption; the electronic potential equation source terms and the ion potential equation source terms are reversed according to the reaction direction; the energy equation source terms are updated with the reaction heat effect according to the endothermic reaction characteristics of the electrolysis mode. An evaporation / condensation phase change source term is introduced in the water-bearing region to maintain coupling between the gas phase water content, liquid water saturation, and local temperature.

[0067] After completing the physical field bridging, key buffer variables are reconstructed, including liquid water saturation, gas saturation, capillary pressure, overpotential, interfacial flux, and electrochemical reaction rate. During reconstruction, saturation and capillary pressure are assigned consistent values ​​to ensure that the gas-liquid saturation is satisfied after bridging and that the capillary pressure is consistent with the gas-pressure difference. The overpotential is recalculated according to the initial potential of the electrolysis mode to ensure a reasonable transition of the electrochemical driving force at the moment of switching.

[0068] After completing the physical field bridging, variable reconstruction, and source term update, the transient calculation in electrolysis mode is directly initiated. The current density is set to the electrolysis mode operating value (e.g., 0.5 A / cm²), the boundary conditions are updated to the electrolysis mode water and gas supply mode, the transient solver is started, the time step is set to 0.01 s, and the total calculation time is 60 s.

[0069] Through transient solution, the voltage response curve after switching, the evolution of gas saturation on the oxygen side, the evolution of liquid water saturation on the hydrogen side, the changes in gas component concentration and temperature are output in real time. Figure 6 This paper illustrates the voltage response characteristics and key physical field evolution during the switch from fuel cell mode to electrolysis mode. The solid line represents the voltage change over time during the switch. It shows that the battery is in a stable fuel cell power generation state from 0 to 20 seconds before the switch, with the voltage maintained at approximately 0.88 V. At the 20-second switch point, a significant overshoot peak occurs, jumping to approximately 1.7 V. This reflects the need to re-establish the electrode double-layer potential and overpotential when the electrochemical reaction direction is instantly reversed, resulting in a significant transient shock. After a brief drop, the voltage gradually stabilizes, reaching around 1.48 V at approximately 40 seconds. The entire voltage response transition time is less than 20 seconds, indicating that the system can quickly achieve electrochemical re-equilibrium.

[0070] The dashed line represents the change in gas saturation over time during the switch from fuel cell mode to electrolysis mode. It can be seen that before the switch, the oxygen-side gas saturation is stable at around 0.9, corresponding to the gas transport-dominated state of more gas and less water in fuel cell mode. After the switch, the gas saturation drops sharply, eventually falling below 0.1. This illustrates the decoupling characteristics of electrochemical and hydrodynamic responses on the time scale during mode switching. Voltage overshoot is dominated by changes in electrochemical driving force, while the subsequent smooth recovery of voltage is constrained by the gas-liquid phase redistribution process within the porous electrode.

[0071] Furthermore, to characterize the spatiotemporal evolution of the oxygen-side gas-liquid two-phase state during the switch from fuel cell mode to electrolysis mode, the gas saturation distribution of the oxygen-side catalyst layer was extracted before the switch and at 0 s, 0.5 s, 1 s, 2 s, 5 s, and 10 s after the switch. The results are as follows: Figure 7 As shown. By Figure 7It can be seen that before the switch, the oxygen-side catalyst layer was in a state of high gas saturation. After switching to electrolysis mode, as the water supply conditions and electrochemical reaction state changed, the overall gas saturation gradually decreased over time, transitioning from a high gas saturation state in fuel cell mode to a low gas saturation state in electrolysis mode. Further observation of its spatial distribution reveals significant differences in response rates across different regions. The gas saturation in the central region of the catalyst layer decreases rapidly, while the change near the edges is relatively lagging, indicating significant spatial non-uniformity in gas-liquid two-phase transport during mode switching. This result demonstrates that the physical field bridging and key variable reconstruction method established in this invention not only maintains a reasonable connection between the gas and liquid states before and after the switch but also characterizes the differences in gas saturation response rates at different spatial locations, thus providing a basis for analyzing local gas-liquid transport processes and determining gas saturation transition time. Finally, based on the transient calculation results, the following parameters are extracted: voltage overshoot peak value (characterizing the maximum positive deviation of the voltage from the steady-state value at the moment of switching), voltage response time (characterizing the time required from the start of switching to the voltage entering the steady-state value within ±5%), gas saturation transition time (characterizing the time required for the oxygen-side gas saturation to transition from the initial value to the steady-state value of the electrolysis mode), and cycle efficiency (characterizing the energy conversion efficiency during the switching process).

[0072] In summary, this embodiment can quantitatively evaluate the voltage overshoot amplitude, response time, and water vapor distribution evolution during the direct switching process from fuel cell mode to electrolysis mode, providing a quantitative basis for optimizing the switching strategy.

[0073] Furthermore, this embodiment still uses the same integrated regenerative fuel cell as Embodiment 1 as the simulation object, and uses the ANSYS Fluent 2022 R1 simulation platform and user-defined functions (UDFs) to complete the full-process transient simulation prediction of the purging switch from electrolysis mode to fuel cell mode.

[0074] This example uses the same mesh from Example 1 for subsequent calculations. First, the basic settings for the materials and battery regions are established: the gas diffusion layer (GDL), catalyst layer (CL), proton exchange membrane (PEM), and cathode catalyst layer (CL) are set with conventional material properties, including electrical conductivity, thermal conductivity, and diffusion coefficient, ensuring that the basic physical properties of each region closely match the actual battery components. Furthermore, corresponding physical equation source terms are configured for each region to ensure correct coupling of the physical equations in each region for calculation.

[0075] Then, steady-state calculations for the electrolytic cell were performed. The inlet and outlet conditions or flow boundary conditions were set as follows: the anode inlet was set as a wall and sealed; liquid water was introduced into the cathode at a stoichiometric ratio of 50 and a temperature of 80°C; the operating pressure was 1 atm absolute pressure, and the outlet boundary was set to atmospheric pressure; the temperature boundary condition was set at 80°C for the battery temperature, and a constant temperature boundary condition of 80°C was set on the outer wall of the battery; the electrical boundary condition was set at 0.1 A / cm² as a constant current boundary condition on the outer boundary at the oxygen-side electrode.

[0076] Subsequently, multiphysics coupling is activated for solution. In electrolysis mode, the governing equations for the hydrogen side are consistent with those for the fuel cell mode; for the oxygen side, due to the abundance of water and scarcity of gas, the complete liquid conservation equation and liquid momentum equation are solved, while the flow channel gas is solved indirectly with a velocity ratio of 1, and the gas saturation of the porous electrode is solved by inverse capillary pressure (LJ equation). The governing equations used include the liquid conservation equation, liquid momentum equation, gas mass conservation equation, gas pressure equation, capillary transport equation, film water equation, electronic potential equation, ion potential equation, and energy equation.

[0077] The control parameters were then set for the solution. The pressure-velocity coupling was handled using the SIMPLE algorithm, and a second-order upwind spatial discretization scheme was chosen. The energy equation residual convergence criterion was 1 × 10⁻⁻⁻⁶. 6 The convergence criterion for the residuals of other equations is 1×10⁻ 4 After completing the above steps, the model is initialized, and then iterative calculations are started, with real-time monitoring of the residual curves and the core physical quantities corresponding to the monitoring points. After approximately 8000 iterative calculations, a steady state is reached. The liquid pressure, gas pressure, gas saturation, liquid water distribution, temperature distribution, film water content, and potential distribution under this steady state are extracted as the final field physical quantities of the electrolysis mode.

[0078] The final field of the electrolysis mode is converted into the initial field of the fuel cell mode using physical field bridging rules. For the hydrogen side, the gas composition and liquid water transport variables are continuously bridging. For the oxygen side, the initial oxygen transport state of the fuel cell mode is determined based on the mapping relationship between liquid water saturation, gas saturation, oxygen content, and gas pressure: the gas saturation in the final field of the electrolysis mode is converted into the initial liquid water saturation of the fuel cell mode, and the oxygen partial pressure in the final field of the electrolysis mode is mapped to the initial oxygen mass fraction of the fuel cell mode, ensuring that the capillary pressure remains consistent before and after bridging. Simultaneously, the temperature distribution, membrane water content, and potential distribution are bridging to the corresponding regions of the fuel cell mode.

[0079] This embodiment employs a purge switching strategy. After completing the physical field bridging, variable reconstruction, and source term update, it does not directly enter fuel cell mode but instead sets up a purge phase. During the purge phase, the current density is set to zero, i.e., the electrochemical reaction source term is shut down, retaining only the flow, mass transfer, heat transfer, and evaporation / condensation phase change processes. The purge gas is a reaction gas of oxygen and saturated water vapor, introduced from the cathode inlet, and the purge time is set to 40 s, starting from 20 s and ending at 60 s.

[0080] During the purging phase, excess liquid water on the cathode side is removed by purging gas, adjusting the water vapor distribution inside the battery and gradually reducing the liquid water saturation on the oxygen side to a level suitable for fuel cell mode startup. The transient solver runs continuously with a time step of 0.01 s, monitoring changes in liquid water saturation and gas component concentrations on the oxygen side in real time. After purging, at 60 s, the fuel cell mode is started. Boundary conditions and electrochemical source terms are updated: hydrogen and oxygen are consumed as reactants, and water is generated as a product, with the energy source terms updated based on the reaction heat effect. The current density is set to the fuel cell mode operating value (e.g., 0.1 A / cm²), and transient calculations continue for 120 s.

[0081] Through transient solution, the voltage response curve, voltage downsampling amplitude, gas component recovery process, and liquid water discharge process are output in real time after switching. Figure 8 This diagram illustrates the voltage response characteristics and key physical field evolution during the purging transition from electrolysis mode to fuel cell mode. The solid line represents the voltage change over time during the transition, while the dashed line represents the liquid saturation change over time. It can be seen that in the steady-state phase of electrolysis mode (from -20 s to 20 s), the voltage stabilizes at approximately 1.48 V, and the liquid water saturation on the oxygen side is at a high level of 0.9, corresponding to the electrolysis mode's operation characterized by abundant water and scarce gas, with liquid water transport being the dominant process. At 20 s, the transition occurs, with the voltage experiencing a step drop and plateauing at approximately 1.24 V, entering the purging phase where the current density is zero and the electrochemical reaction source is shut off. Only purging gas is introduced to regulate the water vapor distribution inside the battery. Simultaneously, the liquid water saturation decreases from 20... The voltage began to show a clear exponential downward trend, which intuitively reflected the process of the purge gas continuously removing excess liquid water from the cathode side, allowing the porous electrode to gradually recover from a water-flooded state to a state conducive to gas transport. The purge ended at 60 seconds, and the fuel cell mode was officially started. At this time, the voltage dropped sharply again, and after a brief and gradual transition, it finally stabilized at about 0.88 V. The liquid water saturation further decreased to about 0.08, indicating that the oxygen side had successfully switched to a gas transport-dominated state with more gas and less water.

[0082] To further analyze the regulatory effect of the purging stage on the distribution of residual liquid water on the oxygen side, the liquid water saturation distribution of the oxygen-side catalyst layer was extracted before purging and at 0 s, 0.5 s, 1 s, 5 s, 10 s, and 20 s after the start of purging. The results are as follows: Figure 9 As shown. By Figure 9 It can be seen that the oxygen-side catalyst layer had a high overall liquid water saturation before the purging began. As the purging process continued, the liquid water saturation gradually decreased, the high liquid water saturation area shrank continuously, and evolved towards a lower liquid water saturation state. Simultaneously, the liquid water discharge process exhibited significant spatial differences: the liquid water saturation decreased more rapidly in the central region of the catalyst layer, while the decrease rate was relatively slower in the two peripheral regions. This indicates that the purging response of different regions is somewhat non-uniform due to differences in local mass transfer paths and drainage conditions. With increasing purging time, the liquid water saturation in all regions further decreased, indicating that purging effectively promoted the discharge and redistribution of residual liquid water on the oxygen side after the electrolysis mode ended.

[0083] Figure 9 The continuous evolution of liquid water saturation over time and space, as shown, demonstrates that the transient calculation method established in this invention for the mode bridging and purging stages can characterize the dynamic changes in the local water phase state during the switch from electrolysis mode to fuel cell mode, and identify differences in drainage response rates in different regions. Combined with... Figure 8 The voltage and liquid water saturation response results show that setting a purging phase before fuel cell mode startup helps reduce the residual liquid water content on the oxygen side, improves the initial water vapor distribution, and thus mitigates the adverse effects of local liquid water accumulation on oxygen transport. Therefore, this invention can provide a quantitative basis for determining the purging duration and related operating parameters by predicting the liquid water distribution at different purging times and spatial locations.

[0084] In summary, this embodiment, through the above process, can quantitatively compare the effects of direct switching and purging switching on voltage downsampling amplitude, response time, and water vapor distribution, verify the effectiveness of purging switching in improving the stability of switching from electrolysis mode to fuel cell mode, and provide a quantitative basis for the design of switching control strategies.

[0085] Example 3 The present invention also provides a response prediction system for mode switching of an integrated regenerative fuel cell, comprising: The control equations construction module is used to establish a multiphysics control equation set based on the operating characteristics of the integrated regenerative fuel cell in fuel cell mode and electrolysis mode.

[0086] The steady-state distribution calculation module is used to solve the multiphysics control equations to obtain the physical field distribution data under steady state before mode switching. The physical field distribution data includes the physical field distribution data on the hydrogen side and the physical field distribution data on the oxygen side.

[0087] The physics field conversion module is used to convert the hydrogen-side physics field distribution data under steady-state conditions before mode switching into the hydrogen-side physics field distribution data under the initial state after mode switching. With the gas-liquid saturation complementarity relationship and capillary pressure consistency relationship as constraints, it converts the oxygen mass fraction and oxygen partial pressure in the hydrogen-side physics field distribution data under steady-state conditions before mode switching, and converts the liquid water saturation and gas saturation in the hydrogen-side physics field distribution data under steady-state conditions before mode switching, to obtain the oxygen-side physics field distribution data under the initial state after mode switching.

[0088] The response prediction module is used to update the electrochemical reaction source terms of the multiphysics control equations based on the switched mode, so as to obtain the updated multiphysics control equations. The physical field distribution data of the initial state are substituted into the updated multiphysics control equations to obtain the response prediction results of the integrated regenerative fuel cell.

[0089] For example, the response prediction system includes a model building module, an equation system building module, a final field extraction module, a bridging and reconstruction module, a transient calculation module, and a response comparison module. The entire system unfolds sequentially around the switching process between the integrated regenerative fuel cell mode and the electrolysis mode, with each module interconnected and operating collaboratively. The model building module, as the system's foundation, establishes a shared three-dimensional computational domain for both the fuel cell and electrolysis modes, providing a unified geometric framework for subsequent multiphysics equation loading and dual-mode switching calculations. The equation system building module establishes and couples the dual-mode multiphysics model equations based on the three-dimensional computational domain, assigning physical constraints such as gas transport, liquid water transport, membrane water transport, charge conservation, energy conservation, electrochemical reactions, and capillary transport to the fuel cell mode, electrolysis mode, and their switching process. The final field extraction module reads the steady-state or quasi-steady-state calculation results before the switching, extracting final field physical quantities such as pressure, temperature, velocity, gas composition, liquid water saturation, gas saturation, membrane water content, potential distribution, and local current density. The bridging and reconstruction module is used to calculate the final field physical quantities based on the fuel cell mode and the electrolysis mode. The switching direction from battery mode to electrolysis mode or from electrolysis mode to fuel cell mode involves bridging the final field physical quantities to the initial field of the target mode, reconstructing key variables for consistency, and updating the gas, water, charge, and heat source terms in the target mode. The transient calculation module is used to perform transient calculations under direct switching, purge switching, or other preset switching strategies after completing the physical field bridging, key variable reconstruction, and source term updates. The response comparison module is used to read the transient calculation results, extract and compare dynamic response indicators such as voltage overshoot or undershoot, response time, gas saturation transition time, liquid water saturation transition time, cycle efficiency, and local mass transfer loss under different switching strategies, thereby forming a complete closed loop for predicting the dynamic response of the integrated regenerative fuel cell dual-mode switching.

[0090] The model building module is used to establish a shared three-dimensional computational domain for both fuel cell and electrolysis modes based on the actual geometry of the integrated regenerative fuel cell. This three-dimensional computational domain includes the hydrogen-side electrode, hydrogen-side flow channel, hydrogen-side gas diffusion layer, hydrogen-side catalyst layer, proton exchange membrane, oxygen-side catalyst layer, oxygen-side gas diffusion layer, oxygen-side flow channel, and oxygen-side electrode. Depending on the actual simulation requirements, the three-dimensional computational domain may also include one or more of the following: a microporous layer, a flow collection region, a fluid inlet / outlet region, or an equivalent porous region. The model building module is also used to partition the flow channel region, porous electrode region, catalyst layer region, proton exchange membrane region, and electrode region, providing a regional basis for subsequently loading flow, mass transfer, charge conduction, heat conduction, and electrochemical reaction source terms.

[0091] The equation construction module is used to establish and couple the dual-mode multiphysics model equations based on the operating characteristics of the fuel cell mode and the electrolysis mode. The dual-mode multiphysics model equations include one or more of the following: gas mass conservation equation, gas momentum conservation equation, gas component transport equation, liquid water mass conservation equation, hydraulic equation, gas pressure equation, capillary transport equation, membrane water transport equation, electronic potential equation, ionic potential equation, energy conservation equation, and electrochemical reaction source terms. The equation construction module is also used to switch the equation source terms and boundary conditions according to the target operating mode, so that hydrogen and oxygen are reactants and water is a product in the fuel cell mode, and water is a reactant and hydrogen and oxygen are products in the electrolysis mode, while ensuring that the reaction heat, charge source terms, and water-gas transport source terms are consistent with the current mode.

[0092] The final field extraction module is used to extract the steady-state or quasi-steady-state final field physical quantities before the mode switching. These final field physical quantities include one or more of the following: pressure, temperature, velocity, hydrogen mass fraction, oxygen mass fraction, water vapor mass fraction, liquid water saturation, gas saturation, membrane water content, electronic potential, ionic potential, local current density, electrochemical reaction rate, phase change source term, and heat source term. The final field extraction module also categorizes and stores the final field physical quantities from different regions according to hydrogen side, oxygen side, membrane electrode region, flow channel region, and porous media region, providing a data foundation for subsequent physical field bridging.

[0093] The bridging and reconstruction module performs physical field bridging, key variable reconstruction, and source term updates based on the switching direction. When switching from fuel cell mode to electrolysis mode, the bridging and reconstruction module converts the pressure, temperature, gas composition, liquid water saturation, membrane water content, and potential distribution in the final field of the fuel cell mode into the initial field of the electrolysis mode. When switching from electrolysis mode to fuel cell mode, the bridging and reconstruction module converts the liquid pressure, gas pressure, gas saturation, liquid water distribution, temperature distribution, membrane water content, and potential distribution in the final field of the electrolysis mode into the initial field of the fuel cell mode. For the hydrogen side, the bridging and reconstruction module maintains continuous bridging of gas composition, liquid water variables, and potential variables. For the oxygen side, the bridging and reconstruction module establishes a mapping relationship between gas pressure, liquid pressure, oxygen pressure, liquid water saturation, gas saturation, and capillary pressure based on the difference in the gas-liquid dominant states in the two modes, and ensures that the initial field of the target mode satisfies the complementary relationship of gas-liquid saturation and the consistent relationship of capillary pressure. Key variables include one or more of the following: liquid water saturation, gas saturation, capillary pressure, overpotential, interfacial flux, electrochemical reaction rate, evaporation / condensation phase change source term, and heat source term. Key variable reconstruction includes uniformly assigning values ​​to saturation and capillary pressure, recalculating the overpotential according to the initial potential of the target model, and recalculating the source terms with different symbols or physical meanings under the old model according to the expression of the target model after clearing them to zero.

[0094] The transient calculation module is used to perform transient calculations according to a preset switching strategy after completing the physical field bridging, key variable reconstruction, and source term updates. The switching strategies include a direct switching strategy and a purge switching strategy. Under the direct switching strategy, the transient calculation module directly enters the transient solution of the target mode after completing the initial field construction of the target mode. Under the purge switching strategy, the transient calculation module sets a purge phase between the two modes. During the purge phase, the current density is set to zero or the electrochemical reaction source term is turned off, while the flow, mass transfer, heat transfer, and evaporation / condensation phase change processes are retained. After adjusting the water vapor distribution inside the battery by purge gas or water supply conditions, it then enters the transient solution of the target mode. The transient calculation module is also used to set the time step, total calculation time, inlet flow rate, inlet humidity, inlet pressure, current density, voltage boundary, and external wall thermal boundary conditions to obtain the changes in voltage, water vapor distribution, temperature, and mass transfer state over time during the switching process.

[0095] The response comparison module is used to read, extract, and compare the dynamic response data output by the transient calculation module, generating dynamic response evaluation results for the integrated regenerative fuel cell under different switching strategies. Dynamic response indicators include one or more of the following: voltage overshoot peak value, voltage undershoot amplitude, voltage response time, gas saturation transition time, liquid water saturation transition time, oxygen concentration recovery time, hydrogen concentration recovery time, temperature fluctuation amplitude, cycle efficiency, and local mass transfer loss. The response comparison module also compares the differences between direct switching and purge switching in terms of voltage stability, water-gas distribution recovery, response speed, and cycle efficiency, and outputs evaluation criteria for switching strategy optimization, boundary condition adjustment, or structural parameter improvement.

[0096] The present invention also provides a computer device, including a memory, a processor, and a computer program stored in the memory. The processor executes the computer program to implement the steps in an embodiment of a response prediction method for mode switching of an integrated regenerative fuel cell. Specific implementation methods can be found in the method embodiments, and will not be repeated here.

[0097] Furthermore, the present invention also provides a non-transitory computer-readable storage medium containing instructions on which a computer program is stored. For example, a memory containing instructions that can be executed by a processor of a computer device to perform the above-described method. For example, the non-transitory computer-readable storage medium may be a ROM, random access memory (RAM), CD-ROM, magnetic tape, floppy disk, and optical data storage device, etc. When the computer program is executed by the processor, it can implement the steps in an embodiment of a response prediction method for mode switching of an integrated regenerative fuel cell. Specific implementation methods can be found in the method embodiments, which will not be repeated here.

[0098] Those skilled in the art will understand that embodiments of the present invention can provide methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0099] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, as well as combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0100] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0101] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0102] It should be noted that the specific embodiments described above enable those skilled in the art to more fully understand the present invention, but do not limit the present invention in any way. Therefore, although the present invention has been described in detail in this specification and embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the present invention; and all technical solutions and improvements that do not depart from the spirit and scope of the present invention are covered within the protection scope of the present invention patent. No reference numerals in the claims should be construed as limiting the scope of the claims. Any simple variations or equivalent substitutions of technical solutions that can be readily obtained by those skilled in the art within the scope of the technology disclosed in the present invention are within the protection scope of the present invention.

Claims

1. A response prediction method for mode switching of an integrated regenerative fuel cell, characterized in that, include: A set of multiphysics control equations was established based on the operating characteristics of the integrated regenerative fuel cell in fuel cell mode and electrolysis mode. Solve the multiphysics control equations to obtain the physical field distribution data under steady state before mode switching, wherein the physical field distribution data includes hydrogen-side physical field distribution data and oxygen-side physical field distribution data; The hydrogen-side physical field distribution data under steady state before mode switching is used as the hydrogen-side physical field distribution data under the initial state after mode switching. With the gas-liquid saturation complementarity relationship and capillary pressure consistency relationship as constraints, the oxygen mass fraction and oxygen partial pressure in the hydrogen-side physical field distribution data under steady state before mode switching are converted, and the liquid water saturation and gas saturation in the hydrogen-side physical field distribution data under steady state before mode switching are converted to obtain the oxygen-side physical field distribution data under the initial state after mode switching. The electrochemical reaction source terms of the multiphysics control equations are updated based on the switched mode to obtain the updated multiphysics control equations. The physical field distribution data of the initial state are substituted into the updated multiphysics control equations to obtain the response prediction results of the integrated regenerative fuel cell.

2. The response prediction method for mode switching of an integrated regenerative fuel cell according to claim 1, characterized in that, The multiphysics control equations are established based on the operating characteristics of integrated regenerative fuel cells in fuel cell mode and electrolysis mode, including: Based on the geometry of the integrated regenerative fuel cell, a three-dimensional computational domain shared by the fuel cell mode and the electrolysis mode is established. Based on the operating characteristics of fuel cell mode and electrolysis mode, gas phase conservation control equation, liquid phase conservation control equation, membrane water transport control equation, charge conservation control equation, and energy conservation control equation are constructed in the three-dimensional computational domain, and capillary pressure control equation and electrochemical reaction source term are introduced. The gas phase conservation control equation, liquid phase conservation control equation, membrane water transport control equation, charge conservation control equation, and energy conservation control equation are coupled with the capillary pressure control equation and the electrochemical reaction source term to obtain the multiphysics field control equation set.

3. The response prediction method for mode switching of an integrated regenerative fuel cell according to claim 1, characterized in that, The hydrogen-side physical field distribution data includes gas composition and liquid water saturation. Using the hydrogen-side physical field distribution data under steady-state conditions before mode switching as the initial hydrogen-side physical field distribution data after mode switching includes: The gas composition and liquid water saturation under steady state before mode switching are respectively used as the gas composition and liquid water saturation under the initial state after mode switching.

4. The response prediction method for mode switching of an integrated regenerative fuel cell according to claim 1, characterized in that, Using the complementary relationship between gas and liquid saturation and the consistent relationship between capillary pressure as constraints, the oxygen mass fraction and oxygen partial pressure in the hydrogen-side physical field distribution data under steady state before mode switching are converted, and the liquid water saturation and gas saturation in the hydrogen-side physical field distribution data under steady state before mode switching are converted, including: When the mode switching direction of the integrated regenerative fuel cell is from fuel cell mode to electrolysis mode, the oxygen mass fraction is converted into oxygen partial pressure and the liquid water saturation is converted into gas saturation, with the gas-liquid saturation complementarity relationship and capillary pressure consistency relationship as constraints. The temperature distribution data, membrane water content distribution data and electric potential field distribution data of the fuel cell mode are mapped to the electrolysis mode. When the mode switching direction of the integrated regenerative fuel cell is from electrolysis mode to fuel cell mode, the oxygen partial pressure is converted into oxygen mass fraction and the gas saturation is converted into liquid water saturation, with the gas-liquid saturation complementarity relationship and capillary pressure consistency relationship as constraints. The temperature distribution data, membrane water content distribution data and electric potential field distribution data of the electrolysis mode are mapped into the fuel cell.

5. The response prediction method for mode switching of an integrated regenerative fuel cell according to claim 1, characterized in that, Updating the electrochemical reaction source terms of the multiphysics governing equations based on the switched mode includes: When the mode switching direction of the integrated regenerative fuel cell is from fuel cell mode to electrolysis mode, the hydrogen component source item and oxygen component source item in the electrochemical reaction source item are switched from consumption items to generation items, the water-related source item in the electrochemical reaction source item is switched from generation items to consumption items, the charge source item sign is reversed, and the energy source item in the electrochemical reaction source item is switched from exothermic source item to endothermic source item. When the mode switching direction of the integrated regenerative fuel cell is from electrolysis mode to fuel cell mode, the hydrogen component source item and oxygen component source item in the electrochemical reaction source item are switched from generation items to consumption items, the water-related source item in the electrochemical reaction source item is switched from consumption items to generation items, the charge source item sign is reversed, and the energy source item in the electrochemical reaction source item is switched from endothermic source item to exothermic source item. An evaporation / condensation phase change source term is introduced into the water-containing region of the integrated regenerative fuel cell.

6. The response prediction method for mode switching of an integrated regenerative fuel cell according to claim 1, characterized in that, Substituting the physical field distribution data under the initial state into the updated multiphysics control equations for the electrochemical reaction source term, the response prediction results of the integrated regenerative fuel cell are obtained, including: When the mode switching mode of the integrated regenerative fuel cell is purge switching, the physical field distribution data in the initial state are substituted into the multiphysics control equation set of the electrochemical reaction source term to obtain the response prediction result. When the mode switching mode of the integrated regenerative fuel cell is direct switching, the physical field distribution data in the initial state are substituted into the multiphysics control equation set after the electrochemical reaction source term is updated to obtain the response prediction result.

7. The response prediction method for mode switching of an integrated regenerative fuel cell according to claim 1, characterized in that, The response prediction results include one or more of the following: voltage overshoot peak value, voltage undershoot amplitude, voltage response time, gas saturation transition time, liquid water saturation transition time, cycle efficiency, and local mass transfer loss.

8. A response prediction system for mode switching of an integrated regenerative fuel cell, characterized in that, include: The control equations construction module is used to establish a multiphysics control equation set based on the operating characteristics of the integrated regenerative fuel cell in fuel cell mode and electrolysis mode. The steady-state distribution calculation module is used to solve the multiphysics control equations to obtain the physical field distribution data under steady state before mode switching, wherein the physical field distribution data includes hydrogen-side physical field distribution data and oxygen-side physical field distribution data. The physical field conversion module is used to use the hydrogen-side physical field distribution data under steady state before mode switching as the hydrogen-side physical field distribution data under the initial state after mode switching; with the gas-liquid saturation complementarity relationship and capillary pressure consistency relationship as constraints, it converts the oxygen mass fraction and oxygen partial pressure in the hydrogen-side physical field distribution data under steady state before mode switching, and converts the liquid water saturation and gas saturation in the hydrogen-side physical field distribution data under steady state before mode switching, to obtain the oxygen-side physical field distribution data under the initial state after mode switching; The response prediction module is used to update the electrochemical reaction source terms of the multiphysics control equation set based on the switched mode, so as to obtain the multiphysics control equation set with updated electrochemical reaction source terms; and to substitute the physical field distribution data of the initial state into the multiphysics control equation set with updated electrochemical reaction source terms to obtain the response prediction result of the integrated regenerative fuel cell.

9. A computer device, comprising a memory, a processor, and a computer program stored in the memory, characterized in that, The processor executes the computer program to implement the steps of the response prediction method for mode switching of an integrated regenerative fuel cell as described in any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is loaded by the processor, it is able to execute the steps of the response prediction method for mode switching of an integrated regenerative fuel cell as described in any one of claims 1 to 7.

Images