Digital Performance Modeling for Fuel Cells

A method to accurately model and analyze the dynamic behavior of solid oxide fuel cell stacks by considering the heat generation and transfer characteristics during gas flow. The method involves establishing a heat exchange model for the stack components like fuel, air supply pipe, and cell. This is used to build a dynamic heat flow model that captures the real-time temperatures and heat capacities of the stack components. The model is then used to determine the dynamic characteristics of the stack in real time.

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Fuel cell stack flow simulation method to accurately predict fluid distribution in fuel cells without using complex, high-grid-count models. The method involves simplifying the stack geometry for simulation by creating a simplified main pipe model that captures the essential fluid flow patterns. This reduces the number of grids needed compared to simulating the entire stack. The simplified model is validated against design requirements and then used for fluid simulation instead of the full stack model. This reduces computational resources needed and allows more accurate and efficient simulation of fuel cell stack fluid flow.

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Method for constructing a simulation model of a hydrogen fuel cell engine system that provides accurate and flexible modeling for hydrogen fuel cell engines. The method involves a step-by-step process that combines mechanism modeling, empirical modeling, and data-driven modeling to capture the internal mechanism, external characteristics, and performance of the hydrogen fuel cell engine system. This hybrid modeling approach allows for detailed simulation of the complex hydrogen fuel cell engine system with high accuracy and flexibility compared to using just one modeling technique.

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Simulation method for modeling oxygen transport in the catalytic layer of proton exchange membrane fuel cells (PEMFCs) using molecular dynamics (MD) to accurately predict oxygen diffusion through the electrolyte and around the catalyst particles. The method involves constructing realistic models of the catalyst, catalyst support, electrolyte, and oxygen molecules. This includes accounting for the true structure of the amorphous carbon catalyst support and the distribution of multiple catalyst particles on it. The simulation steps include creating models of the catalyst, electrolyte, catalyst support, water, hydronium ions, and oxygen. Then building the equilibrium electrolyte model covering the catalyst and support, followed by constructing the oxygen transport model across the electrolyte.

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Method for accurately predicting the transient performance of proton exchange membrane fuel cells (PEMFCs) during load changes, which addresses the issue of low accuracy in predicting the performance changes of PEMFCs throughout the entire operating cycle when traditional methods are used. The method involves characterizing the fuel cell during transient load changes by calculating gas transmission boundary conditions, phase change equilibrium thresholds, and dynamic physical parameters. This allows simulating the fuel cell's steady-state operation and transient voltage overshoot phenomenon accurately, rather than just focusing on steady-state conditions like traditional methods.

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Simulating vehicle-based fuel cell thermal management to improve fuel cell performance and durability in high-power applications. The simulation method comprehensively models the mutual conversion of electrical, kinetic, and thermal energy in the fuel cell system under different conditions. It predicts temperatures, fluid flows, and energy transfers to locate component issues, optimize control, and prevent failures. The simulation considers factors like vehicle speed, ambient temps, coolant properties, etc.

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Simulating heat transfer between components in the hot zone of solid oxide fuel cell systems to improve simulation accuracy. The simulation considers heat exchange between hot zone components like fuel cell, reformer, and heat exchanger. It uses measured or simulated multi-physics domain data to calculate component heat dissipation and update temperatures. This enables more accurate flow rate calculations under steady state conditions.

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Fuel cell algorithm based on linear regression to accurately model fuel cell performance without requiring complex numerical methods. The algorithm uses linear regression to fit experimental data and extract parameters for the fuel cell's polarization curve. This simplified approach avoids issues like grid discretization, random sampling, and numerical stability of more complex methods. The linear regression provides a compact, interpretable model that can easily predict fuel cell behavior without requiring detailed reaction data or specialized simulation tools.

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A method to analyze and optimize current distribution uniformity in a fuel cell stack by using a resistance grid model. The method involves segmenting the fuel cell stack into multiple segments and adding a resistance grid between the segments. This simulates the effect of the bipolar plates' transverse resistance on current flow. By analyzing the inter-segment current through the resistance grid, the method can determine the current distribution uniformity inside the stack. This allows identifying factors and operating conditions that affect current distribution and finding ways to improve uniformity.

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Method to optimize performance of air-cooled proton exchange membrane fuel cells (PEMFCs) by identifying the optimal operating conditions using a combination of computational modeling and experimental analysis. The method involves building a 3D steady-state model of the PEMFC using computational fluid dynamics (CFD) to analyze the impact of operating parameters on cell performance. Orthogonal experiments are then conducted to determine the level combinations that optimize output and internal characteristics. This allows identifying the optimal cathode pressure, humidity, and hydrogen stoichiometry ratio along with anode pressure for maximum cell performance.

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Optimizing the performance of air-cooled metal plate fuel cells using neural networks and genetic algorithms to predict and improve overall cell performance. The method involves using a neural network to predict cell performance indicators based on input variables. Genetic algorithms are then used to optimize the operating parameters and find the maximum cell performance by treating it as an optimization problem with the cell performance as the objective function. This reduces calculation and time costs compared to modeling the cell performance directly.

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Data-driven modeling and simulation method for fuel cell stacks that enables accurate and efficient simulation of fuel cell stacks at different scales by using a battery agent model. The method involves building a multi-physics coupling model for a single fuel cell. This model is used to generate a database of fuel cell performance data under different operating conditions. A battery agent model is then constructed based on this data. This agent model can quickly predict fuel cell performance for different conditions, reducing simulation time and computational resources compared to solving the full PDE system. By using the battery agent model in a stack simulation, accurate stack performance can be obtained at a lower cost than full PDE stack simulations.

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High-speed simulation method for fuel cell systems that reduces calculation time compared to conventional methods without sacrificing accuracy. The method involves a linearized fluid model for the fuel cell system components that uses the gas flow rate and pressure drop instead of nonlinear equations. This reduces calculation time by avoiding iterative convergence steps. The linearization is achieved by dividing the fuel cell system into functional blocks and linearizing the flow rate-pressure relationship for each block using the local flow rate and pressure drop.

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A simulation method for fuel cells that accurately predicts the performance of hydrogen-oxygen fuel cells compared to actual operation. The simulation takes into account mass transfer losses in the cathode side of the fuel cell based on actual current density, theoretical limit current density, and critical current density. This improves the accuracy of fuel cell simulation results compared to prior methods.

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Numerical simulation method to optimize water management in fuel cells using a Janus-type wettability in the catalytic layer. The method involves using a lattice Boltzmann model to simulate two-phase fluid flow in porous catalytic layers with a Janus wetting condition, where the left side is hydrophilic and the right side is hydrophobic. This configuration reduces water content compared to single hydrophobic or hydrophilic surfaces. The simulation results show that a Janus catalytic layer with left hydrophilic and right hydrophobic wetting reduces water by 9.9-12.3% compared to single hydrophobic catalysts. This suggests that choosing a Janus catalyst with left hydrophilic and right hydrophobic properties can minimize water in the fuel cell and improve performance.

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Modeling the cold start process of proton exchange membrane fuel cells (PEMFC) operating in low temperature environments to understand the cold start mechanism and guide development of cold start control strategies. The modeling involves constructing a single cell mechanism model of the PEMFC based on electrochemical, heat and mass transfer, and phase change mechanisms. A stack partition model is then built from the single cell model. This stack model is used to output the heat transfer characteristic curve of the PEMFC. Based on the single cell and stack models, a fuel cell cold start model is developed to fully understand the cold start process.

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Optimizing the flow field design of polymer membrane fuel cells to improve performance and reduce pressure drop. The optimization involves using numerical modeling to simulate the fuel cell flow fields and extract key parameters. These parameters are then used to determine the optimal flow field structure for the fuel cell. The optimization process includes meshing a 3D fuel cell model, running simulations, extracting key parameters like velocity and concentration, and selecting the flow field structure that provides the best gas diffusion speed, concentration, and current density. The optimized flow field design is implemented in the fuel cell to improve its overall performance.

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Numerical simulation method to understand the hydrogen crossover process in fuel cells. The method involves constructing a multi-physics model of a fuel cell that includes the functional layers like catalytic layers and the electrolyte. The model simulates the hydrogen dissolution, diffusion, and reaction in the electrolyte during cell operation. This allows determining the spatial and temporal distribution of dissolved hydrogen concentration and permeation flux in the electrolyte. The simulation reveals the characteristics and spatial distribution of the hydrogen transfer process inside the proton exchange membrane during fuel cell operation.

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Optimizing the performance of proton exchange membrane fuel cells using a combination of simulation and experimentation. The optimization involves calibrating a simulation model with bench test data to accurately predict fuel cell output voltages. Standard operating conditions are identified through simulation that maximize a performance metric. The optimized simulation results are then verified against bench test data to validate the optimization. This allows finding optimal operating conditions for fuel cells that balance factors like humidity, temperature, and gas flow rates to improve performance.

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Rapid method and device for predicting the performance of proton exchange membrane fuel cells to overcome the limitations of existing methods that are too computationally expensive or simplified to accurately analyze fuel cell internal processes. The method involves dividing the fuel cell into network units, building a 2D network model for the overall fuel cell layout and a 1D model for each unit's electrode, and coupling the models to simulate the fuel cell's multi-physics fields.

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A modeling and optimization method for fuel cell cooling systems that allows accurate simulation of fuel cell cooling systems with reduced calculation time and real-time capability. The method involves creating a simplified agent model of the cooling system using response surface modeling. The agent model is built using random sampling techniques like Latin hypercube sampling to approximate the input probability distributions. This allows simplifying the complex cooling system model while still accurately representing the input distributions. The agent model has a first-order delay to simulate the system's dynamic characteristics. This simplified agent model with real-time capability provides a more efficient and protectable alternative to complex simulation models.

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A method to optimize temperature control in proton exchange membrane fuel cells (PEMFC) to improve dynamic life. The method involves modeling and simulating the temperature dynamics inside the fuel cell using a detailed numerical model that considers factors like component diffusion, convective heat transfer, and spatiotemporal fluctuations. By accurately predicting internal temperature behavior, the method enables real-time temperature regulation to prevent hot spots, overshoots, and undershoots. This allows the fuel cell to operate within safe temperature limits during dynamic load conditions, mitigating degradation and improving dynamic life.

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Predicting fuel cell performance in anode recirculation mode to improve accuracy and reduce costs compared to existing models. The prediction method calculates nitrogen permeation inside the fuel cell, characterizes nitrogen and water distribution, and predicts output voltage. This captures nitrogen migration and its effect on anode performance. The predictions can guide anode exhaust strategies to optimize recirculation.

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Optimizing parameters of proton exchange membrane fuel cells (PEMFCs) to improve performance while reducing electrolyte liquid fraction. The optimization involves using neural networks to replace the complex physical models of PEMFCs, enabling faster calculation of power density under different parameters. A multi-objective optimization algorithm is then used to find the optimal parameter sets that maximize power density while minimizing electrolyte liquid fraction. This allows identifying parameter combinations that balance power output and liquid management for better overall fuel cell performance.

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Design method and system for fuel cell cooling channels using topology optimization to improve cooling efficiency and prevent hot spots. The method involves iteratively solving a geometric and mathematical model using topology optimization techniques to find the optimal channel layout for fuel cell cooling. The steps include defining the fuel cell dimensions, specifying heat generation rates, determining material properties, building the geometric and mathematical model, solving the optimization problem, and iteratively refining the model. The system has modules for simulation modeling, heat generation modeling, geometric and mathematical model building, solution, and finite element analysis.

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Optimizing cooling of three-dimensional non-isothermal proton exchange membrane fuel cells used in heavy trucks to improve performance and reliability. The optimization involves numerical simulation to find the best cooling scheme based on factors like coolant flow rate, temperature, and direction. By analyzing the impact of cooling on factors like temperature distribution, current density, and flooding, an optimal cooling plan is determined.

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Analyzing internal parameter distribution of water-cooled fuel cells using a multi-physics model to understand how conditions impact cell performance and identify factors that cause cell inconsistency. The method involves modeling fuel cell components like bipolar plates, membrane electrode assemblies, and cooling channels to estimate parameters like temperature, liquid water saturation, and membrane water content distribution across the cell thickness. It calculates uniformity indices to describe hydrothermal distribution characteristics under different operating conditions. By comparing normal vs abnormal cells, extracting structural differences, and modeling those variations, it analyzes how parameter distribution impacts cell output and consistency.

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High-efficiency proton exchange membrane fuel cell simulation method that accurately represents the complex physical and chemical phenomena in fuel cells. The method involves coupling multiple physical fields like temperature, flow, and electrochemistry instead of solving them separately. This provides a more realistic simulation of fuel cell operation compared to decoupled simulations. The fields are coupled using variables like temperature, concentration, and velocity that interact between fields. This allows optimizing fuel cell performance and design by considering the interdependent physical processes.

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Method to accurately model and simulate solid oxide fuel cell (SOFC) stacks for faster and more efficient development. The method involves using machine learning and high-throughput computing to determine a model of the stack based on multiphysics equations. The model is built by initializing a three-dimensional network representing the stack, then calculating electrochemical reactions, flow resistance, and component updates at a reference temperature. This provides a target stack model that can be used for simulation instead of actual stack measurements. The method involves constructing a unit model with multiphysics equations, modifying parameters based on references, and using neural networks to obtain a source term equation for the stack model.

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Numerical simulation method for solid oxide fuel cells that accurately models the complex physical interactions inside the fuel cell stack. The method involves coupling electrochemistry, fluid flow, heat transfer, and solid mechanics simulations to provide a fast and high-precision simulation. It allows for realistic modeling of the fuel cell components like electrodes, electrolyte, and current collectors, accounting for deformation due to thermal stress. This improves simulation accuracy compared to traditional methods that ignore material deformation and anisotropy.

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Method for modeling vehicle fuel cell stacks that improves simulation speed and accuracy by replacing the complex multi-physics simulation of each cell with a neural network model. The method involves building a manifold 3D fluid dynamics model first, then calculating gas transfer data for each cell using preset current densities, and coupling the manifold and cell neural networks to construct the stack model. This reduces simulation time compared to full physics simulation while maintaining accuracy.

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Method to optimize flow fields for fuel cells by modeling oxygen diffusion and water removal characteristics to find the optimal flow field design. The method involves using numerical simulations to model mass transfer in the fuel cell stack components like the catalyst layer, electrolyte membrane, gas diffusion layer, and flow field. The simulations consider factors like humidity, flow rates, and porous media properties. The modeling results provide insights into oxygen concentration and water removal efficiency in the catalyst layer as the flow field is varied. By identifying the flow fields that maximize oxygen and minimize water, an optimal flow field design for the fuel cell can be selected.

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Method for predicting voltage consistency in fuel cell stacks to improve stability and reliability of fuel cell output performance. The method involves establishing a fuel cell consistency prediction model by simulating the voltage consistency between cells in a fuel cell stack. It involves constructing a fuel cell equivalent circuit electrochemical model to characterize changes in the fuel cell during operation, associating operating parameters with the model using fuel cell mechanism formulas, and building an internal fluid distribution equivalent resistance model for the stack to simulate gas flow and pressure distribution.

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Establishing fuel cell models with embedded fault features to accelerate fault diagnosis research in fuel cell systems. The method involves accurately simulating fuel cell operation under fault conditions by embedding fault functions into the models. It enables accumulation of fault data by explicitly solving conservation equations, energy conservation, and output voltage for electrochemistry, fluid, heat, and temperature fields. This helps accelerate fault diagnosis research by providing realistic fault data for testing and validation of diagnosis algorithms.

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Accurately modeling the electroosmotic drag effect of water transport in fuel cells to improve fuel cell performance predictions. The method involves discretizing the complete electroosmotic drag effect, which is the movement of water molecules carrying protons due to electrostatic forces. By breaking down the complete electroosmotic drag into discrete components, it allows more realistic simulation of water distribution and transport inside the fuel cell. The discretization involves calculating the electroosmotic drag at specific locations within the fuel cell components like the catalyst layers and membrane. This provides a more accurate representation of the complete electroosmotic drag compared to simplified models that neglect certain components.

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Fuel cell thermal management modeling and simulation using AMESim software to design and optimize the thermal subsystem of a hydrogen fuel cell system. The method involves creating a detailed model of the fuel cell's cooling components like pumps, radiators, and fans in AMESim. The model allows simulation of various operating conditions and parameter variations to analyze and optimize the thermal management system. This provides insights into controlling temperatures, flow rates, and pressure to improve fuel cell performance and reliability.

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Simulating the steady-state operation of a methanol reforming hydrogen production fuel cell system to optimize efficiency and start-up time. The simulation involves setting up the flow processes for the fuel cell components like stack, reformer, heat exchangers, etc., to match the steady-state operating conditions. This allows analyzing and optimizing the heat transfer, mass balances, and other parameters during normal operation. It also enables evaluating the start-up and shutdown phases by simulating the transient heating and cooling. This helps to identify bottlenecks, improve system design, and reduce start-up times.

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A method for accurately controlling hydrogen flow in fuel cell systems with injectors that have variable flow areas. The method involves estimating the true flow area of the injector based on hydrogen consumption at steady state and the modeled flow rate. This estimated area is then used to adjust the hydrogen flow command, leak rate, and exhaust valve flow estimates. It compensates for injector flow variations to improve fuel cell performance and stability.

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Optimizing operating conditions of proton exchange membrane fuel cells (PEMFCs) to improve power generation efficiency. The method involves finding the optimal temperature, humidity, and voltage for a given load power, by minimizing the total internal resistance of the fuel cell stack. This is done using a Lagrangian multiplier approach. By operating the fuel cell stack at these conditions, it allows higher power output while maintaining or improving efficiency compared to standard operating points.

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Solid oxide fuel cell (SOFC) system modeling and testing method to improve performance parameters while reducing complexity and calculation amount. The modeling involves dividing the components like heat exchanger, stack, and combustion chamber into nodes with gradually shortened lengths based on the reaction progress. The nodes have customized calculation sub-models for temperature, flow, and mole fraction. This accurately reflects the internal conditions of each component. The stack node model has separate temperature, flow, and mole fraction calculations for the solid oxide layer and gases. The testing involves adjusting parameters like fuel utilization, air ratio, and power to analyze efficiency and stability.

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Fuel cell performance optimization methodology that combines simulation and experiment to find optimal operating conditions for fuel cells. The method involves iteratively calibrating simulation models and comparing simulated and actual cell output voltages to converge on accurate parameters. By iterating between simulation and experiment, it ensures the models accurately represent real-world fuel cell behavior.

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Optimizing the efficiency of fuel cell systems using simulation and genetic algorithms. The method involves building a comprehensive simulation model of the fuel cell system that includes the stack, air, hydrogen, cooling, and water subsystems. An adaptive genetic algorithm is applied to optimize operating parameters like air pressure, flow, and stack power output to find the highest efficiency point while satisfying constraints like power requirements and temperature differences. This allows early virtual optimization of the system design to find the most efficient operating point without expensive experimental testing.

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Modeling method for polymer fuel cells that enables accurate simulation of fuel cell performance and water management. The method involves establishing separate models for gas permeation in the cathode and anode, as well as for water infiltration in the membrane. It also calculates the ohmic pressure drop through the membrane based on the proton conductivity and water content variation. This simplified modeling approach allows responsive verification of fuel cell behavior by adjusting the permeation and ohmic drop modules.

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Computer-based simulation of gas dynamics in fuel cell systems to optimize and understand performance. The simulation models the real fuel cell system using a customized object-oriented approach. The simulation defines classes representing container volumes and flow channels, with instances representing actual components in the fuel cell. The simulation connects the instances to mimic the fuel cell's container network. It then simulates thermodynamic parameters in the volumes using user-defined parameters. This allows dynamic, spatially-accurate simulation of pressure, temperature, and mass flow in the fuel cell's container network.

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A modeling method to simulate the mechanical behavior and performance of fuel cell assemblies during assembly and operation. The method allows predicting the optimal assembly preload for highest performance by simulating the mechanical changes inside the fuel cell stack and how they affect performance. The model considers the contact stress between components like bipolar plates and gas diffusion layers during assembly, and how it affects parameters like contact resistance, porosity, and permeability. By simulating these changes and their impact on performance, the method finds the assembly preload that maximizes cell output.

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Accurately modeling fuel cell aging to predict cell degradation and optimize maintenance. The method involves constructing a fuel cell aging model using electrochemical principles and reversible decay models for catalyst area. It optimizes model parameters using particle swarm optimization to accurately simulate cell aging. This enables predicting cell aging based on operating conditions and proactively maintaining cells.

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Degradation-conscious control for fuel cells that improves durability by formulating a linear time varying model predictive control (MPC) framework for fuel cells with special attention to membrane durability. The MPC uses a reduced-order model for water and heat transport in the fuel cell with linearized dynamics. It solves a quadratic optimization problem at each time step to determine operating conditions that meet power demand while avoiding degradation constraints.

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A dynamic and static optimization analysis method for solid oxide fuel cell (SOFC) systems to balance power generation efficiency and thermal safety. The method involves two steps: static optimization to find the optimal operating point and dynamic optimization to find the optimal control strategy for load tracking. The static optimization involves discretizing the output power range, finding operating points with similar net power, and selecting the one with the highest efficiency. The dynamic optimization involves simulating load changes and finding the best control strategy to track the load while maintaining thermal safety.

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A method for calculating the electrochemical performance of air-cooled proton exchange membrane fuel cell stacks. The method involves a low-dimensional model that accurately reflects the temperature distribution and performance balance of cells in air-cooled stacks. The model treats electrodes as homogeneous media and considers forced and natural convection cooling. The stack is discretized into 3 grid cells along the cathode channel. This 1+1 dimensional steady-state calculation method allows efficient prediction of stack performance for air-cooled stacks.

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Predicting the three-dimensional physical field inside a fuel cell using machine learning to enable online prediction of the internal conditions of fuel cells. The method involves building a digital model of the fuel cell's multiphysics field using snapshots from simulations. This model is then used to predict the internal physical fields based on input operating conditions. The digital model is constructed by decomposing the physical fields into basis functions using eigenorthogonal decomposition. The weights of these basis functions for each physical field are learned from snapshots. This reduces the prediction problem to estimating the weight coefficients for each basis function. Machine learning algorithms like regression or interpolation are then used to predict these weights from the input conditions.

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