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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Simulating the operation of solid oxide electrolysis cells to optimize high-temperature water electrolysis systems. The simulation involves explicitly tracking energy flows like heat and power in the cell stack components. It uses a model with a mixer and heat exchanger to simulate mixing and heat exchange of the outlet materials. This allows visual monitoring of the outlet material temperature to determine heat balance modes. The simulation provides trustworthy results that match real cell behavior.

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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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