Simulating Fuel Cell Operation: Modeling Techniques
10 patents in this list
Updated:
This page provides information about hydrogen fuel cells and explores how simulations are used to study and understand the operation of these fuel cells.
Hydrogen fuel cells are an important technology in the quest for clean and sustainable energy sources. These fuel cells convert hydrogen and oxygen into electricity, with the only byproduct being water. The use of hydrogen fuel cells has the potential to reduce dependence on fossil fuels, decrease greenhouse gas emissions, and mitigate environmental impacts. However, understanding the intricacies of fuel cell operation is crucial for optimizing their performance and improving their efficiency.
Simulating the operation of hydrogen fuel cells presents several challenges. Fuel cell systems are complex and involve various processes such as fluid flow, heat transfer, and electrochemical reactions. Modeling these processes accurately requires precise knowledge of material properties, reaction kinetics, and transport phenomena. Simulations must also account for factors like temperature, pressure, and electrode dynamics. Developing reliable and accurate simulation models is essential for engineers and researchers to analyze and optimize fuel cell performance, leading to advancements in fuel cell technology.
1. Optimization of Proton Exchange Membrane Fuel Cell Performance through Simulation Calibration
CATARC New Energy Vehicle Test Center (Tianjin) Co., Ltd., 2023
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.
2. Optimization of Nuclear Fuel Assembly Design Using Surrogate Modeling and Co-Simulation
DALIAN UNIVERSITY OF TECHNOLOGY, 2023
A method for optimizing the structure of flaky fuel assemblies used in nuclear reactors to improve performance and longevity. The method uses surrogate modeling and co-simulation to optimize fuel assembly design under multidisciplinary coupling conditions of flow, solid, and thermal characteristics. It involves creating a surrogate model using Kriging to predict assembly performance based on key design parameters. This is combined with optimization algorithms like adaptive simulated annealing, multi-island genetic algorithm, and others to efficiently find the optimal assembly structure. This addresses issues like uneven temperature distribution.
3. Hydrogen Flow Control Method for Enhanced Fuel Cell System Performance
GM GLOBAL TECHNOLOGY OPERATIONS LLC, 2023
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.
4. Object-Oriented Simulation of Gas Dynamics in Fuel Cell Systems
dSPACE digital signal processing and control engineering GmbH, 2022
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.
5. Degradation-Conscious Model Predictive Control for Fuel Cell Durability Optimization
THE REGENTS OF THE UNIVERSITY OF MICHIGAN, 2022
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.
6. Dehomogenization Techniques for Optimizing Fluid Flow in Fuel Cell Bipolar Plates
Toyota Motor Engineering & Manufacturing North America, Inc, 2022
Optimizing fluid flow networks in fuel cell bipolar plates using dehomogenization techniques to simultaneously design air, hydrogen, and coolant flow networks. The optimization involves iterative simulations and sensitivity analysis to find the optimal design that satisfies requirements for efficient fluid distribution in the fuel cell. The dehomogenization stage refines the discretization to extract intricate Turing flow channels. The optimization applies design variables to the air and hydrogen layers, with the coolant layer being a function of those variables. This allows simultaneous optimization of all three layers.
7. Optimization of Fuel Cell Plate Design Using Predictive Flooding and Performance Modeling
Toyota Motor Engineering & Manufacturing North America, Inc, 2022
Designing fuel cell plates with optimized flow networks to minimize flooding and maximize power generation. The method involves predicting liquid and vapor regions in the fuel cell channels using a model. Then, simultaneous optimization of the air, hydrogen, and coolant layers using the predicted regions. This iterative process refines the channel structures to prevent flooding while maintaining uniform cooling and reaction.
8. Dimensional Analysis Method for Efficient Fuel Cell Performance Simulation
Xi?an Jiaotong University, 2021
A method to analyze the input-output characteristics of fuel cells using similarity principles that reduces the number of experiments needed to understand fuel cell performance. The method involves applying dimensional analysis and the principle of similarity to analyze fuel cell models and equations. It converts dimensional physical quantities into dimensionless numbers that represent similar sets of working conditions. By changing the dimensionless numbers, you can verify the correction and reduce the number of experiments needed to understand fuel cell behavior.
9. Integrated Homotopic Operating States (IHOS) Model for Sensorless Fuel Cell Stack Behavior Prediction
TOYOTA MOTOR ENGINEERING & MANUFACTURING NORTH AMERICA, INC., 2020
Real-time modeling of a fuel cell stack in a vehicle without the need for additional sensors. The method involves using an interpolation-based model called Integrated Homotopic Operating States (IHOS) to predict fuel cell stack behavior. The IHOS model interpolates output states based on input conditions instead of relying on a lookup table. It reduces storage, processing, and time compared to traditional lookup tables. The model is generated from a dataset of real fuel cell stack data, where each data point has conditions and an associated operating state. The model minimizes the dataset required for interpolation. The vehicle's electronic control unit uses the IHOS model to predict stack behavior without additional sensors.
10. Optimal Actuator Control Path Simulation for Improved Fuel Cell System Performance
TOYOTA MOTOR ENGINEERING & MANUFACTURING NORTH AMERICA, INC., 2020
Determining optimal time-series control paths for multiple actuators in a fuel cell system to achieve desired performance while avoiding issues like compressor surge. The method involves using a physics model of the fuel cell circuit to simulate and analyze control paths. By selecting and comparing multiple time-series actuator states, the best set that meets requirements is identified. This ensures synchronous actuator motion that avoids compressor surge and fuel cell drying.
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