PEM fuel cell modeling and optimization using a genetic algorithm

Catlin, Glenn
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University of Delaware
The future of the world's energy solutions requires a diverse range of ideas relating to the harvest, storage, transmission, implementation, and use of various energy sources. Ideally these sources are incorporated in a renewable and sustain- able manner. An important aspect of the efficient use of limited resources is the design of efficient systems that use these resources. Hydrogen is a potential carrier of clean and renewable energy. It is therefore important to increase the efficiency of the devices that utilize hydrogen as a reactant. This project focuses on effective design of Polymer Electrolyte Membrane Fuel Cells (PEMFCs). The optimization process in this research implements a Genetic Algorithm (GA) to efficiently and effectively search the PEMFC design parameters that have significant influence on performance. This research develops and implements a method of automatic generation of parameterized channel domains that are evaluated for performance by a computational fluid dynamics (CFD) technique. The CFD calculations are conducted by the use of commercially available software from ANSYS. The software package includes GAMBIT as the solid modeling and meshing software, the solver FLUENT, and a PEMFC Add-on Module capable of modeling the relevant physical mechanisms that describe cell operation. The result of the optimization process is a set of optimal channel parameter values for single- and double-serpentine channel configurations. The optimal values for these parameters are identified for a PEMFC of a desired nominal area.