Optimization of catalyst distribution along PEMFC channel through a numerical two-phase model and genetic algorithm

Sasan Ebrahimi, Babak Ghorbani and Krishna Vijayaraghavan

Renewable Energy, 2017, vol. 113, issue C, 846-854

Abstract: In this paper, a new approach is presented to find the optimum catalyst loading distribution along the flow field. The optimization is performed by integrating a computational fluid dynamic (CFD) model and genetic algorithm optimization method. The CFD model is two-dimensional, steady state and two-phase. Multiphase mixture model (M2) is used to model two-phase transport in porous media of a Polymer Electrolyte Membrane Fuel Cell (PEMFC). Numerical domain includes channel, gas diffusion layer (GDL) and catalyst layer (CL) in the cathode side. In the next step, current density is assumed to be proportional with catalyst loading. Catalyst loading is considered as polynomial functions with unknown coefficients. Genetic algorithm optimization method is applied to find the unknown coefficients and as a result the optimum catalyst loading function along the flow field. The results indicate that catalyst loading distribution has a significant effect on the fuel cell performance and it is seen that in the optimum case, maximum PEMFC power density is increased by about 14%.

Keywords: Optimum catalyst loading; PEMFC power density; Two-phase flow; Multiphase mixture model (M2); Non-uniform catalyst distribution; Genetic algorithm (search for similar items in EconPapers)
Date: 2017
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (5)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:renene:v:113:y:2017:i:c:p:846-854

DOI: 10.1016/j.renene.2017.06.067

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