Design of a Polymer Electrolyte Fuel Cell Membrane Electrode Assembly for Maximum Performance under Different Operating Conditions

Abstract

A computational framework for fuel cell analysis and optimization is presented as an innovative alternative to the time consuming trial-and-error process currently used for fuel cell design. The framework is based on a two-dimensional through-the-channel isothermal, isobaric and single phase membrane electrode assembly (MEA) model. The model input parameters are the manufacturing parameters used to build the MEA: platinum loading, platinum to carbon ratio, electrolyte content and gas diffusion layer porosity. The governing equations of the fuel cell model are solved using Netwon’s algorithm and an adaptive finite element method in order to achieve quadratic convergence and a mesh independent solution respectively. The analysis module is used to solve the optimization problem of maximizing performance. To solve these problems a gradientbased optimization algorithm is used in conjunction with analytical sensitivities. The presented computational framework is the first attempt in the fuel cell literature to combine highly efficient analysis and optimization methods to perform optimization in order to tackle large-scale problems. The framework presented is capable of solving a complete MEA optimization problem with state-of-the-art electrode models in approximately 30 minutes. In this article, the optimization framework is applied to obtain an optimal MEA for low, medium and high current density operation. The results show that the optimal MEA design for low current densities is significantly different from the optimal design at medium and high currents. The optimal designs are compared to experimental results from parametric studies in the literature.