Modeling and Optimization of Direct Methanol Fuel Cells using Statistical Design of Experiment Methodology

Abstract

Direct Methanol Fuel Cells (DMFC) are difficult to model due to their complex nonlinear multivariate nature. This paper describes the development of a DMFC system model, which incorporates an optimization approach, based on statistical Design of Experiment (DoE) methodology. It is demonstrated through experimentation with a DMFC setup that DoE provides a very efficient methodology to obtain a model for the studied multivariable system with only a few experiments. A description of a useful procedure to model DMFC systems that can be extended to other Fuel Cell technologies is presented in this work. Central Composite Design (CCD) and the Steepest Ascent Method (SAM), both parts of DoE methodology, are used to build up a DMFC model and to obtain the maximum power point respectively. The results obtained can be used for feasibility study, optimization, and control of the FC system.