Identification of PEM fuel cells based on support vector regression and orthonormal bases

Abstract

Polymer Electrolyte Membrane Fuel Cells (PEMFC) are efficient devices that convert the chemical energy of the reactants in electricity. In this type of fuel cells, the performance of the air supply system is fundamental to improve their efficiency. An accurate mathematical model representing the air filling dynamics for a wide range of operating points is then necessary for control design and analysis. In this paper, a new Wiener model identification method based on Support Vector (SV) Regression and orthonormal bases is introduced and used to estimate a nonlinear dynamical model for the air supply system of a laboratory PEMFC from experimental data. The method is experimentally validated using a PEMFC system based on a ZB 8-cell stack with Nafion 115 membrane electrode assemblies