ANN Modelling of Nonlinear Subsystem of a PEMFC Stack for Dynamic and Steady State Operation

A simple and accurate fuel cell model is required for fuel cell based power electronic applications. An artificial neural network (ANN) model is developed in this paper to model some nonlinear structures within the hybrid model of a PEM fuel cell. It improves accuracy and allows the model to work even under varying operating conditions. What is more, temperature effect on the fuel cell stack are finally represented as current effect by using ANN to represent the relationship between current and temperature. Real-time implementation of the proposed ANN model is realized via a dSPACE processor. Experimental results are provided to verify the validity of the proposed model