Data Driven Model for a Fuel Cell stack development in a complex Multi-source Hybrid Renewable Energy System

Fuel cells based on polymer electrolyte membrane are considered as the most hopeful clean power technology. The operating principles of polymer electrolyte membrane fuel cells (PEMFC) system involve electrochemistry, thermodynamics and hydrodynamics theory for which it is difficult to establish a mathematical model. In this paper a nonlinear data driven model of a PEMFC stack is developed using Neural Networks (NNs). The model presented is a black-box model, based on a set of measurable exogenous inputs and is able to predict the output voltage and cathode temperature of a high power module working at the CNR- ITAE. A 5 kW PEM fuel cell stack is employed to experimentally investigate the dynamic behaviour and to reveal the most influential factors.