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

Abstract

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.