Data driven models for a PEM fuel cell stack performance prediction

The operating principles of polymer electrolyte membrane (PEM) fuel cells system involve electrochemistry, thermodynamics and hydrodynamics theory for which it is not always easy to establish a mathematical model. In this paper two different methods to model a commercial PEM fuel cell stack are discussed and compared. The models presented are nonlinear, derived from a black-box approach based on a set of measurable exogenous inputs and are able to predict the output voltage and cathode temperature of a 5 kW module working at the CNR-ITAE. A PEM fuel cell stack fed with H₂ rich gas is employed to experimentally investigate the dynamic behaviour and to reveal the most influential factors. The performance obtained using a classical Neural Networks (NNs) model are compared with a number of stacking strategies. The results show that both strategies are capable of simulating the effects of different stoichiometric ratio in the output variables under different working conditions.