Neural network modelling of fuel cell systems for vehicles

In this work a nonlinear dynamical model of a fuel cell stack is developed by means of artificial neural networks. The model presented is a black-box model, based on a set of easily measurable exogenous inputs like pressures and temperatures at the stack and is able to predict the output voltage of the fuel cell stack. The model obtained is being exploited as a component of complex control systems able to manage the energy flows between fuel cell stack, battery pack, auxiliary systems and electric engine in a zero-emission vehicle prototype