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 or, 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.
Neural network modelling of fuel cell systems for vehicles / Caponetto, R.; Fortuna, L.; Rizzo, A.. - STAMPA. - (2005), pp. 187-192. (Intervento presentato al convegno 10th IEEE International Conference on Emerging Technologies and Factory Automation, ETFA05 tenutosi a Catania, Italy nel September 19-22, 2005) [10.1109/ETFA.2005.1612519].
Neural network modelling of fuel cell systems for vehicles
A. Rizzo
2005-01-01
Abstract
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 or, 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.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
