Title :
Neural network modelling of fuel cell systems for vehicles
Author :
Caponetto, R. ; Fortuna, L. ; Rizzo, A.
Author_Institution :
D.I.E.E.S., Catania Univ.
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 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
Keywords :
control engineering computing; fuel cell vehicles; fuel cells; multilayer perceptrons; nonlinear dynamical systems; power engineering computing; artificial neural network modelling; black-box model; fuel cell stack; fuel cell system; fuel cell vehicle; nonlinear dynamical model; zero-emission vehicle prototype; Artificial neural networks; Battery charge measurement; Battery management systems; Control system synthesis; Fuel cell vehicles; Fuel cells; Neural networks; Predictive models; Temperature; Voltage;
Conference_Titel :
Emerging Technologies and Factory Automation, 2005. ETFA 2005. 10th IEEE Conference on
Conference_Location :
Catania
Print_ISBN :
0-7803-9401-1
DOI :
10.1109/ETFA.2005.1612519
