A new modeling approach of embedded fuel-cell power generators based on artificial neural network

Among the various kinds of electrical vehicle (EV) prototypes presented by the car manufacturers, fuel-cell EVs seem to be a very promising solution. Five different fuel-cell technologies are available in the research laboratories. Nevertheless, only two technologies can really be considered for transportation applications due to their solid electrolyte, i.e., proton exchange membrane fuel cells (PEMFCs) and solid oxide fuel cells. The PEMFCs are investigated in this paper. When talking about EV design, a simulation model of the whole fuel-cell system is a binding milestone. This would lead in the optimization ability of the complete vehicle (including all ancillaries, output electrical converter, and their dedicated control laws). Nevertheless, the fuel-cell model is strongly dependent on many physicochemical parameters that are difficult to evaluate on a real PEMFC stack. Moreover, the analytical relations governing the behavior of a PEMFC system are also far from being easy. Thus, a "minimal behavioral model" of a fuel-cell system, which is able to evaluate the output variables and their variations, is highly interesting. Artificial neural networks propose a very efficient tool to reach such an aim. In this paper, a PEMFC neural network model is proposed.