Dynamical recurrent neural network towards modeling of on-board fuel cell power supply

Electric vehicle (EV) technologies are a strategic part of research and development in the automotive industry. Among the various kinds of EV prototypes presented by the car manufacturers, fuel cell powered electrical vehicles seem to be a very promising solution. When talking about EV design, a simulation model of the whole fuel cell system is a binding milestone. This would lead in the optimization possibility of the complete vehicle (including all ancillaries, output electrical converter and their dedicated control laws). Nevertheless, the fuel cell system model is strongly dependent of many physico-chemical parameters that are difficult to evaluate on a real proton exchange membrane fuel cell (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, 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. A dynamic recurrent neural network model of a fuel cell system based on proton exchange membrane technology is presented in this paper.