On-board fuel cell power supply modeling on the basis of neural network methodology
Résumé
Proton exchange membranes are one of the most promising fuel cell technologies for transportation applications. Considering the aim of transportation applications, a simulation model of the whole fuel cell system is a major milestone. This would lead to the possibility of optimizing the complete vehicle (including all ancillaries, output electrical converter and their dedicated control laws). In a fuel cell system, there is a strong relationship between available electrical power and actual operating conditions: gas conditioning, membrane hydration state, temperature, current set point . . . 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 (NN) are a very efficient tool to reach such an aim. In this paper, a proton exchange membrane fuel cell (PEMFC) system neural network model is proposed. It is implemented on Matlab/Simulink® software and will be integrated to a complete vehicle powertrain. Thus, it will be possible to carry out the development and the simulation of the control laws in order to drive energy transfers on-board fuel cell vehicles.