Electric powertrain modeling of a fuel cell hybrid electric vehicle and development of a power distribution algorithm based on driving mode recognition

This paper proposes a novel fuzzy controller based on an adaptive membership function for optimum power management of a fuel cell hybrid electric vehicle (FCHEV). In the first phase, an electric powertrain model of the FCHEV is derived and a fuzzy controller is proposed. Then, the fuzzy controller is optimized using a genetic algorithm. The optimization process is accomplished through simulation for a given driving cycle. Since, however, the optimized result may vary according to the applied driving cycle for optimization, it is impossible for one optimized result to cover various driving cycles. In the second phase, an adaptive membership function based on a stochastic approach is proposed to guarantee optimum performance from the presented fuzzy controller, even though the driving cycle changes. This controller is referred to as the ‘Stochastic fuzzy controller’ (SFC) in this study. The SFC employs a stochastic approach where membership functions can be transformed statistically using a probability evaluated from driving pattern recognition. Then, driving cycle analysis is performed through off-line simulation and hardware in a loop simulation (HILS) test for four driving cycles. Finally, the SFC shows the best performance in terms of minimum fuel consumption and state-of-charge (SoC) maintenance.