Power Flow Distribution For Hybrid Fuel Cell Vehicle Via Genetic Algorithm Method

This paper presents a new strategy developed for optimizing the energy flow by using genetic algorithm (GA) method implemented on a hybrid fuel cell vehicle (HFCV) power train system with two energy sources, battery and the stack of fuel cell (FC). This method establishes an energy management between these sources to reach the best performance and acceptable operation of this hybrid structure with respect to fuel economy and overall efficiency.

One of the other goals of this paper is to investigate the applicability of the Evolutionary based algorithms in hybrid system optimization problems. With respect to dynamic behavior of this optimization problem, the system is simulated to demonstrate the validity and the convenience of GA approach. The simulation is done using an OOP Tools that is developed at R&D centre of iran khodro company (IKCO). This package is based on C++ code implemented in Borland C++ Builder V6. The main advantage of object oriented programming is summarized as reusability (inheritance), code reconfigurability (different optimizer, different working space & different driving cycle) and extensibility (addition of more components). It prepares a good environment for supervisory control of stack as a major part of HFCV. The simulation results confirm the feasibility and encourage more research towards an actual application.