Applying (Hybrid) Metaheuristics to Fuel Consumption Optimization of Hybrid Electric Vehicles

This work deals with the application of metaheuristics to the fuel consumption minimization problem of hybrid electric vehicles (HEV) considering exactly specified driving cycles. A genetic algorithm, a downhill-simplex method and an algorithm based on swarm intelligence are used to find appropriate parameter values aiming at fuel consumption minimization. Finally, the individual metaheuristics are combined to a hybrid optimization algorithm taking into account the strengths and weaknesses of the single procedures. Due to the required time-consuming simulations it is crucial to keep the number of candidate solutions to be evaluated low. This is partly achieved by starting the heuristic search with already meaningful solutions identified by a Monte-Carlo procedure. Experimental results indicate that the implemented hybrid algorithm achieves better results than previously existing optimization methods on a simplified HEV model.