Talks and Poster Presentations (with Proceedings-Entry):
T. Krenek, M. Ruthmair, G. Raidl, M. Planer:
"Applying (hybrid) metaheuristics to fuel consumption optimization of hybrid electric vehicles";
- 2012-04-13; in: "Applications of Evolutionary Computation - EvoApplications 2012",
volume 7248 of LNCS
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.
Electronic version of the publication:
Created from the Publication Database of the Vienna University of Technology.