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

  • Thorsten Krenek
  • Mario Ruthmair
  • Günther R. Raidl
  • Michael Planer
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7248)

Abstract

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.

Keywords

hybrid metaheuristic genetic algorithm downhill-simplex particle-swarm-optimization hybrid electric vehicles driving cycles 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Thorsten Krenek
    • 1
  • Mario Ruthmair
    • 2
  • Günther R. Raidl
    • 2
  • Michael Planer
    • 1
  1. 1.Institute for Powertrains and Automotive TechnologyVienna University of TechnologyViennaAustria
  2. 2.Institute of Computer Graphics and AlgorithmsVienna University of TechnologyViennaAustria

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