Multi-objective optimization of hybrid electric vehicles considering fuel consumption and dynamic performance

Sebastian Buerger, Boris Lohmann, Martin Merz, Birgit Vogel-Heuser, Michael Hallmannsegger

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

39 Scopus citations

Abstract

In this paper a new concept for the layout of hybrid-electric-powertrains is developed that includes optimization of the component-sizes as well as control strategies. In contrast to most existing publications, the approach explicitly considers the conflicting goals of low fuel consumption and high vehicle longitudinal dynamics and the trade-off is quantified. Two multiobjective optimization subproblems are solved for one example with a parallelized genetic algorithm (NSGA-II) using the Condor software framework. The analysis of the solutions (Pareto front) shows that combinations exist which improve the fuel consumption with only a slight deterioration of the dynamic performance. So the designers are supported in their decision for a configuration which is attractive for the customers.

Original languageEnglish
Title of host publication2010 IEEE Vehicle Power and Propulsion Conference, VPPC 2010
DOIs
StatePublished - 2010
Event2010 IEEE Vehicle Power and Propulsion Conference, VPPC 2010 - Lille, France
Duration: 1 Sep 20103 Sep 2010

Publication series

Name2010 IEEE Vehicle Power and Propulsion Conference, VPPC 2010

Conference

Conference2010 IEEE Vehicle Power and Propulsion Conference, VPPC 2010
Country/TerritoryFrance
CityLille
Period1/09/103/09/10

Keywords

  • Component sizing
  • Control strategy
  • Dymola
  • Dynamics
  • Energy management
  • Fuel consumption
  • Genetic algorithm
  • Hybrid electric vehicle (HEV)
  • Hybridization
  • Modelica
  • Multi-objective
  • Optimization
  • Simulation

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