• DocumentCode
    508230
  • Title

    An Econiche Genetic Algorism-Based Optimization of HEV Parameters

  • Author

    Deng, Yadong ; Lin, Xiang ; Lian, Zhiwei

  • Author_Institution
    Sch. of Automobile Eng., Wuhan Univ. of Technol., Wuhan, China
  • Volume
    4
  • fYear
    2009
  • fDate
    14-16 Aug. 2009
  • Firstpage
    580
  • Lastpage
    584
  • Abstract
    A synchronous optimization of power system and controller parameter could substantially improve the fuel economy and emission performance of HEV (hybrid electric vehicle). As a typical multi-objective optimization, this type of optimization problem involves a couple of mutually conflicting objectives of optimization and non-linear constraints. In this paper, the Pareto optimal solution set of this optimization problem is sought by adopting econiche genetic algorithm on a specimen vehicle. The result turns out that several groups of Pareto optimal solutions could be obtained in this way, while the fuel economy and emission performance could be remarkably enhanced with vehicle´s given performance being guaranteed.
  • Keywords
    Pareto optimisation; fuel economy; genetic algorithms; hybrid electric vehicles; HEV parameters; econiche genetic algorithm based optimization; emission performance; fuel economy; hybrid electric vehicle; multiobjective optimization; mutually conflicting objectives; nonlinear constraints; pareto optimal solution; specimen vehicle; Acceleration; Battery powered vehicles; Constraint optimization; Control systems; Design optimization; Engines; Genetic algorithms; Hybrid electric vehicles; Pareto optimization; Power systems; Pareto optimal solution; econiche genetic algorithm; multi-objective optimization;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Natural Computation, 2009. ICNC '09. Fifth International Conference on
  • Conference_Location
    Tianjin
  • Print_ISBN
    978-0-7695-3736-8
  • Type

    conf

  • DOI
    10.1109/ICNC.2009.112
  • Filename
    5366098