• DocumentCode
    1706127
  • Title

    Path dependent receding horizon control policies for Hybrid Electric Vehicles

  • Author

    Katsargyri, Georgia-Evangelia ; Kolmanovsky, Ilya ; Michelini, John ; Kuang, Ming ; Phillips, Anthony ; Rinehart, Michael ; Dahleh, Munther

  • Author_Institution
    Dept. of Electr. Eng. & Comput. Sci., Massachusetts Inst. of Technol., Cambridge, MA, USA
  • fYear
    2009
  • Firstpage
    607
  • Lastpage
    612
  • Abstract
    Future hybrid electric vehicles (HEVs) may use path-dependent operating policies to improve fuel economy. In our previous work, we developed a dynamic programming (DP) algorithm for prescribing the battery state of charge (SoC) set-point, which in combination with a novel approach of route decomposition, has been shown to reduce fuel consumption over selected routes. In this paper, we propose and illustrate a receding horizon control (RHC) strategy for the on-board optimization of the fuel consumption. As compared to the DP approach, the computational requirements of the RHC strategy are lower. In addition, the RHC strategy is capable of correcting for differences between characteristics of a predicted route and a route actually traveled. Our numerical results indicate that the fuel economy potential of the RHC solution can approach that of the DP solution.
  • Keywords
    fuel cell vehicles; fuel economy; hybrid electric vehicles; road vehicles; fuel consumption; fuel economy; hybrid electric vehicles; on-board optimization; path dependent receding horizon control policy; Batteries; Control systems; Dynamic programming; Fuel economy; Heuristic algorithms; Hybrid electric vehicles; Navigation; Pattern recognition; Recurrent neural networks; Roads;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Control Applications, (CCA) & Intelligent Control, (ISIC), 2009 IEEE
  • Conference_Location
    St. Petersburg
  • Print_ISBN
    978-1-4244-4601-8
  • Electronic_ISBN
    978-1-4244-4602-5
  • Type

    conf

  • DOI
    10.1109/CCA.2009.5280977
  • Filename
    5280977