• DocumentCode
    848313
  • Title

    Power management strategy for a parallel hybrid electric truck

  • Author

    Lin, Chan-Chiao ; Peng, Huei ; Grizzle, Jessy W. ; Kang, Jun-Mo

  • Author_Institution
    Dept. of Mech. Eng., Univ. of Michigan, Ann Arbor, MI, USA
  • Volume
    11
  • Issue
    6
  • fYear
    2003
  • Firstpage
    839
  • Lastpage
    849
  • Abstract
    Hybrid vehicle techniques have been widely studied recently because of their potential to significantly improve the fuel economy and drivability of future ground vehicles. Due to the dual-power-source nature of these vehicles, control strategies based on engineering intuition frequently fail to fully explore the potential of these advanced vehicles. In this paper, we present a procedure for the design of a near-optimal power management strategy. The design procedure starts by defining a cost function, such as minimizing a combination of fuel consumption and selected emission species over a driving cycle. Dynamic programming (DP) is then utilized to find the optimal control actions including the gear-shifting sequence and the power split between the engine and motor while subject to a battery SOC-sustaining constraint. Through analysis of the behavior of DP control actions, near-optimal rules are extracted, which, unlike DP control signals, are implementable. The performance of this power management control strategy is studied by using the hybrid vehicle model HE-VESIM developed at the Automotive Research Center of the University of Michigan. A tradeoff study between fuel economy and emissions was performed. It was found that significant emission reduction could be achieved at the expense of a small increase in fuel consumption.
  • Keywords
    dynamic programming; hybrid electric vehicles; optimal control; power control; regenerative braking; dynamic programming; fuel economy; gear-shifting sequence; hybrid vehicle model HE-VESIM; hybrid vehicle techniques; near-optimal rules; optimal control; parallel hybrid electric truck; power management strategy; powertrain control; Automotive engineering; Cost function; Dynamic programming; Energy management; Fuel economy; Intelligent vehicles; Land vehicles; Power engineering and energy; Road vehicles; Vehicle driving;
  • fLanguage
    English
  • Journal_Title
    Control Systems Technology, IEEE Transactions on
  • Publisher
    ieee
  • ISSN
    1063-6536
  • Type

    jour

  • DOI
    10.1109/TCST.2003.815606
  • Filename
    1255660