• DocumentCode
    114874
  • Title

    Multi-time scale model predictive control framework for energy management of hybrid electric vehicles

  • Author

    Josevski, Martina ; Abel, Dirk

  • Author_Institution
    Dept. of Mech. Eng., RWTH Aachen Univ., Aachen, Germany
  • fYear
    2014
  • fDate
    15-17 Dec. 2014
  • Firstpage
    2523
  • Lastpage
    2528
  • Abstract
    In this paper a multi-time scale model predictive control framework is proposed and applied in the efficiency and drivability optimization of hybrid electric vehicles. A multi-layer model predictive control concept simultaneously enables a static optimization over a long prediction horizon and the optimization of the transient system response which leads to better drivability. The proposed control architecture is evaluated on a standard driving cycle and on the example of a parallel hybrid electric vehicle configuration. The obtained simulation results indicate an improved performance of the two layer energy management strategy compared to the case when a single layer model predictive control scheme is applied to optimize the fuel economy of a hybrid electric vehicle. Although the concept has been proven on the example of parallel hybrid electric vehicle it holds in general for any other hybrid configuration as well.
  • Keywords
    energy management systems; hybrid electric vehicles; predictive control; transient response; drivability optimization; efficiency optimization; fuel economy; hybrid configuration; long prediction horizon; multilayer model concept; multitime scale model predictive control framework; parallel hybrid electric vehicle configuration; single layer model scheme; standard driving cycle; static optimization; transient system response; two layer energy management strategy; Batteries; Hybrid electric vehicles; Ice; Predictive control; Torque; Vehicle dynamics;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Decision and Control (CDC), 2014 IEEE 53rd Annual Conference on
  • Conference_Location
    Los Angeles, CA
  • Print_ISBN
    978-1-4799-7746-8
  • Type

    conf

  • DOI
    10.1109/CDC.2014.7039774
  • Filename
    7039774