• DocumentCode
    3360515
  • Title

    Study on Braking Force Distribution of Electric Vehicles

  • Author

    Guo, Jingang ; Wang, Junping ; Cao, Binggang

  • Author_Institution
    Sch. of Mech. Eng., Xi´´an Jiaotong Univ., Xi´´an
  • fYear
    2009
  • fDate
    27-31 March 2009
  • Firstpage
    1
  • Lastpage
    4
  • Abstract
    Braking force distribution of an electric vehicle has an important impact on braking performance and energy recovery. With the analysis of braking dynamics and the establishment of motor model, a braking force distribution strategy is proposed from the viewpoint of maximum energy recovery. From another point of view, how to distribute the braking force can be thought as a constrained optimization problem, in which the motor maximum power, the motor maximum angular speed and the requirements of vehicle stability are all taken into account. The task is to find the optimal distribution ratio of the regenerative braking torque and the friction braking torque so that the regenerated energy can be maximized. Genetic algorithm is used to solve the optimization problem. The simulation results show that the braking force distribution by optimization is in accordance with the proposed braking force distribution strategy. The strategy takes advantage of the motor maximum torque and can obtain maximum energy recovery.
  • Keywords
    electric motors; electric vehicles; genetic algorithms; regenerative braking; stability; vehicle dynamics; braking dynamic analysis; braking force distribution strategy; electric vehicles; friction braking torque; genetic algorithm; maximum energy recovery; motor model; regenerative braking torque; vehicle stability; Axles; Constraint optimization; Distribution strategy; Electric vehicles; Friction; Genetic algorithms; Hybrid electric vehicles; Land vehicles; Torque; Vehicle dynamics;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Power and Energy Engineering Conference, 2009. APPEEC 2009. Asia-Pacific
  • Conference_Location
    Wuhan
  • Print_ISBN
    978-1-4244-2486-3
  • Electronic_ISBN
    978-1-4244-2487-0
  • Type

    conf

  • DOI
    10.1109/APPEEC.2009.4918806
  • Filename
    4918806