• DocumentCode
    575400
  • Title

    State of charge estimation of HEV/EV battery with Series Kalman Filter

  • Author

    Baba, Atsushi ; Adachi, Shuichi

  • Author_Institution
    Dept. of Appl. Phys. & Physico-Inf., Keio Univ., Yokohama, Japan
  • fYear
    2012
  • fDate
    20-23 Aug. 2012
  • Firstpage
    845
  • Lastpage
    850
  • Abstract
    This paper proposes a method of accurately estimating the state of charge (SOC) of lithium-ion rechargeable batteries in high fuel efficiency vehicles, such as hybrid electric vehicles (HEVs) and electric vehicles (EVs). Although it is important to accurately estimate SOC of the battery to maximize efficiency and safety, there exist many problems for conventional methods. To address this issue, a model-based approach using “Series Kalman Filters” is proposed and implemented in this paper. Its approach is verified with series of simulations under the basic HEV operating environment. A discussion on a limitation of the method is also included in this paper. The ultimate goal is to design a state estimator capable of providing accurate state estimation of batteries under any possible user conditions.
  • Keywords
    Kalman filters; battery powered vehicles; hybrid electric vehicles; secondary cells; HEV/EV battery; Li; SOC; high fuel efficiency vehicles; hybrid electric vehicles; lithium-ion rechargeable batteries; model-based approach; series Kalman filter; state estimator; state of charge estimation; ultimate goal; Batteries; Estimation; Hybrid electric vehicles; Integrated circuit modeling; Kalman filters; System-on-a-chip; Voltage measurement; Electric vehicle (EV); Hybrid electric vehicle (HEV); Kalman filter; Parameter estimation; Rechargeable Battery; State of Charge (SOC);
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    SICE Annual Conference (SICE), 2012 Proceedings of
  • Conference_Location
    Akita
  • ISSN
    pending
  • Print_ISBN
    978-1-4673-2259-1
  • Type

    conf

  • Filename
    6318559