• DocumentCode
    1709808
  • Title

    Model-based fault detection of a battery system in a hybrid electric vehicle

  • Author

    Gadsden, S.A. ; Habibi, S.R.

  • Author_Institution
    Dept. of Mech. Eng., McMaster Univ., Hamilton, ON, Canada
  • fYear
    2011
  • Firstpage
    1
  • Lastpage
    6
  • Abstract
    Recently, a new type of interacting multiple model (IMM) method was introduced based on the relatively new smooth variable structure filter (SVSF), and is referred to as the IMM-SVSF. The SVSF is a type of sliding mode estimator that is formulated in a predictor-corrector fashion. This strategy keeps the estimated state bounded within a region of the true state trajectory, thus creating a stable and robust estimation process. The IMM method may be utilized for fault detection and diagnosis, and is classified as a model-based method. In this paper, for the purposes of fault detection, the IMM-SVSF is applied through simulation on a simple battery system which is modeled from a hybrid electric vehicle.
  • Keywords
    estimation theory; hybrid electric vehicles; secondary cells; variable structure systems; battery system; hybrid electric vehicle; interacting multiple model; model-based fault detection; predictor-corrector fashion; robust estimation; sliding mode estimator; smooth variable structure filter; Batteries; Equations; Estimation; Fault detection; Hybrid electric vehicles; Mathematical model; Probability;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Vehicle Power and Propulsion Conference (VPPC), 2011 IEEE
  • Conference_Location
    Chicago, IL
  • ISSN
    Pending
  • Print_ISBN
    978-1-61284-248-6
  • Electronic_ISBN
    Pending
  • Type

    conf

  • DOI
    10.1109/VPPC.2011.6043175
  • Filename
    6043175