• DocumentCode
    1567152
  • Title

    State Estimation of a Lithium-Ion Battery Through Kalman Filter

  • Author

    Urbain, M. ; Raël, S. ; Davat, B. ; Desprez, P.

  • Author_Institution
    GREEN-INPL-CNRS (UMR 7037) 2, Vandceuvre-les-Nancy
  • fYear
    2007
  • Firstpage
    2804
  • Lastpage
    2810
  • Abstract
    Online evaluation of operating conditions is crucial for battery management system. For this purpose, the resistance and the capacity best characterize the state-of-health of a lithium-ion cell, whereas the state-of-charge is a reliable information about its remaining stored energy. This paper describes the use of Kalman filter in order to estimate these parameters for photovoltaic applications, and hybrid electric vehicle applications. Rather than computing heavy models incompatible with embedded microcontroller capabilities, some assumptions associated to theses kinds of applications allow to implement a simple model to track parameters. Experimental validation of this process is fully depicted.
  • Keywords
    Kalman filters; hybrid electric vehicles; microcontrollers; power system state estimation; secondary cells; Kalman filter; battery management; embedded microcontroller; hybrid electric vehicle; lithium-ion battery; photovoltaics; state estimation; Battery management systems; Computer applications; Electric resistance; Embedded computing; Hybrid electric vehicles; Microcontrollers; Parameter estimation; Photovoltaic systems; Solar power generation; State estimation;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Power Electronics Specialists Conference, 2007. PESC 2007. IEEE
  • Conference_Location
    Orlando, FL
  • ISSN
    0275-9306
  • Print_ISBN
    978-1-4244-0654-8
  • Electronic_ISBN
    0275-9306
  • Type

    conf

  • DOI
    10.1109/PESC.2007.4342463
  • Filename
    4342463