• DocumentCode
    1800527
  • Title

    Kalman Filter SoC estimation for Li-Ion batteries

  • Author

    Spagnol, P. ; Rossi, S. ; Savaresi, S.M.

  • Author_Institution
    DEI, Politec. di Milano, Milan, Italy
  • fYear
    2011
  • fDate
    28-30 Sept. 2011
  • Firstpage
    587
  • Lastpage
    592
  • Abstract
    State-of-Charge (SoC) estimation is a key factor for correct and safe battery management, particularly for the development of the Battery Management System (BMS) [1]. The paper deals with this problem for the currently most promising technology in the battery filed: Lithium-Ion batteries. An electric model of the cell is identified and verified, in order to apply Kalman Filter Theory and design an algorithm for estimating SoC. Particularly the aim of the algorithm is to reject measurement noise and parametric uncertainties and be applicable to different cells of the same manufacturer and technology. In this purpose, design criterions that speed up the convergence time or make the estimation robust to noise measurements are presented. Results on two different cells of the same manufacturer and typology are shown, focusing on the different behaviors of the estimation due to different design choices.
  • Keywords
    Kalman filters; battery management systems; lithium; secondary cells; Kalman Filter Theory; Kalman filter SoC estimation; Li; battery management system; lithium-ion batteries; noise measurements; parametric uncertainties; state-of-charge estimation; Batteries; Battery charge measurement; Current measurement; Estimation; Kalman filters; System-on-a-chip; Voltage measurement;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Control Applications (CCA), 2011 IEEE International Conference on
  • Conference_Location
    Denver, CO
  • Print_ISBN
    978-1-4577-1062-9
  • Electronic_ISBN
    978-1-4577-1061-2
  • Type

    conf

  • DOI
    10.1109/CCA.2011.6044480
  • Filename
    6044480