• DocumentCode
    234163
  • Title

    Robust battery fuel gauge algorithm development, part 2: Online battery-capacity estimation

  • Author

    Balasingam, B. ; Avvari, G.V. ; Pattipati, B. ; Pattipati, K. ; Bar-Shalom, Y.

  • Author_Institution
    Dept. of Electr. & Comput. Eng., Univ. of Connectiut, Storrs, CT, USA
  • fYear
    2014
  • fDate
    19-22 Oct. 2014
  • Firstpage
    104
  • Lastpage
    109
  • Abstract
    In this paper we present an approach for robust, real time capacity estimation in Li-ion batteries. The proposed capacity estimation scheme has the following novel features: it employes total least squares (TLS) estimation in order to account for uncertainties in both model and the observations in capacity estimation. The TLS method can adaptively track changes in battery capacity. We propose a second approach to estimate battery capacity by exploiting rest states in the battery. This approach is devised to minimize the effect of hysteresis in capacity estimation. Finally, we propose a novel approach for optimally fusing capacity estimates obtained through different methods. We demonstrate the performance of the algorithm through objective experiments.
  • Keywords
    battery management systems; least squares approximations; secondary cells; TLS estimation; lithium-ion batteries; online battery-capacity estimation; real time capacity estimation; robust battery fuel gauge algorithm development; total least squares estimation; Batteries; Current measurement; Estimation error; Hysteresis; Robustness; System-on-chip; Battery fuel gauge (BFG); Battery management system (BMS); Li-ion battery; capacity estimation; capacity fade; extended Kalman filter (EKF); state of charge (SOC); total least squares (TLS);
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Renewable Energy Research and Application (ICRERA), 2014 International Conference on
  • Conference_Location
    Milwaukee, WI
  • Type

    conf

  • DOI
    10.1109/ICRERA.2014.7016539
  • Filename
    7016539