• DocumentCode
    2897197
  • Title

    Model-based predictive diagnostics for primary and secondary batteries

  • Author

    Kozlowski, James D. ; Byington, Carl S. ; Garga, Amulya K. ; Watson, Matthew J. ; Hay, Todd A.

  • Author_Institution
    Appl. Res. Lab., Pennsylvania State Univ., State Coll., PA, USA
  • fYear
    2001
  • fDate
    2001
  • Firstpage
    251
  • Lastpage
    256
  • Abstract
    The model-based effort described here is focused on predictive diagnostics for primary and secondary batteries. However, this novel approach can also be applied to other electrochemical energy sources such as fuel cells. This method is based on accurate parametric modeling of the transport mechanisms within the battery. This system knowledge was used for the careful development of electrochemical and thermal models. These models have been used to extract new features to be used in conjunction with several traditional measured parameters to assess the condition of the battery. The resulting output and any usable information available about the battery is then evaluated using hybrid automated reasoning schemes consisting of neural network and decision theoretic methods. The focus of this paper is on the model identification and data fusion of the monitored and virtual sensor data. The methodology and analysis presented in this paper is applicable to mechanical systems where multiple sensor types are used for diagnostic assessment
  • Keywords
    computerised monitoring; decision theory; electrochemical analysis; neural nets; power engineering computing; primary cells; secondary cells; sensor fusion; thermal analysis; data fusion; decision theoretic methods; diagnostic assessment; electrochemical energy sources; electrochemical models; fuel cells; hybrid automated reasoning schemes; model identification; model-based predictive diagnostics; multiple sensor types; neural network; parametric modeling; primary batteries; secondary batteries; thermal models; transport mechanisms; virtual sensor data; Battery charge measurement; Data mining; Feature extraction; Fuel cells; Mechanical sensors; Mechanical systems; Neural networks; Parametric statistics; Predictive models; Sensor fusion;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Applications and Advances, 2001. The Sixteenth Annual Battery Conference on
  • Conference_Location
    Long Beach, CA
  • ISSN
    1089-8182
  • Print_ISBN
    0-7803-6545-3
  • Type

    conf

  • DOI
    10.1109/BCAA.2001.905133
  • Filename
    905133