• DocumentCode
    1985640
  • Title

    The ANN models for SOC/BRC estimation of Li-ion battery

  • Author

    Shi, Pu ; Bu, Chunguang ; Zhao, Yiwen

  • Author_Institution
    Shenyang Inst. of Autom., Chinese Acad. of Sci., Shenyang, China
  • fYear
    2005
  • fDate
    27 June-3 July 2005
  • Abstract
    Lithium-ion battery is a kind of advanced sources and is a quite complex and nonlinear system comprised of interacting physical and chemical processes. Its state-of-charge (SOC)/ battery residual capacity (BRC), which is parameters to describe how much energy battery has, is key factors in applications; its estimations is an important and challenging task. To achieve this goal, the traditional and ANN system will be presented. Firstly, the paper defines the concepts of SOC/BRC. Secondly, the paper compares the traditional approaches with ANN dynamic techniques used to estimate the remaining battery capacity of lithium-ion battery and describes the latter in detail. Finally, a conclusion is given.
  • Keywords
    lithium; neural nets; power engineering computing; secondary cells; ANN model; SOC-BRC estimation; battery residual capacity; chemical process; lithium-ion battery; nonlinear system; physical process; state-of-charge; Artificial neural networks; Automation; Batteries; Chemical processes; Electrodes; IEEE news; Lithium; Nonlinear systems; State estimation; Voltage;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Information Acquisition, 2005 IEEE International Conference on
  • Print_ISBN
    0-7803-9303-1
  • Type

    conf

  • DOI
    10.1109/ICIA.2005.1635151
  • Filename
    1635151