• DocumentCode
    67694
  • Title

    Mathematical Modeling of Li-Ion Battery Using Genetic Algorithm Approach for V2G Applications

  • Author

    Thirugnanam, Kannan ; Ezhil Reena, Joy T. P. ; Singh, Monika ; Kumar, Pranaw

  • Author_Institution
    Dept. of Electr. & Electron. Eng., Indian Inst. of Technol. Guwahati, Guwahati, India
  • Volume
    29
  • Issue
    2
  • fYear
    2014
  • fDate
    Jun-14
  • Firstpage
    332
  • Lastpage
    343
  • Abstract
    This paper presents an electric circuit-based battery and a capacity fade model suitable for electric vehicles (EVs) in vehicle-to-grid applications. The circuit parameters of the battery model (BM) are extracted using genetic algorithm-based optimization method. A control algorithm has been developed for the battery, which calculates the processed energy, charge or discharge rate, and state of charge limits of the battery in order to satisfy the future requirements of EVs. A complete capacity fade analysis has been carried out to quantify the capacity loss with respect to processed energy and cycling. The BM is tested by simulation and its characteristics such as charge and discharge voltage, available and stored energy, battery power, and its capacity loss are extracted. The propriety of the proposed model is validated by superimposing the results with four typical manufacturers´ data. The battery profiles of different manufacturers´ like EIG, Sony, Panasonic, and Sanyo have been taken and their characteristics are compared with proposed models. The obtained battery characteristics are in close agreement with the measured (manufacturers´ catalogue) characteristics.
  • Keywords
    battery powered vehicles; battery storage plants; genetic algorithms; secondary cells; V2G applications; battery model; battery profiles; capacity fade model; capacity loss; electric circuit based battery; electric vehicles; genetic algorithm; vehicle to grid applications; Batteries; Discharges (electric); Genetic algorithms; Integrated circuit modeling; Mathematical model; Polynomials; Batteries; capacity loss; electric vehicles (EVs); genetic algorithm (GA); vehicle-to-grid (V2G);
  • fLanguage
    English
  • Journal_Title
    Energy Conversion, IEEE Transactions on
  • Publisher
    ieee
  • ISSN
    0885-8969
  • Type

    jour

  • DOI
    10.1109/TEC.2014.2298460
  • Filename
    6716979