• DocumentCode
    676257
  • Title

    State of charge estimation of a Lithium-ion battery for electric vehicle based on particle swarm optimization

  • Author

    Ismail, Nur Hazima Faezaa ; Toha, S.F.

  • Author_Institution
    Dept. of Mechatron., Int. Islamic Univ. Malaysia, Kuala Lumpur, Malaysia
  • fYear
    2013
  • fDate
    25-27 Nov. 2013
  • Firstpage
    1
  • Lastpage
    4
  • Abstract
    Lithium-ion battery plays important roles in electric drive vehicles. It has several advantages among other battery technologies such as high energy density and specific energy. The primary concerns of Lithium-ion batteries are to maintain optimum battery performance and extend the battery´s life. An accurate state of charge (SOC) estimation can improve the performance of Lithium-ion battery. In this paper, a method for SOC estimation for LiFePO4 using the particle swarm optimization (PSO) algorithm is presented. The results indicate the SOC estimation using PSO optimized algorithm has good performance. The simulation result has also been validated and complies within specific confidence level.
  • Keywords
    battery powered vehicles; lithium compounds; particle swarm optimisation; secondary cells; LiFePO4; PSO algorithm; SOC estimation; electric drive vehicle; high energy density; lithium-ion battery; optimum battery performance; particle swarm optimization; state of charge estimation; Batteries; Battery charge measurement; Estimation; Integrated circuit modeling; Mathematical model; Particle swarm optimization; System-on-chip; LiFePO4 battery; PSO; State of Charge; electric vehicle;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Smart Instrumentation, Measurement and Applications (ICSIMA), 2013 IEEE International Conference on
  • Conference_Location
    Kuala Lumpur
  • Print_ISBN
    978-1-4799-0842-4
  • Type

    conf

  • DOI
    10.1109/ICSIMA.2013.6717978
  • Filename
    6717978