Review of Lithium-Ion Battery State of Charge Estimation Methodologies for Electric Vehicle Application

  • Authors

    • M.S. Hossain Lipu
    • M.A. Hannan
    • A. Ayob
    • M.H.M. Saad
    • A. Hussain
    https://doi.org/10.14419/ijet.v7i3.17.21909

  • Abstract

    With the ever-rising concerns over energy conservation, global warming, and climate change issues, extensive research on electric vehicles (EVs) is being actively performed. EVs are considered to be the most promising replacement of gasoline-based vehicles in reducing CO2 emissions in recent decades. The number of EV run by rechargeable batteries has been increased significantly due to enhanced performances and efficiencies. The lithium-ion battery has some advantageous features such as lightweight, long lifespan, low self-discharge, high voltage, high energy density, and low memory effect. However, EVs using lithium-ion batteries have drawbacks with regard to short mileage, slow charging, and high cost. Moreover, load variation, temperature variations, and battery aging can degrade EV performance and efficiency. Therefore, there is an urgent necessity to develop an efficient energy storage management system that can evaluate the charging state and health condition of lithium-ion batteries. The state of charge (SOC) is an essential parameter of EV which determines the remaining charge of lithium-ion batteries. Also, SOC provides information about the charging/discharging approach and thus protects the battery from being overcharged/over discharged. This paper comprehensively reviews the different estimation methodologies to evaluate SOC. The estimation approaches of SOC are discussed in detail on their algorithm, mathematical model, strength, weakness and error rate. Finally, the review delivers some important proposals for the future development of SOC estimation in EV application.

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  • How to Cite

    Lipu, M. H., Hannan, M., Ayob, A., Saad, M., & Hussain, A. (2018). Review of Lithium-Ion Battery State of Charge Estimation Methodologies for Electric Vehicle Application. International Journal of Engineering & Technology, 7(3.17), 219-224. https://doi.org/10.14419/ijet.v7i3.17.21909

    Received date: 2018-11-27

    Accepted date: 2018-11-27