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Towards a Smarter Battery Management System for Electric Vehicle Applications: A Critical Review of Lithium-Ion Battery State of Charge Estimation

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  • Muhammad Umair Ali

    (School of Electrical Engineering, Pusan National University, San 30, ChangJeon 2 Dong, KeumJeong-gu, Pusan 46241, Korea)

  • Amad Zafar

    (Department of Electrical Engineering, Wah Engineering College, University of Wah, Wah Cantt 47040, Pakistan)

  • Sarvar Hussain Nengroo

    (School of Electrical Engineering, Pusan National University, San 30, ChangJeon 2 Dong, KeumJeong-gu, Pusan 46241, Korea)

  • Sadam Hussain

    (School of Electrical Engineering, Pusan National University, San 30, ChangJeon 2 Dong, KeumJeong-gu, Pusan 46241, Korea)

  • Muhammad Junaid Alvi

    (Department of Electrical Engineering, University of Engineering and Technology, Lahore 54890, Pakistan)

  • Hee-Je Kim

    (School of Electrical Engineering, Pusan National University, San 30, ChangJeon 2 Dong, KeumJeong-gu, Pusan 46241, Korea)

Abstract

Energy storage system (ESS) technology is still the logjam for the electric vehicle (EV) industry. Lithium-ion (Li-ion) batteries have attracted considerable attention in the EV industry owing to their high energy density, lifespan, nominal voltage, power density, and cost. In EVs, a smart battery management system (BMS) is one of the essential components; it not only measures the states of battery accurately, but also ensures safe operation and prolongs the battery life. The accurate estimation of the state of charge (SOC) of a Li-ion battery is a very challenging task because the Li-ion battery is a highly time variant, non-linear, and complex electrochemical system. This paper explains the workings of a Li-ion battery, provides the main features of a smart BMS, and comprehensively reviews its SOC estimation methods. These SOC estimation methods have been classified into four main categories depending on their nature. A critical explanation, including their merits, limitations, and their estimation errors from other studies, is provided. Some recommendations depending on the development of technology are suggested to improve the online estimation.

Suggested Citation

  • Muhammad Umair Ali & Amad Zafar & Sarvar Hussain Nengroo & Sadam Hussain & Muhammad Junaid Alvi & Hee-Je Kim, 2019. "Towards a Smarter Battery Management System for Electric Vehicle Applications: A Critical Review of Lithium-Ion Battery State of Charge Estimation," Energies, MDPI, vol. 12(3), pages 1-33, January.
    • Handle: RePEc:gam:jeners:v:12:y:2019:i:3:p:446-:d:202152
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