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

Muhammad Umair Ali, Amad Zafar, Sarvar Hussain Nengroo, Sadam Hussain, Muhammad Junaid Alvi and Hee-Je Kim
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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

Energies, 2019, vol. 12, issue 3, 1-33

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.

Keywords: battery management system; energy storage system; electric vehicle; lithium-ion battery; state of charge (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (11)

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