Next Article in Journal
A Rare-Earth Free Magnetically Geared Generator for Direct-Drive Wind Turbines
Next Article in Special Issue
An Optimized Impedance Model for the Estimation of the State-of-Charge of a Li-Ion Cell: The Case of a LiFePO4 (ANR26650)
Previous Article in Journal
A Non-Uniform Transmission Line Model of the ±1100 kV UHV Tower
 
 
Review

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

1
School of Electrical Engineering, Pusan National University, San 30, ChangJeon 2 Dong, KeumJeong-gu, Pusan 46241, Korea
2
Department of Electrical Engineering, Wah Engineering College, University of Wah, Wah Cantt 47040, Pakistan
3
Department of Electrical Engineering, University of Engineering and Technology, Lahore 54890, Pakistan
*
Author to whom correspondence should be addressed.
Energies 2019, 12(3), 446; https://doi.org/10.3390/en12030446
Received: 4 January 2019 / Revised: 23 January 2019 / Accepted: 29 January 2019 / Published: 30 January 2019
(This article belongs to the Special Issue State of Charge Estimation for Battery Systems)
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. View Full-Text
Keywords: battery management system; energy storage system; electric vehicle; lithium-ion battery; state of charge battery management system; energy storage system; electric vehicle; lithium-ion battery; state of charge
Show Figures

Figure 1

MDPI and ACS Style

Ali, M.U.; Zafar, A.; Nengroo, S.H.; Hussain, S.; Junaid Alvi, M.; Kim, H.-J. Towards a Smarter Battery Management System for Electric Vehicle Applications: A Critical Review of Lithium-Ion Battery State of Charge Estimation. Energies 2019, 12, 446. https://doi.org/10.3390/en12030446

AMA Style

Ali MU, Zafar A, Nengroo SH, Hussain S, Junaid Alvi M, Kim H-J. Towards a Smarter Battery Management System for Electric Vehicle Applications: A Critical Review of Lithium-Ion Battery State of Charge Estimation. Energies. 2019; 12(3):446. https://doi.org/10.3390/en12030446

Chicago/Turabian Style

Ali, Muhammad Umair, Amad Zafar, Sarvar Hussain Nengroo, Sadam Hussain, Muhammad Junaid Alvi, and 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 12, no. 3: 446. https://doi.org/10.3390/en12030446

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop