State-of-Charge Estimation Methods for Li-ion Batteries in Electric Vehicles
Prakash Venugopal1, Vigneswaran T2
1Prakash Venugopal, Ph.D student and working as an Assistant Professorat Vellore Institute of Technology (VIT), Chennai (Tamil Nadu), India.
2Dr. Vigneswaran. T, Ph.D from SRM University, Professor in Vellore Institute of Technology, Chennai (Tamil Nadu), India.
Manuscript received on 01 May 2019 | Revised Manuscript received on 15 May 2019 | Manuscript published on 30 May 2019 | PP: 37-46 | Volume-8 Issue-7, May 2019 | Retrieval Number: F3430048619/19©BEIESP
Open Access | Ethics and Policies | Cite | Mendeley | Indexing and Abstracting
© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: In recent years, due to zero caron emission Electric Vehicles (EVs) are considered as a best alternative choice for gasoline and diesel based vehicles in automotive industries. Despite the fact that, Li-ion batteries are preferred choice for EVs they have few drawbacks such as temperature dependent, slow charging and battery aging which degrade performance and operational efficiency of EVs. In real-cars, estimation of accurate battery State of Charge (SOC) is considered as most essential task to be performed by Battery Management System (BMS) because of the nonlinear battery characteristics and unpredictable operating conditions. The main objective of this paper is to comprehensively present common methods currently adapted by researchers in estimating battery SOC by analyzing their pros and cons. This investigation also highlights various issues and challenges associated to SOC estimation with possible recommendations to overcome them. All recommended insights can be amended into advanced BMS with accurate SOC estimation in next-generation EVs.
Keyword: Electric Vehicle, State of Charge, Battery Management System, SOC Estimation, Li-ion battery.
Scope of the Article: Artificial Intelligent Methods, Models, Techniques.