Critical Review on the Battery State of Charge Estimation Methods for Electric Vehicles

• Published in: IEEE access 2018-01, Vol.6, p.1832-1843
• Main Authors: Xiong, Rui, Cao, Jiayi, Yu, Quanqing, He, Hongwen, Sun, Fengchun
• Format: Article
• Language: English
• Subjects: Algorithms; Batteries; Battery charge measurement; data-driven estimation; Driving conditions; Electric vehicles; Energy management; Energy storage; Estimation; Integrated circuit modeling; Life cycle costs; Management systems; Mathematical model; model based estimation; multi-scale; Power management; State estimation; State of charge
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2017.2780258

Battery technology is the bottleneck of the electric vehicles (EVs). It is important, both in theory and practical application, to do research on the modeling and state estimation of batteries, which is essential to optimizing energy management, extending the life cycle, reducing cost, and safeguarding the safe application of batteries in EVs. However, the batteries, with strong time-variables and nonlinear characteristics, are further influenced by such random factors such as driving loads, operational conditions, in the application of EVs. The real-time, accurate estimation of their state is challenging. The classification of the estimation methodologies for estimating state-of-charge (SoC) of battery focusing with the estimation method/algorithm, advantages, drawbacks, and estimation error are systematically and separately discussed. Especially for the battery packs existing of the inevitable inconsistency in cell capacity, resistance and voltage, the advanced characterizing monomer selection, and bias correction-based method has been described and discussed. The review also presents the key feedback factors that are indispensable for accurate estimation of battery SoC, it will be helpful for ensuring the SoC estimation accuracy. It will be very helpful for choosing an appropriate method to develop a reliable and safe battery management system and energy management strategy of the EVs. Finally, the paper also highlights a number of key factors and challenges, and presents the possible recommendations for the development of next generation of smart SoC estimation and battery management systems for electric vehicles and battery energy storage system.