State of the Art of Lithium-Ion Battery SOC Estimation for Electrical Vehicles
by
Ruifeng Zhang 1,2,3,*
, Bizhong Xia 1, Baohua Li 1, Libo Cao 2, Yongzhi Lai 3, Weiwei Zheng 3, Huawen Wang 3 and Wei Wang 3
Ruifeng Zhang 1,2,3,*, Bizhong Xia 1, Baohua Li 1, Libo Cao 2, Yongzhi Lai 3, Weiwei Zheng 3, Huawen Wang 3 and Wei Wang 3
1
Graduate School at Shenzhen, Tsinghua University, Shenzhen 518055, China
2
College of Mechanical and Vehicle Engineering, Hunan University, Changsha 410082, China
3
Sunwoda Electronic Co., Ltd., Shenzhen 518108, China
*
Author to whom correspondence should be addressed.
Energies 2018, 11(7), 1820; https://doi.org/10.3390/en11071820
Received: 16 June 2018 / Revised: 28 June 2018 / Accepted: 5 July 2018 / Published: 11 July 2018
Sate of charge (SOC) accurate estimation is one of the most important functions in a battery management system for battery packs used in electrical vehicles. This paper focuses on battery SOC estimation and its issues and challenges by exploring different existing estimation methodologies. The key technologies of lithium-ion battery state estimation methodologies of the electrical vehicles categorized under five groups, such as the conventional method, adaptive filter algorithm, learning algorithm, nonlinear observer, and the hybrid method, are explored in an in-depth analysis. Lithium-ion battery characteristic, battery model, estimation algorithm, and cell unbalancing are the most important factors that affect the accuracy and robustness of SOC estimation. Finally, this paper concludes with the challenges of SOC estimation and suggests other directions for possible research efforts.
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Keywords:
lithium-ion battery; sate of charge; estimation algorithm; battery management system; electric vehicle
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MDPI and ACS Style
Zhang, R.; Xia, B.; Li, B.; Cao, L.; Lai, Y.; Zheng, W.; Wang, H.; Wang, W. State of the Art of Lithium-Ion Battery SOC Estimation for Electrical Vehicles. Energies 2018, 11, 1820.
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