A Novel Battery State of Charge Estimation Method Based on a Super-Twisting Sliding Mode Observer

Yigeng Huangfu, Jiani Xu, Dongdong Zhao, Yuntian Liu and Fei Gao
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Yigeng Huangfu: School of Automation, Northwestern Polytechnical University, Xi’an 710072, China
Jiani Xu: School of Automation, Northwestern Polytechnical University, Xi’an 710072, China
Dongdong Zhao: School of Automation, Northwestern Polytechnical University, Xi’an 710072, China
Yuntian Liu: School of Automation, Northwestern Polytechnical University, Xi’an 710072, China
Fei Gao: Institute of FEMTO-ST (UMR CNRS 6174), Energy Department, University of Bourgogne Franche-Comte, UTBM, 90010 Belfort, France

Energies, 2018, vol. 11, issue 5, 1-21

Abstract: A novel method for Li-ion battery state of charge (SOC) estimation based on a super-twisting sliding mode observer (STSMO) is proposed in this paper. To design the STSMO, the state equation of a second-order RC equivalent circuit model (SRCECM) is derived to represent the dynamic behaviors of the Li-ion battery, and the model parameters are determined by the pulse current discharge approach. The convergence of the STSMO is proven by Lyapunov stability theory. The experiments under three different discharge profiles are conducted on the Li-ion battery. Through comparisons with a conventional sliding mode observer (CSMO) and adaptive extended Kalman filter (AEKF), the superiority of the proposed observer for SOC estimation is validated.

Keywords: super-twisting algorithm; sliding mode observer; second-order RC equivalent circuit model; Li-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: 2018
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (11)

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