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

被引：57

作者：

Huangfu, Yigeng ^{[1
]}

Xu, Jiani ^{[1
]}

Zhao, Dongdong ^{[1
]}

Liu, Yuntian ^{[1
]}

Gao, Fei ^{[2
]}

机构：

[1] Northwestern Polytech Univ, Sch Automat, Xian 710072, Shaanxi, Peoples R China

[2] Univ Bourgogne Franche Comte, UTBM, Inst FEMTO ST, Energy Dept,UMR CNRS 6174, F-90010 Belfort, France

来源：

ENERGIES | 2018年 / 11卷 / 05期

关键词：

super-twisting algorithm; sliding mode observer; second-order RC equivalent circuit model; Li-ion battery; state of charge; LITHIUM-ION BATTERIES; EXTENDED KALMAN FILTER; ELECTRIC VEHICLES; OF-CHARGE; HEALTH ESTIMATION; JOINT ESTIMATION; ONLINE STATE; HYBRID; PARAMETERS; ALGORITHM;

D O I：

10.3390/en11051211

中图分类号：

TE [石油、天然气工业]; TK [能源与动力工程];

学科分类号：

080707 [能源环境工程]; 082001 [油气井工程];

摘要：

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.

引用

页数：21

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