A Novel State of Charge Estimation Algorithm for Lithium-Ion Battery Packs of Electric Vehicles

Zheng Chen, Xiaoyu Li, Jiangwei Shen, Wensheng Yan and Renxin Xiao
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Zheng Chen: Faculty of Transportation Engineering, Kunming University of Science and Technology, Kunming 650500, China
Xiaoyu Li: Faculty of Transportation Engineering, Kunming University of Science and Technology, Kunming 650500, China
Jiangwei Shen: Faculty of Transportation Engineering, Kunming University of Science and Technology, Kunming 650500, China
Wensheng Yan: Faculty of Transportation Engineering, Kunming University of Science and Technology, Kunming 650500, China
Renxin Xiao: Faculty of Transportation Engineering, Kunming University of Science and Technology, Kunming 650500, China

Energies, 2016, vol. 9, issue 9, 1-15

Abstract: This paper focuses on state of charge ( SOC ) estimation for the battery packs of electric vehicles (EVs). By modeling a battery based on the equivalent circuit model (ECM), the adaptive extended Kalman filter (AEKF) method can be applied to estimate the battery cell SOC . By adaptively setting different weighed coefficients, a battery pack SOC estimation algorithm is established based on the single cell estimation. The proposed method can not only precisely estimate the battery pack SOC , but also effectively prevent the battery pack from overcharge and over-discharge, thus providing safe operation. Experiment results verify the feasibility of the proposed algorithm.

Keywords: adaptive extended Kalman filter (AEKF); electric vehicle (EV); state of charge ( SOC ); weighed coefficients (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: 2016
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (13)

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