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Energies 2016, 9(9), 710; doi:10.3390/en9090710

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

Faculty of Transportation Engineering, Kunming University of Science and Technology, Kunming 650500, China
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Author to whom correspondence should be addressed.
Academic Editor: Sheng S. Zhang
Received: 16 June 2016 / Revised: 17 August 2016 / Accepted: 30 August 2016 / Published: 5 September 2016
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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. View Full-Text
Keywords: adaptive extended Kalman filter (AEKF); electric vehicle (EV); state of charge (SOC); weighed coefficients adaptive extended Kalman filter (AEKF); electric vehicle (EV); state of charge (SOC); weighed coefficients
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Chen, Z.; Li, X.; Shen, J.; Yan, W.; Xiao, R. A Novel State of Charge Estimation Algorithm for Lithium-Ion Battery Packs of Electric Vehicles. Energies 2016, 9, 710.

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