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Correction: Juan, A.A.; Mendez, C.A.; Faulin, J.; de Armas, J.; Grasman, S.E. Electric Vehicles in Logistics and Transportation: A Survey on Emerging Environmental, Strategic, and Operational Challenges. Energies 2016, 9, 86
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Energies 2016, 9(9), 675; doi:10.3390/en9090675

State-of-Charge Estimation for Li-Ion Power Batteries Based on a Tuning Free Observer

1
Department of Chemical and Biomolecular Engineering, The Hong Kong University of Science and Technology, Clear Water Bay, Kowloon 999077, Hong Kong, China
2
Guangzhou HKUST Fok Ying Tung Research Institute, Guangzhou 511458, China
*
Author to whom correspondence should be addressed.
Academic Editor: Peter J S Foot
Received: 20 July 2016 / Revised: 15 August 2016 / Accepted: 16 August 2016 / Published: 24 August 2016
(This article belongs to the Collection Electric and Hybrid Vehicles Collection)
View Full-Text   |   Download PDF [5015 KB, uploaded 24 August 2016]   |  

Abstract

A battery’s state-of-charge (SOC) can be used to estimate the mileage an electric vehicle (EV) can travel. It is desirable to make such an estimation not only accurate, but also economical in computation, so that the battery management system (BMS) can be cost-effective in its implementation. Existing computationally-efficient SOC estimation algorithms, such as the Luenberger observer, suffer from low accuracy and require tuning of the feedback gain by trial-and-error. In this study, an algorithm named lazy-extended Kalman filter (LEKF) is proposed, to allow the Luenberger observer to learn periodically from the extended Kalman filter (EKF) and solve the problems, while maintaining computational efficiency. We demonstrated the effectiveness and high performance of LEKF by both numerical simulation and experiments under different load conditions. The results show that LEKF can have 50% less computational complexity than the conventional EKF and a near-optimal estimation error of less than 2%. View Full-Text
Keywords: state-of-charge (SOC); tuning-free; electronic vehicle; lazy-extended Kalman filter (LEKF); battery management system (BMS) state-of-charge (SOC); tuning-free; electronic vehicle; lazy-extended Kalman filter (LEKF); battery management system (BMS)
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Tang, X.; Liu, B.; Gao, F.; Lv, Z. State-of-Charge Estimation for Li-Ion Power Batteries Based on a Tuning Free Observer. Energies 2016, 9, 675.

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