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Energies 2013, 6(8), 4134-4151; doi:10.3390/en6084134
Article

Adaptive State of Charge Estimation for Li-Ion Batteries Based on an Unscented Kalman Filter with an Enhanced Battery Model

1,* , 1
,
2
,
2
 and
1
1 College of Electric Information, Hangzhou Dianzi University, 2nd Street, Xiasha Higher Education Zone, Hangzhou 310018, China 2 Department of Electrical and Computer Engineering, Wayne State University, 5050 Anthony Wayne Drive, Detroit, MI 48202, USA
* Author to whom correspondence should be addressed.
Received: 28 May 2013 / Revised: 30 July 2013 / Accepted: 6 August 2013 / Published: 12 August 2013
(This article belongs to the Special Issue Li-ion Batteries and Energy Storage Devices)
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Abstract

Accurate estimation of the state of charge (SOC) of batteries is one of the key problems in a battery management system. This paper proposes an adaptive SOC estimation method based on unscented Kalman filter algorithms for lithium (Li)-ion batteries. First, an enhanced battery model is proposed to include the impacts due to different discharge rates and temperatures. An adaptive joint estimation of the battery SOC and battery internal resistance is then presented to enhance system robustness with battery aging. The SOC estimation algorithm has been developed and verified through experiments on different types of Li-ion batteries. The results indicate that the proposed method provides an accurate SOC estimation and is computationally efficient, making it suitable for embedded system implementation.
Keywords: battery; state of charge; online estimation; unscented Kalman filter battery; state of charge; online estimation; unscented Kalman filter
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.

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He, Z.; Gao, M.; Wang, C.; Wang, L.; Liu, Y. Adaptive State of Charge Estimation for Li-Ion Batteries Based on an Unscented Kalman Filter with an Enhanced Battery Model. Energies 2013, 6, 4134-4151.

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