期刊
ENERGIES
卷 6, 期 8, 页码 4134-4151出版社
MDPI
DOI: 10.3390/en6084134
关键词
battery; state of charge; online estimation; unscented Kalman filter
资金
- Nature Science Foundation of China (NSFC) [60871088, 61001216, 61172132]
- Zhejiang Provincial Natural Science Foundation of China [Z1110741, LQ13F010011]
- Zhejiang Educational Committee Foundation of China [Z201122745]
- National Science Foundation of USA [ECCS-1202133]
- Directorate For Engineering
- Div Of Electrical, Commun & Cyber Sys [1202133] Funding Source: National Science Foundation
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.
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