Journal
SCIENCE CHINA-TECHNOLOGICAL SCIENCES
Volume 57, Issue 2, Pages 403-413Publisher
SCIENCE PRESS
DOI: 10.1007/s11431-013-5431-y
Keywords
Lithium-ion battery; battery model; recursive identification; recursive extended least squares (RELS); electric vehicle
Funding
- National High Technology Research and Development Program(863 Project) [2011AA05A109]
- International Science and Technology Cooperation Program of China [2011DFA70570, 2010DFA72760]
- National Natural Science Foundation of China [51007088]
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For safe and reliable operation of lithium-ion batteries in electric vehicles, the real-time monitoring of their internal states is important. The purpose of our study is to find an easily implementable, online identification method for lithium-ion batteries in electric vehicles. In this article, we propose an equivalent circuit model structure. Based on the model structure we derive the recursive mathematical description. The recursive extended least square algorithm is introduced to estimate the model parameters online. The accuracy and robustness are validated through experiments and simulations. Real-road driving cycle experiment shows that the proposed online identification method can achieve acceptable accuracy with the maximum error of less than 5.52%. In addition, it is proved that the proposed method can also be used to estimate the real-time SOH and SOC of the batteries.
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