期刊
JOURNAL OF POWER SOURCES
卷 269, 期 -, 页码 682-693出版社
ELSEVIER
DOI: 10.1016/j.jpowsour.2014.07.016
关键词
State of charge; Support vector machine for regression; Battery management system; Electric vehicle; Double step search; Driving conditions
资金
- National Natural Science Foundation of China
- Natural Science Foundation of Guangdong Province [U1134002]
- National Natural Science Foundation [21273084]
- Natural Science Fund of Guangdong Province [10351063101000001]
- key project of Science and Technology in Guangdong Province [2011A010801001]
- scientific research project of Department of Education of Guangdong Province [2013CXZDA013]
State-of-charge (SOC) estimation is one of the most challengeable tasks for battery management system (BMS) in electric vehicles. Since the external factors (voltage, current, temperature, arrangement of the batteries, etc.) are complicated, the formula of SOC is difficult to deduce and the existent SOC estimation methods are not generally suitable for the same vehicle running in different road conditions. In this paper, we propose a new SOC estimation based on an optimized support vector machine for regression (SVR) with double search optimization process. Our developed method is tested by simulation experiments in the ADVISOR, with a comparison of the estimations based on artificial neural network (ANN). It is demonstrated that our method is simpler and more accurate than that based on ANN to deal with the SOC estimation task. (C) 2014 Elsevier B.V. All rights reserved.
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