期刊
JOURNAL OF POWER SOURCES
卷 401, 期 -, 页码 49-54出版社
ELSEVIER SCIENCE BV
DOI: 10.1016/j.jpowsour.2018.08.073
关键词
Lithium-ion battery; Remaining useful life prediction; Support vector regression; Differential evolution
资金
- Ministry of Science and Technology, Taiwan [MOST-107-2221-E-011-100-MY3]
Remaining useful life prediction plays an important role in battery management system. The fusion prognostics method has become a main research direction for improving the prediction performance. We present a hybrid model based on support vector regression and differential evolution to predict the remaining useful life of Li-ion battery, where differential evolution algorithm is used to obtain the support vector regression kernel parameters. The capacity, voltage, and current on discharge operation are considered in this study. Three Li-ion batteries from NASA Ames Prognostics Center of Excellence are used to illustrate the application. The results show that the proposed method has better prediction accuracy than the ten published methods. Regeneration factor has insignificant influence on the prediction accuracy of the proposed hybrid model.
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