期刊
EXPERT SYSTEMS WITH APPLICATIONS
卷 38, 期 9, 页码 11763-11769出版社
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.eswa.2011.03.063
关键词
Battery health prognostics; Support vector machine; Relevance vector machine; Sample entropy
类别
资金
- Ministry of Education, Science & Technology (MEST)
- National Research Foundation of Korea (NRF)
In this paper, an intelligent prognostic for battery health based on sample entropy (SampEn) feature of discharge voltage is proposed. SampEn can provide computational means for assessing the predictability of a time series and also can quantity the regularity of a data sequence. Therefore, when it is applied to discharge voltage battery data, it could serve an indicator for battery health. In this work, the intelligent ability is introduced by utilizing machine learning methods namely support vector machine (SVM) and relevance vector machine (RVM). SampEn and estimated state of charge (SOH) are employed as data input and target vector of learning algorithms, respectively. The results show that the proposed method is plausible due to the good performance of SVM and RVM in SOH prediction. In our study, RVM outperforms SVM based battery health prognostics. (C) 2011 Elsevier Ltd. All rights reserved.
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