Intelligent prognostics for battery health monitoring based on sample entropy

Authors:
Achmad Widodo;Min-Chan Shim;Wahyu Caesarendra;Bo-Suk Yang
Affiliations:
Mechanical Engineering Department, Diponegoro University, Tembalang, Semarang 50275, Indonesia;Department of Mechanical and Automobile Engineering, Pukyong National University, San 100, Yongdang-dong, Nam-gu, Busan 608-739, South Korea;Department of Mechanical and Automobile Engineering, Pukyong National University, San 100, Yongdang-dong, Nam-gu, Busan 608-739, South Korea;Department of Mechanical and Automobile Engineering, Pukyong National University, San 100, Yongdang-dong, Nam-gu, Busan 608-739, South Korea
Venue:
Expert Systems with Applications: An International Journal
Year:
2011

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Abstract

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.