Intelligent prognostics for battery health monitoring based on sample entropy

► Sample entropy feature is used for assessing SOH battery based on discharge voltage data. ► Intelligent battery prognostics is served by SVM and RVM. ► Performance matrices used are RMSE, RMSPE, RMSRE, and correlation (R) matrices. ► RVM outperforms SVM based on performance matrices. 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.