Intelligent prognostics for battery health monitoring based on sample entropy
Intelligent prognostics for battery health monitoring based on sample entropy
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DOI：
10.1016/j.eswa.2011.03.063
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发表时间：
2011-09-01
影响因子：
8.5
通讯作者：
Yang, Bo-Suk
中科院分区：
文献类型：
--
作者：
Widodo, Achmad;Shim, Min-Chan;Yang, Bo-Suk
提出了一种基于放电电压样本熵特征的电池健康智能预测方法。SampEn可以提供评估时间序列可预测性的计算手段，也可以量化数据序列的规律性。因此，当将其应用于放电电压电池数据时，它可以作为电池健康的指标。在这项工作中，智能能力引入利用机器学习方法，即支持向量机（SVM）和相关向量机（RVM）。SampEn和估计的荷电状态（SOH）分别作为学习算法的数据输入和目标向量。结果表明，由于支持向量机和RVM在SOH预测中的良好性能，所提出的方法是合理的。在我们的研究中，RVM优于基于SVM的电池健康预测。(C)2011爱思唯尔有限公司保留所有权利。
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.