an on-board remaining useful life estimation algorithm for lithium-ion batteries of electric vehicles

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ID: 214153
2017
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Abstract
Battery remaining useful life (RUL) estimation is critical to battery management and performance optimization of electric vehicles (EVs). In this paper, we present an effective way to estimate RUL online by using the support vector machine (SVM) algorithm. By studying the characteristics of the battery degradation process, the rising of the terminal voltage and changing characteristics of the voltage derivative (DV) during the charging process are introduced as the training variables of the SVM algorithm to determine the battery RUL. The SVM is then applied to build the battery degradation model and predict the battery real cycle numbers. Experimental results prove that the built battery degradation model shows higher accuracy and less computation time compared with those of the neural network (NN) method, thereby making it a potential candidate for realizing online RUL estimation in a battery management system (BMS).
Reference Key
li2017energiesan Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors ;Xiaoyu Li;Xing Shu;Jiangwei Shen;Renxin Xiao;Wensheng Yan;Zheng Chen
Journal acs combinatorial science
Year 2017
DOI
10.3390/en10050691
URL
http://www.mdpi.com/1996-1073/10/5/691
Keywords
support vector machine (svm) remaining useful life (rul)

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