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Energies 2017, 10(5), 691; doi:10.3390/en10050691

An On-Board Remaining Useful Life Estimation Algorithm for Lithium-Ion Batteries of Electric Vehicles

Faculty of Transportation Engineering, Kunming University of Science and Technology, Kunming 650500, China
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Academic Editor: K.T. Chau
Received: 20 March 2017 / Revised: 8 May 2017 / Accepted: 11 May 2017 / Published: 14 May 2017
(This article belongs to the Section Energy Storage and Application)
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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). View Full-Text
Keywords: mean square error (MSE); remaining useful life (RUL); support vector machine (SVM); voltage derivative (DV) mean square error (MSE); remaining useful life (RUL); support vector machine (SVM); voltage derivative (DV)
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Li, X.; Shu, X.; Shen, J.; Xiao, R.; Yan, W.; Chen, Z. An On-Board Remaining Useful Life Estimation Algorithm for Lithium-Ion Batteries of Electric Vehicles. Energies 2017, 10, 691.

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