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Appl. Sci. 2018, 8(6), 925; https://doi.org/10.3390/app8060925

Online State of Health Estimation for Lithium-Ion Batteries Based on Support Vector Machine

Faculty of Transportation Engineering, Kunming University of Science and Technology, Kunming 650500, China
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Received: 8 April 2018 / Revised: 23 May 2018 / Accepted: 31 May 2018 / Published: 4 June 2018
(This article belongs to the Special Issue Battery Management and State Estimation)
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Abstract

In this paper, a novel state of health (SOH) estimation method based on partial charge voltage and current data is proposed. The extraction of feature variables, which are energy signal, the Ah-throughput, and the charge duration, is discussed and analyzed. The support vector machine (SVM) with radial basis function (RBF) as kernel function is applied for the SOH estimation. The predictive performance of the SOH by the SVM are performed with full and partial charging data. Experiment results show that the addressed approach enables estimating the SOH accurately for practical application. View Full-Text
Keywords: capacity degradation; charge voltage; state of health (SOH); support vector machine (SVM) capacity degradation; charge voltage; state of health (SOH); support vector machine (SVM)
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Chen, Z.; Sun, M.; Shu, X.; Xiao, R.; Shen, J. Online State of Health Estimation for Lithium-Ion Batteries Based on Support Vector Machine. Appl. Sci. 2018, 8, 925.

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