Battery state-of-charge estimator using the SVM technique |
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| Authors: | J.C. Á lvarez Antó n,P.J. Garcí a Nieto,F.J. de Cos Juez,F. Sá nchez Lasheras,M. Gonzá lez Vega,M.N. Roqueñ í Gutié rrez |
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| Affiliation: | 1. Department of Electrical Engineering, Campus de Viesques, University of Oviedo, 33204 Gijón, Spain;2. Department of Mathematics, Faculty of Sciences, University of Oviedo, 33007 Oviedo, Spain;3. Mining Exploitation and Prospecting Department, University of Oviedo, 33004 Oviedo, Spain;4. Department of Construction and Manufacturing Engineering, University of Oviedo, 33204 Gijón, Spain |
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| Abstract: | State-of-charge (SOC) is the equivalent of a fuel gauge for a battery pack in an electric vehicle. Determining the state-of-charge becomes an important issue in all battery applications including electric vehicles (EV), hybrid electric vehicles (HEV) or portable devices. The aim of this innovative study is to estimate the SOC of a high capacity lithium iron phosphate (LiFePO4) battery cell from an experimental data-set obtained in the University of Oviedo Battery Laboratory (UOB Lab) using support vector machine (SVM) approach. The SOC of a battery cannot be measured directly and must be estimated from measurable battery parameters such as current, voltage or temperature. An accurate predictive model able to forecast the SOC in the short term is obtained. The agreement of the SVM model with the experimental data-set confirmed its good performance. |
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| Keywords: | Lithium batteries Modeling State-of-charge (SOC) Support vector machine (SVM) Support vector regression |
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