Battery state-of-charge estimator using the SVM technique

被引:259
作者
Alvarez Anton, J. C. [1 ]
Garcia Nieto, P. J. [2 ]
de Cos Juez, F. J. [3 ]
Sanchez Lasheras, F. [4 ]
Gonzalez Vega, M. [1 ]
Roqueni Gutierrez, M. N. [3 ]
机构
[1] Univ Oviedo, Dept Elect Engn, Gijon 33204, Spain
[2] Univ Oviedo, Fac Sci, Dept Math, Oviedo 33007, Spain
[3] Univ Oviedo, Min Exploitat & Prospecting Dept, Oviedo 33004, Spain
[4] Univ Oviedo, Dept Construct & Mfg Engn, Gijon 33204, Spain
关键词
Lithium batteries; Modeling; State-of-charge (SOC); Support vector machine (SVM); Support vector regression; SUPPORT; VALIDATION; PACKS;
D O I
10.1016/j.apm.2013.01.024
中图分类号
T [工业技术];
学科分类号
120111 [工业工程];
摘要
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. (C) 2013 Elsevier Inc. All rights reserved.
引用
收藏
页码:6244 / 6253
页数:10
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