State-of-charge estimation for battery management system using optimized support vector machine for regression

被引：234

作者：

Hu, J. N. ^{[1
,2
]}

Hu, J. J. ^{[3
]}

Lin, H. B. ^{[1
,2
]}

Li, X. P. ^{[1
,2
]}

Jiang, C. L. ^{[1
,2
]}

Qiu, X. H. ^{[1
,2
]}

Li, W. S. ^{[1
,2
]}

机构：

[1] S China Normal Univ, Sch Chem & Environm, Key Lab Electrochem Technol Energy Storage & Powe, Guangzhou 510006, Guangdong, Peoples R China

[2] S China Normal Univ, Engn Res Ctr Mat & Technol Electrochem Energy Sto, Minist Educ, Guangzhou 510006, Guangdong, Peoples R China

[3] S China Univ Technol, Sch Comp Sci & Engn, Guangzhou 510006, Guangdong, Peoples R China

来源：

JOURNAL OF POWER SOURCES | 2014年 / 269卷

基金：

中国国家自然科学基金;

关键词：

State of charge; Support vector machine for regression; Battery management system; Electric vehicle; Double step search; Driving conditions; SOC ESTIMATION; ION; PACKS; COMPOSITE; HEALTH; MODEL;

D O I：

10.1016/j.jpowsour.2014.07.016

中图分类号：

O64 [物理化学（理论化学）、化学物理学];

学科分类号：

070305 [高分子化学与物理];

摘要：

State-of-charge (SOC) estimation is one of the most challengeable tasks for battery management system (BMS) in electric vehicles. Since the external factors (voltage, current, temperature, arrangement of the batteries, etc.) are complicated, the formula of SOC is difficult to deduce and the existent SOC estimation methods are not generally suitable for the same vehicle running in different road conditions. In this paper, we propose a new SOC estimation based on an optimized support vector machine for regression (SVR) with double search optimization process. Our developed method is tested by simulation experiments in the ADVISOR, with a comparison of the estimations based on artificial neural network (ANN). It is demonstrated that our method is simpler and more accurate than that based on ANN to deal with the SOC estimation task. (C) 2014 Elsevier B.V. All rights reserved.

引用

页码：682 / 693

页数：12

共 27 条

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Advanced mathematical methods of SOC and SOH estimation for lithium-ion batteries [J].