A New Hybrid Filter-Based Online Condition Monitoring for Lithium-Ion Batteries

被引：0

作者：

Kim, Taesic ^{[1
]}

Adhikaree, Amit ^{[1
]}

Kang, Daewook ^{[2
]}

Kim, Myoungho ^{[2
]}

Baek, Juwon ^{[2
]}

机构：

[1] Texas A&M Univ Kingsville, Dept Elect Engn & Comp Sci, Kingsville, TX 78363 USA

[2] Korea Electrotechnol Res Inst, 12 Bulmosan Ro, Changwon Si 642120, South Korea

来源：

2017 IEEE INTERNATIONAL CONFERENCE ON ELECTRO INFORMATION TECHNOLOGY (EIT) | 2017年

关键词：

Battery condition monitoring; battery model; dual extended Kalman filter (DEKF); hysteresis; smooth variable structure filter (SVSF); state of charge (SOC); STATE-OF-HEALTH; MANAGEMENT-SYSTEMS; PARAMETER-IDENTIFICATION; CHARGE ESTIMATION; PART; MODEL; PACKS;

D O I：

暂无

中图分类号：

TP39 [计算机的应用];

学科分类号：

081203 ; 0835 ;

摘要：

This paper proposes a novel online condition monitoring algorithm estimating battery states and model parameters. The proposed method includes: 1) an electrical circuit battery model incorporating the hysteresis effect, 2) an extended Kalman Filter-based online parameter identification algorithm for the electrical battery model, and 3) a smooth variable structure filter (SVSF)-based state estimation algorithm for state of charge (SOC) estimation. The proposed method enables an accurate and robust condition monitoring for lithium-ion batteries. Since the proposed hybrid filter further reduces the complexity compared to existing dual extended Kalman filter (DEKF), it is much more suitable for the real-time embedded battery management system (BMS) application. Simulation studies validate the effectiveness of the proposed strategy.

引用

页码：22 / 27

页数：6

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