State of Charge Estimation of Lithium Ion Batteries Using an Extended Single Particle Model and Sigma-Point Kalman Filter

被引：0

作者：

Ngoc Tham Tran ^{[1
]}

Vilathgamuwa, Mahinda ^{[1
]}

Li, Yang ^{[1
]}

Farrell, Troy ^{[2
]}

Choi, San Shing ^{[1
]}

Teague, Joseph ^{[2
]}

机构：

[1] Queensland Univ Technol, Sch Elect Engn & Comp Sci, Brisbane, Qld, Australia

[2] Queensland Univ Technol, Sch Math Sci, Brisbane, Qld, Australia

来源：

2017 IEEE SOUTHERN POWER ELECTRONICS CONFERENCE (SPEC) | 2017年

基金：

澳大利亚研究理事会;

关键词：

Lithium ion cell; SOC estimation; extended single particle model; Sigma-point Kalman filter; ELECTROCHEMICAL MODEL; MANAGEMENT-SYSTEMS; DISCHARGE; CATHODES;

D O I：

暂无

中图分类号：

TE [石油、天然气工业]; TK [能源与动力工程];

学科分类号：

0807 ; 0820 ;

摘要：

We present a method to estimate the state of charge (SOC) of lithium ion batteries using an extended single particle model (ESPM) and the sigma-point Kalman filter (SPKF) algorithm. ESPM is a computationally efficient battery electrochemical model while the SPKF algorithm allows the battery SOC to be estimated with high accuracy and stability. The proposed ESPM-SPKF method is stable, efficient and can be adopted for advanced battery management systems in large-scale battery energy storage systems.

引用

页码：405 / 410

页数：6

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