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
收藏
页码：405 / 410
页数：6
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