Online estimation of state-of-charge based on the H infinity and unscented Kalman filters for lithium ion batteries

被引：35
|
作者：
Yu, Quanqing [1 ]
Xiong, Rui [1 ]
Lin, Cheng [1 ]
机构：
[1] Beijing Inst Technol, Natl Engn Lab Elect Vehicles, Sch Mech Engn, Beijing 100081, Peoples R China
来源：
8TH INTERNATIONAL CONFERENCE ON APPLIED ENERGY (ICAE2016) | 2017年 / 105卷
关键词：
state of charge; H infinity filter; unscented Kalman filter; lithium-ion battery; PARAMETERS;
D O I：
10.1016/j.egypro.2017.03.600
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：
The state of charge (SOC) is a key indicator for the battery management system (BMS) of electric vehicles. A SOC joint estimation method based on the H infinity filter (HF) and unscented Kalman filter (UKF) algorithms is proposed in this paper, HF based parameters identification can trace the parameters online according to the working conditions while he UKF based state estimation method does not require the jacobian matrix derivation and the linearization for nonlinear model. The HF-UKF SOC joint estimation method has been experimentally validated at different temperatures. The results show that this method is robust to the inaccurate initial SOC value and the different working temperatures. (C) 2017 The Authors Published by Elsevier Ltd.
引用
收藏
页码：2791 / 2796
页数：6
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