Estimation of State of Charge of Battery Based on Extended Kalman Filtering

被引：0

作者：

Chen Huangjie ^{[1
]}

Ma Yan ^{[1
,2
]}

Zhao Haiyan ^{[1
,2
]}

机构：

[1] Jilin Univ, Dept Control Sci & Engn, Changchun 130025, Peoples R China

[2] Natl Automobile Dynam Simulat Lab, Changchun 130022, Peoples R China

来源：

2013 32ND CHINESE CONTROL CONFERENCE (CCC) | 2013年

关键词：

Electric Vehicle; State of Charge (SOC); AMESim; Extended Kalman Filtering;

D O I：

暂无

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

Estimation of state of charge (SOC) is the key technique for electric vehicle power management system. A simple battery nonlinear equivalent model under AMESim is built. By using Extended Kalman Filter method, battery SOC is estimated on-line through optimal estimation in the sense of minimum-variance when battery current fluctuates wildly. Co-simulation result shows that Extended Kalman Filter method which can estimate SOC with error less than 1.8% has high accuracy.

引用

页码：7632 / 7635

页数：4