Battery State Estimation Using Unscented Kalman Filter

被引：0
|
作者：
Zhang, Fei [1 ]
Liu, Guangjun [2 ]
Fang, Lijin [1 ]
机构：
[1] Chinese Acad Sci, State Key Lab Robot, Shenyang Inst Automat, Shenyang 110016, Peoples R China
[2] Ryerson Univ, Dept Aerosp Engn, Toronto, ON M5B 2K3, Canada
来源：
ICRA: 2009 IEEE INTERNATIONAL CONFERENCE ON ROBOTICS AND AUTOMATION, VOLS 1-7 | 2009年
基金：
国家高技术研究发展计划(863计划);
关键词：
LEAD-ACID-BATTERIES; PREDICTING STATE; OF-CHARGE; HEALTH;
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：
Online evaluation of battery State of Function (SOF) is crucial for battery management systems of autonomous mobile robots. Battery State of Charge (SOC) represents its remaining energy available, whereas internal resistance and capacity reflect its State of Health (SOH). In this paper, an improved equivalent circuit model is proposed to estimate SOC, internal resistance and capacity using an Unscented Kalman Filter (UKF). The proposed method not only estimates SOC, but also evaluates SOH and SOF. Experimental results have shown the effectiveness of the proposed method using resistive loads and a robot prototype for inspecting power transmission line.
引用
收藏
页码：3574 / +
页数：2
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