ICRA'09 Paper Abstract

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Paper FrA10.4

Zhang, Fei (Shenyang Institute of Automation,Chinese Academy ofSciences), Liu, Guangjun (Ryerson University), Fang, Lijin (Chinese Academy of Sciences)

Battery State Estimation Using Unscented Kalman Filter

Scheduled for presentation during the Regular Sessions "Field Robots - III" (FrA10), Friday, May 15, 2009, 09:30−09:50, Room: 502

2009 IEEE International Conference on Robotics and Automation, May 12 - 17, 2009, Kobe, Japan

This information is tentative and subject to change. Compiled on January 24, 2022

Keywords Robotics in Hazardous Fields

Abstract

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.

 

 

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