Battery State Estimation for Applications in Intelligent Warehouses
When it comes to AGVs (Automated Guide Vehicles) working in intelligent warehouse systems it is necessary to take into account that the use of batteries may impact the performance of the overall system. They need to be recharged or changed, and the time required to execute these operations might interfere in the AGV availability. Therefore, it is necessary to carry out a battery management procedure to ensure that the batteries have sufficient charges to perform the desired tasks. This paper describes a method based on the Extended Kalman Filter (EKF) to estimate the Batteries State of Charge (SOC). The estimated consumption is compared with the SOC obtained by the EKF. A series of experiments using mini-robotic forklifts were performed to evaluate the method. The experimental results have shown its effectiveness using resistive loads. This methodology allowed estimating the battery consumption for a certain route of the mini-robotic forklift in the warehouse and verifying the load capacity available for the mini-robotic forklift to accomplish a task assigned by the warehouse routing system.
M. M. Oliveira J. P. M. Galdames K. T. Vivaldini D. V. Magalh(a)es M. Becker
Mobile Robotics Lab - Mechanical Engineering Department,EESC,University of Sao Paulo - USP,SP,13566-900 Brazil
国际会议
2011 IEEE International Conference on Robotics and Automation(2011年IEEE世界机器人与自动化大会 ICRA 2011)
上海
英文
5511-5516
2011-05-09（万方平台首次上网日期，不代表论文的发表时间）