DocumentCode :
46389
Title :
An Automated Battery Management System to Enable Persistent Missions With Multiple Aerial Vehicles
Author :
Ure, N. Kemal ; Chowdhary, Girish ; Toksoz, Tuna ; How, Jonathan P. ; Vavrina, Matthew A. ; Vian, John
Author_Institution :
Aerosp. Controls Lab., Massachusetts Inst. of Technol., Cambridge, MA, USA
Volume :
20
Issue :
1
fYear :
2015
fDate :
Feb. 2015
Firstpage :
275
Lastpage :
286
Abstract :
This paper presents the development and hardware implementation of an autonomous battery maintenance mechatronic system that significantly extends the operational time of battery powered small-scaled unmanned aerial vehicles (UAVs). A simultaneous change and charge approach is used to overcome the significant downtime experienced by existing charge-only approaches. The automated system quickly swaps a depleted battery of a UAV with a replenished one while simultaneously recharging several other batteries. This results in a battery maintenance system with low UAV downtime, arbitrarily extensible operation time, and a compact footprint. Hence, the system can enable multi-agent UAV missions that require persistent presence. This capability is illustrated by developing and testing in flight a centralized autonomous planning and learning algorithm that incorporates a probabilistic health model dependent on vehicle battery health that is updated during the mission, and replans to improve the performance based on the improved model. Flight test results are presented for a 3-h-long persistent mission with three UAVs that each has an endurance of 8-10 min on a single battery charge (more than 100 battery swaps).
Keywords :
aerospace testing; autonomous aerial vehicles; battery management systems; battery powered vehicles; mechatronics; secondary cells; UAV downtime; automated battery management system; autonomous battery maintenance mechatronic system; battery powered small-scaled unmanned aerial vehicles; battery recharging; centralized autonomous planning; compact footprint; flight testing; hardware implementation; learning algorithm; multiagent UAV missions; multiple aerial vehicles; operational time; persistent missions; probabilistic health model; vehicle battery health; Algorithm design and analysis; Batteries; Battery management systems; Discharges (electric); Hardware; Receivers; Vehicles; Battery management systems; Markov processes; learning (artificial intelligence); multiagent systems; unmanned aerial vehicles (UAVs);
fLanguage :
English
Journal_Title :
Mechatronics, IEEE/ASME Transactions on
Publisher :
ieee
ISSN :
1083-4435
Type :
jour
DOI :
10.1109/TMECH.2013.2294805
Filename :
6701199
Link To Document :
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