東華大學圖書館 |

語系: 繁體中文

說明(常見問題)

回圖書館首頁

手機版館藏查詢

登入

回首頁

切換: 標籤 | MARC模式 | ISBD

A Novel Battery Management & Chargin...

Mian, Sami.

FindBook

Google Book

Amazon

博客來

A Novel Battery Management & Charging Solution for Autonomous UAV Systems.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	A Novel Battery Management & Charging Solution for Autonomous UAV Systems./
作者:	Mian, Sami.
出版者:	Ann Arbor : ProQuest Dissertations & Theses, : 2018,
面頁冊數:	117 p.
附註:	Source: Masters Abstracts International, Volume: 79-11.
Contained By:	Masters Abstracts International79-11.
標題:	Mathematics. -
電子資源:	https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10790304
ISBN:	9780355929812

A Novel Battery Management & Charging Solution for Autonomous UAV Systems.
Mian, Sami.

A Novel Battery Management & Charging Solution for Autonomous UAV Systems. - Ann Arbor : ProQuest Dissertations & Theses, 2018 - 117 p.

Source: Masters Abstracts International, Volume: 79-11.

Thesis (M.S.)--Arizona State University, 2018.

This item is not available from ProQuest Dissertations & Theses.

Currently, one of the biggest limiting factors for long-term deployment of autonomous systems is the power constraints of a platform. In particular, for aerial robots such as unmanned aerial vehicles (UAVs), the energy resource is the main driver of mission planning and operation definitions, as everything revolved around flight time. The focus of this work is to develop a new method of energy storage and charging for autonomous UAV systems, for use during long-term deployments in a constrained environment. We developed a charging solution that allows pre-equipped UAV system to land on top of designated charging pads and rapidly replenish their battery reserves, using a contact charging point. This system is designed to work with all types of rechargeable batteries, focusing on Lithium Polymer (LiPo) packs, that incorporate a battery management system for increased reliability. The project also explores optimization methods for fleets of UAV systems, to increase charging efficiency and extend battery lifespans. Each component of this project was first designed and tested in computer simulation. Following positive feedback and results, prototypes for each part of this system were developed and rigorously tested. Results show that the contact charging method is able to charge LiPo batteries at a 1-C rate, which is the industry standard rate, maintaining the same safety and efficiency standards as modern day direct connection chargers. Control software for these base stations was also created, to be integrated with a fleet management system, and optimizes UAV charge levels and distribution to extend LiPo battery lifetimes while still meeting expected mission demand. Each component of this project (hardware/software) was designed for manufacturing and implementation using industry standard tools, making it ideal for large-scale implementations. This system has been successfully tested with a fleet of UAV systems at Arizona State University, and is currently being integrated into an Arizona smart city environment for deployment.

ISBN: 9780355929812Subjects--Topical Terms:

515831
Mathematics.
Subjects--Index Terms:

Autonomous systems

A Novel Battery Management & Charging Solution for Autonomous UAV Systems.
LDR:03417nmm a2200421 4500 001 2282479
005 20211012150138.5
008 220723s2018 ||||||||||||||||| ||eng d
020 $a 9780355929812
035 $a (MiAaPQ)AAI10790304
035 $a (MiAaPQ)asu:17663
035 $a AAI10790304
040 $a MiAaPQ $c MiAaPQ
100 1 $a Mian, Sami. $3 3561281
245 1 0 $a A Novel Battery Management & Charging Solution for Autonomous UAV Systems.
260 1 $a Ann Arbor : $b ProQuest Dissertations & Theses, $c 2018
300 $a 117 p.
500 $a Source: Masters Abstracts International, Volume: 79-11.
500 $a Publisher info.: Dissertation/Thesis.
500 $a Advisor: Panchanathan, Sethuraman.
502 $a Thesis (M.S.)--Arizona State University, 2018.
506 $a This item is not available from ProQuest Dissertations & Theses.
506 $a This item must not be sold to any third party vendors.
520 $a Currently, one of the biggest limiting factors for long-term deployment of autonomous systems is the power constraints of a platform. In particular, for aerial robots such as unmanned aerial vehicles (UAVs), the energy resource is the main driver of mission planning and operation definitions, as everything revolved around flight time. The focus of this work is to develop a new method of energy storage and charging for autonomous UAV systems, for use during long-term deployments in a constrained environment. We developed a charging solution that allows pre-equipped UAV system to land on top of designated charging pads and rapidly replenish their battery reserves, using a contact charging point. This system is designed to work with all types of rechargeable batteries, focusing on Lithium Polymer (LiPo) packs, that incorporate a battery management system for increased reliability. The project also explores optimization methods for fleets of UAV systems, to increase charging efficiency and extend battery lifespans. Each component of this project was first designed and tested in computer simulation. Following positive feedback and results, prototypes for each part of this system were developed and rigorously tested. Results show that the contact charging method is able to charge LiPo batteries at a 1-C rate, which is the industry standard rate, maintaining the same safety and efficiency standards as modern day direct connection chargers. Control software for these base stations was also created, to be integrated with a fleet management system, and optimizes UAV charge levels and distribution to extend LiPo battery lifetimes while still meeting expected mission demand. Each component of this project (hardware/software) was designed for manufacturing and implementation using industry standard tools, making it ideal for large-scale implementations. This system has been successfully tested with a fleet of UAV systems at Arizona State University, and is currently being integrated into an Arizona smart city environment for deployment.
590 $a School code: 0010.
650 4 $a Mathematics. $3 515831
650 4 $a Computer Engineering. $3 1567821
650 4 $a Robotics. $3 519753
653 $a Autonomous systems
653 $a Battery technologies
653 $a Power optimization
653 $a Smart cities
653 $a Swarm robotics
653 $a Unmanned aerial vechiles
690 $a 0405
690 $a 0464
690 $a 0771
710 2 $a Arizona State University. $b Computer Engineering. $3 3289092
773 0 $t Masters Abstracts International $g 79-11.
790 $a 0010
791 $a M.S.
792 $a 2018
793 $a English
856 4 0 $u https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10790304