Research Article
Optimizing Hybrid Energy Harvesting Mechanisms for UAVs
@ARTICLE{10.4108/eai.13-7-2018.164629, author={Toan V. Quyen and Cuong V. Nguyen and Anh M. Le and Minh T. Nguyen}, title={Optimizing Hybrid Energy Harvesting Mechanisms for UAVs}, journal={EAI Endorsed Transactions on Energy Web}, volume={7}, number={30}, publisher={EAI}, journal_a={EW}, year={2020}, month={5}, keywords={Unmanned Aerial Vehicle (UAVs) - Drones, Solar cells, RF energy harvesting, Hybrid energy harvesting, Rechargeable batteries}, doi={10.4108/eai.13-7-2018.164629} }
- Toan V. Quyen
Cuong V. Nguyen
Anh M. Le
Minh T. Nguyen
Year: 2020
Optimizing Hybrid Energy Harvesting Mechanisms for UAVs
EW
EAI
DOI: 10.4108/eai.13-7-2018.164629
Abstract
Drones or unmanned aerial vehicles (UAVs) are often limited in range and duration since they can carry limited fuel or small size batteries. While other technologies focusing on batteries, control algorithms, and devices, the immediate improvements could be energy harvesting in which the ambient resources would provide sufficient power for UAVs. This proposed study aims to overcome the limitations in terms of energy and to optimize the energy harvested to prolong the flight time. The proposed mechanism focuses on two resources as radio frequency (RF) and solar energy and optimizes the energy harvesting systems. Two standalone systems are used to extract electricity from surrounding environment sources. RF harvesting energy is supported by a multi-stage of the voltage multiplier. The solar energy is absorbed by solar panels with mounting more the boost converter and using the maximum power point tracking method to achieve better efficiency. The output values of the hybrid energy system will be adjusted by stabilizer and boost the current system to have proper voltage and current with the battery inputs. By adding the supported circuit and methods, the simulation results are suitable to charge electricity for the battery having the capacity 7660 mAh and show promise.
Copyright © 2020 Toan V. Quyen et al., licensed to EAI. This is an open access article distributed under the terms of the Creative Commons Attribution license (http://creativecommons.org/licenses/by/3.0/), which permits unlimited use, distribution and reproduction in any medium so long as the original work is properly cited.