Energy Consumption Modeling and Flight Time Analysis of Micro Drones

Unmanned aerial vehicles have emerged as revolutionary technology with a wide range of applications in different areas. One of the interesting drone types is miniature or lightweight micro drones which have palm-size small dimensions. Due to their small size, micro drones can navigate confined spaces and reach inaccessible areas, providing valuable data and insights. However, the small size of these drones limits their ability to carry heavy equipment, including batteries and many sensors. Increasing the flight time of micro drones using limited energy sources is a challenging task that requires advanced hardware technologies and efficient software methods. In this paper, we propose a voltage-based regression model to estimate the energy consumption and flight time of micro drones, using sampled voltage data obtained from experimental evaluations in ground, take-off, hovering, and flying modes. The mean absolute error of the proposed regression models is less than 0.3 V for all flying modes. The results from 150 second flight tests indicated an estimated total energy consumption of 2000 J, corresponding to an average power of 13.3 W. The unit mass power consumption was calculated as 0.38 W/g, highlighting the suitability of miniature UAVs for energy-constrained tasks. Also, the experiments showed that due to the voltage drop of the batteries, the drones cannot fly anymore when the battery energy drops to about 50% of the total battery capacity. These results emphasize the importance of lightweight high capacity batteries for developing micro drones.

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Publication Details

Journal
IEEE Access
Published
2025-01-01
DOI
https://doi.org/10.1109/access.2025.3581944
Citations
2
Primary Topic
UAV Applications and Optimization
Type
article
Field-Weighted Citation Impact
9.99

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article

Energy Consumption Modeling and Flight Time Analysis of Micro Drones

Orhan Dağdevıren, Moharram Challenger, Nusin Akram, Mina Khoshrangbaf
2 citations
IEEE Access
UAV Applications and Optimization
9.99
article

Energy Consumption Modeling and Flight Time Analysis of Micro Drones

Orhan Dağdevıren, Moharram Challenger, Nusin Akram, Mina Khoshrangbaf
article en
2 citations

Abstract

Unmanned aerial vehicles have emerged as revolutionary technology with a wide range of applications in different areas. One of the interesting drone types is miniature or lightweight micro drones which have palm-size small dimensions. Due to their small size, micro drones can navigate confined spaces and reach inaccessible areas, providing valuable data and insights. However, the small size of these drones limits their ability to carry heavy equipment, including batteries and many sensors. Increasing the flight time of micro drones using limited energy sources is a challenging task that requires advanced hardware technologies and efficient software methods. In this paper, we propose a voltage-based regression model to estimate the energy consumption and flight time of micro drones, using sampled voltage data obtained from experimental evaluations in ground, take-off, hovering, and flying modes. The mean absolute error of the proposed regression models is less than 0.3 V for all flying modes. The results from 150 second flight tests indicated an estimated total energy consumption of 2000 J, corresponding to an average power of 13.3 W. The unit mass power consumption was calculated as 0.38 W/g, highlighting the suitability of miniature UAVs for energy-constrained tasks. Also, the experiments showed that due to the voltage drop of the batteries, the drones cannot fly anymore when the battery energy drops to about 50% of the total battery capacity. These results emphasize the importance of lightweight high capacity batteries for developing micro drones.

IEEE AccessVol. 13
University of Antwerp (BE), Ege University (TR), Flanders Make (Belgium) (BE), Xuzhou Construction Machinery Group (China) (CN)
Science and Engineering Research Council
Affordable and clean energy
Openalex Percentile: Top 3%
UAV Applications and Optimization
9.99
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