.png){kind=icon}
.png){kind=icon}
Return
Prediction of energy consumption in unmanned aerial vehicles
.png){kind=icon}
PRE
AI
A
M
W
DOI：10.1016/j.epsr.2026.113008.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
This study addresses the problem of predicting energy consumption in Unmanned Aerial Vehicles (UAVs), with a particular focus on identifying the parameters that exert the greatest influence on battery performance, while maintaining model interpretability. A hybrid modeling strategy was employed, combining data-driven machine learning techniques with a physics-based theoretical foundation. The resulting framework not only quantifies the impact of weather conditions on UAV energy consumption and battery dynamics, but also highlights the importance of trajectory design and velocity control in optimizing flight range. The results of the model are presented in a straightforward and intuitive manner. Furthermore, the proposed approach is lightweight, adaptable and adaptable to other quadrotor systems, offering both practical usability and scalability.
Keywords:
Forecasting
Time series
Drone
Power consumption
Battery state of charge
Machine learning
Journal
IF:
4.2
Papers:
1.1W
Citations:
2.2W
Organization
Cited Papers