Energies, Vol. 18, Pages 6521: Charting Smarter Skies—A Review of Computational Strategies for Energy-Saving Flights in Electric UAVs

Energies, Vol. 18, Pages 6521: Charting Smarter Skies—A Review of Computational Strategies for Energy-Saving Flights in Electric UAVs

Energies doi: 10.3390/en18246521

Authors:
Graheeth Hazare
Mohamed Thariq Hameed Sultan
Andrzej Łukaszewicz
Marek Nowakowski
Farah Syazwani Shahar

This review surveys the past five years of research on energy-aware path optimization for both solar-powered and battery-only UAVs. First, the energy constraints of these two platforms are contrasted. Next, advanced computational frameworks—including model predictive control, deep reinforcement learning, and bio-inspired metaheuristics—are examined along with their hardware implementations. Recent studies show that hybrid methods combining neural networks with bio-inspired search can boost net energy efficiency by 15–25% while maintaining real-time feasibility on embedded GPUs or FPGAs. Among the remaining challenges are federated learning at the edge, multi-UAV coordination under partial observability, and field trials on ultra-long-endurance platforms like the Airbus Zephyr HAPS. Addressing these issues will accelerate the deployment of truly persistent and green aerial services.