Abstract—This paper presents a systematic literature review of recent developments in the optimization of electric propulsion systems for Unmanned Aerial Vehicles (UAVs). This review uniquely identifies underdeveloped areas such as real-world testing, integrated aerodynamic optimization, and lifecycle modeling in UAV electric propulsion systems, providing a new roadmap for future research. A total of 42 peer-reviewed articles, published between 2014 and 2024, were analyzed using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) method to ensure transparency and rigor in the selection process. The findings show a significant shift toward intelligent optimization techniques, especially Particle Swarm Optimization (PSO) and its hybrid variants, which are widely used for tuning Fuzzy PID controllers to enhance system performance and stability. Furthermore, innovations in lightweight composite materials and the integration of Internet of Things (IoT) technologies have demonstrated improved endurance and adaptability of UAV propulsion systems. Despite these advancements, most studies remain at the simulation stage, with limited real-world implementation. Environmental modeling, thermal-aware control, and lifecycle analysis are identified as key areas requiring further investigation. This review highlights the necessity of cross-disciplinary approaches to address the complexity of electric propulsion optimization. Recommendations for future work include the adoption of embedded Machine Learning (ML) models, experimental validation frameworks, and system-level integration with aerodynamic and mission planning subsystems.

Keywords—particle swarm optimization, Fuzzy Proportional-Integral-Derivative (PID) controller, composite materials, Internet of Things (IoT) integration, machine learning

Cite: Erwan Eko Prasetiyo, Rustam Asnawi, and Moh. Khairudin, "Optimization Strategies for Electric Propulsion in UAVs: A Review of Technologies, Control Systems, and Environmental Impacts," International Journal of Mechanical Engineering and Robotics Research, Vol. 14, No. 6, pp. 657-666, 2025. doi: 10.18178/ijmerr.14.6.657-666

Copyright © 2025 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).