Experimental Development and Optimization of Hybrid-Electric Multirotors for Extended Flight Endurance and Payload

Abstract This study presents the experimental development and system-level optimization of a hybrid-electric multirotor Uncrewed Aerial System (UAS), tailored for resilient and extended-duration operations in disaster-affected environments. The validated multi-objective optimization framework addresses conventional battery-powered systems' inherent endurance and payload limitations. To address the limited endurance and payload of conventional electric UAVs, two custom hybrid-electric generator (HEG) modules, based on internal combustion engines (DLE20 and DLE30) and brushless DC motors, were designed, fabricated, and validated using a sensor-integrated experimental testbed. A third commercial generator (Nova 2400W) was included for comparative benchmarking. A multi-objective evolutionary algorithm (NSGA-II) is employed to simultaneously minimize maximum takeoff weight, maximize flight endurance, and maximize payload capacity. The resulting Pareto-optimal configurations were evaluated, and three representative designs were further refined, supporting mission-critical tasks in post-disaster scenarios. Ground and flight tests confirm the reliability and scalability of the hybrid platform, particularly the DLE30-based configuration, which exhibited superior endurance and energy conversion performance. These UAVs are envisioned as resilient aerial platforms capable of supporting emergency logistics, real-time situational awareness, and rapid infrastructure assessments. The study contributes a replicable design methodology and validated dataset to enhance the disaster resilience capabilities of transportation systems.