Hybrid Renewable Power Integration (Solar+Wind+Thermal+Microwave + Fuel Cell) in eVTOL and Satellites (International Journal of Scientific Research and Management (IJSRM), 2025)
This research explores the intelligent integration and optimal scheduling of hybrid renewable energy sources—solar, wind, thermal, microwave, and fuel cell—for electric vertical take-off and landing (eVTOL) aircraft and satellite systems. With growing interest from organizations such as NASA, the Canadian Space Agency (CSA), Bombardier, and Boeing, the demand for weight-efficient, AI-driven energy autonomy has become critical. Leveraging cutting-edge deep learning architectures including deep reinforcement learning, federated learning, and neural combinatorial optimization, this study proposes a unified model to enhance the energy efficiency of solar-powered UAVs, wind-harvesting aerial vehicles, and deep-space exploration platforms. Our methodology is grounded in an in-depth review and synthesis of the most recent and impactful research (2020–2024) across IEEE and related peer-reviewed journals, including ten key papers that span energy optimization, trajectory scheduling, federated UAV learning, and hybrid microgrid control.