Aviation is under increasing pressure to reduce carbon emissions in conventional transports and support the growth of low-altitude operations such as long-endurance eVTOLs. Hybrid-electric propulsion addresses these challenges by integrating the high specific energy of fuels or hydrogen with the controllability and efficiency of electrified powertrains. At present, the field of hybrid-electric aircraft is developing rapidly. To systematically study hybrid-electric propulsion control in aviation, this review focuses on practical aspects of system development, including propulsion architectures, system- and component-level modeling approaches, and energy management strategies. Key technologies in the future are examined, with emphasis on aircraft power-demand prediction, multi-timescale control, and thermal integrated energy management. This review aims to serve as a reference for configuration design, modeling and control simulation, as well as energy management strategy design of hybrid-electric propulsion systems. Building on this reference role, the review presents a coherent guidance scheme from architectures through modeling to energy-management control, with a practical roadmap toward flight-ready deployment.

Due to the limitations of pure electric power endurance, turbo-electric hybrid power systems, which offer a high power-to-weight ratio, present a reliable solution for medium- and large-sized vertical take-off and landing (VTOL) aircraft. Traditional energy management strategies often fail to minimize fuel consumption across the entire flight profile while meeting power demands under varying flight conditions. To address this issue, this paper proposes a deep reinforcement learning (DRL)-based energy management strategy (EMS) specifically designed for turbo-electric hybrid propulsion systems. Firstly, the proposed strategy employs a Prior Knowledge-Guided Deep Reinforcement Learning (PKGDRL) method, which integrates domain-specific knowledge into the Deep Deterministic Policy Gradient (DDPG) algorithm to improve learning efficiency and enhance fuel economy. Then, by narrowing the exploration space, the PKGDRL method accelerates convergence and achieves superior fuel and energy efficiency. Simulation results show that PKGDRL has a strong generalization capability in all operating conditions, with a fuel economy difference of only 1.6% from the offline benchmark of the optimization algorithm, and in addition, the PKG module enables the DRL method to achieve a huge improvement in terms of fuel economy and convergence rate. In particular, the prospect theory (PT) in the PKG module improves fuel economy by 0.81%. Future research will explore the application of PKGDRL in the direction of real-time total power prediction and adaptive energy management under complex operating conditions to enhance the generalization capability of EMS.

In the Machine Learning (ML) era, faced with challenges, including exponential multi-sensor data, an increasing number of actuators, and data-intensive algorithms, the development of Unmanned Aerial Vehicles (UAVs) is standing on a new footing. In particular, the Flight Management System (FMS) plays an essential role in UAV design. However, the trade-offs between performance and SWaP-C (Size, Weight, Power, and Cost) and reliability–efficiency are challenging to determine for such a complex system. To address these issues, the identification of a successful approach to managing heterogeneity emerges as the critical question to be answered. This paper investigates Heterogeneous Computing (HC) integration in FMS in the UAV domain from academia to industry. The overview of cross-layer FMS design is firstly described from top–down in the abstraction layer to left–right in the figurative layer. In addition, the HC advantages from Light-ML, accelerated Federated Learning (FL), and hardware accelerators are highlighted. Accordingly, three distinct research focuses detailed with visual-guided landing, intelligent Fault Diagnosis and Detection (FDD), and controller-embeddable Power Electronics (PE) to distinctly illustrate advancements of the next-generation FMS design from sensing, and computing, to driving. Finally, recommendations for future research and opportunities are discussed. In summary, this article draws a road map that considers the heterogeneous advantages to conducting the Flight-Management-as-a-Service (FMaaS) platform for UAVs.

Maritime activities have become increasingly frequent with the deepening of economic globalization, highlighting the burgeoning significance of maritime rescue. However, in practical applications, UAVs for maritime rescue face numerous challenges, such as limited endurance and inadequate autonomous planning capabilities. To optimize flight routes and circumvent adverse sea conditions, an improved Pied Kingfisher Optimizer (IPKO) that incorporates refraction reverse learning, variable spiral search, and Cauchy mutation strategies was proposed. Comparative experiments conducted on CEC2005 and CEC2022 datasets with seven traditional algorithms demonstrate that the proposed algorithm exhibits superior precision and convergence speed. Subsequently, a path planning objective function was constructed based on trajectory cost and threat cost to simulate a 3D space for UAV maritime rescue missions, and the IPKO algorithm was applied to address the UAV path planning problem. The results showed that the total cost incurred by the IPKO algorithm decreased by 5.77% compared to the PKO algorithm and by 51.19% compared to the SCA algorithm. Finally, through UAV flight tests validating its practical applicability, it is ascertained that IPKO can enhance rescue efficiency in complex maritime rescue environments.