All-electric aircraft face limitations in achieving long range due to the energy density constraints of current lithium batteries. Range-extended aircraft, employing an "electric-dominant, fuel-supplement" powertrain architecture, offer a balance between environmental benefits and extended range, positioning them as a crucial transitional solution for aviation decarbonization. This review focuses on the range-extended technology pathway. It provides an overview of the technological roadmaps adopted by representative range-extended aircraft models both domestically and internationally; it compares and analyzes five typical hybrid powertrain architectures, elucidating the definition and operational principles of the range-extended concept. It explores the advances and challenges of key enabling technologies that are driving its development, including high-power-density permanent magnet motors, intelligent energy management systems, efficient thermal management systems, and high-voltage partial discharge monitoring and protection technologies. This review clarifies the current landscape and core barriers of range-extended technology. It provides essential insights to inform the technological evolution towards long-range adaptability for all-electric aircraft and serves as a valuable reference for formulating strategies aimed at the low-carbon transition of the aviation industry.

An experiment of Pd/γ-Al₂O₃ performance is carried out to get its reaction dynamics function. A two-dimensional monolithic catalyst transient model is established and solved with Modelica. Catalyst transition performance has been studied by simulation. The influence of high temperature and catalyst deactivation on a monolithic catalyst is researched. It was investigated how the reactor was affected by the oxygen content while in flight. The result shows that wall cooling can reduce high temperatures and reactor efficiency at the same time. The catalyst deactivation due to high temperature occurs on the surface near the outlet of the reactor, where the sharpest reaction takes place. Deactivation in Less than half of the catalyst surface area cuts down the efficiency by 80%. The conversion rate of fuel steam can be effectively increased by the increase in oxygen concentration brought on by the loss of pressure during aircraft ascent; however, precautions should be taken to prevent the acceleration of catalyst deactivation caused by high oxygen concentration.

Due to the long experimental period and complex operating conditions, it is necessary to study the precise prediction of membrane performance using limited data in airborne hollow fiber membrane separation performance experiments. An artificial neural network-based prediction model for hollow fiber membrane separation performance has been developed. Error back propagation (BP) neural networks, Elman neural networks, and genetic algorithm optimized BP (GA-BP) neural networks have been constructed to investigate the variation of airborne hollow fiber membrane separation performance with bleed pressure, flight altitude, and bleed flow rate. Compare and predict the performance of hollow fiber membranes under different algorithms. The research results indicate that the three network algorithms constructed can accurately predict the separation characteristics of hollow fiber membranes, with an mean absolute percentage error (MAPE) of less than 1%. With an average prediction accuracy 1.43% greater than the other two, GA-BP neural networks typically produce the best prediction results when selecting 5%, 10%, and 20% of the complete dataset. Based on the research results, suitable algorithms can be selected in practical work to process membrane experimental data and efficiently analyze performance.