Navigating Technological Risks: An Uncertainty Analysis of Powertrain Technology in Hybrid-Electric Commuter Aircraft

Abstract

Abstract This study addresses the uncertainties in hybrid-electric powertrain technology for a 19-passenger commuter aircraft, focusing on two future Entry-Into-Service timeframes: 2030 and 2040. The methodology is split into a preliminary optimization of aircraft design based on nominal technology scenarios followed by Monte Carlo simulations to investigate the impact of diverse technology projections and distribution types. Advanced surrogate modeling techniques, leveraging deep neural networks (DNN) trained on a dataset from an aircraft design framework, are employed. Key outcomes from this work reveal a marked increase in computational efficiency, with a speed-up factor of approximately 500 times when utilizing surrogate models. The results indicate that the 2040 entry-into-service (EIS) scenario could achieve larger reductions in fuel and total energy consumption—20.4% and 15.8% respectively—relative to the 2030 scenario, but with higher uncertainty. Across all scenarios examined, the hybrid-electric model showcased superior performance compared to its conventional counterpart. The battery-specific energy density is proved to be a critical parameter of the aircraft’s performance across both timeframes. The findings emphasize the importance of continuous innovation in battery and motor technologies to target toward greater system-level efficiency and reduced environmental impact.