Energy Demand Analysis for eVTOLs in Cluttered and Dynamic Environments based on Adaptive Trajectory Prediction

Abstract

The increasing interest in passenger transportation by electric vertical take-off and landing aircraft (eVTOLs) in urban airspace has led to the need for further development of realistic energy demand planning for electric drives, considering non-nominal flight scenarios. This gap poses a critical challenge to efficient air traffic management and can lead to operational complications. These complications include emergency procedures, identifying alternative landing zones, considering regulatory nofly zones, and managing extended hover phases to allow for the passage of moving obstacles such as aircraft or flocks of birds. In response to this problem, the study presented here introduces a path planning algorithm based on model predictive control in which an eVTOL operates in cluttered, dynamic, and threedimensional urban environments. The energy demand of the eVTOL was analyzed and simulated based on various scenarios, focusing on the specific use case for an Airport Shuttle service at Frankfurt Airport. In this study, it was found that for an eVTOL, especially a quadcopter configuration with space for four passengers, every additional minute of hovering can increase the energy demand by up to 3.64 kWh/min, and each additional flight kilometer leads to an increase in energy demand of up to 3.0 kWh/km. The results showed that while the path planning algorithm can generate safe paths for the eVTOL, a realistic mapping of the required energy reserves in the context of the regulatory framework is needed to ensure safe and sustainable air traffic management considering energy demand based on physical principles.