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

The increasing interest in passenger transportation \nby electric vertical take-off and landing aircraft (eVTOLs) in \nurban airspace has led to the need for further development of \nrealistic energy demand planning for electric drives, considering \nnon-nominal flight scenarios. This gap poses a critical challenge to \nefficient air traffic management and can lead to operational complications. These complications include emergency procedures, \nidentifying alternative landing zones, considering regulatory nofly zones, and managing extended hover phases to allow for the \npassage of moving obstacles such as aircraft or flocks of birds. \nIn response to this problem, the study presented here introduces \na path planning algorithm based on model predictive control \nin which an eVTOL operates in cluttered, dynamic, and threedimensional urban environments. The energy demand of the \neVTOL was analyzed and simulated based on various scenarios, \nfocusing on the specific use case for an Airport Shuttle service \nat Frankfurt Airport. In this study, it was found that for an \neVTOL, especially a quadcopter configuration with space for \nfour passengers, every additional minute of hovering can increase \nthe energy demand by up to 3.64 kWh/min, and each additional \nflight kilometer leads to an increase in energy demand of up to \n3.0 kWh/km. The results showed that while the path planning \nalgorithm can generate safe paths for the eVTOL, a realistic \nmapping of the required energy reserves in the context of the \nregulatory framework is needed to ensure safe and sustainable air \ntraffic management considering energy demand based on physical \nprinciples.