eVTOL Aircraft Energy Overhead Estimation under Conflict Resolution in High-Density Airspaces

TL;DR

This study quantifies the energy overhead of conflict resolution in eVTOL aircraft using the MVP algorithm, demonstrating it is generally energy-efficient with a machine learning model to estimate overhead for operational planning.

Contribution

It provides a systematic analysis of energy costs for conflict resolution in high-density airspace and introduces a machine learning model for energy overhead estimation.

Findings

Median energy overhead remains below 1.5% across densities.

Tail cases can reach up to 44% overhead at high densities.

A machine learning model with conservative bounds estimates energy overhead effectively.

Abstract

Electric vertical takeoff and landing (eVTOL) aircraft operating in high-density urban airspace must maintain safe separation through tactical conflict resolution, yet the energy cost of such maneuvers has not been systematically quantified. This paper investigates how conflict-resolution maneuvers under the Modified Voltage Potential (MVP) algorithm affect eVTOL energy consumption. Using a physics-based power model integrated within a traffic simulation, we analyze approximately 71,767 en route sections within a sector, across traffic densities of 10-60 simultaneous aircraft. The main finding is that MVP-based deconfliction is energy-efficient: median energy overhead remains below 1.5% across all density levels, and the majority of en route flights within the sector incur negligible penalty. However, the distribution exhibits pronounced right-skewness, with tail cases reaching 44% overhead at the highest densities due to sustained multi-aircraft conflicts. The 95th percentile ranges from 3.84% to 5.3%, suggesting that a 4-5% reserve margin accommodates the vast majority of tactical deconfliction scenarios. To support operational planning, we develop a machine learning model that estimates energy overhead at mission initiation. Because conflict outcomes depend on future traffic interactions that cannot be known in advance, the model provides both point estimates and uncertainty bounds. These bounds are conservative; actual outcomes fall within the predicted range more often than the stated confidence level, making them suitable for safety-critical reserve planning. Together, these results validate MVP's suitability for energy-constrained eVTOL operations and provide quantitative guidance for reserve energy determination in Advanced Air Mobility.