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.
Figures & tables
Fig. 1 : Power-velocity relationship for the baseline tilt-rotor configuration in cruise flight ( V≥85 kt), showing contributions from induced, profile, parasite, and hotel power. The available shaft power (dashed) provides margin throughout the operating envelope.
Parameter
Value
Parameter
Value
Number of rotors
6
Wing aspect ratio
10.8
Rotor diameter (m)
2.9
Max takeoff mass (kg)
2,177
Wing area (m 2 )
10.83
Battery capacity (kWh)
136
Wing airfoil geometry
NACA2418
Wing span (m)
10.66
Max RPM
800
# of blades per rotor
5
Cruise RPM
300
Cruise altitude (ft)
2,000
TABLE I : Baseline eVTOL Configuration Parameters. See [ 15 , 23 , 9 ] for additional details.
Parameter
Symbol
Value
Sector radius
Rsector
10 nm
Max-range cruise speed
Vbr
157 kt
Initial separation factor
α
0.9π≈1.60
Exit bearing range
—
[60∘,180∘]
Traffic count
N
10−60
TABLE II : Simulation Environment Parameters
Fig. 2 : A traffic-level scenario where the eVTOL aircraft (in green) are cruising, inside a 10 nm radius airspace sector, towards their exit (red dots). The circle around each aircraft represents the protected zone. (see supplementary video 2 2 2 Supplementary video (https://youtu.be/L_CJrEOIxFg) )
Fig. 3 : Modified Voltage Potential (MVP) conflict resolution geometry adapted from [ 6 ] .
Parameter
Symbol
Value
Protected zone radius
rpz
0.6 nm
Permitted speed range
—
85-185 kt
Look-ahead time
tlook
90 s
TABLE III : Conflict Detection and Resolution Parameters
Category
Features
Aircraft State
Normalized speed deviation from Vbr ; radial position from sector center; route distance; the variance of relative bearing and speed among neighbors; angle between ownship’s planned heading and mean neighbor heading
TABLE IV : Input Features for Energy Prediction Model
Fig. 4 : Architecture of the energy prediction neural network. Fully connected (FC) layers with feed-forward network (FFN) blocks and residual connections produce distributional parameters for the transformed energy overhead variable.
Component
Value
Input dimension
d0 (number of features)
Hidden dimension
128
FFN blocks
4
Activation
SiLU
Dropout
0.05
Log-variance clamp
[−8,3]
TABLE V : Model Configuration and Training Hyperparameters
Fig. 5 : Distribution of relative energy overhead by traffic density, shown in log-space to reveal structure obscured by right-skew. Dashed vertical lines indicate 95 th Percentile (P95) for each scenario. Medians cluster tightly near 1% across all densities, while P95 values spread from 4.5% to 5.3% , illustrating that density primarily affects tail behavior rather than typical energy overhead.
Fig. 6 : Fraction of aircraft detected in conflict by MVP versus conflict-free at the beginning of cruise flight, as a function of traffic density. At low densities, the majority of aircraft begin their en route flight with no active conflict detections. The crossover near N=40 coincides with the onset of heavier energy overhead tails in Fig. 5 .
N
Mean (%)
Median (%)
P90 (%)
P95 (%)
Max (%)
10
1.42
0.98
3.36
4.51
6.98
20
1.34
0.94
3.06
3.84
12.56
30
1.43
1.00
3.07
3.87
24.87
40
1.61
1.13
3.52
4.38
22.97
50
1.85
1.29
3.76
4.72
36.49
60
2.12
1.44
4.06
5.30
44.23
TABLE VI : Energy Overhead Statistics by Traffic Density. P90 and P95 represent the 90th and 95th percentiles, respectively.
Metric
Value
MAE (overall)
1.0%
MAE (conflict-free)
0.7%
MAE (conflict-involved)
1.3%
RMSE
1.8%
Pearson correlation
0.50
R2
0.22
TABLE VII : Prediction Performance on Validation Set
Metric
Nominal
Empirical
80% interval coverage
80%
96%
90% interval coverage
90%
98%
80% interval width
—
5.3%
90% interval width
—
6.8%
z -residual mean
0
0.08
z -residual std
1
0.68
TABLE VIII : Uncertainty Calibration on Validation Set
The Hong Kong University of Science and Technology (Guangzhou), China · Southern University of Science and Technology, China · Intelligent Transportation Thrust, Systems Hub, The Hong Kong University of Science and Technology (Guangzhou), China
School of Computer Science and Technology, Tianjin University, Tianjin, China · School of Computer Software, Tianjin University, Tianjin, China · School of Information Science and Engineering, Shandong Agriculture and Engineering University, Shandong, China