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
State-of-charge (SoC) estimation for electric vertical take-off and landing (eVTOL) aircraft must run on the vehicle under hard energy and certification budgets, on a duty cycle unlike anything in the automotive literature. In this study an event-driven spiking network, the state-estimation core of the nBMS neuromorphic battery management architecture, is designed for per-timestep SoC estimation and evaluated on a public 22-cell eVTOL dataset with an automotive cross-check. A delta and population encoder, a second-order sigma-delta spiking layer, and a rate-accumulator readout hold 34{,}433 int16 parameters with zero dense multiply-accumulate (MAC) operations. The estimator reaches 2.45% root-mean-square error (RMSE) against 1.74% for a tuned long short-term memory (LSTM) baseline, a gap characterized as a temporal-mixing limit in cruise; in exchange it degrades 1.6 times slower than an adaptive leaky integrate-and-fire control under sensor noise and needs roughly 3{,}500 additions per step where the LSTM needs 67{,}700 multiply-accumulates. The full core is deployed on a low-cost automotive-qualified Artix-7 field-programmable gate array at 87% block-RAM utilization, meets timing at 50MHz, and reproduces the frozen fixed-point reference bit-exactly over a real 491-step flight segment on silicon. Fully annotated post-implementation analysis gives 1.30μJ per inference, an average of 0.65~μW at the 0.5~Hz mission cadence.
İsmail Can Dikmen
Department of Electrical and Electronics Engineering Istinye University, Istanbul, Turkiye
Energy feasibility under wind uncertainty is a critical safety issue for low-altitude air-ground delivery. In truck-UAV systems, UAVs complete assigned deliveries and safely return to a mobile truck or depot, while wind-induced propulsion costs vary online and are only partially observable. Existing routing methods often rely on static or deterministic energy models, which may underestimate headwind, crosswind, battery-voltage, and return-feasibility risks. This paper proposes Energy-Aware Wind-Resilient Routing (EWR), an online risk-sensitive planning framework for wind-aware and energy-safe UAV routing. The delivery environment is represented as a time-dependent directed energy graph whose edge costs are updated using delayed noisy wind estimates, payload states, and conservative uncertainty margins. Experiments using synthetic delivery graphs with replayed wind logs from a public truck-UAV delivery dataset show that EWR improves mission success rates and reduces wind-induced return failures.
Tianshun Li, Yanggang Sheng, Hongliang Lu +3
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
A UAV mobile edge computing (MEC) fleet trades energy against delay, and its schedules form a Pareto front; we call a scheduler operable when the fleet can be asked for any point on that front at run time. We propose PrefDT, to the best of our knowledge the first preference-conditioned Decision Transformer for the problem of joint trajectory, association and offloading scheduling. Its idea comes from language modeling: we hand the model the desired trade-off as an input, such that a single model only needs to be trained once offline to return any desired point on the curve in one rollout. The fleet's state is summarized by attention pooling with a per-user bypass, so the scheduler keeps working when user reports are lost. The energy target is a running budget decremented by what the fleet actually spends. As a result, when wind or load pushes consumption off the plan, the policy can track the difference and hold its budget. Because no corpus of preference-labeled flights exists, we design a distillation pipeline and build the corpus by ourselves. In simulation against 26 method variants, PrefDT produces the best trade-off curve of any learned method and holds its energy budget to within 0.6% when propulsion cost rises by half in mid-flight.
Qiao Liao, Zhiyong Feng, Bin Wu +1
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