ICNS 2026 · Herndon, Virginia

eVTOL Aircraft Energy Consumption Estimation with Conflict Resolution in High-Density Airspaces

What tactical deconfliction costs an electric aircraft, and how much reserve to plan for

Alex Zongo, Peng Wei

Intelligent Aerospace Systems Lab · The George Washington University

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How much energy does staying separated cost?

Every deviation from a nominal path draws on a limited battery. We couple an eVTOL power model with MVP conflict resolution in high-density traffic simulation, measure the energy overhead across about 72,000 en-route transits, and learn per-flight reserve bounds before the flight starts.

Energy cost of tactical deconfliction for an eVTOL (schematic)An eVTOL flying between two vertiports deviates around crossing traffic. A bar chart accumulates energy: the deconfliction maneuver adds a small overhead to the nominal energy. V V deconfliction maneuver energy used nominal flown Median overhead < 1.5% · 95th percentile 3.8–5.3% ~72k en-route sections, 10–60 aircraft (ICNS 2026)
Schematic. A deconfliction maneuver adds a small energy overhead to the nominal energy of the cruise segment; statistics from the study.
median energy overhead, all densities
< 1.5%
95th-percentile overhead
3.8–5.3%
near mid-air collisions in ~70k simulated flights
0

BlueSky simulation of a 10 nm sector at 2,000 ft with 10–60 aircraft and a Joby S4-class tilt-rotor power model. The power model uses published parameters and is not validated against flight-test data.

01 / The question

Flight plans assume nominal paths. Traffic does not.

Advanced air mobility will put dozens of electric aircraft in the same airspace, so tactical deconfliction is unavoidable. Energy density leaves little margin: too small a reserve is a safety risk, too large a reserve slows operations. Energy models and conflict resolution have advanced separately, but their intersection, the energy cost of resolving conflicts, had not been quantified.

MVP conflict resolution and the energy-overhead metricLeft: two aircraft are predicted to pass inside the protected zone at the closest point of approach. MVP applies a minimal velocity change to each aircraft, moving the closest point of approach to the zone boundary. Right: the flown path deviates from the nominal path, and the energy overhead is the relative difference in energy. Modified Voltage Potential (MVP) look-ahead 90 s · protected zone 0.6 nm · speed 85–185 kt Δv predicted CPA inside the zone minimal change: CPA on the boundary Energy overhead relative to the conflict-free cruise segment eb nominal ea flown δE = (ea − eb) / eb
MVP resolves a predicted conflict with the minimal velocity change that moves the closest point of approach to the protected-zone boundary. The energy overhead compares the flown and nominal cruise segments.

02 / Framework

Power model, deconfliction, and traffic in one loop.

eVTOL power model

Joby S4-class tilt-rotor: induced, profile, and parasite power plus hotel loads, over the cruise envelope (≥ 85 kt).

MVP deconfliction

Decentralized, deterministic, and auditable. Conflicts detected 90 s ahead against a 0.6 nm protected zone.

Traffic simulation

BlueSky, 10 nm sector at 2,000 ft, 10 to 60 simultaneous aircraft, about 71,767 transits.

03 / Empirical characterization

Cheap on average, heavy in the tail.

MVP-based deconfliction is energy-efficient: the median overhead stays below 1.5% at every density, and most transits pay a negligible penalty. The distribution is strongly right-skewed, however, with tail cases reaching 44% at the highest densities. Around 40 aircraft, most flights begin cruise already in conflict, and overlapping conflicts cascade. A 4–5% reserve covers about 95% of tactical scenarios.

Summary statistics of energy overhead
StatisticValue
Median overhead (all densities)< 1.5%
95th percentile (by density)3.8% to 5.3%
Largest observed overhead≈ 44% (highest densities)
Conflict-free at cruise start, N < 4055% to 87% of flights

From the paper and the ICNS 2026 presentation.

04 / Uncertainty-aware prediction

From one blanket margin to per-flight reserves.

At mission start the traffic state is known, but how conflicts will evolve is not, so a point prediction is unreliable. The model instead predicts the parameters of a log-normal distribution of overhead from pre-flight features (speed deviation, position, route distance, conflict severity, initial MVP resolution, and sector congestion) and reads reserve margins from its upper quantiles. The bounds are conservative: actual outcomes fall inside them more often than the stated confidence level, which is the desirable direction for safety-critical planning.

Per-flight energy-overhead bounds from pre-flight featuresThe model predicts a log-normal distribution of energy overhead for each flight. A flight entering a quiet sector with no initial conflicts gets a 90% upper bound of about 3%; a flight entering a congested sector with multiple initial conflicts gets about 7%. 0%2%4%6%8%10%12% energy overhead δE 90% bound ≈ 3% quiet sector, no initial conflicts 90% bound ≈ 7% congested sector, multiple initial conflicts z = log(1 + δE) ~ 𝒩(μz, σz²) δEq = exp(μz + σz Φ⁻¹(q)) − 1 Illustrative distributions matched to the reported example bounds.
Two flights in different traffic conditions receive different 90% reserve bounds (about 3% and 7% in the presented examples).
Prediction performance
MetricValue
Mean absolute error1.0%
Root mean squared error1.8%
80% interval width5.3%
90% interval width6.8%

From the ICNS 2026 presentation.

05 / Limits and next steps

Cruise-only, MVP-specific, simulation-based.

Results cover cruise at constant altitude and the MVP algorithm; transitions near vertiports and other resolution methods may behave differently. Follow-up work extends the power model to the full flight envelope (SciTech 2027) and moves from pre-flight to in-flight prediction.

Reference

Cite this work

Zongo, A., & Wei, P. (2026). "eVTOL Aircraft Energy Consumption Estimation with Conflict Resolution in High-Density Airspaces." 2026 Integrated Communications, Navigation and Surveillance Conference (ICNS).

BibTeX 
@misc{zongo2026evtol,
  title         = {eVTOL Aircraft Energy Overhead Estimation under Conflict Resolution
                   in High-Density Airspaces},
  author        = {Alex Zongo and Peng Wei},
  year          = {2026},
  eprint        = {2604.06093},
  archivePrefix = {arXiv}
}

Acknowledgement. This work was supported by NASA under grant 80NSSC23M0059.