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.

翻译：在高密度城市空域中运行的电动垂直起降（eVTOL）飞行器必须通过战术冲突解决来维持安全间隔，然而此类机动行为的能量成本尚未被系统量化。本文研究了基于修正电势（MVP）算法的冲突解决机动对eVTOL能量消耗的影响。通过将基于物理的功率模型集成到交通仿真中，我们分析了扇区内约71,767个航路段，覆盖10-60架同时运行飞行器的交通密度。主要发现是：基于MVP的冲突解除具有能量效率优势——在所有密度水平下，能量开销中位数低于1.5%，扇区内绝大多数航路飞行几乎无额外能量代价。然而，分布呈现显著右偏特征：在高密度情境下，由于持续的多机冲突，尾部案例可达44%的开销。第95百分位数范围为3.84%-5.3%，表明4-5%的储备裕度可覆盖绝大多数战术冲突解决场景。为支持运行规划，我们开发了机器学习模型，可在任务起始阶段预估能量开销。由于冲突结果取决于无法预先获知的未来交通交互，该模型同时提供点估计值与不确定性边界。这些边界具有保守性：实际结果落在预测范围内的频率高于标称置信水平，使其适用于安全关键的储备规划。综上，这些结果验证了MVP在能量受限的eVTOL运行中的适用性，并为先进空中交通的储备能量确定提供了量化指导。