Optimizing Airport Electric Ground Support Fleets with Digital-Twin-Guided Predictive Control

Electrifying airport ground operations is a key step toward reducing aviation-related emissions, but it makes task assignment and charging coordination strongly coupled in real time. This paper develops a digital twin of airport ground handling operations for electric ground-support vehicles (EGSVs), combining an agent-based model of airport operations with live flight-schedule updates and vehicle-energy dynamics. On this platform, we compare three control strategies for EGSV dispatching and charging: a rule-based heuristic, a rolling-horizon model predictive controller (MPC), and a Monte Carlo tree search (MCTS) controller. The controllers are evaluated on three representative Schiphol Airport operating days and for multiple fleet sizes. The results show a clear trade-off between delays and energy use. Across all tested scenarios, MCTS yields the lowest average flight delay, with a pooled delay of 4.09 min, improving on the heuristic and MPC controllers by 64.7% and 31.5%, but at an increased energy consumption of 1% and 0.3%, respectively.The MPC and MCTS controllers coordinate charging more effectively throughout the day and reduce the peak charging infrastructure required by 6.0 and 4.7 charger units, respectively. Additionally, we find more vehicles can result in lower overall energy consumption and lower charging infrastructure requirements for all controllers. These findings highlight the value of digital twins not only as offline design evaluation tools, but also as practical environments for real-time control policies in electrified airport operations.