Battery Power Load Forecasting for Electric Vertical Take-Off and Landing Operations Based on Four-Dimensional Trajectory

Abstract

Electric vertical take-off and landing (eVTOL) aircrafts are emerging as the core enabler of urban air mobility (UAM), and their safe and efficient energy management relies on accurate knowledge of the battery power load. Existing research has focused on battery state estimation or mission-level energy modeling, leaving the second-level power demand of disturbed flight essentially unpredicted. This paper proposes a four-dimensional (4D) trajectory-based framework for eVTOL battery power forecasting. A physics-based power model first translates 4D trajectories into reference power profiles; multi-factor perturbations—three-dimensional wind, temperature, and mass—are then applied under emulated closed-loop flight control, yielding a trajectory–power database of 1,047,970 samples from 1000 simulated missions of the certified EHang 216-S multirotor, whose reference mission stays inside the certified envelope of 200 m above ground level, 30 km, and 25 min. A two-layer long short-term memory (LSTM) network maps a 10 s window of trajectory and power history, together with the flight phase and the distance remaining to the next phase boundary, to the battery power over the next 5 s. On the held-out test set it attains a root-mean-square error (RMSE) of 3.483 kW—50.9% below the physics baseline (7.089 kW) and 16.9% below a linear predictor given exactly the same inputs—with the residual tail dominated by flight-phase transitions. Ablations show that the power-history input channel is decisive, whereas network depth and residual connections matter little; extending the horizon to 10 and 15 s preserves 39.6% and 33.5% error reductions, with sub-linear growth. The framework combines physical interpretability with data-driven adaptability, offering a prospective forecasting capability for the battery management systems of electric aircraft.