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Electric Take-Off and Landing (eVTOL) aircraft is considered as the majoraircraft type in the emerging urban air mobility. Accurate power consumptionestimation is crucial to eVTOL, supporting advanced power management strategiesand improving the efficiency and safety performance of flight operations. Inthis study, a framework for power consumption modeling of eVTOL aircraft wasestablished. We employed an ensemble learning method, namely stacking, todevelop a data-driven model using flight records of three different types ofquadrotors. Random forest and extreme gradient boosting, showing advantages inprediction, were chosen as base-models, and a linear regression model was usedas the meta-model. The established stacking model can accurately estimate thepower of a quadrotor. Error analysis shows that about 80% prediction errorsfall within one standard deviation interval and less than 0.5% error in theprediction for an entire flight can be expected with a confidence of more than80%. Our model outperforms the existing models in two aspects: firstly, ourmodel has a better prediction performance, and secondly, our model is moredata-efficient, requiring a much smaller dataset. Our model provides a powerfultool for operators of eVTOL aircraft in mission management and contributes topromoting safe and energy-efficient urban air traffic.

Data-Efficient Modeling for Precise Power Consumption Estimation of Quadrotor Operations Using Ensemble Learning