A Data-Driven Approach for Performance Evaluation of Autonomous eVTOLs

In this article, we develop a data-driven performance-based evaluation framework for an electric vertical takeoff and landing (eVTOL) aircraft in the context of urban air mobility applications. First, a two-stage comprehensive simulation framework is developed to generate a benchmark database for the performance evaluation of both autonomous aircraft system (AAS) traffic management (AAS-TM) algorithms and high-fidelity eVTOL dynamical models. In the developed simulation framework, we implement AAS-TM algorithms and incorporate real-world constraints, e.g., vertiport infrastructures and different wind conditions. From the developed simulation framework, we generate 1 213 010 flight profiles. These flight profiles are used in a model-based eVTOL performance evaluation tool as inputs to compute the physical performance of three types of eVTOLs. Due to the high computational cost of model-based eVTOL performance evaluation approaches, a clustering-based sampling procedure is employed to reduce the redundancy in the generated flight profiles and utilize the resampled flight profiles to form an eVTOL performance analysis dataset. We then train and compare several machine learning models on the eVTOL performance analysis dataset to predict: performance variables-flight conditions, aerodynamic coefficients, aircraft electronics, and electric motor and propeller efficiencies. Finally, we deploy the proposed data-driven models in the framework and reduce the eVTOL performance inference time to real time.