AeroPowerNet: Fixed-Wing UAV Power Consumption Estimation with an AI-Driven Hybrid Deep Learning Framework

The instantaneous power consumption of electric-powered aerial vehicles in the aviation industry is crucial for optimizing flight activities. However, devising physics-based power consumption models requires deep insight into the dynamics of an unmanned aerial vehicle (UAV). This becomes challenging due to the variability and complexity of the parameters of airspeed, altitude, and motion of the flight controls. Therefore, a power consumption model is needed to map the influence of flight parameters on power utilization during varying flight phases. This model is crucial for mission planning, optimization, and extending the endurance of UAVs. This study introduces the AeroPowerNet framework, employing a data-driven approach based on deep learning to model UAV power consumption utilizing real-world flight data, which can serve as a foundation for future integration into fixed-wing UAV flight operations. We implemented a test of four different models: RNN-LSTM, GRU, Transformer, and a hybrid model, which were trained and compared. Experimental results show that the hybrid model outperforms all other models, achieving the best performance with MAE of 3.38W, R<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> of 99.31%, and NRMSE of 0.21% for the first UAV flight, and an MAE of 7.52W, R<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> of 96.68%, and NRMSE of 1.03% for the second flight.