Remaining Flying Time Prediction of Unmanned Aerial Vehicles Under Different Load Conditions

Abstract

Unmanned aerial vehicles (UAVs) are forecast to be widely used in the military and civilian domains. The remaining flying time is a critical parameter to monitor during a flight to ensure the safety of electric UAVs (e-UAVs). However, accurate remaining flying time prediction under different load conditions requires a large amount of data and is computationally expensive for online applications. To address these issues, a deep learning approach based on temporal convolutional networks and transfer learning is developed for lithium-ion battery systems for e-UAVs. A temporal convolutional network is used to extract features from monitoring data and predict the remaining flying time of flights under one load condition. A layer transfer strategy is then used to transfer the knowledge learned from one load condition to another load condition. Battery health monitoring data collected from a fixed-wing e-UAV are used to demonstrate the effectiveness of the proposed method. Experimental results show that the proposed temporal convolutional network with the transfer learning strategy can predict the remaining flying time of the e-UAV under two load conditions more efficiently and accurately than a temporal convolutional network without transfer learning.