Ground simulation of fuel cell/battery hybrid propulsion system for small unmanned air vehicles

Purpose The endurance of small unmanned air vehicles (UAVs) is directly associated with the energy density of the propulsion system used. As the batteries commonly used in small UAVs have a relatively low energy density, they are not sufficient for long-term endurance tasks. The purpose of this paper is to offer a solution to increase the endurance of a concept small UAV with combination of different power sources. The design, construction and ground tests of fuel cell-powered hybrid propulsion systems are presented in this paper. Design/methodology/approach The power requirements of a concept UAV were calculated according to aerodynamic calculations and then, hybrid propulsion system sources are determined. The hybrid system consists of a 100 W scale proton-exchange membrane (PEM) type fuel cell stack, lithium-polymer battery, solar cells and power management system (PMS). Subsequently, this hybrid power system was integrated with the new design of PMS and then series of ground tests were carried out. Findings This experimental study proved that it is theoretically possible to obtain an endurance of around 3 h for concept UAV with the proposed hybrid system. Practical implications The research study shows that fuel cell-based hybrid propulsion system with the proposed PMS can be widely used to obtain extended endurance in small UAVs. Originality/value A hybrid propulsion system with a novel PMS unit is proposed for small UAVs and the ground tests were implemented.

Hear-and-avoid for unmanned air vehicles using convolutional neural networks

To investigate how an unmanned air vehicle can detect manned aircraft with a single microphone, an audio data set is created in which unmanned air vehicle ego-sound and recorded aircraft sound are mixed together. A convolutional neural network is used to perform air traffic detection. Due to restrictions on flying unmanned air vehicles close to aircraft, the data set has to be artificially produced, so the unmanned air vehicle sound is captured separately from the aircraft sound. They are then mixed with unmanned air vehicle recordings, during which labels are given indicating whether the mixed recording contains aircraft audio or not. The model is a convolutional neural network that uses the features Mel frequency cepstral coefficient, spectrogram or Mel spectrogram as input. For each feature, the effect of unmanned air vehicle/aircraft amplitude ratio, the type of labeling, the window length and the addition of third party aircraft sound database recordings are explored. The results show that the best performance is achieved using the Mel spectrogram feature. The performance increases when the unmanned air vehicle/aircraft amplitude ratio is decreased, when the time window is increased or when the data set is extended with aircraft audio recordings from a third party sound database. Although the currently presented approach has a number of false positives and false negatives that is still too high for real-world application, this study indicates multiple paths forward that can lead to an interesting performance. Finally, the data set is provided as open access.