Power management of hybrid fuel cell fixed wing UAVs using a fuzzy reinforcement learning system optimized with...

Unmanned aerial vehicles (UAVs) are increasingly being powered by fuel cells, which provide a zero-emission green energy source, improve endurance, and reduce charging/refuelling times. These gadgets are used for anything from aerial photography to military activities. By tackling two problems-power management (PM) of the resources and design optimization (DO) of a hybrid electric source (HES) made up of a fuel cell (FC) and a battery-this study seeks to increase endurance and energy efficiency. It is designed for a fixed-wing electric unmanned aerial vehicle (EUAV) and uses a novel method to lighten the drone. For hybrid electric drones, energy management techniques are crucial. Using fuzzy logic-based programming and Multi-Factor Reinforcement Learning (MFRL), we will apply a reinforcement learning system to regulate the drone's fuel consumption between the fuel cell and the battery. The Harris Hawk Optimization (HHO) algorithm is used by DO to determine the fuel cell and battery's maximum power and capacity in order to reduce resource use. In order to choose the best management system, this PMS will use the HHO method to optimize the MFRL parameters and membership functions in the fuzzy logic structure. We have considered the uncertainty that governs the drone's mobility and the effect of variations in wind speed in order to produce a realistic model. In the fuzzy system, we have also incorporated the wind speed variable for the energy management problem. Through the use of a modelling platform that integrates the UAV and hybrid power system models with a Matlab tool, the proposed method is assessed and yields an 8% weight reduction, saving a total of 70.43 kJ of energy, which can extend the "endurance phase" by more than 30 min. Additionally, the proposed method reduces the amplitude of SoC fluctuations, saving up to 40% of FC energy and enabling the UAV to operate for longer missions.