Artificial neural network control strategies for fuel cell hybrid system - Oheda, Hakim (Cranfield University, 2013)
The greening of air transport is the driver for developing technologies to reduce the environmental impact of aviation with the aim of halving the amount of carbon dioxide (COଶ) emitted by air transport, cutting specific emissions of nitrogen oxides (NO୶) by 80% and halving perceived noise by the year 2020. Fuel Cells (FC) play an important role in the new power generation field as inherently clean, efficient and reliable source of power especially when comparing with the traditional fossil-fuel based technologies. The project investigates the feasibility of using an electric hybrid system consisting of a fuel cell and battery to power a small model aircraft (PiperCub J3). In order to meet the desired power requirements at different phases of flight efficiently, a simulation model of the complete system was first developed, consisting of a Proton Exchange Membrane hybrid fuel cell system, 6DoF aircraft model and neural network based controller. The system was then integrated in one simulation environment to run in real-time and finally was also tested in hardware-in-the-loop with real-time control. The control strategy developed is based on a neural network model identification technique; specifically Model Reference Control (MRC), since neural network is well suited to nonlinear systems. To meet the power demands at different phases of flight, the controller controls the battery current and rate of charging/discharging. Three case studies were used to validate and assess the performance of the hybrid system: battery fully charged (high SOC), worst case scenario and taking into account the external factors such as wind speeds and wind direction. In addition, the performance of the Artificial Neural Network Controller was compared to that of a Fuzzy Logic controller. In all cases the fuel cell act as the main power source for the PiperCub J3 aircraft. The tests were carried-out in both simulation and hardware-in-the-loop.
Keywords: PEM Fuel cell; Modelling and Simulation PEM; Dynamic performance; Transient response; Artificial Neural Network; Fuzzy Logic; Hybrid systems