An Online Energy Management Strategy Based on SOC Fluctuation Optimization for Fuel Cell UAV

Focusing on maintaining the state of charge (SOC) of the energy storage devices on fuel cell unmanned aerial vehicles (UAV), a novel hierarchical energy management strategy (EMS) is proposed to guarantee the safe and efficient operation of the fuel cell hybrid power system. Combined with the mission profile analysis, state machine-based power distribution rules and an improved Q-Learning-based power distribution algorithm were applied to non-cruising conditions and cruising conditions, respectively. After emerging the above two power distribution methods into a hierarchical one, a 1.2kW proton exchange membrane fuel cell-lithium battery UAV experimental platform was built in the laboratory. Load demand power tests indicated that the proposed EMS can realize online and accurate matching of the UAV load demand power demand. In addition, the optimization goal of minimizing energy storage level variation without online estimation of the lithium battery state of charge can be realized. Further experiments validated that the proposed strategy has better performance in load demand power matching error, PEMFC operating stress, and system overall efficiency when compared with other traditional methods. The proposed EMS can be suitable for fuel cell UAV optimization control, which can help to contribute to its rapid commercial applications.