Intelligent energy management for solar-powered unmanned aerial vehicle using multi-objective genetic algorithm

•Intelligent energy management for solar-powered UAVs using GA was proposed.•Details of complex energy flow model in solar-powered UAVs were considered.•Complex factors on energy distribution and flight trajectories were analyzed.•Optimal design condition for energy saving in solar-powered UAVs was identified. Comprehensive energy efficiency is the primary factor that determines the high-cruise endurance of solar-powered unmanned aerial vehicles (UAVs). In this study, a complete simulated environment of a solar-powered UAV for multi-objective genetic algorithm, including the solar radiation model, photovoltaic cell model coupling the attitude angle and thermal effect, battery model, propeller model, and dynamics and kinematic model, is proposed. The solar energy captured by the UAVs, remaining charge of the battery, and electricity released by the battery are treated as multi-objective functions. The energy distribution and flight trajectories are analyzed. The results show that the maximum energy output of the photovoltaic cells, output electricity released by the battery, and state of charge in the battery receive 5.79, –7.00, and 10.94 %, respectively, for the optimal design condition compared with those in the basic flight condition obtained by using traditional optimization method. Additionally, the solar-powered UAVs realize permanent flight for optimal design conditions. Thus, the results of this study can help design a solar-powered UAV with high comprehensive energy efficiency.