A method to analyze and optimize hybrid electric architectures applied to unmanned aerial vehicles

This paper addressed some critical issues in the development of hybrid electric powertrains for aircraft and propose a design methodology based on multi-objective optimization algorithms and mission-based simulations.

Scalable models were used for the main components of the powertrain, namely, the (two stroke diesel) engine, the (lithium) batteries and the (permanent magnet) motor. The optimization was performed with the NSGA-II genetic algorithm coupled with an in-house MATLAB tool. The input parameters were the size of engine, the hybridization degree and the specification of the battery (typology, nominal capacity, bus voltage, etc.). The outputs were electric endurance, additional volume, performance parameters and fuel consumption over a specified mission.

Electric endurance was below 30 min in the two test cases (unmanned aerial vehicles [UAVs]) but, thanks to the recharging of the batteries on-board, the total electric time was higher. Fuel consumption was very high for the largest UAV, while an improvement of 11 per cent with respect to a conventional configuration was obtained for the smallest one.

The research used a simplified approach for flight mechanics. Some components were not sized in the proposed test cases.

The results of the test cases stressed the importance of improving energy density and power density of the electric path.

The proposed methodology is aimed at minimizing the environmental impact of aircraft.

The proposed methodology was obtained from the automotive field with several original contributions to account for the aircraft application.