Multiobjective Optimization Method to Maximize Power Density and Efficiency of an Electric Propulsion Motor in UAV Applications

This article proposes a new multiobjective optimization method to maximize the power density and efficiency of an electric propulsion motor in unmanned aerial vehicle (UAV) applications. The high power density permanent magnet electric propulsion motor (PM-EPM) topology is firstly proposed with the multilayer wavy heat dissipation structure to improve the motor cooling performance. Then the electromagnetic-thermal analytic model of the PM-EPM is proposed, which can accurately predict the electromagnetic and thermal performance under various conditions. To enhance the global optimization performance, a novel multiobjective dynamic neighborhood genetic learning strategy based on particle swarm optimization (MODNGL-PSO) method is proposed to optimize the power density and efficiency of the PM-EPM, which has low computation burden, high optimization efficiency, and global optimization performance. Finally, a 20 kW 3000 r/min PM-EPM is designed and manufactured. The experimental results show that the proposed motor has excellent electromagnetic performance with a rated power density of 2.25 kW/kg and an efficiency of 95.52%.