• CN: 11-2187/TH
  • ISSN: 0577-6686

Journal of Mechanical Engineering ›› 2023, Vol. 59 ›› Issue (8): 181-195.doi: 10.3901/JME.2023.08.181

Previous Articles     Next Articles

Design Method of High Efficiency Lightweight Permanent Magnet Synchronous Motor for Electric Propulsion

ZHANG Xin-tong, ZHANG Cheng-ming, LI Li-yi, FU Peng-rui   

  1. School of Electrical Engineering and Automation, Harbin Institute of Technology, Harbin 150001
  • Received:2022-03-26 Revised:2022-11-20 Online:2023-04-20 Published:2023-06-16

Abstract: Stratospheric aircraft can only rely on batteries to obtain energy at night, so its propulsion motor is required to have the characteristics of high efficiency and high torque density. To meet this requirement, a design method of high efficiency lightweight permanent magnet synchronous motor for electric propulsion is proposed. According to the propeller load of the motor, the strength and stiffness analytical model of lightweight structure for external rotor surface-mounted permanent magnet synchronous motor(ERSPMSM) is established. Based on the equivalent magnetic network, the electromagnetic model of ERSPMSM considering the influence of high-frequency current harmonics is established, and combined with the analytical model of lightweight structure, the electromagnetic-structural analytical model of ERSPMSM is obtained. Based on the model, the multi-objective optimization of motor efficiency and overall mass is carried out and the differential evolution algorithm is used to obtain the Pareto front. The correlations between design parameters and optimization objectives are analyzed. A solution in the Pareto front is selected as the motor scheme,and the validity of the proposed design method is verified by finite element analysis and prototype experiment. The designed 10kW prototype has a mass of 25.7 kg, a torque density of 10N·m/kg and a rated efficiency of 94%.

Key words: external rotor surface-mounted permanent magnet synchronous motor, lightweight structure, efficiency, overall mass, multi-objective optimization, differential evolution algorithm

CLC Number: