Coupled Magnetic Field-Thermal Network Analysis of Modular-Spoke-Type Permanent-Magnet Machine for Electric Motorcycle

• Published in: IEEE transactions on energy conversion Vol. 36; no. 1; pp. 120 - 130
• Main Authors: Yu, Wenfei, Hua, Wei, Qi, Ji, Zhang, Hengliang, Zhang, Gan, Xiao, Huafeng, Xu, Shuai, Ma, Guangtong
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.03.2021; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: coupled electromagnetic-thermal analysis; Electric motorcycles; Electric vehicles; Finite element analysis; Finite element method; in-wheel; lumped parameter thermal network; Mathematical models; modular; Motorcycles; Network analysis; permanent magnet machines; Permanent magnets; Rotors; Stator windings; Steel; Temperature distribution; Thermal analysis; Thermodynamic properties; Three dimensional models; Wheels
• ISSN: 0885-8969; 1558-0059
• DOI: 10.1109/TEC.2020.3006098

Brushless modular spoke type permanent magnet (MSTPM) machine has exhibited prior electromagnetic (EM) performance over conventional surface-mounted PM machines for in-wheel traction system in electric vehicles (EVs). However, analysis of thermal behavior of MSTPM machines is rather insufficient, even accurate thermal analysis is of significant importance due to poor heat dissipation condition inside the wheel hub. Conventionally, the losses produced in electrical machines by EM prediction is equivalent to the heat source in normal thermal analysis, and only the resultant thermal behavior is investigated. However, the reaction of temperature rising on the EM performance is neglected. In this article, a bi-directional coupled electromagnetic-thermal analysis method is proposed and carried out by a combination of lumped parameter thermal network (LPTN) model and finite element method (FEM), where both steady-state temperature distribution and transient-state temperature rise are investigated for MSTPM machines. By finite number of iterations between magnetic and thermal fields, the electromagnetic performance and thermal behavior can be predicted more accurately due to the coupling effect considered. The coupled model-predicted results are verified by 3D-FEM and experimental measurement, which shows that the proposed method has advantages in both computational efficiency and accuracy as well as can be applied to other electrical machines.