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.

中文翻译：

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电动摩托车模幅式永磁电机的磁场热网络耦合分析

与用于电动汽车（EV）的轮内牵引系统的传统表面安装式PM机器相比，无刷模块化轮辐式永磁（MSTPM）机器具有先验的电磁（EM）性能。但是，MSTPM机器的热性能分析还远远不够，由于轮毂内部的散热条件不佳，即使是精确的热分析也非常重要。常规上，通过EM预测在电机中产生的损耗与正常热分析中的热源等效，并且仅研究所得的热行为。然而，温度升高对EM性能的反应被忽略了。在本文中，提出了一种集总参数热网络模型和有限元方法相结合的双向耦合电磁热分析方法，研究了稳态温度分布和瞬态温度升高两方面。用于MSTPM机器。通过磁场和热场之间的有限次迭代，由于考虑了耦合效应，可以更准确地预测电磁性能和热行为。通过3D-FEM和实验测量验证了模型预测结果的耦合性，表明所提出的方法在计算效率和准确性方面均具有优势，可应用于其他电机。对于MSTPM机器，研究了稳态温度分布和瞬态温度上升。通过磁场和热场之间的有限次迭代，由于考虑了耦合效应，可以更准确地预测电磁性能和热行为。通过3D-FEM和实验测量验证了模型预测结果的耦合性，表明所提出的方法在计算效率和准确性方面均具有优势，可应用于其他电机。对于MSTPM机器，研究了稳态温度分布和瞬态温度上升。通过磁场和热场之间的有限次迭代，由于考虑了耦合效应，可以更准确地预测电磁性能和热行为。通过3D-FEM和实验测量验证了模型预测结果的耦合性，表明所提出的方法在计算效率和准确性方面均具有优势，可应用于其他电机。