Thermal Analysis of the Yokeless and Segmented Armature Axial Flux In-wheel Motor

Li, Tao; Zhang, Youtong; Liang, Yuxiu; Ai, Qiang; Dou, Haishi

doi:10.1109/icaice51518.2020.00094

Cited by 3 publications

(1 citation statement)

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“…Electric vehicles driven by four in-wheel electrical machines make the traction system more simplified and flexible [1]. In the limited volume, the yokeless and segmented armature (YASA) axial flux machine is capable of producing larger torque compared with radial flux machines [2][3][4][5]. The yokeless segmented stator core and the centralized shortdistance windings make it more efficient and give it a higher power density [6,7].…”

Section: Introductionmentioning

confidence: 99%

Thermal Model Approach to the YASA Machine for In-Wheel Traction Applications

Wang¹,

Wang²,

Gao

et al. 2022

Energies

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The axial-flux permanent magnet (AFPM) machines with yokeless and segmented armature (YASA) topology are suitable for in-wheel traction systems due to the high power density and efficiency. To guarantee the reliable operation of the YASA machines, an accurate thermal analysis should be undertaken in detail during the electrical machine design phase. The technical contribution of this paper is to establish a detailed thermal analysis model of the YASA machine by the lumped parameter thermal network (LPTN) method. Compared with the computational fluid dynamics (CFD) method and the finite element (FE) method, the LPTN method can obtain an accurate temperature distribution with low time consumption. Firstly, the LPTN model of each component of the YASA machine is constructed with technical details. Secondly, the losses of the YASA machine are obtained by the electromagnetic FE analysis. Then, the temperature distribution of the machine can be calculated by the LPTN model and loss information. Finally, a prototype of the YASA machine is manufactured and its temperature distribution under different operating conditions is tested by TT-K-30 thermocouple temperature sensors. The experimental data matches the LPTN results well.

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