Electromagnetic design, sensitivity analysis, optimization and Multiphysics capability of rare-earth-free synchronous reluctance motor for electric trike vehicle

被引:0
作者
Rajini, V [1 ]
Nagarajan, V. S. [2 ]
Harikrishnan, Karunya [3 ]
Kolhe, Mohan Lal [4 ]
机构
[1] Sri Sivasubramaniya Nadar Coll Engn, Dept Elect & Elect Engn, Kalavakkam 603110, Tamil Nadu, India
[2] Sri Sivasubramaniya Nadar Coll Kalavakkam, Dept Elect & Elect Engn, Kalavakkam 603110, Tamil Nadu, India
[3] Shiv Nadar Univ, Dept Artificial Intelligence DS, Chennai 603110, Tamil Nadu, India
[4] Univ Agder, Fac Engn & Sci, POB 422, N-O4604 Kristiansand, Norway
关键词
design optimization; electric vehicle; ferrites; finite element analysis; traction motors; TRACTION MOTOR; MACHINE; EFFICIENCY; SYSTEM;
D O I
10.3934/energy.2024049
中图分类号
TE [石油、天然气工业]; TK [能源与动力工程];
学科分类号
0807 ; 0820 ;
摘要
As a part of last-mile transportation, electric three-wheelers, popularly known as trikes, are considered as an important aspect of the future of the auto industry. The appropriate choice of electric traction motor drive is imperative to the design of an electric motor power train. In this paper, six main types of sinusoidal-fed electric motor power trains-Ferrite-Assisted Synchronous Reluctance Motor (FASRM), Synchronous Reluctance Motor (SyncRelM), Ferrite-based Spoke motor, Ferrite-based Surface-Mounted Permanent Magnet Synchronous Motor (SMPMSM), Wound-Field Synchronous Motor (WFSM), and Induction Motor (IM)-are compared for their performance in electric trike applications. The permanent magnet machines presented here utilize ferrite magnets, in line with the recent trend of not using rare-earth magnets. The comprehensive comparative analysis establishes that SyncRelM, FASRM, and Spoke motors are superior in terms of overall electromagnetic, thermal, and vibration performance. This paper also focuses on an optimal design of the FASRM due to its high torque density, lower torque ripple, and minimal use of ferrite magnets. A Design of Experiments (DoE)-based statistical analysis tool is used to identify the key parameters needed for robust motor performance in the optimization step. Furthermore, an Extreme Learning Machine (ELM)-based interpolation technique is employed for estimating the performance parameters during each step of the optimization routine. A 1.2 kW FASR motor prototype is developed and tested. A comparison of Finite-Element-Analysis-based modeling results is presented along with Indian drive-cycle results for the FASRM for trike application.
引用
收藏
页码:1027 / 1053
页数:27
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