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Multi-Objective Optimal Design of Permanent Magnet Synchronous Motor for High Efficiency and High Dynamic Performance
Author(s) -
Guo Hong,
Tian Wei,
Xiaofeng Ding
Publication year - 2018
Publication title -
ieee access
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.587
H-Index - 127
ISSN - 2169-3536
DOI - 10.1109/access.2018.2828802
Subject(s) - aerospace , bioengineering , communication, networking and broadcast technologies , components, circuits, devices and systems , computing and processing , engineered materials, dielectrics and plasmas , engineering profession , fields, waves and electromagnetics , general topics for engineers , geoscience , nuclear engineering , photonics and electrooptics , power, energy and industry applications , robotics and control systems , signal processing and analysis , transportation
There is a strong demand for the research of electric vehicles (EVs) in automotive industry, because of an increased concern of the energy depletion and environmental pollution problems caused by oil-fueled automotive. The traction motor drive system is one of the core components of EVs, and a motor with superior dynamic performance and high efficiency could significantly reduce energy consumption and improve riding comfort of EVs. Therefore, in order to achieve high dynamic performance and high efficiency of permanent magnet synchronous motor (PMSM), a multi-objective optimization design method for PMSM based on the artificial bee colony algorithm was proposed in this paper. First, based on the magnetic field analytical model of PMSM, the analytical expressions of the key parameters were deduced, namely, mechanical time constant and electrical time constant. Second, the efficiency and electrical and mechanical time constant were defined as optimization objectives. Third, the efficiency and dynamic performance of the original motor and optimized motor were compared applying the finite-element analysis. Furthermore, one prototype machine was manufactured according to the results of optimization. The dynamic performance and efficiency of the prototype had been tested. The experiments show confident results that the efficiency increased about 1% and the mechanical time constant reduced to 31.4% of initial value.

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