Multi-objective optimal design of interior permanent magnet motors using finite element analysis

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Multi-objective optimal design of interior permanent magnet motors using finite element analysis

 

Author: Wu, Huihuan
Title: Multi-objective optimal design of interior permanent magnet motors using finite element analysis
Degree: M.Sc.
Year: 2016
Subject: Permanent magnet motors -- Design.
Electric vehicles.
Hong Kong Polytechnic University -- Dissertations
Department: Faculty of Engineering
Pages: xi, 71 pages : color illustrations
Language: English
InnoPac Record: http://library.polyu.edu.hk/record=b2952764
URI: http://theses.lib.polyu.edu.hk/handle/200/8899
Abstract: Electricity revolution created a modern information society. With the increasing popularity of electric power in the industrial, agricultural and residential, how to save energy and reduce the impact of human activities on the natural environment has gradually become a focus of scientists and the public in recent years. Electric motors, as one type of the most electrical appliances all over the world, are extremely necessary to improve their efficiency, thereby reducing energy consumption. In recent decades, an interior permanent magnet (IPM) motor, due to its high efficiency, high torque and high flux weakening capacity, has arouse more and more public attention in the market. Therefore, this dissertation began a development paths that included a literature review, development and utilization of parametric models of interior permanent magnet (IPM) motors, verification of the modeling methodology, development of an IPM motor optimal design and performance analyses for candidate models. In this dissertation, a multi-objective design optimization method combining with large-scale parallel computing system is presented. Several multi-objective genetic algorithms were tested and non-dominate sorting genetic algorithm 2 (NSGA2) was chosen as optimal algorithm to search a set of optimized solutions. Furthermore, dramatically reducing computational time was another breakthrough of utilizing the optimal design. There are two barriers that influence the computational time, evaluation process in genetic algorithm and meshing generation in finite element analysis. To accelerate the performance evaluation process, a parallel framework was developed for acceleration. Also, different settings of mesh generation of finite element analysis for IPM motor were tested and compared separately and an optimized setting was found in the end.

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