Geometry Analysis and Optimization of PM-Assisted Reluctance Motors

Wang, Yawei; Bacco, Giacomo; Bianchi, Nicola

doi:10.1109/tia.2017.2702111

Cited by 75 publications

(32 citation statements)

References 15 publications

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“…For optimization algorithms, there are two main types, classic and modern intelligent algorithms. Regarding the optimization methods or strategies, there are many options as well, such as multi-level and multi-disciplinary [2,7,[24][25][26][27][28][29][30][31][32][33][34][35]. After the optimization, compare all optimal designs and output the best one with detailed design scheme and the simulated performances.…”

Section: An Overview Of Design and Optimization Of Electrical Machinesmentioning

confidence: 99%

A Review of Design Optimization Methods for Electrical Machines

et al. 2017

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Electrical machines are the hearts of many appliances, industrial equipment and systems. In the context of global sustainability, they must fulfill various requirements, not only physically and technologically but also environmentally. Therefore, their design optimization process becomes more and more complex as more engineering disciplines/domains and constraints are involved, such as electromagnetics, structural mechanics and heat transfer. This paper aims to present a review of the design optimization methods for electrical machines, including design analysis methods and models, optimization models, algorithms and methods/strategies. Several efficient optimization methods/strategies are highlighted with comments, including surrogate-model based and multi-level optimization methods. In addition, two promising and challenging topics in both academic and industrial communities are discussed, and two novel optimization methods are introduced for advanced design optimization of electrical machines. First, a system-level design optimization method is introduced for the development of advanced electric drive systems. Second, a robust design optimization method based on the design for six-sigma technique is introduced for high-quality manufacturing of electrical machines in production. Meanwhile, a proposal is presented for the development of a robust design optimization service based on industrial big data and cloud computing services. Finally, five future directions are proposed, including smart design optimization method for future intelligent design and production of electrical machines.

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Section: An Overview Of Design and Optimization Of Electrical Machinesmentioning

confidence: 99%

A Review of Design Optimization Methods for Electrical Machines

et al. 2017

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“…The motor capability within the flux-weakening region is related with the magnet contribution [33,34]. It means that the magnets can be or not be inserted in all the barriers, depending on the requirements.…”

Section: New Trends In Electrical Vehicle Powertrainsmentioning

confidence: 99%

Reliable Design of PMaSynRM

López-Torres¹,

Garcia-Espinosa²,

Riba³

2019

New Trends in Electrical Vehicle Powertrains

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Electric vehicles require highly reliable and resilient electric motors, due to the harsh operating conditions they must withstand. To this end, there is a current trend to design rareearth-free machines. Traction electric motors must be optimized in terms of efficiency, torque density, power factor, constant power speed ratio, and cost. Although different technologies are available, permanent magnet assisted synchronous reluctance motors (PMa-SynRM) are promising candidates for such applications. Nowadays, the optimal design process of electrical motors is based on finite element method (FEM) simulations. However, it is very time consuming with a heavy computational burden process, so in order to speed up the optimization process, it is very appealing to have an accurate pre-design of the machine. In this chapter, the electromagnetic pre-design of a PMaSynRM is developed. In the proposed electromagnetic pre-design process, the geometry of the machine is calculated based on analytical equations that take into account the thermal, electrical, magnetic, and mechanical behavior of the machine to ensure a suitable and reliable design.

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“…The application of these motors is widespread from small home applications to high power industrial applications 1‐3 . The essential step in the design of the PM motor is geometry optimization to achieve the desired output response 4 . There are some crucial subjects in the design procedure as follow: Generally, the optimization procedure of the PM motors is the multiobjective (MO) problem.…”

Section: Introductionmentioning

confidence: 99%

Sensitivity analysis and multiobjective design optimization of flux switching permanent magnet motor using MLP‐ANN modeling and NSGA‐II algorithm

Mahmouditabar

Vahedi

Mosavi

et al. 2020

Int Trans Electr Energ Syst

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Summary The flux switching permanent magnet (FSPM) motor is relatively a new topology of the permanent magnet (PM) motors, which both PM and armature winding are placed at the stator. This feature leads to more robust design, better heat dissipation, and a proper option for a wide range of industrial applications. The critical part of the development of FSPM motor is the design optimization of the structure to improve the electromagnetic performance of the motor. In this article, first to reduce the computation time and required memory of the optimization procedure, the multiobjective sensitivity analysis based on design of experiment is performed to specify the most effective parameters on the objectives. Then, the initial samples data of the optimization procedure is obtained by 2D finite element method (FEM) model of the FSPM motor, which is validated by the prototype of the motor. Furthermore, based on FEM results the multilayers perceptron artificial neural network for the approximation of relation between design variables and objectives is implemented. Finally, using the nondominated sorting genetic algorithm‐II, the optimization procedure of the FSPM motor is done. The accuracy of the presented optimization procedure is validated by a comparison of the initial prototype and final design of the motor.

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Geometry Analysis and Optimization of PM-Assisted Reluctance Motors

Cited by 75 publications

References 15 publications

A Review of Design Optimization Methods for Electrical Machines

A Review of Design Optimization Methods for Electrical Machines

Reliable Design of PMaSynRM

Sensitivity analysis and multiobjective design optimization of flux switching permanent magnet motor using MLP‐ANN modeling and NSGA‐II algorithm

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