Cogging Torque Optimization of Axial Flux Permanent Magnet Motor

Woo, Dong-Kyun; Kim, Il-Woo; Lim, Dong-Kuk; Ro, Jong‐Suk; Jung, Hyun–Kyo

doi:10.1109/tmag.2013.2237762

Cited by 18 publications

(5 citation statements)

References 10 publications

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“…In Ref. [98], an improved climb method was proposed using a multi-contour strategy to quickly determine the optimal motor parameters for coggingtorque minimization in a multidimensional optimization problem. Xu et al established a response-surface model and combined it with a genetic algorithm to determine the optimal combination of the stator shoe-width ratio, magnet skew angle, and stator-shoe shift angle to minimize the cogging torque [59].…”

Section: Minimization Of Cogging Torquementioning

confidence: 99%

Review of Design and Control Optimization of Axial Flux PMSM in Renewable-energy Applications

Zhao

Wang

et al. 2023

Chin. J. Mech. Eng.

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Axial flux permanent magnet synchronous motors (AFPMSMs) have been widely used in wind-power generation, electric vehicles, aircraft, and other renewable-energy applications owing to their high power density, operating efficiency, and integrability. To facilitate comprehensive research on AFPMSM, this article reviews the developments in the research on the design and control optimization of AFPMSMs. First, the basic topologies of AFPMSMs are introduced and classified. Second, the key points of the design optimization of core and coreless AFPMSMs are summarized from the aspects of parameter design, structure design, and material optimization. Third, because efficiency improvement is an issue that needs to be addressed when AFPMSMs are applied to electric or other vehicles, the development status of efficiency-optimization control strategies is reviewed. Moreover, control strategies proposed to suppress torque ripple caused by the small inductance of disc coreless permanent magnet synchronous motors (DCPMSMs) are summarized. An overview of the rotor-synchronization control strategies for disc contra-rotating permanent magnet synchronous motors (CRPMSMs) is presented. Finally, the current difficulties and development trends revealed in this review are discussed.

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Section: Minimization Of Cogging Torquementioning

confidence: 99%

Review of Design and Control Optimization of Axial Flux PMSM in Renewable-energy Applications

Zhao

Wang

et al. 2023

Chin. J. Mech. Eng.

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“…In the literature, there are various techniques for reducing the cogging torque components of PM motors [20,21]. In this study, effective and simple rotor-side cogging torque reduction techniques, which are magnet skewing and shifting and rotor rotating, were implemented in the designed AFPM motor and torque quality of the motor was improved by eliminating the cogging component.…”

Section: Zero Cogging Torque For Afpm Motormentioning

confidence: 99%

Modeling based on 3D finite element analysis and experimental study of a 24-slot 8-pole axial-flux permanent-magnet synchronous motor for no cogging torque and sinusoidal back-EMF

Güleç¹,

Yolaçan²,

Demir³

et al. 2016

Turk J Elec Eng & Comp Sci

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This paper discusses cogging torque minimization techniques in surface-mounted, axial-flux, multirotor permanent magnet synchronous disc motors in order to eliminate cogging components and obtain sinusoidal back-EMF for low-speed applications. Cogging torque minimization techniques are examined in detail with a focus on magnet skewing and pole and rotor shifting in order to obtain zero cogging. Some magnet shape and rotor design alternatives with conventional skew planar magnets were investigated based on 3D finite element analysis (FEA). Improvements compared to unskewed reference axial-flux permanent magnet (AFPM) motor design are presented. A prototype AFPM motor was built and tested based on the analyses. Results obtained from FEA and experimental study are well matched and it was shown that zero cogging and sinusoidal back-EMF can be obtained even for integer slot axial-flux PM motors with the proposed low-cost cogging torque minimization methods.

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“…The reference machine parameters are given in Table V. The objective functions of optimization are the reduction of cogging torque and total harmonic distortion (THD) of back electromotive force (EMF) to reduce noise, vibration, and stable control [6]- [8]. The cogging torque should be <1% of the rated torque.…”

Section: Optimal Design Of Ipmsm Using Samoomentioning

confidence: 99%

A Novel Surrogate-Assisted Multi-Objective Optimization Algorithm for an Electromagnetic Machine Design

et al. 2015

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To design electric machines, the motor performance, cost, and manufacturing have to be considered. Hence, researchers have called this the multi-objective optimization (MOO) problem in which the goal is to minimize or maximize several objective functions at the same time. In order to solve the MOO problem, various algorithms, such as nondominated sorting genetic algorithm II and multi-objective particle swarm optimization, have been widely used. When these algorithms are applied to the electric machine design, much time consumption is inevitable due to many times of function evaluations using a finite-element method. To solve this problem, a novel surrogate-assisted MOO algorithm is proposed. Its validity is confirmed by comparing the optimization results of test functions with conventional optimization methods. To verify the feasibility of its application to a practical electric machine, an interior permanent magnet synchronous motor is designed.Index Terms-Interior permanent magnet synchronous motor (IPMSM), Kriging, multi-objective, surrogate model.

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Cogging Torque Optimization of Axial Flux Permanent Magnet Motor

Cited by 18 publications

References 10 publications

Review of Design and Control Optimization of Axial Flux PMSM in Renewable-energy Applications

Review of Design and Control Optimization of Axial Flux PMSM in Renewable-energy Applications

Modeling based on 3D finite element analysis and experimental study of a 24-slot 8-pole axial-flux permanent-magnet synchronous motor for no cogging torque and sinusoidal back-EMF

A Novel Surrogate-Assisted Multi-Objective Optimization Algorithm for an Electromagnetic Machine Design

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