Design Optimization of a Homopolar Salient-Pole Brushless DC Machine: Analysis, Simulation, and Experimental Tests

CheshmehBeigi, Hassan Moradi; Afjei, E.

doi:10.1109/tec.2013.2249584

Cited by 19 publications

(13 citation statements)

References 13 publications

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“…Therefore, it is assumed that averaging the value of (10) over various radiuses gives more precise results. Its value is defined using the double integral over the entire computational area: (11) Since the magnetic field lines are frozen in the back iron, the linear density of the magnetic charge is assumed to be constant in time. And the MMF drop across the SRSC matches the average value (11) over all considered rotor positions (8):…”

Section: Calculation Of Excitation Magnetic Circuit Of the Shmmentioning

confidence: 99%

“…In some parts of the motor, flux flows in the axial direction, while in other parts, it flows in the transverse plane and changes its direction to the axial one when coming from laminated parts to the stator back iron or the magnetic sleeve on the rotor shaft. Therefore, the finite element model (FEM) in 3D was used to compute the operation points of these motors [1], [11], [12].…”

Section: Introductionmentioning

confidence: 99%

“…The experimental verification of the 3D FEM computation was performed in [11] for the 0.75 kW, 3600 rpm SHM. However, only a limited analysis was performed due to the huge complexity of computations.…”

Section: Introductionmentioning

confidence: 99%

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Traction Synchronous Homopolar Motor: Simplified Computation Technique and Experimental Validation

et al. 2020

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The synchronous homopolar motor (SHM) has been attracting the attention of researchers for many decades. Various mathematical models of SHM have been proposed to deal with its complicated magnetic circuit. Among them, there are time-consuming 3D finite element models (FEM), equivalent circuit models neglecting some significant features of the machine design, and 2D FEM models with virtual excitation winding distorting magnetic field picture. This paper proposes a novel 2D FEM of SHM and shows that since there are no sources of excitation in the cross-sections of the rotor and stator stacks, no virtual elements are required. This model uses the general solution of the Gauss's law for magnetism containing excitation flux. The model is based on a set of magnetostatic boundary value problems for various rotor positions. The set of boundary problems is completed with the excitation equivalent circuit. The losses in the armature and field windings and in the stator and rotor magnetic cores are computed in postprocessing. All these computations are carried out for a single combination of stator and rotor stack. A symmetrization algorithm is proposed to extend the obtained results to the whole SHM. A comparison of the theoretical and experimental data for a nine-phase three-section 320 kW SHM is carried out. These SHMs were used in a mining truck with a carrying capacity of 90 tons. INDEX TERMS AC machines, automotive applications, brushless motors, electric vehicles, electromagnetic modeling, mining industry, traction motor.

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Section: Calculation Of Excitation Magnetic Circuit Of the Shmmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Traction Synchronous Homopolar Motor: Simplified Computation Technique and Experimental Validation

et al. 2020

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“…Three kinds of models were proposed for the evaluation of the characteristics of the SHMs: the first is the 3D finite element method (FEM) [6,7]; the second is the 2D FEM [8,9], where the axial and radial fluxes are evaluated using a magnetic circuit; and the third is the 1D [6,10] lumped parameters-equivalent circuit. The 3D FEM provides the most accurate field calculation; however, the use of any method of mathematical optimization together with it is barely possible due to the very long time required by one calculation.…”

Section: Introductionmentioning

confidence: 99%

Design Optimization of a Traction Synchronous Homopolar Motor

et al. 2021

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Synchronous homopolar motors (SHMs) have been attracting the attention of researchers for many decades. They are used in a variety of equipment such as aircraft and train generators, welding inverters, and as traction motors. Various mathematical models of SHMs have been proposed to deal with their complicated magnetic circuit. However, mathematical techniques for optimizing SHMs have not yet been proposed. This paper discusses various aspects of the optimal design of traction SHMs, applying the one-criterion unconstrained Nelder–Mead method. The considered motor is intended for use in a mining dump truck with a carrying capacity of 90 tons. The objective function for the SHM optimization was designed to reduce/improve the following main characteristics: total motor power loss, maximum winding current, and torque ripple. One of the difficulties in optimizing SHMs is the three-dimensional structure of their magnetic core, which usually requires the use of a three-dimensional finite element model. However, in this study, an original two-dimensional finite element model of a SHM was used; it allowed the drastic reduction in the computational burden, enabling objective optimization. As a result of optimization, the total losses in the motor decreased by up to 1.16 times and the torque ripple decreased by up to 1.34 times; the maximum armature winding current in the motor mode decreased by 8%.

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“…Variety of methods to assuage the cogging torque during the machine design phase itself has been reported [1]. These methods include skewing of the stator laminations or rotor magnets, varying slot width, varying magnet width, shifting alternate pair of poles and notching of teeth [13][14][15][16].…”

Section: Introductionmentioning

confidence: 99%

Data Interpolation and Design Optimisation of Brushless DC Motor Using Generalized Regression Neural Network

Umadevi¹,

Balaji²,

Kamaraj³

et al. 2015

Journal of Electrical Engineering and Technology

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-This paper proposes a generalized regression neural network (GRNN) based algorithm for data interpolation and design optimization of brushless dc (BLDC) motor. The procedure makes use of magnet length, stator slot opening and air gap length as design variables. Cogging torque and average torque are treated as performance indices. The optimal design necessitates mitigating the cogging torque and maximizing the average torque by varying design variables. The data set for interpolation and ensuing design optimisation using GRNN is obtained by modeling a standard BLDC motor using finite element analysis (FEA) tool MagNet 7.1.1. The performance indices of the standard motor obtained using FEA are validated with an experimental model and an analytical method. The optimal design is authenticated using particle swarm optimization (PSO) algorithm and the performance indices of the optimal design obtained using GRNN is validated using FEA. The results indicate the suitability of GRNN as an interpolation and design optimization tool for a BLDC motor.

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Design Optimization of a Homopolar Salient-Pole Brushless DC Machine: Analysis, Simulation, and Experimental Tests

Cited by 19 publications

References 13 publications

Traction Synchronous Homopolar Motor: Simplified Computation Technique and Experimental Validation

Traction Synchronous Homopolar Motor: Simplified Computation Technique and Experimental Validation

Design Optimization of a Traction Synchronous Homopolar Motor

Data Interpolation and Design Optimisation of Brushless DC Motor Using Generalized Regression Neural Network

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