Integrated Taguchi method‐assisted polynomial Metamodelling &amp; Genetic Algorithm based optimisation of a surface inset permanent synchronous motor for performance improvement

Verma, Monika; Singh, Madhusudan; Sreejeth, Mini

doi:10.1049/els2.12035

Cited by 4 publications

(5 citation statements)

References 34 publications

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“…where L Fe is the axial distance of the armature, R m1 is the inner radius of the magnet, R m2 is the outer radius of the magnet, and z is the number of slots. Equation (11) shows that parameters such as residual flux density, number of slots, the axial distance of the armature, inner and outer radius of the magnet play an essential role in the cogging torque. Cogging torque is the main cause of torque ripple in PM machines.…”

Section: Cogging Torque Of Afspm Motormentioning

confidence: 99%

“…Cogging torque is the main cause of torque ripple in PM machines. Considering Equations ( 5) and (11), the objective function for optimisation can be defined as the (Te/Tc) ratio. Therefore, this paper proposes a novel hybrid structure to reduce unwanted cogging torque while simultaneously improving electromagnetic torque.…”

Section: Cogging Torque Of Afspm Motormentioning

confidence: 99%

“…On the other hand, high cogging torque will rise the losses and reduce the efficiency of the electric motor. Because, since that high torque density and high efficiency are the two main design rules for an electric motor used in an electric vehicle [11, 12], therefore, the simultaneous improvement of torque density and cogging torque to improve efficiency must be considered. So far, various approaches have been proposed in the references to decrease the cogging torque of the AFSPM motor.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Simultaneous improvement of cogging torque and torque density in axial flux‐switching permanent magnet motor

Ebrahimi,

Torkaman,

Javadi

2023

IET Electric Power Appl

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Axial Flux‐Switching Permanent Magnet motors are broadly used in various industrial applications due to their high torque density and low rotor inertia. However, one major drawback of these motors is their cogging torque, which results in torque ripple and vibration. Existing methods for reducing cogging torque have often led to a decrease in useful electromagnetic torque, thereby compromising motor efficiency. To address this issue, a novel hybrid structure for the simultaneous improvement of cogging torque and electromagnetic torque in Axial Flux‐Switching Permanent Magnet motors is presented. The proposed structure combines the optimal arc coefficient of the rotor pole technique with the notching rotor technique, resulting in a new motor configuration that exhibits both reduced cogging torque and increased torque density. Analytical equations for calculating cogging torque are derived, and 3D finite element analysis is conducted to evaluate the effectiveness of the proposed structure design. Furthermore, the optimal values of the arc coefficient and forming angle of the rotor pole are determined using the Taguchi analysis. Comparing the results of the optimal motor structure with the previous design shows that the new structure can improve the cogging torque, average electromagnetic torque, torque ripple, torque density, and peak torque output of the motor, which confirms the effectiveness of the proposed structure.

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Section: Cogging Torque Of Afspm Motormentioning

confidence: 99%

Section: Cogging Torque Of Afspm Motormentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Simultaneous improvement of cogging torque and torque density in axial flux‐switching permanent magnet motor

Ebrahimi,

Torkaman,

Javadi

2023

IET Electric Power Appl

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“…Out of so many inspirations, stochastic search based have raised the interest of researcherfor obtaining advancement in the performance of existing frameworks. By doing so, the updated versions of existing metaheuristic frameworks become more efficient for solving many complex real world problems [8][9][10][11][12][13][14][15]. The evolutionary algorithms are based on the laws of natural evolutions.…”

Section: A Motivation and Prior Work Figure 1taxonomy Of State-of-the...mentioning

confidence: 99%

Chaotic Mapping Based Advanced Aquila Optimizer With Single Stage Evolutionary Algorithm

et al. 2022

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The intelligent optimization techniques have been introduced by carefully observing the behavior of various hunters like a whale, grey wolf, Aquila, and lizards for estimating global optimum solutions in fair time by forming appropriate mathematical models. However, hunting-based algorithms suffer from slow and pre-requisite convergence and get caught up in local optima. Aquila Optimizer (AO) is one of the recently developed hunting-based methods that encounter a similar type of shortcoming in a few situations. This research introduces the concept of chaotic mapping to the standard AO in order to increase the convergence speed. Also to maintain the balance of exploration performed by AO with its exploitation capability, a single stage evolutionary algorithm is also integrated with it. The performance of standard AO and modified AO are tested for well-defined unimodal and multimodal Benchmark functions. The proposed framework produces one population by standard AO and a new population by single stage genetic algorithm based evolutionary concept in which binary tournament selection, roulette wheel selection, shuffle crossing over and displacement mutationoccur to generate a new population.The chaotic mapping criteria are then applied to obtain various variants of the standard AO technique. The general results obtained from the proposed novel chaotic mapping-based advanced AO with single stage evolutionary algorithm shows that it outperforms the standard AO. This advanced technique is thus applied to real-world design engineering problems to study its significance from an industrial point of view.

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“…The result of the measurement is usually a set of data representing the measured cogging torque at different angular positions. This allows a closed-form model to be derived by approximation procedure, by harmonic analysis [23] or polynomial [24], as reported in Equation (3).…”

Section: Cogging Torque Modeling Approachesmentioning

confidence: 99%

Review on Model Based Design of Advanced Control Algorithms for Cogging Torque Reduction in Power Drive Systems

Dini

Saponara

2022

Energies

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This review of the state of the art aims to collect the description and main research results in the field of development and validation of control algorithms with the main purpose to solve the problem of cogging torque and main sources of electromagnetic torque ripple. In particular, we focus on electric drives for advanced and modern mechatronic applications such as industrial automation, robotics, and automotive applications, with special emphasis on work that exploits model-based design. A great added value of this paper is to explicitly show the operational steps required for the model-based design design of optimized control algorithms for electric drives where it is necessary to make up for electromagnetic torque oscillations due to the main sources of ripple, particularly cogging torque. The ultimate goal of this paper is to provide researchers approaching this particular problem with a comprehensive collection of the most effective solutions reported in the state of the art and also a summary for effectively applying the model-based design methodology.

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Integrated Taguchi method‐assisted polynomial Metamodelling & Genetic Algorithm based optimisation of a surface inset permanent synchronous motor for performance improvement

Cited by 4 publications

References 34 publications

Simultaneous improvement of cogging torque and torque density in axial flux‐switching permanent magnet motor

Simultaneous improvement of cogging torque and torque density in axial flux‐switching permanent magnet motor

Chaotic Mapping Based Advanced Aquila Optimizer With Single Stage Evolutionary Algorithm

Review on Model Based Design of Advanced Control Algorithms for Cogging Torque Reduction in Power Drive Systems

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