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Proceeding Paper

GA Optimization for Regression Modeling of Electromagnetic Performances Predicted by a Subdomain Model for SMPMSM in an Electric Vehicle †

by
Syauqina Akmar Mohd-Shafri
1,
Tow Leong Tiang
1,*,
Choo Jun Tan
2,
Dahaman Ishak
3 and
Mohd Saufi Ahmad
1
1
Faculty of Electrical Engineering Technology, Universiti Malaysia Perlis (UniMAP), Perlis 02600, Malaysia
2
School of Science and Technology, Wawasan Open University (WOU), Penang 10050, Malaysia
3
School of Electrical and Electronic Engineering, Engineering Campus, Universiti Sains Malaysia (USM), Nibong Tebal, Penang 14300, Malaysia
*
Author to whom correspondence should be addressed.
†
Presented at the 1st International Conference on Energy, Power and Environment, Gujrat, Pakistan, 11–12 November 2021.
Eng. Proc. 2021, 12(1), 73; https://doi.org/10.3390/engproc2021012073
Published: 6 January 2022
(This article belongs to the Proceedings of The 1st International Conference on Energy, Power and Environment)
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Abstract

This paper investigates a nonlinear modeling optimization of 12s/8p surface-mounted permanent magnet synchronous machines (SMPMSM) with a radial magnetization pattern. The modeling is based on subdomain model (SDM) computation, where the analytical models are developed to predict the electromagnetic (EM) performances, such as, average EM torque and EM torque ripple in PM machines. A genetic algorithm is applied to the proposed model in order to search for the optimal solutions. The objective function of the optimizations is obtaining a higher average EM torque and achieving the minimum EM torque ripple. The data, viz, and the average EM torque and its ripples predicted by SDM are employed in regression analysis in order to find the model of best fit. After that, the most suitable fit of the computing equation is selected. The preliminary and optimal designs of 12s/8p PM motors are also compared in terms of parameters and motor performance. As a result, the regression model and GA framework has reduced the use of magnet materials and the EM torque ripple of the SMPMSM, making it ideal for use in an electric car. Lastly, the proposed model can determine the appropriate configuration design parameters for SMPMSM in order to achieve the best motor performance.
Keywords: regression model; genetic algorithm; subdomain model; surface-mounted; permanent magnet synchronous machine; electric vehicle; EM performance regression model; genetic algorithm; subdomain model; surface-mounted; permanent magnet synchronous machine; electric vehicle; EM performance

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MDPI and ACS Style

Mohd-Shafri, S.A.; Tiang, T.L.; Tan, C.J.; Ishak, D.; Ahmad, M.S. GA Optimization for Regression Modeling of Electromagnetic Performances Predicted by a Subdomain Model for SMPMSM in an Electric Vehicle. Eng. Proc. 2021, 12, 73. https://doi.org/10.3390/engproc2021012073

AMA Style

Mohd-Shafri SA, Tiang TL, Tan CJ, Ishak D, Ahmad MS. GA Optimization for Regression Modeling of Electromagnetic Performances Predicted by a Subdomain Model for SMPMSM in an Electric Vehicle. Engineering Proceedings. 2021; 12(1):73. https://doi.org/10.3390/engproc2021012073

Chicago/Turabian Style

Mohd-Shafri, Syauqina Akmar, Tow Leong Tiang, Choo Jun Tan, Dahaman Ishak, and Mohd Saufi Ahmad. 2021. "GA Optimization for Regression Modeling of Electromagnetic Performances Predicted by a Subdomain Model for SMPMSM in an Electric Vehicle" Engineering Proceedings 12, no. 1: 73. https://doi.org/10.3390/engproc2021012073

APA Style

Mohd-Shafri, S. A., Tiang, T. L., Tan, C. J., Ishak, D., & Ahmad, M. S. (2021). GA Optimization for Regression Modeling of Electromagnetic Performances Predicted by a Subdomain Model for SMPMSM in an Electric Vehicle. Engineering Proceedings, 12(1), 73. https://doi.org/10.3390/engproc2021012073

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