Robust Explorative Particle Swarm Optimization for Optimal Design of EV Traction Motor

This paper proposes a robust optimization algorithm customized for the optimal design of electric machines. The proposed algorithm, termed “robust explorative particle swarm optimization” (RePSO), is a hybrid algorithm that affords high accuracy and a high search speed when determining robust optima...

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Autores principales:	Jin-Hwan Lee, Woo-Jung Kim, Sang-Yong Jung
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	robust optimization algorithm electric machine electric vehicle traction motor hybrid optimization algorithm particle swarm optimization Chemical technology TP1-1185 Chemistry QD1-999
Acceso en línea:	https://doaj.org/article/8bc09bd0b10f445f9372d2db099b3876
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id	oai:doaj.org-article:8bc09bd0b10f445f9372d2db099b3876
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spelling	oai:doaj.org-article:8bc09bd0b10f445f9372d2db099b38762021-11-25T18:51:22ZRobust Explorative Particle Swarm Optimization for Optimal Design of EV Traction Motor10.3390/pr91120002227-9717https://doaj.org/article/8bc09bd0b10f445f9372d2db099b38762021-11-01T00:00:00Zhttps://www.mdpi.com/2227-9717/9/11/2000https://doaj.org/toc/2227-9717This paper proposes a robust optimization algorithm customized for the optimal design of electric machines. The proposed algorithm, termed “robust explorative particle swarm optimization” (RePSO), is a hybrid algorithm that affords high accuracy and a high search speed when determining robust optimal solutions. To ensure the robustness of the determined optimal solution, RePSO employs the rate of change of the cost function. When this rate is high, the cost function appears as a steep curve, indicating low robustness; in contrast, when the rate is low, the cost function takes the form of a gradual curve, indicating high robustness. For verification, the performance of the proposed algorithm was compared with those of the conventional methods of robust particle swarm optimization and explorative particle swarm optimization with a Gaussian basis test function. The target performance of the traction motor for the optimal design was derived using a simulation of vehicle driving performance. Based on the simulation results, the target performance of the traction motor requires a maximum torque and power of 294 Nm and 88 kW, respectively. The base model, an 8-pole 72-slot permanent magnet synchronous machine, was designed considering the target performance. Accordingly, an optimal design was realized using the proposed algorithm. The cost function for this optimal design was selected such that the torque ripple, total harmonic distortion of back-electromotive force, and cogging torque were minimized. Finally, experiments were performed on the manufactured optimal model. The robustness and effectiveness of the proposed algorithm were validated by comparing the analytical and experimental results.Jin-Hwan LeeWoo-Jung KimSang-Yong JungMDPI AGarticlerobust optimization algorithmelectric machineelectric vehicletraction motorhybrid optimization algorithmparticle swarm optimizationChemical technologyTP1-1185ChemistryQD1-999ENProcesses, Vol 9, Iss 2000, p 2000 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	robust optimization algorithm electric machine electric vehicle traction motor hybrid optimization algorithm particle swarm optimization Chemical technology TP1-1185 Chemistry QD1-999
spellingShingle	robust optimization algorithm electric machine electric vehicle traction motor hybrid optimization algorithm particle swarm optimization Chemical technology TP1-1185 Chemistry QD1-999 Jin-Hwan Lee Woo-Jung Kim Sang-Yong Jung Robust Explorative Particle Swarm Optimization for Optimal Design of EV Traction Motor
description	This paper proposes a robust optimization algorithm customized for the optimal design of electric machines. The proposed algorithm, termed “robust explorative particle swarm optimization” (RePSO), is a hybrid algorithm that affords high accuracy and a high search speed when determining robust optimal solutions. To ensure the robustness of the determined optimal solution, RePSO employs the rate of change of the cost function. When this rate is high, the cost function appears as a steep curve, indicating low robustness; in contrast, when the rate is low, the cost function takes the form of a gradual curve, indicating high robustness. For verification, the performance of the proposed algorithm was compared with those of the conventional methods of robust particle swarm optimization and explorative particle swarm optimization with a Gaussian basis test function. The target performance of the traction motor for the optimal design was derived using a simulation of vehicle driving performance. Based on the simulation results, the target performance of the traction motor requires a maximum torque and power of 294 Nm and 88 kW, respectively. The base model, an 8-pole 72-slot permanent magnet synchronous machine, was designed considering the target performance. Accordingly, an optimal design was realized using the proposed algorithm. The cost function for this optimal design was selected such that the torque ripple, total harmonic distortion of back-electromotive force, and cogging torque were minimized. Finally, experiments were performed on the manufactured optimal model. The robustness and effectiveness of the proposed algorithm were validated by comparing the analytical and experimental results.
format	article
author	Jin-Hwan Lee Woo-Jung Kim Sang-Yong Jung
author_facet	Jin-Hwan Lee Woo-Jung Kim Sang-Yong Jung
author_sort	Jin-Hwan Lee
title	Robust Explorative Particle Swarm Optimization for Optimal Design of EV Traction Motor
title_short	Robust Explorative Particle Swarm Optimization for Optimal Design of EV Traction Motor
title_full	Robust Explorative Particle Swarm Optimization for Optimal Design of EV Traction Motor
title_fullStr	Robust Explorative Particle Swarm Optimization for Optimal Design of EV Traction Motor
title_full_unstemmed	Robust Explorative Particle Swarm Optimization for Optimal Design of EV Traction Motor
title_sort	robust explorative particle swarm optimization for optimal design of ev traction motor
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/8bc09bd0b10f445f9372d2db099b3876
work_keys_str_mv	AT jinhwanlee robustexplorativeparticleswarmoptimizationforoptimaldesignofevtractionmotor AT woojungkim robustexplorativeparticleswarmoptimizationforoptimaldesignofevtractionmotor AT sangyongjung robustexplorativeparticleswarmoptimizationforoptimaldesignofevtractionmotor
_version_	1718410658402271232

Robust Explorative Particle Swarm Optimization for Optimal Design of EV Traction Motor

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