Rotor profile optimization for high-performance IPMSMs in electric vehicles

Abstract Interior Permanent Magnet Synchronous Motors (IPMSMs) are a leading technology for electric vehicles (EVs) owing to their high efficiency and power density, yet their performance is limited by torque ripple, drive losses, and sensitivity to parameter variations. This paper presents a robust rotor profile optimization (RPO) framework that integrates a modified adaptive particle swarm optimization (MAPSO) algorithm with finite-element analysis to enhance torque production and minimize torque fluctuations. Unlike conventional optimization approaches, the proposed framework incorporates sensitivity and robustness evaluation, ensuring stable performance under input uncertainties. The optimized rotor design achieves an 8.65% increase in average torque (92.45 → 100.45 Nm), a 69.61% reduction in torque ripple (22.17% → 6.74%), and a slight efficiency improvement (97.08% → 97.12%) while maintaining the same 25 kW power rating and 2250 rpm base speed. Additionally, the 6% refers to the overall or representative robustness level, confirming robustness against parameter deviations. These results demonstrate that the proposed optimization method enables reliable rotor geometry refinement within the same motor envelope, leading to higher torque density, reduced pulsations, and improved suitability for high-performance EV traction applications.

Institutions

Publication Details

Journal
Journal of Electrical Systems and Information Technology
Published
2025-12-11
DOI
https://doi.org/10.1186/s43067-025-00304-y
Citations
1
Primary Topic
Electric Motor Design and Analysis
Type
article
Field-Weighted Citation Impact
0.73
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Rotor profile optimization for high-performance IPMSMs in electric vehicles

1 citations
Journal of Electrical Systems and Information Technology
Electric Motor Design and Analysis
0.73
article

Rotor profile optimization for high-performance IPMSMs in electric vehicles

article en
1 citations

Abstract

Abstract Interior Permanent Magnet Synchronous Motors (IPMSMs) are a leading technology for electric vehicles (EVs) owing to their high efficiency and power density, yet their performance is limited by torque ripple, drive losses, and sensitivity to parameter variations. This paper presents a robust rotor profile optimization (RPO) framework that integrates a modified adaptive particle swarm optimization (MAPSO) algorithm with finite-element analysis to enhance torque production and minimize torque fluctuations. Unlike conventional optimization approaches, the proposed framework incorporates sensitivity and robustness evaluation, ensuring stable performance under input uncertainties. The optimized rotor design achieves an 8.65% increase in average torque (92.45 → 100.45 Nm), a 69.61% reduction in torque ripple (22.17% → 6.74%), and a slight efficiency improvement (97.08% → 97.12%) while maintaining the same 25 kW power rating and 2250 rpm base speed. Additionally, the 6% refers to the overall or representative robustness level, confirming robustness against parameter deviations. These results demonstrate that the proposed optimization method enables reliable rotor geometry refinement within the same motor envelope, leading to higher torque density, reduced pulsations, and improved suitability for high-performance EV traction applications.

Journal of Electrical Systems and Information TechnologyVol. 12(1)
Tallinn University of Technology (EE), King Fahd University of Petroleum and Minerals (SA), Southwest Jiaotong University (CN)
Affordable and clean energy
Openalex Percentile: Top 20%
Electric Motor Design and Analysis
0.73
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.