Hybrid FEA and Meta-modeling for DE Optimization of a Highly Saturated Spoke IPM

Authors

• Oluwaseun A. Badewa, University of KentuckyFollow
• Marcelo Silva, Uppsala UniversityFollow
• Rosemary E. Alden, University of KentucktyFollow
• Pedram Asef, University College LondonFollow
• Dan M. Ionel, University of KentuckyFollow

Abstract

This paper introduces a novel approach for high performance electric motor design that combines machine learning (ML)-based meta-modeling with a differential evolution (DE) optimization algorithm. The method leverages finite element analysis (FEA) results to train the ML meta-model, enabling efficient design optimization for high-power density cored machines, such as spoke interior permanent magnet motors (IPM), which exhibit complex nonlinearities and saturation effects. This hybrid ML-DE framework seeks to provide an alternative for physics-based electric motor design and optimization, offering significant reductions in computational effort while maintaining accuracy. The meta-model’s accuracy in capturing the nonlinear relationships between design parameters, core losses, and torque is assessed using metrics such as R-squared (R²), normalized root mean square error (NRMSE), and mean absolute percentage error (MAPE), showing promising performance.

Document Type

Conference Proceeding

Publication Date

5-2025

Digital Object Identifier (DOI)

doi: 10.1109/IEMDC60492.2025.11060953

Repository Citation

Badewa, Oluwaseun A.; Silva, Marcelo; Alden, Rosemary E.; Asef, Pedram; and Ionel, Dan M., "Hybrid FEA and Meta-modeling for DE Optimization of a Highly Saturated Spoke IPM" (2025). Electrical and Computer Engineering Graduate Research. 46.
https://uknowledge.uky.edu/ece_gradpub/46