Badewa, Oluwaseun A; Silva, Marcelo D; Alden, Rosemary E; Asef, Pedram; Ionel, Dan M; (2025) Hybrid FEA and Meta-modeling for DE Optimization of a Highly Saturated Spoke IPM. In: 2025 International Electric Machines and Drives Conference (IEMDC). IEEE (In press).

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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.

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