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Abstract

This paper presents an innovative method for designing high-performance electric motors by integrating machine learning (ML) based meta-modeling with a differential evolution (DE) optimization algorithm. The approach utilizes finite element analysis (FEA) data to train the ML meta-model, allowing for efficient optimization of high-power-density machines, such as the reluctance rotor and permanent magnet (PM) stator combined excitation motor, which is characterized by nonlinearities. The meta-modeling process employs an Artificial Neural Network (ANN) with 3 hidden layers and uses the motor’s geometrical variables as inputs. The accuracy of the meta-model 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. This hybrid ML-DE framework aims to serve as an alternative approach for physics-based electric motor design and optimization, delivering substantial reductions in computational effort without compromising accuracy. Drive cycle analysis, which could benefit from a system-level optimization integration with the meta-model, was also investigated for a 100kW rated experimentally tested prototype of the studied motor topology with promising results.

Document Type

Conference Proceeding

Publication Date

6-2025

Digital Object Identifier (DOI)

doi: 10.1109/ITEC63604.2025.11098025

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