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<p>Permanent magnet assisted synchronous reluctance motors (PMa-SynRMs) are promising traction candidates due to their high efficiency, power density, and reduced reliance on rare-earth magnets; however, multi-objective design in large variable spaces demands extensive Finite Element Method (FEM) evaluations. We propose a dual-output surrogate framework that decouples feasibility classification from performance regression. A classifier first filters infeasible designs induced by geometric and physical constraints; the regressor then predicts key metrics (energy consumption, torque, torque ripple) only on feasible samples. This separation preserves physical validity near active constraints and improves sample efficiency in high-dimensional searches. The approach is validated on FEM-generated datasets of a traction-scale PMa-SynRM, where it reliably reconstructs the constrained Pareto structure—including the knee region relevant to trade-offs—while maintaining robust discrimination of infeasible designs. Overall, the framework reduces reliance on expensive FEM calls without sacrificing decision quality, providing a practical foundation for scalable, constraint-aware optimization of railway traction motors.</p>

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A Dual-Output Surrogate Modeling Framework for the Energy Efficiency Optimization of a PMa-SynRM

  • 期刊论文

  • 发表日期：

    2026年1月28日

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  • Sung-Chang Lee,
  • Do Hyun Kang,
  • Seok-Won Jung,
  • Sang-Yong Jung

摘要

Permanent magnet assisted synchronous reluctance motors (PMa-SynRMs) are promising traction candidates due to their high efficiency, power density, and reduced reliance on rare-earth magnets; however, multi-objective design in large variable spaces demands extensive Finite Element Method (FEM) evaluations. We propose a dual-output surrogate framework that decouples feasibility classification from performance regression. A classifier first filters infeasible designs induced by geometric and physical constraints; the regressor then predicts key metrics (energy consumption, torque, torque ripple) only on feasible samples. This separation preserves physical validity near active constraints and improves sample efficiency in high-dimensional searches. The approach is validated on FEM-generated datasets of a traction-scale PMa-SynRM, where it reliably reconstructs the constrained Pareto structure—including the knee region relevant to trade-offs—while maintaining robust discrimination of infeasible designs. Overall, the framework reduces reliance on expensive FEM calls without sacrificing decision quality, providing a practical foundation for scalable, constraint-aware optimization of railway traction motors.