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<p>This study proposes an FE-based modeling approach for predicting the dynamic behavior of hairpin-type stator core assemblies in electric vehicle traction motors. A detailed FE model was developed using the actual geometry and experimentally measured material properties of electrical steel, adhesive, rectangular conductors, insulators, slot liners, and varnish. To enable accurate and efficient modeling, equivalent orthotropic properties of the stator core and winding regions were derived through representative volume elements. These properties were used to construct a simplified model that preserves the mass and stiffness distributions, including geometric and material asymmetry in the winding regions. The simplified model maintains high accuracy in predicting natural frequencies and mode shapes, with frequency errors remaining within 1.3 %. The framework provides a practical solution for efficient and accurate dynamic analysis in the early design stage of electric powertrain systems.</p>

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Dynamic modeling of hairpin-type stator core assemblies using finite element-based estimation of equivalent orthotropic properties

  • 期刊论文

  • 发表日期：

    2025年10月9日

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  • Seonbin Lim,
  • Kunsoo Jung,
  • No-Cheol Park

摘要

This study proposes an FE-based modeling approach for predicting the dynamic behavior of hairpin-type stator core assemblies in electric vehicle traction motors. A detailed FE model was developed using the actual geometry and experimentally measured material properties of electrical steel, adhesive, rectangular conductors, insulators, slot liners, and varnish. To enable accurate and efficient modeling, equivalent orthotropic properties of the stator core and winding regions were derived through representative volume elements. These properties were used to construct a simplified model that preserves the mass and stiffness distributions, including geometric and material asymmetry in the winding regions. The simplified model maintains high accuracy in predicting natural frequencies and mode shapes, with frequency errors remaining within 1.3 %. The framework provides a practical solution for efficient and accurate dynamic analysis in the early design stage of electric powertrain systems.