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<p>This work presents a&#xa0;surrogate-based optimization approach for designing magnetic components, specifically a&#xa0;coupled inductor (CI) and a&#xa0;three-phase common-mode choke (CMC), used in an interleaved AC-DC converter for electric vehicle (EV) charging. The goal is to minimize component volume while avoiding core saturation, maximizing magnetic core utilization, and ensuring electromagnetic compatibility (EMC). The simulation workflow integrates analytical modeling, LTSpice circuit simulation, and both 2D magnetostatic and 3D electrostatic finite element analysis. To efficiently navigate the complex design space and reduce computational effort, surrogate models are developed for the magnetic components and the overall circuit. These models enable rapid evaluation of design variants and facilitate multi-objective optimization. The paper outlines the modeling strategies, training procedures, and application of surrogates within a&#xa0;simulation-driven design workflow.</p>

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Surrogate-based optimization of coupled inductor and common mode choke for e-vehicle fast charging application

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年2月3日

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  • Herbert Hackl,
  • Christian Manfred Riener,
  • Mehtab Hussain

摘要

This work presents a surrogate-based optimization approach for designing magnetic components, specifically a coupled inductor (CI) and a three-phase common-mode choke (CMC), used in an interleaved AC-DC converter for electric vehicle (EV) charging. The goal is to minimize component volume while avoiding core saturation, maximizing magnetic core utilization, and ensuring electromagnetic compatibility (EMC). The simulation workflow integrates analytical modeling, LTSpice circuit simulation, and both 2D magnetostatic and 3D electrostatic finite element analysis. To efficiently navigate the complex design space and reduce computational effort, surrogate models are developed for the magnetic components and the overall circuit. These models enable rapid evaluation of design variants and facilitate multi-objective optimization. The paper outlines the modeling strategies, training procedures, and application of surrogates within a simulation-driven design workflow.