ECIE Lab Publications at IEEE CPEEE 2026 & Best Paper Recognition

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ECIE Lab at the National Taipei University of Technology is proud to announce that our researchers presented three papers at the 2026 IEEE International Conference on Power, Energy, and Electrical Engineering (CPEEE 2026), where one paper was honored with the Best Paper Award.

This recognition highlights the laboratory’s continued contributions in high-frequency magnetics, converter modeling, and high-efficiency power electronics design, particularly for data-center and high-power applications.


🏆 Best Paper

Design Methodology and Modeling of Matrix-Based Magnetically Integrated Resonant Inductors for Server Power Applications

Authors

  • Prof. Yu-Chen Liu — Advisor
  • Shang-Syun Wu — Author and Presenter
  • Sheng-Chieh Lan — Author

Research Overview

This work presents a structured design methodology for a 1.5 kW, 380/12 V LLC resonant converter, with particular emphasis on the integration of a matrix-type resonant inductor.

The proposed approach analytically determines resonant tank parameters by considering the coupled effects of dead time, resonant current, and voltage gain. This enables the reduction of circulating energy and overall loss while maintaining required gain characteristics.

To address high-frequency magnetic challenges, a matrix-based resonant inductor is introduced to improve flux distribution and mitigate AC winding losses caused by magnetomotive force (MMF) accumulation, proximity effect, and current imbalance.

Finite element analysis (FEA) is employed to evaluate leakage inductance, AC resistance (ACR), and electromagnetic behavior, including the influence of PCB via design on current sharing. Experimental validation confirms stable operation and demonstrates the effectiveness of the proposed methodology for high-power LLC applications in data centers.

📄 Paper 2

Loss Analysis of a Wide Output-Voltage-Range PSFB Converter

Authors

  • Prof. Yu-Chen Liu — Advisor
  • Tzu-Chieh Hsu — Author and Presenter

Research Overview

This paper proposes a systematic loss analysis methodology based on mathematical modeling combined with MATLAB iterative computation.

The approach accounts for conduction, switching, magnetic, and gate-drive losses, enabling comprehensive evaluation under varying load and output voltage conditions. By generating loss contour maps, the method provides intuitive visualization of loss distribution, helping identify dominant loss mechanisms.

Compared with conventional point-based estimation, this framework significantly improves analysis efficiency and accuracy, offering practical guidance for device selection, thermal design, and efficiency optimization in high-performance converters.

📄 Paper 3

A Design and Analysis Method for an Inverse-Coupled Inductor Core Used in Interleaved Boost Converters

Authors

  • Prof. Yu-Chen Liu — Advisor
  • Tuo-Chen Lin — Author
  • Tzu-Chieh Hsu — Author and Presenter

Research Overview

This work investigates the application of inverse-coupled inductors in interleaved boost converters, focusing on implementations using low-permeability powder cores without additional air gaps.

By integrating multiple inductors into a shared magnetic path, the study analyzes coupling behavior and its effect on input current ripple. The proposed design demonstrates improved ripple suppression and enhanced current distribution, making it suitable for multiphase power conversion systems.

Reflection on CPEEE 2026

CPEEE 2026 provided a valuable platform for ECIE Lab to present our latest research, exchange ideas with international scholars, and explore emerging trends in power electronics and energy systems.

Receiving the Best Paper Award, along with presenting multiple contributions, represents an important milestone for our research team. It reflects the collective efforts of our students and advisors in advancing:

  • High-frequency magnetic component design
  • Multi-physics modeling and simulation
  • High-efficiency and high-power-density converter technologies

ECIE Lab will continue to push forward in developing innovative solutions for AI servers, data centers, electric vehicles, and renewable energy systems, while cultivating the next generation of engineers and researchers in power electronics.

#ECIELab #TaipeiTech #PowerElectronics #CPEEE2026 #BestPaperAward #LLCConverter #MagneticDesign #FEA #DataCenter

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