Why MagNet: Quantifying the Complexity of Modeling Power Magnetic Material Characteristics

Diego Serrano, Haoran Li, Shukai Wang, Thomas Guillod, Min Luo, Vineet Bansal, Niraj K. Jha, Yuxin Chen, Charles R. Sullivan, Minjie Chen

Research output: Contribution to journalArticlepeer-review

16 Scopus citations

Abstract

This article motivates the development of sophisticated data-driven models for power magnetic material characteristics. Core losses and hysteresis loops are critical information in the design process of power magnetics, yet the physics behind them is not fully understood or directly applicable. Both losses and hysteresis loops change for each magnetic material and depend heavily on electrical operating conditions (e.g., waveform, frequency, amplitude, and dc bias), mechanical properties (e.g., pressure and vibration), temperature, and geometry of the magnetic components, and in a nonlinear and coupled fashion. Understanding the complex and intertwined relationship these factors have on core loss is important for the development of accurate models and their applicability and limitations. Existing studies on power magnetics are usually developed based on a small amount of data and do not reveal the full magnetic behavior across a wide range of operating conditions. In this article, based on a recently developed large-scale open-source database-MagNet-the core losses and hysteresis loops of Mn-Zn ferrites are analyzed over a wide range of amplitudes, frequencies, waveform shapes, dc bias levels, and temperatures, to quantify the complexity of modeling magnetic core losses, amplitude permeability, and hysteresis loops and provide guidelines for modeling power magnetics with data-driven methods.

Original languageEnglish (US)
Pages (from-to)14292-14316
Number of pages25
JournalIEEE Transactions on Power Electronics
Volume38
Issue number11
DOIs
StatePublished - Nov 1 2023

All Science Journal Classification (ASJC) codes

  • Electrical and Electronic Engineering

Keywords

  • B-H loop
  • core loss
  • data visualization
  • data-driven methods
  • hysteresis
  • open-source database
  • power magnetics
  • soft ferrites.

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