MagNet-AI: Neural Network as Datasheet for Magnetics Modeling and Material Recommendation

Haoran Li, Diego Serrano, Shukai Wang, Minjie Chen

Research output: Contribution to journalArticlepeer-review

7 Scopus citations

Abstract

This article presents the MagNet-AI platform as an online platform to demonstrate the 'neural network as datasheet' concept for B-H loop modeling and material recommendation of power magnetics across wide operation range. Instead of directly presenting the measured characteristics of magnetic core materials as time sequences, we employ a neural network to capture the B-H loop mapping relationships of magnetic materials under different excitation waveforms at different temperatures and dc bias. Long short-term memory and transformer-based neural network models are developed, verified, and compared. The neural network can be used to rapidly predict hysteresis loops and core losses under different operating conditions, compare materials, and recommend materials for design. The neural network model is also proved effective in reconstructing the raw measurement while accurately maintaining the magnetic characteristics, enabling rapid material evaluation and comparison.

Original languageEnglish (US)
Pages (from-to)15854-15869
Number of pages16
JournalIEEE Transactions on Power Electronics
Volume38
Issue number12
DOIs
StatePublished - Dec 1 2023

All Science Journal Classification (ASJC) codes

  • Electrical and Electronic Engineering

Keywords

  • Core loss
  • hysteresis loop
  • machine learning
  • neural network
  • power magnetics

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