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Why MagNet: Quantifying the Complexity of Modeling Power Magnetic Material Characteristics
This paper 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 lo...
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Published in: | IEEE transactions on power electronics 2023-11, Vol.38 (11), p.1-25 |
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Main Authors: | , , , , , , , , , |
Format: | Article |
Language: | English |
Subjects: | |
Citations: | Items that this one cites Items that cite this one |
Online Access: | Get full text |
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Summary: | This paper 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, dc bias), mechanical properties (e.g., pressure,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 paper, based on a recently dev loped 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 datadriven methods. |
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ISSN: | 0885-8993 1941-0107 |
DOI: | 10.1109/TPEL.2023.3291084 |