@inproceedings{4fa0ae5537384b27af4247729aaa5106,
title = "Investigating the Mutual Impact of Waveform, Temperature, and Dc-Bias on Magnetic Core Loss using Neural Network Models",
abstract = "This paper investigates the mutual impact of waveform, temperature and dc-bias on the magnetic core loss using data and models from the MagNet database and MagNet-AI platform. The impact of varying temperatures and dc-biases on localized Steinmetz parameters (k, α, and β) are visualized, considering weighted impact on core losses from different waveform excitations. It is found that the temperature and dc-bias impact on core losses are strongly correlated, while the impact of waveform shapes on the magnetic core loss is generally independent from temperature and dc-bias. This paper verified the limitations of existing methods on evaluating the power magnetics performance and the importance of incorporating temperature, waveform, and dc-bias as mutually correlated factors in modeling power magnetics material characteristics.",
keywords = "core loss, dc-bias, hysteresis loop, power magnetics, soft magnetic materials",
author = "Joe Li and Edward Deleu and Wonju Lee and Haoran Li and Minjie Chen and Shukai Wang",
note = "Publisher Copyright: {\textcopyright} 2024 IEEE.; 39th Annual IEEE Applied Power Electronics Conference and Exposition, APEC 2024 ; Conference date: 25-02-2024 Through 29-02-2024",
year = "2024",
doi = "10.1109/APEC48139.2024.10509194",
language = "English (US)",
series = "Conference Proceedings - IEEE Applied Power Electronics Conference and Exposition - APEC",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "391--395",
booktitle = "2024 IEEE Applied Power Electronics Conference and Exposition, APEC 2024",
address = "United States",
}