Investigating the Mutual Impact of Waveform, Temperature, and Dc-Bias on Magnetic Core Loss using Neural Network Models

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.