In this paper, decision tree, ANOVA, least squares and other modeling algorithms are used to study the core loss of magnetic components. For the excitation waveform classification, the decision tree model is used to realize high-precision classification based on the extracted waveform features; in the correction of the Steinmetz equation, the temperature factor is added to construct three kinds of correction equations, and the optimal correction equations are selected by the least squares method to improve the loss prediction accuracy; the one-factor and two-factor ANOVA models are used to investigate the effects of temperature, excitation waveform, and core material on the magnetic core loss, and the independent and synergistic effects of each factor are clarified. The effects of temperature, excitation waveform and core material on core loss are investigated using one-factor and two-factor ANOVA models, and the independent and synergistic effects of each factor are clarified. The research results provide a basis for the in-depth understanding of the core loss mechanism, and also lay a foundation for the design and optimization of magnetic components, which will help to improve the performance of power converters.

Zhengliang Lv, Dexun Li, Haoshuai Gao. Study and Optimization of Core Loss Characteristics of Magnetic Components Based on Multi-model Algorithm. Academic Journal of Mathematical Sciences (2025), Vol. 6, Issue 2: 1-10. https://doi.org/10.25236/AJMS.2025.060201.

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