Accurate core loss modeling is critical for the design of high-efficiency power electronic systems. Traditional core loss modeling methods have limitations in prediction accuracy. To advance this field, the IEEE Power Electronics Society launched the MagNet Challenge in 2023, the first international competition focused on data-driven power electronics design methods, aiming to uncover complex loss patterns in magnetic components through a data-driven paradigm. Although purely data-driven models demonstrate strong fitting performance, their interpretability and cross-distribution generalization capabilities remain limited. To address these issues, this paper proposes a hybrid model, SEPI-TFPNet, which integrates empirical models with deep learning. The physical-prior submodule employs a spectral entropy discrimination mechanism to select the most suitable empirical model under different excitation waveforms. The data-driven submodule incorporates convolutional neural networks, multi-head attention mechanisms, and bidirectional long short-term memory networks to extract flux-density time-series features. An adaptive feature fusion module is introduced to improve multimodal feature interaction and integration. Using the MagNet dataset containing various magnetic materials, this paper evaluates the proposed method and compares it with 21 representative models from the 2023 challenge and three advanced methods from 2024-2025. The results show that the proposed method achieves improved modeling accuracy and robustness.

翻译：精确的磁芯损耗建模对于高效电力电子系统的设计至关重要。传统的磁芯损耗建模方法在预测精度方面存在局限。为推动该领域发展，IEEE电力电子学会于2023年发起首届聚焦数据驱动电力电子设计方法的国际竞赛——MagNet挑战赛，旨在通过数据驱动范式揭示磁性元件中的复杂损耗规律。尽管纯数据驱动模型展现出较强的拟合性能，但其可解释性与跨分布泛化能力仍显不足。为解决这些问题，本文提出一种融合经验模型与深度学习的混合模型SEPI-TFPNet。其物理先验子模块采用谱熵判别机制，以选择不同激励波形下最适用的经验模型；数据驱动子模块结合卷积神经网络、多头注意力机制与双向长短期记忆网络，以提取磁通密度时间序列特征。本文引入自适应特征融合模块以增强多模态特征的交互与整合。基于包含多种磁性材料的MagNet数据集，本文评估了所提方法，并与2023年挑战赛的21个代表性模型及2024-2025年的三种先进方法进行对比。结果表明，所提方法在建模精度与鲁棒性方面均获得提升。