For the performance modeling of power converters, the mainstream approaches are essentially knowledge-based, suffering from heavy manpower burden and low modeling accuracy. Recent emerging data-driven techniques greatly relieve human reliance by automatic modeling from simulation data. However, model discrepancy may occur due to unmodeled parasitics, deficient thermal and magnetic models, unpredictable ambient conditions, etc. These inaccurate data-driven models based on pure simulation cannot represent the practical performance in physical world, hindering their applications in power converter modeling. To alleviate model discrepancy and improve accuracy in practice, this paper proposes a novel data-driven modeling with experimental augmentation (D2EA), leveraging both simulation data and experimental data. In D2EA, simulation data aims to establish basic functional landscape, and experimental data focuses on matching actual performance in real world. The D2EA approach is instantiated for the efficiency optimization of a hybrid modulation for neutral-point-clamped dual-active-bridge (NPC-DAB) converter. The proposed D2EA approach realizes 99.92% efficiency modeling accuracy, and its feasibility is comprehensively validated in 2-kW hardware experiments, where the peak efficiency of 98.45% is attained. Overall, D2EA is data-light and can achieve highly accurate and highly practical data-driven models in one shot, and it is scalable to other applications, effortlessly.

翻译：针对电力变换器性能建模，主流方法本质上是基于知识的，存在人力负担重、建模精度低的问题。新兴的数据驱动技术通过仿真数据自动建模，极大减轻了对人工的依赖。然而，由于未建模的寄生参数、热模型与磁模型缺陷、不可预测的环境条件等因素，可能导致模型偏差。这些基于纯仿真的非精确数据驱动模型无法表征物理世界中的实际性能，阻碍了其在电力变换器建模中的应用。为缓解模型偏差并在实践中提高精度，本文提出一种新颖的数据驱动结合实验增强建模方法（D2EA），该方法同时利用仿真数据和实验数据。在D2EA中，仿真数据用于建立基本功能拓扑，实验数据则专注于匹配真实世界中的实际性能。将该D2EA方法实例化，用于中点钳位型双有源桥（NPC-DAB）变换器混合调制的效率优化。所提D2EA方法实现了99.92%的效率建模精度，并通过2-kW硬件实验全面验证了其可行性，实验中获得了98.45%的峰值效率。总体而言，D2EA方法数据需求轻量，可一次性获得高精度且高实用性的数据驱动模型，并且可轻松扩展至其他应用。