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：针对功率变换器性能建模，主流方法本质上基于知识驱动，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，由于未建模寄生参数、不完善的热磁模型以及不可预测的环境条件等因素，可能导致模型偏差。这些基于纯仿真的不准确数据驱动模型无法反映物理世界中的实际性能，从而阻碍了其在功率变换器建模中的应用。为缓解模型偏差并提高实际建模精度，本文提出一种基于实验增强的新型数据驱动建模方法（D2EA），该方法同时利用仿真数据和实验数据。在D2EA中，仿真数据旨在建立基础功能框架，而实验数据则侧重于匹配真实世界中的实际性能。该D2EA方法被具体应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。所提出的D2EA方法实现了99.92%的效率建模精度，并通过2千瓦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具有数据需求轻的特点，能够一次性构建高精度、高实用性的数据驱动模型，并可轻松扩展至其他应用场景。