Data-Driven Modeling With Experimental Augmentation for the Modulation Strategy of the Dual-Active-Bridge Converter Article Swipe
Xinze Li
,
Josep Pou
,
Jiaxin Dong
,
Fanfan Lin
,
Changyun Wen
,
Suvajit Mukherjee
,
Xin Zhang
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1109/tie.2023.3265027
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1109/tie.2023.3265027
For the performance modeling of power converters, the mainstream approaches\nare essentially knowledge-based, suffering from heavy manpower burden and low\nmodeling accuracy. Recent emerging data-driven techniques greatly relieve human\nreliance by automatic modeling from simulation data. However, model discrepancy\nmay occur due to unmodeled parasitics, deficient thermal and magnetic models,\nunpredictable ambient conditions, etc. These inaccurate data-driven models\nbased on pure simulation cannot represent the practical performance in physical\nworld, hindering their applications in power converter modeling. To alleviate\nmodel discrepancy and improve accuracy in practice, this paper proposes a novel\ndata-driven modeling with experimental augmentation (D2EA), leveraging both\nsimulation data and experimental data. In D2EA, simulation data aims to\nestablish basic functional landscape, and experimental data focuses on matching\nactual performance in real world. The D2EA approach is instantiated for the\nefficiency optimization of a hybrid modulation for neutral-point-clamped\ndual-active-bridge (NPC-DAB) converter. The proposed D2EA approach realizes\n99.92% efficiency modeling accuracy, and its feasibility is comprehensively\nvalidated in 2-kW hardware experiments, where the peak efficiency of 98.45% is\nattained. Overall, D2EA is data-light and can achieve highly accurate and\nhighly practical data-driven models in one shot, and it is scalable to other\napplications, effortlessly.\n
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1109/tie.2023.3265027
- OA Status
- green
- Cited By
- 28
- References
- 30
- Related Works
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- OpenAlex ID
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All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4365128697Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1109/tie.2023.3265027Digital Object Identifier
- Title
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Data-Driven Modeling With Experimental Augmentation for the Modulation Strategy of the Dual-Active-Bridge ConverterWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2023Year of publication
- Publication date
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2023-04-11Full publication date if available
- Authors
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Xinze Li, Josep Pou, Jiaxin Dong, Fanfan Lin, Changyun Wen, Suvajit Mukherjee, Xin ZhangList of authors in order
- Landing page
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https://doi.org/10.1109/tie.2023.3265027Publisher landing page
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YesWhether a free full text is available
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greenOpen access status per OpenAlex
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https://arxiv.org/pdf/2307.16173Direct OA link when available
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Dual (grammatical number), Bridge (graph theory), Modulation (music), Computer science, Electronic engineering, Engineering, Physics, Biology, Acoustics, Anatomy, Art, LiteratureTop concepts (fields/topics) attached by OpenAlex
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28Total citation count in OpenAlex
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2025: 11, 2024: 13, 2023: 4Per-year citation counts (last 5 years)
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30Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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