Data-driven modeling with experimental augmentation for the modulation strategy of the dual-active-bridge converter

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, m...

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Main Authors: Li, Xinze, Pou, Josep, Dong, Jiaxin, Lin, Fanfan, Wen, Changyun, Mukherjee, Suvajit, Zhang, Xin
Other Authors: School of Electrical and Electronic Engineering
Format: Article
Language:English
Published: 2023
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Online Access:https://hdl.handle.net/10356/172335
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Institution: Nanyang Technological University
Language: English
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spelling sg-ntu-dr.10356-1723352023-12-06T06:12:41Z Data-driven modeling with experimental augmentation for the modulation strategy of the dual-active-bridge converter Li, Xinze Pou, Josep Dong, Jiaxin Lin, Fanfan Wen, Changyun Mukherjee, Suvajit Zhang, Xin School of Electrical and Electronic Engineering Interdisciplinary Graduate School (IGS) Rolls-Royce@NTU Corporate Lab Engineering::Electrical and electronic engineering Engineering::Computer science and engineering Artificial Intelligence Experimental Augmentation 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. 2023-12-06T06:12:41Z 2023-12-06T06:12:41Z 2024 Journal Article Li, X., Pou, J., Dong, J., Lin, F., Wen, C., Mukherjee, S. & Zhang, X. (2024). Data-driven modeling with experimental augmentation for the modulation strategy of the dual-active-bridge converter. IEEE Transactions On Industrial Electronics, 71(3), 2626-2637. https://dx.doi.org/10.1109/TIE.2023.3265027 0278-0046 https://hdl.handle.net/10356/172335 10.1109/TIE.2023.3265027 2-s2.0-85153401237 3 71 2626 2637 en IEEE Transactions on Industrial Electronics © 2023 IEEE. All rights reserved.
institution Nanyang Technological University
building NTU Library
continent Asia
country Singapore
Singapore
content_provider NTU Library
collection DR-NTU
language English
topic Engineering::Electrical and electronic engineering
Engineering::Computer science and engineering
Artificial Intelligence
Experimental Augmentation
spellingShingle Engineering::Electrical and electronic engineering
Engineering::Computer science and engineering
Artificial Intelligence
Experimental Augmentation
Li, Xinze
Pou, Josep
Dong, Jiaxin
Lin, Fanfan
Wen, Changyun
Mukherjee, Suvajit
Zhang, Xin
Data-driven modeling with experimental augmentation for the modulation strategy of the dual-active-bridge converter
description 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.
author2 School of Electrical and Electronic Engineering
author_facet School of Electrical and Electronic Engineering
Li, Xinze
Pou, Josep
Dong, Jiaxin
Lin, Fanfan
Wen, Changyun
Mukherjee, Suvajit
Zhang, Xin
format Article
author Li, Xinze
Pou, Josep
Dong, Jiaxin
Lin, Fanfan
Wen, Changyun
Mukherjee, Suvajit
Zhang, Xin
author_sort Li, Xinze
title Data-driven modeling with experimental augmentation for the modulation strategy of the dual-active-bridge converter
title_short Data-driven modeling with experimental augmentation for the modulation strategy of the dual-active-bridge converter
title_full Data-driven modeling with experimental augmentation for the modulation strategy of the dual-active-bridge converter
title_fullStr Data-driven modeling with experimental augmentation for the modulation strategy of the dual-active-bridge converter
title_full_unstemmed Data-driven modeling with experimental augmentation for the modulation strategy of the dual-active-bridge converter
title_sort data-driven modeling with experimental augmentation for the modulation strategy of the dual-active-bridge converter
publishDate 2023
url https://hdl.handle.net/10356/172335
_version_ 1784855598000504832