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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2411.17748 |
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| _version_ | 1866913587093569536 |
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| author | Berramdane, Mohammed Riadh Battiston, Alexandre Bardi, Michele Blet, Nicolas Rémy, Benjamin Urbain, Matthieu |
| author_facet | Berramdane, Mohammed Riadh Battiston, Alexandre Bardi, Michele Blet, Nicolas Rémy, Benjamin Urbain, Matthieu |
| contents | Facing the thermal management challenges of Wide Bandgap (WBG) semiconductors, this study highlights the use of ARX parametric models, which provide accurate temperature predictions without requiring detailed understanding of component thickness disparities or material physical properties, relying solely on experimental measurements. These parametric models emerge as a reliable alternative to FEM simulations and conventional thermal models, significantly simplifying system identification while ensuring high result accuracy. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2411_17748 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Deployment of ARX Models for Thermal Forecasting in Power Electronics Boards Using WBG Semiconductors Berramdane, Mohammed Riadh Battiston, Alexandre Bardi, Michele Blet, Nicolas Rémy, Benjamin Urbain, Matthieu Signal Processing Machine Learning Facing the thermal management challenges of Wide Bandgap (WBG) semiconductors, this study highlights the use of ARX parametric models, which provide accurate temperature predictions without requiring detailed understanding of component thickness disparities or material physical properties, relying solely on experimental measurements. These parametric models emerge as a reliable alternative to FEM simulations and conventional thermal models, significantly simplifying system identification while ensuring high result accuracy. |
| title | Deployment of ARX Models for Thermal Forecasting in Power Electronics Boards Using WBG Semiconductors |
| topic | Signal Processing Machine Learning |
| url | https://arxiv.org/abs/2411.17748 |