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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2509.02366 |
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| _version_ | 1866909987182215168 |
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| author | Zhu, Tianwen Wang, Hao Cao, Zhiwei See, Simon Wen, Yonggang |
| author_facet | Zhu, Tianwen Wang, Hao Cao, Zhiwei See, Simon Wen, Yonggang |
| contents | As digital twin technologies are increasingly incorporated into battery management systems to meet the growing need for transparent and lifecycle-aware operation, existing battery digital twins still suffer from fragmented operational processes and lack an architectural perspective to coordinate modeling, inference, and decision-making throughout the battery lifecycle. To this end, we develop a unified five-tier battery digital twin framework that integrates key functionalities into a coherent pipeline and facilitates a clearer architectural understanding of digital twins. The five-tier comprises geometric modeling, descriptive analytics, physics-informed prediction, prescriptive optimization, and autonomous control. In quantitative evaluation, the resulting architecture achieves high-fidelity multi-physics calibration with 0.92\% voltage and 0.18\% temperature prediction error, and provides state-of-health estimation with 1.09\% MAPE and calibrated uncertainty. As the first battery digital twin system empowered by the NVIDIA ecosystem with physics-AI technologies, our proposed five-tier framework shifts battery management from reactive protection to an interpretable, predictive, and autonomous paradigm, paving the path to develop next-generation battery management and energy management systems. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_02366 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Towards Intelligent Systems for Battery Management: A Five-Tier Digital Twin Architecture Zhu, Tianwen Wang, Hao Cao, Zhiwei See, Simon Wen, Yonggang Networking and Internet Architecture As digital twin technologies are increasingly incorporated into battery management systems to meet the growing need for transparent and lifecycle-aware operation, existing battery digital twins still suffer from fragmented operational processes and lack an architectural perspective to coordinate modeling, inference, and decision-making throughout the battery lifecycle. To this end, we develop a unified five-tier battery digital twin framework that integrates key functionalities into a coherent pipeline and facilitates a clearer architectural understanding of digital twins. The five-tier comprises geometric modeling, descriptive analytics, physics-informed prediction, prescriptive optimization, and autonomous control. In quantitative evaluation, the resulting architecture achieves high-fidelity multi-physics calibration with 0.92\% voltage and 0.18\% temperature prediction error, and provides state-of-health estimation with 1.09\% MAPE and calibrated uncertainty. As the first battery digital twin system empowered by the NVIDIA ecosystem with physics-AI technologies, our proposed five-tier framework shifts battery management from reactive protection to an interpretable, predictive, and autonomous paradigm, paving the path to develop next-generation battery management and energy management systems. |
| title | Towards Intelligent Systems for Battery Management: A Five-Tier Digital Twin Architecture |
| topic | Networking and Internet Architecture |
| url | https://arxiv.org/abs/2509.02366 |