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arXiv · 2509.02366

Towards Intelligent Systems for Battery Management: A Five-Tier Digital Twin Architecture

Tianwen Zhu·Hao Wang·Zhiwei Cao·Simon See·Yonggang Wen
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Abstract

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.

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Tianwen Zhu, Hao Wang, Zhiwei Cao, Simon See, Yonggang Wen. 2026-01-12. Towards Intelligent Systems for Battery Management: A Five-Tier Digital Twin Architecture. https://arxiv.org/abs/2509.02366

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