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.

翻译：随着数字孪生技术日益融入电池管理系统，以满足对透明化与全生命周期感知运行日益增长的需求，现有电池数字孪生仍存在运行流程碎片化的问题，且缺乏一个贯穿电池全生命周期的、用于协调建模、推理与决策的架构视角。为此，我们开发了一个统一的五层电池数字孪生框架，该框架将关键功能集成到一个连贯的流程中，并促进对数字孪生更清晰的架构理解。这五层包括几何建模、描述性分析、物理信息预测、规范性优化与自主控制。在定量评估中，所提出的架构实现了高保真多物理场校准，电压与温度预测误差分别为0.92%和0.18%，并以1.09%的平均绝对百分比误差及经过校准的不确定性提供健康状态估计。作为首个由NVIDIA生态系统及物理-AI技术赋能的电池数字孪生系统，我们提出的五层框架将电池管理从被动保护转向可解释、可预测且自主的范式，为开发下一代电池管理与能源管理系统铺平了道路。