AI 中文总结
研究针对锂离子电池健康监测与设计脱节问题，提出基于虚拟传感和物理信息学习的框架，能推断设计参数，嵌入机制降低预测误差，虚拟传感减少相关误差，建立反馈回路，助力电池设计与评估。
AI 中文摘要
锂离子电池的快速充电需要强大的健康监测，然而电池管理与老化的材料和结构根源脱节。本文提出一个具有虚拟传感的物理信息学习框架，能从标准电池管理系统测量中推断难以测量的设计参数。通过嵌入数字孪生衍生的颗粒破裂机制作为软约束，降低了轨迹和寿命预测误差。虚拟传感无需额外传感器，减少了容量损失等误差，建立了部署与开发之间的反馈回路。
英文摘要
Supercharging of lithium-ion batteries (LiBs) requires robust health monitoring to ensure durability, safety, and user confidence, particularly for emerging vehicle-to-grid applications with bidirectional energy flows. Yet battery management remains largely disconnected from the material and structural origins of aging, limiting both interpretable health assessment and informed battery design. Here we propose a physics-informed learning framework with virtual sensing that infers hard-to-measure design parameters, including solid-state diffusion coefficient, electrode thickness, ion concentration, and particle size, directly from standard battery management system (BMS) measurements. Across diverse fast-charging strategies and driving profiles, embedding a digital-twin-derived particle-cracking mechanism as a soft constraint reduces trajectory and lifetime prediction errors by 6-8 times relative to state-of-the-art machine learning baselines using only 2% early-life observations. We further show that accurate degradation extrapolation does not require fully resolved governing equations; validated partial mechanisms, jointly refined with limited data, provide sufficient guidance. Virtual sensing transforms standard charging signals into latent design variables without additional sensors, bridging observable battery behavior and underlying aging processes while reducing capacity loss error by up to 39%, end-of-life (EOL) error by 17%, and prediction variability by up to 54%, enabling real-time exploration of new battery configurations. More broadly, the proposed framework establishes a practical feedback loop between deployment and development, demonstrating how real-world operation can continuously inform upstream design decisions across complex multiphysics systems.
Comments28 pages, 7 figures
