用电池管理数据反推关键设计参数，实现电池健康与设计的闭环优化。
Bridging battery design and health assessment through virtual sensing and physics-informed learning
- 基于物理约束的虚拟传感，从标准信号推断扩散系数、电极厚度等难测参数。
- 仅用2%早期数据，寿命预测误差降低6-8倍，容量衰减误差减少39%。
- 适合电池研发与智能管理领域，推动真实使用反馈驱动设计迭代。
快充锂离子电池（LiBs）需可靠健康监测以保障耐久性、安全性和用户信心，尤其在具有双向能量流动的车网互动应用中。然而，电池管理仍与老化材料和结构根源脱节，限制了可解释的健康评估与指导性设计。本文提出一种融合虚拟传感与物理信息学习的框架，直接从标准电池管理系统（BMS）测量中推断难以测量的设计参数，包括固态扩散系数、电极厚度、离子浓度和颗粒尺寸。在多种快速充电策略和驾驶工况下，嵌入由数字孪生导出的颗粒开裂机制作为软约束，使轨迹与寿命预测误差相比现有机器学习基线降低6-8倍，仅需2%早期观测数据。进一步表明，精确退化外推无需完整控制方程；经验证的部分机理与有限数据联合优化即可提供足够引导。虚拟传感将标准充电信号转化为潜在设计变量，无需额外传感器，连接可观测行为与深层老化过程，使容量损失误差降低至39%，寿命终点（EOL）误差减少17%，预测变异性降低54%，支持实时探索新电池构型。该框架建立了部署与开发间的实用反馈回路，展示了如何通过实际运行持续优化复杂多物理系统的设计决策。
原文摘要 · Abstract (English)
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.
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