.png){kind=icon}
.png){kind=icon}
Return
A Cyber-Physical Digital Twin Framework for State Estimation of Dendrite-Risk Prediction and Resilient Control in Solid-State Batteries
.png){kind=icon}
OA
AI
S
P
R
L
H
M
H
J
S
DOI：10.1002/gch2.70141.png)
.png){kind=icon}
.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Solid-state batteries (SSBs) offer high energy density and improved safety but remain vulnerable to hidden electro-chemo-thermo-mechanical degradation, lithium dendrite formation, and cyber-physical attacks that cannot be reliably detected using conventional battery-management systems. This work presents a cyber-physical digital twin framework for real-time state estimation, dendrite-risk prediction, and resilient control of SSBs. A physics-regularized reduced-order model integrated with Moving Horizon Estimation reconstructs unmeasurable internal concentration, potential, temperature, stress, and interfacial degradation states from limited terminal measurements. A physics-informed Dendrite-Risk Forecast Index (DRFI) is developed by combining stress evolution, current-density variance, interfacial impedance growth, and thermal-gradient severity to provide early degradation warning. Physics-consistent anomaly detection identifies measurement manipulation and cyber-attacks, while a DRFI-aware Model Predictive Controller adaptively regulates battery operation to mitigate degradation. Simulation studies under normal operation, accelerated degradation, and cyber-attack scenarios demonstrate state-estimation errors of 5%–7% during normal cycling and less than 10% under accelerated ageing, 0.8–1.2 cycles of early degradation prediction, 40%–55% reduction in stress concentration, and attack detection within 80–110 ms. These results demonstrate that the proposed physics-guided cyber-physical digital twin provides an interpretable and computationally efficient framework for predictive battery management, degradation forecasting, and resilient operation of next-generation solid-state batteries.
Keywords:
cyber-physical anomaly detection
dendrite-risk forecast index
physics-embedded digital twin
physics-regularized battery management
solid-state batteries
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
6.4
Papers:
695
Citations:
2.7K
Organization
Cited Papers