A Cyber‐Physical Digital Twin Framework for State Estimation of Dendrite‐Risk Prediction and Resilient Control in Solid‐State Batteries

ABSTRACT

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.