Actively Regulated Electrochemical Interfaces for Robust All-Solid-State Batteries Using Physics-Informed Learning

All-solid-state batteries have the potential to transform energy storage by providing safer, higher-energy alternatives to today's lithium-ion batteries. However, their widespread adoption is limited by a gradual loss of electrical contact between solid state battery components during operation. The performance decline is caused by increased resistance that accelerates degradation. Ultimately, this defect can lead to battery failure. This project will develop a new strategy involving active monitoring and regulation of the internal interfaces during battery operation. The researchers will design a control system to maintain strong electrical contact and high battery performance. This will be accomplished by applying pressure to mitigate failure of interfacial contact during fast charging and discharging. This mechanical strategy does not rely on new materials to prevent or delay failure. By combining physics-based modeling, intelligent monitoring, and real-time control, the project will extend battery life while maintaining high performance. The project will also provide interdisciplinary research and educational opportunities that integrate electrochemistry, control systems, and artificial intelligence tools. This project will develop a physics-informed framework for actively regulating electrochemical interfaces in all-solid-state batteries through integrated modeling, estimation, learning, and control. The research will establish a multiphysics model describing the coupled electrochemical and mechanical evolution of interfacial contact during battery cycling. Physics-informed state observers and data-driven learning methods will reconstruct the evolving interface using accessible electrochemical measurements, including voltage and impedance. These state estimates will enable event-triggered optimal control and deep reinforcement learning algorithms that adapt charging current profiles and stack pressure (feedback) to mitigate contact loss and restore interfacial integrity. The proposed framework will be validated experimentally using symmetric and full-cell all-solid-state batteries. By departing from passive materials optimization to active interface regulation, this project will establish a new paradigm for intelligent battery management and provide broadly applicable methodologies for monitoring and controlling complex electrochemical systems with evolving interfaces. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.