Our research bridges electrochemistry, multiphysics modeling, mechanics, and data-driven analytics to advance practical battery diagnostics and safety assessment for electric vehicles and energy storage systems. A central focus is battery aging and lifetime prediction, where we link resistance evolution and temperature-dependent storage effects to SOH degradation and long-term cycling behavior. Building on this foundation, we develop coupled electrochemical, thermal, and mechanical models to investigate failure modes and safety-critical scenarios — including internal short circuits, mechanical abuse, dendrite growth, and SEI evolution — while also designing real-time algorithms to reliably estimate key battery states such as SOC, SOH, and temperature under practical operating conditions. Taken together, our work aims to generate both deployable technologies and deeper mechanistic understanding, ultimately strengthening battery reliability and safety and accelerating the translation of laboratory research into real-world BMS solutions.
