Digital modeling and intelligent control methods for lithium deposition evolutions
Yefan Sun, Zhaoxia Peng, Xiaopeng Zhu, Xinkai Zhang, Xinhua Liu, Shichun Yang, Xiaoyu Yan, Justice Delali Akoto, Nadeen S. B. M. Alotaibi, Rui Tan
Abstract
Abstract Lithium metal anodes are critical for next generation high energy density batteries due to their ultrahigh theoretical capacity and low electrochemical potential. However, uncontrolled dendritic lithium growth during deposition causes severe issues such as internal short circuits, reduced Coulombic efficiency, and rapid capacity fading, significantly hindering practical application. Conventional experimental methods struggle to capture the dynamic, nanoscale interfacial reactions and complex three‐dimensional lithium morphological evolution during cycling. In this context, digital modeling and intelligent control offer promising new avenues for investigating and managing lithium deposition behavior. This review systematically summarizes recent advances in digital characterization techniques, multiphysics modeling, and simulations for lithium metal anodes, focusing on elucidating the thermodynamic and kinetic mechanisms behind dendrite nucleation, growth, and suppression. Moreover, we highlight how intelligent regulation strategies—particularly those utilizing machine learning and data driven closed loop feedback, which can guide uniform lithium deposition—enable real‐time optimization of interfacial conditions. We envision future directions for digital battery research, emphasizing three transformative trends: “Reliable data replaces expert experience, Computing power surpasses human brainpower and Machine substitution for human labor”, laying a theoretical foundation for developing safe, long life lithium metal batteries.