Abstract:As a complex electrochemical system with strong multiphysics coupling, the performance of lithium batteries is affected by the intricate interplay of physicochemical processes spanning atomic, mesoscopic, and macroscopic scales. Conventional single-scale simulations are insufficient to elucidate such intricate couplings or fully explain the complex failure mechanisms. To systematically characterize the internal multi-field couplings, it is necessary to establish a comprehensive multiscale computational modeling framework that integrates these key physicochemical processes. Therefore, multiscale computational modeling has emerged as an essential approach for understanding electrochemical-mechanical interactions, predicting performance degradation, and guiding the design of next-generation materials and structures. This review summarizes the overall research framework and recent advances in multiscale modeling of lithium-ion batteries, with a focus on the key scientific questions and mainstream methodologies across three hierarchical levels, i., microscopic, mesoscopic, and macroscopic. Key methodologies include density functional theory, molecular dynamics, phase-field modeling, Kinetic Monte Carlo, and the foundational porous electrode theory. Models at each scale play crucial roles in elucidating electrode material structure and mechanical behavior, ion transport and solvation, the formation and evolution of solid-electrolyte interphases, dendrite growth, and the multiphysics coupling phenomena within battery cells. Density Functional Theory, at the quantum level, serves as the cornerstone for resolving intrinsic material structures, thermodynamic properties, and mechanical characteristics. At the atomic scale, Molecular dynamics is crucial for clarifying ion transport mechanisms within electrolytes and the complex dynamics of solvation structures. Moving to the mesoscopic scale, phase-field modeling shows unique advantages in simulating the dynamic formation and evolution of the solid electrolyte interphase and the complex morphological growth of lithium dendrites. Finally, macroscopic porous electrode theory provides the core framework for linking all these microscopic mechanisms to the overall cell-level multiphysical coupling behavior. Furthermore, key information transfer strategies for bridging these different scales-such as hierarchical (top-down) and concurrent (handshaking) modeling, as well as emerging machine-learning surrogate models-are discussed in detail. We emphasize that, despite the power of multiscale approaches, current simulations still face substantial challenges in reconciling high fidelity with computational efficiency. Key limitations include the prohibitive computational cost of fine-grained models, persistent difficulties in validating model consistency across scales, and the limited ability to accurately represent the complex, heterogeneous microstructures of practical electrodes. We conclude that the deep integration of physics-informed machine learning, the development of high-fidelity real-time battery digital twins, and the closed-loop integration of computation with advanced characterization techniques will be the key pathways to overcoming these challenges and accelerating the design of next-generation high-performance batteries.
