锂电池多尺度计算模拟研究进展

Progress in multiscale computational modeling of lithium batteries

关键词:  锂离子电池  多尺度模拟  密度泛函理论  分子动力学  相场法

Key words:  lithium-ion batteries  multiscale modeling  density functional theory  molecular dynamics  phase-field method

摘要:

锂电池作为复杂的多场耦合电化学系统,其性能受跨越原子、介观至宏观多尺度的物理化学过程协同影响。单一尺度方法难以揭示内部的多场耦合与失效机制。文中为系统性地阐明锂电池内部的多场耦合机制,需要建立一个整合关键物理化学过程的多尺度计算模拟框架。因此,多尺度计算模拟已成为理解电池工作原理、预测性能衰退,以及指导材料结构优化的重要工具。首先,综述了锂电池多尺度模拟的研究框架与最新进展,重点梳理了 微观、介观与宏观3个层级的核心问题与主流方法,包括密度泛函理论、分子动力学、相场法、动力学蒙特卡洛,以及多孔电极理论等。其次,各尺度模型在揭示电极材料结构与力学特性、离子输运与溶剂化、固态电解质界面的形成与演化、枝晶生长及电芯多物理场耦合行为中均发挥了关键作用。最后,介绍了不同尺度间的信息传递策略,如层级式建模、并发式建模,以及新兴的机器学习代理模型。结果表明,当前多尺度模拟在实现高保真度与计算效率的平衡方面,仍面临计算成本高昂、跨尺度模型验证困难,以及真实电极微观结构不均匀性表征不足等挑战,而物理信息机器学习、电池数字孪生、计算与先进表征闭环融合是解决上述挑战、加速下一代高性能电池设计的关键发展方向。

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.