Thermal runaway modeling of lithium-ion batteries at different scales: Recent advances and perspectives

热失控 材料科学 锂(药物) 比例(比率) 热的 建模与仿真 多尺度建模 工程物理 领域(数学) 机制(生物学) 过程(计算) 航空航天工程 系统工程 生化工程 工艺工程 纳米技术 计算机科学 热力学 电池(电) 化学 工程类 功率(物理) 物理 数学 纯数学 操作系统 计算化学 内分泌学 量子力学 医学
作者
Rongqi Peng,Depeng Kong,Ping Ping,Gongquan Wang,Xinzeng Gao,Hongpeng Lv,Hengle Zhao,Xu He,Yue Zhang,Xinyi Dai
出处
期刊:Energy Storage Materials [Elsevier BV]
卷期号:69: 103417-103417 被引量:25
标识
DOI:10.1016/j.ensm.2024.103417
摘要

Large-scale application of lithium-ion batteries (LIBs) is limited by the safety concerns induced by thermal runaway (TR). In the field of TR research, numerical simulation, with its low risk and suitable cost, has become a key method to study the characteristics and mechanism of TR in LIBs. Early endeavors in TR modeling mainly concentrated on individual cells or a single scale, which may not completely predict the failure of cells in applications at the system scale, where various physical phenomena can take place simultaneously in a multitude of cells. This paper presents a comprehensive review of TR modeling technologies for LIBs from multi-scale perspectives. Firstly, the mechanism of LIBs' internal heat generation and the modeling process of the reaction kinetics are elucidated at the particle scale. Subsequently, TR triggering mechanisms of LIBs are expounded under various abuse conditions at the cell-scale, and the related models from single-physical to multi-physical fields are introduced. Evolution processes and underlying mechanisms of gas generation, venting, and combustion induced by TR are also analyzed, along with the latest modeling research. For the module scale, three technologies for the TR propagation are introduced, and the modeling studies are reviewed for the prediction of various behaviors affecting TR propagation. Then the discussion is conducted on TR modeling studies for gas diffusion, fire propagation, and gas explosion involved at the system scale. Finally, several strategies have been proposed to accelerate TR modeling technologies to embrace the trend of multi-scale models and multi-physics field coupled models.
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