Advances and challenges in thermal runaway modeling of lithium-ion batteries

热失控 多物理 可扩展性 计算机科学 多尺度建模 系统工程 建模与仿真 风险分析(工程) 模拟 物理 工程类 电池(电) 有限元法 生物信息学 生物 数据库 量子力学 结构工程 功率(物理) 医学
作者
Gongquan Wang,Ping Ping,Depeng Kong,Rongqi Peng,Xu He,Yue Zhang,Xinyi Dai,Jennifer Wen
出处
期刊:The Innovation [Elsevier BV]
卷期号:5 (4): 100624-100624 被引量:86
标识
DOI:10.1016/j.xinn.2024.100624
摘要

The broader application of lithium-ion batteries (LIBs) is constrained by safety concerns arising from thermal runaway (TR). Accurate prediction of TR is essential to comprehend its underlying mechanisms, expedite battery design, and enhance safety protocols, thereby significantly promoting the safer use of LIBs. The complex, nonlinear nature of LIB systems presents substantial challenges in TR modeling, stemming from the need to address multiscale simulations, multiphysics coupling, and computing efficiency issues. This paper provides an extensive review and outlook on TR modeling technologies, focusing on recent advances, current challenges, and potential future directions. We begin with an overview of the evolutionary processes and underlying mechanisms of TR from multiscale perspectives, laying the foundation for TR modeling. Following a comprehensive understanding of TR phenomena and mechanisms, we introduce a multiphysics coupling model framework to encapsulate these aspects. Within this framework, we detail four fundamental physics modeling approaches: thermal, electrical, mechanical, and fluid dynamic models, highlighting the primary challenges in developing and integrating these models. To address the intrinsic trade-off between computational accuracy and efficiency, we discuss several promising modeling strategies to accelerate TR simulations and explore the role of AI in advancing next-generation TR models. Last, we discuss challenges related to data availability, model scalability, and safety standards and regulations.
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