储能科学与技术 ›› 2023, Vol. 12 ›› Issue (2): 366-382.doi: 10.19799/j.cnki.2095-4239.2022.0504
张慧敏1(), 王京2, 王一博1, 郑家新3, 邱景义1, 曹高萍1, 张浩1()
收稿日期:
2022-09-05
修回日期:
2022-09-15
出版日期:
2023-02-05
发布日期:
2023-02-24
通讯作者:
张浩
E-mail:zhanghuimin_506@126.com;dr.h.zhang@hotmail.com
作者简介:
张慧敏(1989—),女,博士,助理研究员,研究方向为高性能储能材料,E-mail:zhanghuimin_506@126.com;
基金资助:
Huimin ZHANG1(), Jing WANG2, Yibo WANG1, Jiaxin ZHENG3, Jingyi QIU1, Gaoping CAO1, Hao ZHANG1()
Received:
2022-09-05
Revised:
2022-09-15
Online:
2023-02-05
Published:
2023-02-24
Contact:
Hao ZHANG
E-mail:zhanghuimin_506@126.com;dr.h.zhang@hotmail.com
摘要:
锂离子电池高还原性负极表面的固体电解质界面膜(SEI)是影响电池电化学性能与稳定性的关键组分,但SEI的形成涉及多尺度、多物理场下的复杂过程,且组分异常复杂。在电池外壳“黑箱”环境下,现有的非原位技术对其表征无能为力,而原位技术又难以得到较高真实度的结果,难以深入理解SEI的相关机制。采用数学的方法对SEI进行建模研究,有望将复杂的物理场进行解耦,进而精准描述SEI的形成和演化的机制与过程,是近年来电池领域的研究热点。本文按对象尺度由小到大从原子到介观尺度逐渐增大的顺序分别总结了第一性原理分子动力学、反应力场分子动力学、经典分子动力学、蒙特卡罗算法、宏观性质建模在SEI建模研究中的应用进展,介绍其在指导电极材料开发及电解液改性方面的成功案例,着重讨论分析了多尺度建模研究SEI的难点与不足。并提出针对SEI的电化学势场特性建立力场算法平台,采用动力学蒙特卡罗方法和机器学习辅助将模型拓展到数万直至数亿原子,并通过逐级计算结合试验验证及专家评估促使收敛,获得具有量子力学精度且带电化学势场的SEI模型,有望实现SEI的长时域建模。
中图分类号:
张慧敏, 王京, 王一博, 郑家新, 邱景义, 曹高萍, 张浩. 锂离子电池SEI多尺度建模研究展望[J]. 储能科学与技术, 2023, 12(2): 366-382.
Huimin ZHANG, Jing WANG, Yibo WANG, Jiaxin ZHENG, Jingyi QIU, Gaoping CAO, Hao ZHANG. Multiscale modeling of the SEI of lithium-ion batteries[J]. Energy Storage Science and Technology, 2023, 12(2): 366-382.
表 1
用于研究SEI的主流多尺度模拟方法及其分类[1]"
Methods | MSM types | Scientific problems of SEI studied |
---|---|---|
First principle MD | MSMSL | Composition and decomposition reactions |
Reactive MD | MSMSL | Decomposition reactions |
Classical MD | MSMSL | Transport and mechanical properties |
Hybrid MC MD | MSMIC | Formation process |
kMC | MSMSL | Formation process, ion diffusion |
Macroscopic models | MSMTC | Cell capacity face |
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