锂固态电池中空隙演化的研究:集成高通量相场建模、实验验证和机器学习

IF 26 1区 材料科学 Q1 CHEMISTRY, PHYSICAL
Yu Wang, Jiashun Shi, Haowen Gao, Ming-Sheng Wang, Chen Lin
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引用次数: 0

摘要

本研究建立了一个力学-电化学-多相场耦合模型来描述固态锂电池放电过程中界面空隙的演变。该模型考虑了金属锂在堆积力作用下的粘塑性流动引起的空隙坍塌,结合了空位形成、扩散和聚集等微观机制,揭示了空位积累与空隙生长之间的关系。它还准确地捕捉了应力对空位聚集的影响。利用Butler - Volmer方程,研究了电化学剥离过程中界面处空隙的动态收缩。在此模型的基础上,系统考察了堆积力和外加电流对孔隙形成和演化的协同作用。此外,采用高通量相场模拟、实验验证和机器学习技术来分析不同电解质材料在不同操作条件下的空隙生长模式。结果表明,在Li‐LLZO体系中,孔隙的生长主要受堆积压力和电流密度的共同影响,而在Li‐Argyrodite体系中,堆积压力的作用更为主要。这些发现加深了对固态电池中空隙演化的理解,并为优化电池运行和增强界面稳定性提供了定量基础。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Study of Void Evolution in Lithium Solid-State Batteries: Integrating High-Throughput Phase-Field Modeling, Experimental Validation, and Machine Learning

Study of Void Evolution in Lithium Solid-State Batteries: Integrating High-Throughput Phase-Field Modeling, Experimental Validation, and Machine Learning

Study of Void Evolution in Lithium Solid-State Batteries: Integrating High-Throughput Phase-Field Modeling, Experimental Validation, and Machine Learning

Study of Void Evolution in Lithium Solid-State Batteries: Integrating High-Throughput Phase-Field Modeling, Experimental Validation, and Machine Learning

This study develops a coupled mechanical-electrochemical multiphase-field model to describe the evolution of interface voids during the discharge of solid-state lithium batteries. The model accounts for void collapse induced by the viscoplastic flow of lithium metal under stacking forces and incorporates the microscopic mechanisms of vacancy formation, diffusion, and aggregation, revealing the relationship between vacancy accumulation and void growth. It also accurately captures the effect of stress on vacancy aggregation. Using the Butler-Volmer equation, the study explores the dynamic shrinkage of voids during the electrochemical stripping process at the interface. Based on this model, the synergistic effects of stacking force and external current on void formation and evolution are systematically examined. Additionally, high-throughput phase-field simulations, experimental validation, and machine learning techniques are employed to analyze void growth patterns in different electrolyte materials under varying operating conditions. The results show that in the Li-LLZO system, void growth is primarily influenced by the combined effects of stacking pressure and current density, whereas in the Li-Argyrodite system, stacking pressure plays a more dominant role. These findings deepen the understanding of void evolution in solid-state batteries and provide a quantitative foundation for optimizing battery operation and enhancing interface stability.

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来源期刊
Advanced Energy Materials
Advanced Energy Materials CHEMISTRY, PHYSICAL-ENERGY & FUELS
CiteScore
41.90
自引率
4.00%
发文量
889
审稿时长
1.4 months
期刊介绍: Established in 2011, Advanced Energy Materials is an international, interdisciplinary, English-language journal that focuses on materials used in energy harvesting, conversion, and storage. It is regarded as a top-quality journal alongside Advanced Materials, Advanced Functional Materials, and Small. With a 2022 Impact Factor of 27.8, Advanced Energy Materials is considered a prime source for the best energy-related research. The journal covers a wide range of topics in energy-related research, including organic and inorganic photovoltaics, batteries and supercapacitors, fuel cells, hydrogen generation and storage, thermoelectrics, water splitting and photocatalysis, solar fuels and thermosolar power, magnetocalorics, and piezoelectronics. The readership of Advanced Energy Materials includes materials scientists, chemists, physicists, and engineers in both academia and industry. The journal is indexed in various databases and collections, such as Advanced Technologies & Aerospace Database, FIZ Karlsruhe, INSPEC (IET), Science Citation Index Expanded, Technology Collection, and Web of Science, among others.
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