基于机器学习的锂离子电池电极润湿性优化

IF 5.4 Q2 CHEMISTRY, PHYSICAL
Amina El Malki , Mark Asch , Oier Arcelus , Abbos Shodiev , Jia Yu , Alejandro A. Franco
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引用次数: 2

摘要

电极润湿是锂离子电池制造过程中的关键步骤。在电极孔隙中注入电解质需要采用压力-真空抽吸策略,但不能保证在该工艺步骤结束时,整个孔隙将被电解质完全占据。电极的润湿性在很大程度上取决于电解质与电极之间的接触角、以电极的孔隙度、孔网和弯曲系数为特征的电极微观结构、电解质的粘度和密度。晶格玻尔兹曼方法等计算流体动力学方法可以提供填充过程的相关信息,但这些方法的计算成本很高。机器学习技术的使用可以为这种依赖于化学和物理性质的多参数过程的优化提供替代模型。在此背景下,我们提出了实现这一目标的一般工作流程,并提供了详细的基于模拟的实验。这些物理信息代理模型为参数识别和设计优化问题的易于处理,快速解决方案开辟了道路。它们还为其他最佳电池材料设计问题的应用提供了一个通用的工作流程。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Machine learning for optimal electrode wettability in lithium ion batteries

Electrode wetting is a critical step in the Lithium-Ion Battery manufacturing process. The injection of electrolyte in the electrodes’ porosity requires the application of pressure-vacuum pumping strategies without warranty that the full porosity will be fully occupied with electrolyte at the end of this process step. The electrode wettability strongly depends on the contact angle between the electrolyte and the electrode, the electrode microstructure characterized by its porosity, pore network and tortuosity factor, the electrolyte viscosity and density. Computational fluid dynamics approaches such as the Lattice Boltzmann Method can provide relevant information of the filling process, yet these approaches come with significant computational cost. The use of machine learning techniques can provide surrogate models for the optimization of this multi-parameter process that depends on both chemical and physical properties. Within this context, we propose a general workflow for realizing this objective and provide detailed simulation-based experiments. These physics-informed surrogate models open the path to tractable, rapid solutions of parameter identification and design optimization problems. They also provide a general workflow for applications on other optimal battery material design problems.

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来源期刊
CiteScore
9.10
自引率
0.00%
发文量
18
审稿时长
64 days
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