期刊
ADVANCED ENERGY MATERIALS
卷 13, 期 10, 页码 -出版社
WILEY-V C H VERLAG GMBH
DOI: 10.1002/aenm.202203767
关键词
charging protocols; electrochemical models; lithium deposition; lithium stripping; particle heterogeneity
This work establishes a mechanism model to characterize the Li deposition-stripping process and enables the quantitative prediction of Li deposition during charging-discharging cycles. A smart charging strategy is proposed based on the understanding of the Li deposition-stripping process. The influence of anode heterogeneity on Li plating is also quantified. The results highlight the promise of physics-based mechanistic modeling for battery design and charging protocols.
Metallic Lithium deposited on graphite particles is the major phenomenon responsible for the degradation of cell capacity, triggering of internal short circuit (ISC), and exacerbating thermal runaway (TR) in lithium-ion batteries (LIBs). However, currently, no available physics-based model can provide an accurate quantitative description of lithium-plating behavior. Herein, this work establishes a mechanism model to characterize the Li deposition-stripping process, especially the formation of dead Li and the reversibility of deposited Li. By the combination of the battery model and 3D particle model with the Li deposition-stripping model, this work enables the quantitative prediction of Li deposition during charging-discharging cycles at various charging rates. Based on the revealed understanding of the Li deposition-stripping process, a smart charging strategy with the optimization of the minimized Li-deposition and expedited charging time is proposed. Furthermore, this work also quantifies the influence of anode heterogeneity on Li plating. The results highlight the promise of physics-based mechanistic modeling for the quantification of the Li disposition-stripping process and provide fundamental guidance on battery design and charging protocols for next-generation long cycle life Li-ion cells.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据