Insights into soft short circuit-based degradation of lithium metal batteries

Svetlana Menkin, Jana B. Fritzke, Rebecca Larner, Cas de Leeuw, Yoonseong Choi,Anna B. Gunnarsdottir,Clare P. Grey

FARADAY DISCUSSIONS(2024)

引用 0|浏览0
暂无评分
摘要
The demand for electric vehicles with extended ranges has created a renaissance of interest in replacing the common metal-ion with higher energy-density metal-anode batteries. However, the potential battery safety issues associated with lithium metal must be addressed to enable lithium metal battery chemistries. A considerable performance gap between lithium (Li) symmetric cells and practical Li batteries motivated us to explore the correlation between the shape of voltage traces and degradation. We coupled impedance spectroscopy and operando NMR and used the new approach to show that transient (i.e., soft) shorts form in realistic conditions for battery applications; however, they are typically overlooked, as their electrochemical signatures are often not distinct. The typical rectangular-shaped voltage trace, widely considered ideal, was proven, under the conditions studied here, to be a result of soft shorts. Recoverable soft-shorted cells were demonstrated during a symmetric cell polarisation experiment, defining a new type of critical current density: the current density at which the soft shorts are not reversible. Moreover, we demonstrated that soft shorts, detected via electrochemical impedance spectroscopy (EIS) and validated via operando NMR, are predictive towards the formation of hard shorts, showing the potential use of EIS as a relatively low-cost and non-destructive method for early detection of catastrophic shorts and battery failure while demonstrating the strength of operando NMR as a research tool for metal plating in lithium batteries. A considerable performance gap between Li symmetric cells and practical Li batteries motivated us to explore the correlation between the shape of voltage traces and degradation.
更多
查看译文
关键词
lithium metal batteries,degradation,circuit-based
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要