4.8 Article

Model-based lithium deposition detection method using differential voltage analysis

期刊

JOURNAL OF POWER SOURCES
卷 512, 期 -, 页码 -

出版社

ELSEVIER
DOI: 10.1016/j.jpowsour.2021.230449

关键词

Lithium-ion battery; Lithium deposition; Lithium plating; Fast charging; Differential voltage analysis; Equivalent circuit model

向作者/读者索取更多资源

A novel model-based approach was developed to distinguish between normal and lithium deposition-affected relaxation processes during fast charging of lithium-ion batteries. This method enables the classification of charging processes based on accelerated aging, with remarkable sensitivity and automated detection of lithium deposition.
During fast charging of lithium-ion batteries with graphite electrodes, kinetic limitations of the desired intercalation process force the Li ions to deposit metallically on the electrode's surface. The degradation process of lithium deposition (LD) leads to rapid capacity fade and may lead to safety-critical conditions. Hence, a reliable and sensitive method for the operando detection of LD is needed that allows to adjust the charging current and to preserve the lifetime of the battery. Differential voltage analysis is a state-of-the-art electrochemical characterisation technique that enables the retrospective detection of LD during relaxation and discharge. However, in its current use it reveals weaknesses in terms of reliability and sensitivity. Our novel, model-based approach distinguishes normal, i.e., after intercalation only, and LD-affected relaxation. Hence, the charging process can be classified as critical or non-critical in terms of accelerated ageing. To test the method on real fast charging cycles, a measurement series with six commercial cells and different applied charging currents is performed, and the subsequent voltage behaviour during relaxation and discharge is investigated. As reference, the irreversible charge loss is measured for each cycle using coulomb counting. A detection algorithm is developed, which shows a remarkable sensitivity and furthermore enables an automated detection of LD.

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。

评论

主要评分

4.8
评分不足

次要评分

新颖性
-
重要性
-
科学严谨性
-
评价这篇论文

推荐

暂无数据
暂无数据