Diagnosis of Current Flow Patterns Inside Fault-Simulated Li-Ion Batteries via Non-Invasive, In Operando Magnetic Field Imaging Mingyu Lee§, Yewon Shin§, Hongjun Chang, Dahee Jin, Hyuntae Lee, Minhong Lim, Jiyeon Seo, Tino Band, Kai Kaufmann, Janghyuk Moon*, Yong Min Lee*, and Hongkyung Lee* Small Methods 7.11 (2023): 2300748 IF: 12.4 JCR: 14.28
With the growing popularity of Li-ion batteries in large-scale applications, building a safer battery has become a common goal of the battery community. Although the small errors inside the cells trigger catastrophic failures, tracing them and distinguishing cell failure modes without knowledge of cell anatomy can be challenging using conventional methods. In this study, a real-time, non-invasive magnetic field imaging (MFI) analysis that can signal the battery current-induced magnetic field and visualize the current flow within Li-ion cells is developed. A high-speed, spatially resolved MFI scan is used to derive the current distribution pattern from cells with different tab positions at a current load. Current maps are collected to determine possible cell failures using fault-simulated batteries that intentionally possess manufacturing faults such as lead-tab connection failures, electrode misalignment, and stacking faults (electrode folding). A modified MFI analysis exploiting the magnetic field interference with the countercurrent-carrying plate enables the direct identification of defect spots where abnormal current flow occurs within the pouch cells.
https://doi.org/10.1002/smtd.202300748 |
