Magnetically Guided Diagnosis of Current Flow Patterns inside Fault-Simulated Li-Ion Batteries
Authors: Lee Mingyu , Lee Hongkyung 
Published: December 2024

As the energy industry shifts towards cleaner and more electrified solutions, there's a growing demand for improved batteries, especially for electric vehicles (EVs) and stationary energy storage systems (ESSs). Lithium-ion batteries (LIBs) have been dominant due to their advantages such as high energy/power density, long lifespan, and fast charging capabilities. Despite advancements in cell design making them more efficient and compact, current LIBs face new challenges, particularly in improving reliability and addressing safety concerns. Recent instances of battery recalls due to explosions and fires have underscored the need to address potential failures stemming from internal faults in large-format cell designs and manufacturing, or from "latent" defects arising from misuse or aging under extreme conditions. Therefore, understanding failure scenarios and developing diagnostic tools are crucial for facilitating smoother troubleshooting for battery manufacturers and ensuring the development of safer battery technologies.

Advanced imaging methods, both postmortem and in situ, have been developed to reveal the underlying causes of battery failures at the material level. These techniques help identify physical and chemical defects in active materials, degradation of electrolytes, and structural damage in inactive components like separators and current collectors. However, connecting these material-level defects with overall cell failure is difficult due to their random occurrence within complex cell structures. Additionally, many microscale imaging tools designed for customized cells are impractical for commercial cells, making it nearly impossible to detect small errors in large-scale cells during postmortem analysis. Therefore, global imaging techniques are more effective for identifying abnormalities and examining local faults. One powerful tool for this purpose is cell-penetrating X-ray computed tomography, which provides 3D images of the cell's internal structures, although it is time-consuming. However, this method may struggle to detect invisible errors that can lead to abnormal current flow in areas where latent defects exist. Thermal imaging is another useful tool for detecting abnormal heat generation in batteries caused by current flow, but without information on current direction, determining the type and extent of defects and cell degradation may be limited.

This research introduces a real-time, noninvasive method for imaging magnetic fields (MFI) generated by current flow within Li-ion pouch cells. This technique provides fast scanning speeds (~100 mm/min), high spatial resolution (0.161 mm2), and a simple setup without the need for magnetic field shielding. Initially, we verified the correlation between magnetic fields and current by conducting MFI studies on 1D straight wires and 2D sheets carrying current. Subsequently, we identified an appropriate pouch cell geometry for analyzing current flow patterns using MFI, cross-referencing with current distribution maps generated by COMSOL simulations. To illustrate the effectiveness of current pattern analysis in identifying failure modes, we analyzed MFI data from fault-simulated batteries (FSBs) deliberately manufactured with faults like lead-tab contact failure, electrode misalignment, and stacking process folds. By using MFI destructive interference with a countercurrent-carrying cell-shaped 2D sheet below, we could selectively enhance areas of failure where abnormal current flows occur. This MFI-guided visualization of current distribution patterns in FSBs provides a nondestructive, immediate diagnostic approach to identify fault types that may not be discernible through electrochemical analysis alone.