Ultrasonic Tomography Study of Metal Defect Detection in Lithium-Ion Battery

Abstract

Lithium-ion batteries are widely used in electric vehicles and energy storage systems. Sudden fire accident is one of the most serious issue, which is mainly caused by unpredicted internal short circuit. Metal particle defect is a key factor in internal short circuit it will not show an obvious abnormal change in battery external characteristic just like mechanical and thermal abuse. So, a non-destructive testing of battery internal metal defect is very necessary. This study is first time to scan and analyze different types of defects inside a battery by using ultrasonic technology, and it shows the detection capability boundary of this methodology. A non-contact ultrasonic scanning system with multi-channel was built to scan the battery sample with aluminum foil, copper foil and copper powder defects. The position and shape of those defects were clearly shown by using tomography methodology. It was found that the acoustic properties difference between metal defects and battery active materials has a strong influence on detection sensitivity. Compared with aluminum foil, copper foil and copper powder are easier to be detected and change the ultrasonic signal greatly, they will produce an obvious shadowing artifacts and speed displacement phenomena in tomography images. Ultrasonic tomography technology is an effective method for non-destructive testing of lithium-ion batteries.

Nondestructive Defect Detection in Battery Pouch Cells: A Comparative Study of Scanning Acoustic Microscopy and X‐Ray Computed Tomography

“…A reflectogram, or A‐scan, is obtained for each measured point of the scanned sample. [ 25 ] With the data saved during the measurements, several images and calculation can be afterward generated. The investigation of one sample using CT demands duration in the range of multiple hours.…”

“…[ 16–19 ] It has been used for laboratory analyses for the determination of the state of charge of battery cells, [ 20,21 ] electrode aging, [ 22 ] electrolyte wetting of electrodes, [ 23,24 ] and location of internal mechanical defects. [ 25,26 ]…”

“…[16][17][18][19] It has been used for laboratory analyses for the determination of the state of charge of battery cells, [20,21] electrode aging, [22] electrolyte wetting of electrodes, [23,24] and location of internal mechanical defects. [25,26] SAM is performed here on the reflection mode. By this mode, information about the relative depth of inhomogeneities on interfaces is obtained.…”

A real-time method for detecting bottom defects of lithium batteries based on an improved YOLOv5 model

“…In recent years, many new methods for defect detection of lithium batteries have emerged. Yi et al [1] built a noncontact ultrasonic scanning system with multi-channel to scan the battery sample with aluminum foil, copper foil, and copper powder defects. To realize the automatic detection of surface defects of lithium battery pole pieces.…”

A K-Value Dynamic Detection Method Based on Machine Learning for Lithium-Ion Battery Manufacturing

“…Yi et al attempted to use an ultrasonic nondestructive testing (NDT) technique to detect foreign matter defects inside a cell. However, the ability of this technology is restricted to the thickness and structure of the cell, as well as the morphology of the defect [27]. Fink et al evaluated the impacts of various metal impurities on the electrochemical and thermal performance of the lithium-ion cell, including Fe, Cu, Mg, Al, and Si.…”