Sound-Based Non-Destructive Evaluation to Detect Damage in Lithium-Ion Batteries

Abstract

In recent years, lithium-ion batteries (LIBs) have played an essential role in nowadays energy storage system, especially electric vehicles (EVs) and portable electronics because of its high energy density and long cycle life [1, 2]. However, one of the biggest challenges is how to guarantee their dependability and trustworthiness. In the present investigation, Acoustic Emission (AE) and Ultrasound Testing (UT) techniques are systematically employed to verify probable critical defects in the LIBs. Where AE technology is able to record the stress waves produced by the growth of the defects, UT uses high-frequency sound waves to penetrate the batteries and provide an indication of the internal voids. The performances of these approaches were systematically tested on as-received, pre-damaged and cold-soaked batteries. Different AE and UT activity patterns were shown in the results under various environmental conditions that influenced battery performance. Combining Acoustic Emission (AE) and Ultrasound Testing (UT) with clustering and outlier analysis machine learning algorithms improved defect detection effectiveness. Such research highlights that AE and UT can be robust noninvasive techniques for on-line health monitoring of LIBs that should aid in maintaining the longevity and operability of LIBs.