Advanced Classification of Lithium-Ion Battery Defects Using Electrochemical Impedance Spectroscopy and Machine Learning

Metallic particle contaminants have been shown to have a detrimental effect on the reliability, performance and capacity of lithium-ion battery cells. In addition, they pose a significant safety risk. Typical contaminants, such as iron (Fe), copper (Cu) and aluminium (Al), often enter the cell via mechanical abrasion from production equipment, as burrs during electrode cutting, or through environmental exposure during handling. In such instances, the degradation mechanisms are known to accelerate, dendrite formation is increased, and, in the most unfavourable circumstances, thermal runaway is the likely outcome. Contaminants that do not affect cell behavior during formation and the initial cycles, yet only compromise safety at a subsequent stage, are of particular concern. Affected cells are known to pass end-of-line testing and make their way into the market as latent safety risks. Consequently, there is an urgent requirement for non-destructive diagnostic methods that are capable of identifying latent defects. The issue under discussion is approached in the present paper through the utilization of an innovative methodology that integrates the distribution of relaxation time (DRT) analysis of electrochemical impedance spectroscopy (EIS) data with machine learning techniques. The objective of this integrated approach is to facilitate the detection of critically contaminated pouch cells with a high degree of sensitivity.