The degradation and safety study of lithium-ion batteries is becoming increasingly important given that these batteries are widely used not only in electronic devices but also in automotive vehicles. Consequently, the detection of degradation modes that could lead to safety alerts is essential. Existing methodologies are diverse, experimental based, model based, and the new trends of artificial intelligence. This review aims to analyze the existing methodologies and compare them, opening the spectrum to those based on artificial intelligence (AI). AI-based studies are increasing in number and have a wide variety of applications, but no classification, in-depth analysis, or comparison with existing methodologies is yet available.

Solid-state batteries represent a promising technology in the field of high-energy-density and safe storage systems. Improving the understanding of how defects form within these cells would greatly facilitate future development, which would be best served by applying nondestructive analytical tools capable of characterization of the key components and their changes during cycling and/or aging. Spatially offset Raman spectroscopy (SORS) represents a potentially useful technique, but currently there is a lack of knowledge regarding its use in this field. To fill this gap, we present an investigation into the use of simple defocused micro-SORS on systems constructed using typical components found within solid-state cells. By analyzing the constituents and the assembled system, it was possible to obtain depth profiling spectra and show that spectra may be obtained from layers which are normally obscured, demonstrating the technique’s potential for nondestructive chemical analysis of the subsurface. In this way, the results presented validate the potential of micro-SORS as a technique to develop to support future solid-state battery development, as well as the nondestructive battery analytical field.