In-Situ Monitoring of Lithium-Ion Battery Health Using Acoustic Emissions

The durability and dependability of lithium-ion batteries (LIBs) play a crucial role in advancing energy storage technologies that power modern applications, including electric vehicles (EVs), consumer electronics, and large-scale energy storage systems. Effective battery health monitoring is essential not only for ensuring safe and efficient operation but also for optimizing battery performance and facilitating second-life applications. Conventional diagnostic techniques, such as voltage and current profiling or impedance spectroscopy, have been widely utilized for LIB assessment. However, these methods often face challenges, such as susceptibility to environmental influences, the need for offline analysis, and dependence on direct electrical connections, limiting their suitability for real-time monitoring. Acoustic Emission (AE) technology has gained attention as a promising, non-destructive approach for real-time monitoring of LIB degradation. By detecting mechanical waves generated during battery operation, AE provides insight into internal structural changes and helps identify key degradation phenomena, including electrode cracking, gas formation, and electrolyte leakage. These acoustic signals offer a unique perspective on stress accumulation and material transformations within LIBs, contributing to a deeper understanding of aging mechanisms. In this study, AE was employed to investigate LIB degradation mechanisms, beginning with graphite/Li and NMC/Li coin cells assembled in a glovebox using LiPF6 in a 1:1 EC:DEC electrolyte. During charging at a 0.5 C-rate, a differential wideband sensor was affixed to the coin cell surface to capture AE signals. The recorded acoustic fluctuations were analyzed, and identical coin cells were aged under similar conditions for validation. Observations revealed significant graphite exfoliation at the fully charged state, with an amplitude of 65 dB. Incremental analysis indicated that the highest amplitude occurred at the peak value, corresponding to the phase transition point, suggesting a strong correlation between AE signals and structural changes during cycling. Post-mortem analysis was conducted to establish correlations between AE signals and electrode structural changes, providing a preliminary link between acoustic signatures and degradation processes. Subsequent experiments focused on commercial LIBs, where testing was carried out within a specially designed foam enclosure to minimize background noise. Advanced digital processing techniques were applied to the AE data to filter out interference and categorize acoustic events based on coin cell spectra. Initial results suggest that AE signals, such as those associated with microcracking, provide distinct markers of electrode degradation. These findings demonstrate AE’s potential as a diagnostic tool for tracking LIB health and detecting critical aging phenomena. Future research will aim to establish more precise correlations between AE events and specific electrochemical reactions occurring within LIBs. By integrating AE with complementary characterization techniques, a more comprehensive understanding of battery aging mechanisms can be achieved. Overall, this study underscores the potential of AE technology to transform LIB health monitoring by providing a real-time, non-invasive, and scalable diagnostic solution. This advancement contributes to improving battery reliability, extending operational lifetimes, and supporting the development of more sustainable and efficient energy storage solutions for future applications.