Lithium ion batteries are widely used for electrical devices. However, their degradation causes serious accidents such as fires or explosions. In order to detect the degradation of a lithium ion battery during charging and discharging in real time, an acoustic emission (AE) technique was applied and a clustering method was developed to extract AE signals caused by battery degradation. In this study, highly oriented pyrolytic graphite (HOPG) and a lithium metal were used as working and counter electrodes, respectively, and a glass fiber sheet was used as a separator. An ethylene carbonate / diethyl carbonate (EC/DEC) solution, which was used as the electrolyte solution, was sealed in a metallic shell. Degradation of this type of battery mainly occurred by gas evolution and fracture or exfoliation of the graphite electrode. During the first charging and discharging cycle, 499 AE signals were detected. AE signals were clustered by a method that was developed based on the waveform polarity, power spectrum, and enveloped waveform. These features were first clustered by correlation coefficients. AE signals which the Euclidean distance of each feature was close were clustered into the same cluster. AE signals were clustered into 43 clusters by this method. In order to characterize clustered AE signals from a degrading battery, the clustered signals were compared to artificial AE signals created by simulating degradation behavior in the battery. Gas evolution on an electrode in the battery was simulated by electrolysis of water. AE signals due to graphite fracture were simulated by mechanically fracturing the graphite in a Vickers indentation test. As a result, AE signals characterizing gas evolution were detected continuously during the cycle. This result constituted gas evolution phenomena in the battery. Fracture-related AE signals due to graphite electrode exfoliation tended to occur when the lithium intercalation rate changed. Thus, fracture or exfoliation of graphite was attributed to the formation of a solid electrolyte interface (SEI) or volume expansion/contraction due to lithium intercalation, respectively. Other classes AE signals were also discussed, of which some were attributed to the distortion of graphite.
CITATION STYLE
MATSUO, T., UCHIDA, M., & CHO, H. (2011). Development of Acoustic Emission Clustering Method to Detect Degradation of Lithium Ion Batteries. Journal of Solid Mechanics and Materials Engineering, 5(12), 678–689. https://doi.org/10.1299/jmmp.5.678
Mendeley helps you to discover research relevant for your work.