Acoustic emission monitoring of lithium-ion battery aging and failure mode transition under low-temperature conditions

Low-temperature charging induces lithium plating and stress accumulation, posing severe safety challenges for lithium-ion batteries (LIBs). However, the mechanisms underlying their internal damage evolution and failure mode transitions remain unclear. This study employs acoustic emission (AE) technology to develop an innovative hybrid classification model integrating K-means clustering, linear classification, and Gaussian kernel support vector machines. This approach enables adaptive recognition and dynamic tracking of multiple damage modes in LIBs. Results indicate that AE signals during charging exhibit distinctive dual-burst waveform characteristics. Pearson correlation analysis reveals that two burst signals share similar waveform features, originating from correlated waveforms of the same damage event. Classification results reveal that damage modes evolve from tensile-dominated patterns in the early charging stage to shear and mixed modes in the later stage. Furthermore, the coupled effects of low temperature and high charging rates significantly accelerate the accumulation of shear and mixed damage. Furthermore, continuous wavelet transform (CWT) analysis revealed a time-frequency evolution pattern where AE signals transitioned from high-frequency short-duration to low-frequency long-duration signals, aligning with the transformation of damage modes. This study established a multiscale acoustic emission analysis framework integrating hybrid learning classification and time-frequency analysis, providing novel insights and technical support for elucidating low-temperature failure mechanisms and enabling early warning in lithium-ion batteries.