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With the increase of the operating life of lithium-ion batteries (LIBs), a large number of LIBs will enter the recycling and decommissioning stage in the next few years. Battery recycling, processing, and reuse are important issues that need to be solved in the field of new energy vehicle battery applications. As an emerging solution, the echelon utilization of LIBs is gradually receiving attention. This article proposes a LIB echelon utilization evaluation method based on acoustic emission (AE). This research conducts battery cycle aging tests to diminish the state of health (SOH) of the batteries to 50%. An improved empirical mode decomposition (EMD) algorithm is then employed to decompose the effective AE signals, followed by the extraction and analysis of frequency-domain characteristic parameters. Subsequently, principal component analysis (PCA) is utilized for stratifying LIBs into different echelon utilization levels based on the characteristic parameters of AE signal. This article applies improved EMD and PCA in the analysis and processing of AE signals for LIBs, thereby enhancing the methodology for detecting the health status of LIBs. This proposed approach not only offers fresh perspectives for data-driven research but also achieves real-time, online, non-destructive monitoring of LIB health status and facilitates the stratification for battery echelon utilization.
Zhang et al. (Thu,) studied this question.