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This research aims to develop a status imaging system for a Li-Ion battery by utilizing guided ultrasonic waves with an embedded sensor network. Li-Ion Battery (LIB) has emerged as an essential powering element in the future mobility industry including electric vehicles, unmanned aerial vehicles, and urban air mobility. Conventional safety monitoring of LIB mostly depends on the electric signals of each LIB unit, yet the electric signal-based monitoring has shown its technical limitations in detecting local mechanical/chemical status in LIBs. Therefore, this study investigated a status imaging system to detect local changes in an LIB using an active sensing system. The research scope of this study is to detect and localize the simulated mechanical degradation within a LiFePo4 (LFP) battery having a relatively large dimension (300×210×12 mm3 ). Nine piezoelectric wafers were embedded on the LIB surface. The excitation frequency was determined by observing the signal-to-noise ratio in the frequency range from 60 to 280kHz. As for the status imaging algorithm, we employed a probabilistic reconstruction algorithm, where the index was developed based on the Continuous Wavelet Transform (CWT). The local mechanical change in the LIB was realized by placing a heavy (~ 0.5 kg) weight on a certain spot. The experiment results showed that the proposed imaging method (i.e., CWT-based imaging) could detect the localized mechanical degradation of the LFP battery in a more significant imaging contrast (⪆+20%) compared to other existing methods. This research will provide a new methodology to monitor the localized state-of-health of a large LIB.
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