Estimating Lithium-ion Battery State of Charge and Health with Ultrasonic Guided Waves Using an Efficient Matching Pursuit Technique

This paper presents a novel guided-wave-based framework for estimating state of charge (SoC) and state of health (SoH) of lithium-ion (Li-ion) batteries. The guided waves are propagated and sensed using low-profile, surface-mounted piezoelectric transducers on off-the-shelf Li-ion pouch cells. Special emphasis is given to the development of an efficient feature extraction strategy based on the Matching Pursuit (MP) technique. The proposed method decomposes complex waveforms into constituent `atoms', allowing the time-frequency information of the signals to be mined. The descriptive parameters of the decomposed atoms can be extracted to show strong and meaningful correlations with SoC and SoH. Statistical prediction models for estimating SoC/SoH are built using the atom parameters as predictive features in conjunction with the traditional voltage data. It is shown that the atom parameters can augment the voltage data and statistically improve the prediction of SoC by two-fold and, in particular, of SoH by twenty-fold. The results signify the importance of encapsulating proper signal processing and feature extraction into future guided-wave-based SoC/SoH prediction frameworks.