Autoregressive model-based parameter correlation for state of charge and state of health of lithium-ion batteries using built-in piezoelectric transducer induced ultrasonic waves

Abstract

It is crucial to accurately monitor the performance, health, and lifespan of lithium-ion batteries to ensure reliable, efficient, and on-demand delivery of stored electrical energy for hybrid and electric vehicle technologies. This paper presents a method to monitor the state of charge (SoC) and state of health (SoH) of lithium-ion batteries by utilizing ultrasonic guided wave propagation signals. The lithium-ion battery is modeled as a time-varying single-degree-of-freedom vibrating system and the time-varying nature of the system is captured by successive utilization of the time-invariant autoregressive models. The proposed technique focuses on extracting the time-dependent natural frequencies and damping ratios of the lithium-ion batteries from the ultrasonic guided wave signals through the use of autoregressive model-based modal analysis techniques. The extracted natural frequencies and damping ratios are then correlated with the battery SoC and SoH to assess the battery conditions accurately. Lengthwise signals exhibit a decreasing trend in natural frequencies as the SoC increases, whereas, the thickness direction signals show the opposite trend, frequencies increase with rising SoC. This method elucidates the potential of ultrasonic guided wave-based real-time monitoring of battery SoC and SoH in its life cycle.