Li-ion Battery Health Estimation Using Ultrasonic Guided Wave Data and an Extended Kalman Filter

Abstract

State estimation for Li-ion batteries is difficult due to the limited number of measurement parameters available: voltage and current. Furthermore, thus far, there is not a physical model to accurately and directly link the measurement parameters to the two battery states we would like to estimate, state of charge (SoC) and state of health (SoH). Several approximate models such as the equivalent circuit model and single-particle model have been developed to correlate SoC with the measurable parameters, but no such model exists for correlating SoH. Ultrasonic guided waves have been used in many nondestructive testing and evaluation applications. This work uses ultrasonic guided wave features to detect battery degradation and thereby measure battery SoH, introducing the opportunity to make online SoH estimation feasible. A novel set of state estimation parameters and transition equations is proposed and used with an Extended Kalman Filter (EKF) to compare with results from using only current and voltage measurements.