Date of Award
Open Access Thesis
Ralph E White
A particle filter (PF) is shown to be more accurate than non-linear least squares (NLLS) and an unscented Kalman filter (UKF) for predicting the remaining useful life (RUL) and time until end of discharge voltage (EODV) of a Lithium-ion battery. The three algorithms track four states with correct initial guesses and 5% variation on the initial guesses. The more accurate prediction performance of PF over NLLS and UKF is reported for three Lithium-ion battery models: a data-driven empirical model, an equivalent circuit model, and a physics-based single particle (SP) model.
Walker, E. A.(2013). Comparison of a Particle Filter and Other State Estimation Methods for Prognostics of Lithium-Ion Batteries. (Master's thesis). Retrieved from https://scholarcommons.sc.edu/etd/2565