Research on Remaining Useful Life Prediction Based on Nonlinear Filtering for Lithium-ion Battery

Abstract

With the widespread application of lithium-ion batteries in industries around the world, lithium-ion battery performance degradation prediction and remaining useful life (RUL) estimation methods are receiving much more attention. This paper summarizes the nonlinear filtering algorithms used in RUL estimation of lithium-ion batteries, which compares and analyzes the applicable conditions and performance of the commonly used nonlinear filtering algorithms, including extended Kalman filtering (EKF), unscented Kalman filtering (UKF), particle filtering (PF), extended particle filtering (EPF) and unscented particle filtering(UPF). Simulations are obtained by lithium-ion battery performance degradation model and the performance of these algorithms are verified.