State Estimation Method of Lithium-ion Battery Based on Electro-thermal Model and Strong Tracking Particle Filter

Abstract

Accurate estimation of battery state is crucial for battery management system. Lithium-ion battery is a complex electrochemical system with coupled electrothermal characteristics and strong nonlinearity. Therefore a state estimation method based on electrothermal model and strong tracking particle filter is proposed in this article. The calorimetric method is employed to realize fast identification for thermal model parameter. By introducing strong tracking filter into particle filter, an estimator based on strong tracking particle filter is proposed to improve the estimation accuracy and tracking capability of saltatory state. The simulation and experiments are conducted to verify the performance of proposed method under dynamic characterization schedules.