State of charge estimation of electrical vehicle battery by Using Box Particle Filter Signal Tracking Method

Abstract

ABSTRACT This paper concentrates on the effective study of the online state of charge (SoC) estimation of lithium-ion batteries used in electric vehicles (EVs). Box Particle filters (BPF) are commonly used in the estimation of multivariate systems under noisy environments. In this study, a Joint-estimator is proposed which uses the forgetting factor recursive least square (FF-RLS) method for the 1-RC battery model online parameter estimation and the lithium-ion battery SoC is estimated using battery terminal voltage signal tracking with (BPF). The implemented method is compared with a Sequential Importance Resampling Particle Filter (SIR-PF) which shows that the proposed BPF has reduced the number of particles and computational time and maintained the efficiency comparable to the SIR-PF. The BPF performance is further evaluated at assorted voltage signal tracking accuracy, the number of boxes and the initial dimension of the boxes. The BPF method requires less number of boxes as compared to the SIR-PF with compatible efficiency. Therefore, BPF can be used as an alternative to the complex algorithms used in the SoC estimation in battery management systems (BMS) of electric vehicles.