Exploring the Attributes of Particle Filter vs Nonlinear Kalman Filter for Battery State of Charge Estimation

Abstract

Accurately knowing the State of Charge (SoC) of a Lithium Ion Battery is critical for many applications, and various methods for determining it have been proposed. This paper examines the differences between the Central Difference Kalman Filter (CDKF) and the Bootstrap Particle Filter (BPF) for SoC estimation. A numerical analysis of nonlinearities in a 2RC Equivalent Cell Model is presented as a basis for the comparison. The model is found to exhibit strong nonlinearity when the estimated SoC is below 0.12. The implemented CDKF shows a SoC RMSE of 0.88% and the BPF an average SoC RMSE of 0.91% over 50 runs.