A new soc estimation for lfp batteries: Application in a 10 ah cell (hw 38120 l/s) as a hysteresis case study

Abstract

An accurate state of charge (SOC) estimation of the lithium iron phosphate battery (LiFePO4) is one of the most important functions for the battery management system (BMS) for electric vehicles (EVs) and energy storage systems (ESSs). However, an accurate estimation of the SOC of LiFePO4 is challenging due to the hysteresis phenomenon occurring during the charge and dis-charge. Therefore, an accurate modeling of the hysteresis phenomenon is essential for reliable SOC estimation. The conventional hysteresis modeling methods, such as one-state hysteresis modeling and parallelogram modeling, are not good enough to achieve high-accuracy SOC estimation due to their errors in the approximation of the hysteresis contour. This paper proposes a novel method for accurate hysteresis modeling, which can provide a significant improvement in terms of the accuracy of the SOC estimation compared with the conventional methods. The SOC estimation is performed by using an extended Kalman filter (EKF) and the parameters of the battery are estimated by using auto regressive exogenous (ARX) model and the recursive least square (RLS) filter. The experimental results with the conventional and proposed methods are compared to show the superiority of the proposed method.