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 discharge. 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.

An accurate state of charge (SOC) estimation of the battery is one of the most important techniques in battery-based power systems, such as electric vehicles (EVs) and energy storage systems (ESSs). The Kalman filter is a preferred algorithm in estimating the SOC of the battery due to the capability of including the time-varying coefficients in the model and its superior performance in the SOC estimation. However, since its performance highly depends on the measurement noise (MN) and process noise (PN) values, it is difficult to obtain highly accurate estimation results with the battery having a flat plateau OCV (open-circuit voltage) area in the SOC-OCV curve, such as the Lithium iron phosphate battery. In this paper, a new integrated estimation method is proposed by combining an unscented Kalman filter and a particle filter (UKF-PF) to estimate the SOC of the Lithium iron phosphate battery. The equivalent circuit of the battery used is composed of a series resistor and two R-C parallel circuits. Then, it is modeled by a second-order autoregressive exogenous (ARX) model, and the parameters are identified by using the recursive least square (RLS) identification method. The validity of the proposed algorithm is verified by comparing the experimental results obtained with the proposed method and the conventional methods.