Unscented Kalman Filter-Based Online State-of-Charge Estimation in LiFePO4 Battery-Powered Electric Vehicles

Abstract

Lithium iron phosphate (LiFePO4) battery has gained popularity due to its desirable characteristics such as low pollution, long cycling life, large power density, high safety and relatively low production costs. The estimation precision of State of Charge (SoC) of lithium iron phosphate (LiFePO4) battery determines the life and working efficiency of energy storage system. In practical applications, the battery capacity deteriorates with temperature drift and charge/discharge cycles, which make the accurate estimation of SoC a challenging issue. In this paper, a novel online estimation technique for SoC has been developed. Based on a simplified RC model, the Open Circuit Voltage (OCV) of the battery is calculated through two adaptive filtering stage, which is then used to determine the SoC via a dynamic OCV–SoC curve. In the first stage a variable step-size Least Mean Square (VSS-LMS) algorithm is employed to adaptively estimate the model parameters in real-time; in the second stage, an Unscented Kalman Filter (UKF) is used to estimate the OCV from the parameters. UKF can provide a more accurate and stable performance compared to Extended Kalman Filter at an equal computational complexity by utilizing deterministic sample points to calculate mean and covariance terms which are propagated through the true nonlinearity, without approximation. Further, the influence of polarization potential on OCV parameter measurement is analyzed and the averaged battery terminal voltage and current are introduced to avoid the large error of OCV estimate causing by the inconsistency of batteries. The impact of polarization over potential variation in battery capacity is eliminated through a smooth filter so as to achieve a stable online estimation process. Plenty of experiments under data acquisition systems with different precisions were carried out in order to confirm the effectiveness of the proposed system. The errors in estimating the battery’s SoC were below 2% compared to more than 5% and 9% in the case of using an extended Kalman filter and ordinary Kalman filter. The proposed SOC estimation method with its simplified model was transferred to DSP system also to verify the accuracy, simplicity and feasibility of SoC estimation for real-time application.