언센티드 칼만 필터와 파티클 필터에 기반한 리튬 인산철 배터리의 정확한 충전 상태 추정

Accurate State of Charge Estimation of LiFePO₄ Battery Based on the Unscented Kalman Filter and the Particle Filter

An accurate State Of Charge (SOC) estimation of battery is the most important technique for Electric Vehicles (EVs) and Energy Storage Systems (ESSs). In this paper a new integrated Unscented Kalman Filter-Particle Filter (UKF-PF) is employed to estimate the SOC of a $LiFePO_4$ battery cell and a significant improvement is obtained as compared to the other methods. The parameters of the battery is modeled by the second order Auto Regressive eXogenous (ARX) model and estimated by using Recursive Least Square (RLS) method to calculate value of each element in the model. The proposed algorithm is established by combining a parameter identification technique using RLS method with ARX model and an SOC estimation technique using UKF-PF.