SOC Estimation of Li-ion Battery by Adaptive Dual Kalman Filter under Typical Working Conditions

Estimating the state of charge (SOC) of Li-ion battery by the traditional extend Kalman filter (EKF), the unreasonable noise settings and inaccurate model parameters may decrease the accuracy. Here, an adaptive dual extended Kalman filter which can dynamically adjust the process noise covariance and battery model parameters is established by the second order RC model. The algorithm uses the EKF algorithm as the main body, fixes the measurement noise covariance, and adaptively adjusts the process noise covariance based on the maximum likelihood estimation criterion. Another Kalman filter (KF) is simultaneously used to estimate battery model parameter in real time to constitute a joint algorithm. In this paper, the charging and discharging experiments of lithium iron phosphate batteries are carried out. The SOC estimation by different adaptive adjustment strategies of noise covariance are compared and analyzed under actual operating condition. Meanwhile, the SOC under different aging conditions is also estimated and compared. The results show that the proposed joint algorithm has higher estimation accuracy, faster convergence speed and more extensive applicability.