Performance Comparison of the General the Dual and the Joint Extended Kalman Filter on State Estimation of Li-ion Battery Cells for BMS

The aim of the work is to compare the performance of the general Extended Kalman Filter (EKF), the joint EKF and the dual EKF for state and parameter estimation of a Samsung 18650INR13L Li-ion battery cell. The used battery model is based on a simplified Randles equivalent circuit equipped with R<inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">s</inf>, R<inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ct</inf> and C<inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">dl</inf> parameters. The initial values of Randles parameters were identified from constant charge, discharge and transient discharge measurements. The performance of the implemented Kalman Filters were compared for the state and parameter estimation of the cell during WLTP test condition. While the general EKF uses only the identified initial values of the parameters, the joint and dual EKF are able to estimate them to compensate for the drift during the operation which leads to more accurate state estimation and compensates the parameters change due to heat and the aging of the cell. From the SOC estimation performance comparison we found out that both the dual and joint EKF perform similarly well, but the dual EKF has about 22% less computational cost which is very significant in the case of the future implementation on a real Battery Management System (BMS) which was the target of this investigation.