Estimation of state-of-charge of lithium-ion cell using extended and sigma-point kalman filters for battery management system

Lithium-ion batteries are state-of-the-art energy storage technology. Instead of having remarkable features, a highly accurate, reliable, and cost-effective battery monitoring technology should continuously monitor the battery cell parameters and ensure the parameters are within the safe operating area recommended by the manufacturer. Direct measurement of the Li-ion cell's state of charge or SOC is impossible but can be estimated with reasonable accuracy with the help of state estimation algorithms. A precise estimation of SOC is always needed to enhance the cycle life and safety of lithium-ion batteries. Since the internal electrochemical kinetics of the Li-Ion cells are highly complex and non-linear, the non-linear variants of the Kalman filter, such as the Extended Kalman filter (EKF) and Sigma-Point Kalman filter (SPKF) perform exceptionally well in the presence of uncertainties in initial estimates and sensor measurements. This article evaluates the performance of EKF and SPKF for SOC estimation accuracy in terms of RMSE error. The experiment results show that SPKF slightly outperforms EKF. Both EKF and KF demonstrate strong robustness against current noise.