State-of-Charge Estimation of Lithium-ion Batteries by Lebesgue Sampling-Based EKF Method

Estimation State-of-Charge (SOC) of Lithium-ion batteries is a main function of battery management system (BMS), which play critical roles in the application of batteries. The applications in electrical vehicles and consumer electronics require a time efficient algorithm to produce accurate SOC estimation. Extended Kalman filter (EKF) is widely used in state estimation because it provides a simple and efficient solution for nonlinear systems. In order to further reduce the computation cost, Lebesgue sampling based EKF (LS-EKF) is developed, which is able to eliminate unnecessary computations. In this paper, the SOC is estimated by the proposed LS-EKF method based on an equivalent circuit model. By this means, the SOC estimation is much faster than traditional EKF method, which makes it feasible for online applications. This method is verified by SOC experimental results. The results show that the LS-EKF based algorithm has good performance and low computation cost.