State of Charge (SoC) is the important criterion which reflects the actual battery usage. So, the State of Charge (SoC) has to be precisely estimated for improving the life and the rate of utilization of the battery. During normal operation of the battery, parameters like charge and discharge efficiency, temperature, etc., tend to affect the accurate estimation of SoC. In this paper, for estimating battery SoC with higher accuracy, Strong Tracking Cubature Kalman Filtering (STCKF) is used and the battery model parameters are identified using the method of Recursive Least Square (RLS). Simulation results indicate, STCKF estimates the SoC values as that of Ampere-Hour (AH) method with very minimal error and the dynamically modeled battery parameter values follows the same discharge characteristics as that of real batteries. © 2019, Springer Nature Singapore Pte Ltd.
cited By 0; Conference of 4th International Symposium on Signal Processing and Intelligent Recognition Systems, SIRS 2018 ; Conference Date: 19 September 2018 Through 22 September 2018; Conference Code:222839
R. Ramprasath and Shanmughasundaram, R., “STCKF algorithm based SOC estimation of Li-Ion battery by dynamic parameter modeling”, Communications in Computer and Information Science, 4th International Symposium on Signal Processing and Intelligent Recognition Systems, SIRS 2018; , vol. 968, pp. 229-239, 2019.