Comparison of robustness of different state of charge estimation algorithms

Whether an algorithm of state of charge (SOC) estimation being able to use successfully in a real usage or not is highly determined by its robustness, because various errors including sensors’ errors, initial data errors, model parameters errors and so on disturb the calculation, and thus make the accuracy of estimation decrease significantly and even a wrong SOC be given out. However, systemic analysis of robustness of the algorithms that have been comprehensive studied or even been used in real applications still remains unsolved until now. In this study, various scenarios in which the random error such as current sensor bias, voltage sensor bias, and initial parameter identification errors exists are tested by using an ampere hour integration (Ah), the extended Kalman Filter (EKF) and a dual extended Kalman filter (DEKF) algorithm. Results show the robustness of three algorithms in the scenarios give different deviations. In case of the disturbance of the random error, the SOC can be effectively estimated by using the DEKF algorithm, since the maximum error of the SOC estimation from the algorithm is less than 3%, which is better than the results by using the EKF and the Ah integration.