Online Estimation of Model Parameters and State-of-Charge of Lithium-Ion Battery Using Unscented Kalman Filter

For the operation of Autonomous Mobile Robot (AMR) in unknown environments, accurate estimation of internal parameters and consequently precise prediction of the battery state of charge (SoC) are critical issues for power management. Battery performance can be affected by factors such as temperature deviation, discharge/charge current, Coulombic efficiency losses, and aging. Thus, in order to increase the model accuracy, it is important to update the model parameters online. In this paper, the Unscented Kalman Filter (UKF) is employed for the online estimation of the Lithium-Ion battery model parameters and the battery SoC based on the updated model. The proposed method is evaluated experimentally, and the results are compared with that of the Extended Kalman Filter (EKF). The comparison with the EKF shows that UKF provides better accuracy both in battery parameters estimation and the battery SoC estimation.