improvement of the battery state of charge estimation using recursive ‎ least square-based adaptive extended kalman filter ‎

Battery management system (bms) including measurements errors that causes decrease in ‎the quality of ‎calculated state of the charge (soc). it will limit the accurate estimation of ‎the soc that is a critical challenge in ‎some of the engineering fields such as medical science, ‎robotics, navigation and industrial applications. these ‎facts implies on the significance of ‎soc estimation from battery measurements that is the matter of the literature ‎through the ‎recent years. due to the dependency of the ekf to the system model, the change in the ‎battery ‎parameters and noise information cause losing performance in the soc estimation ‎over the time. in this paper, we ‎assume that the battery parameters including internal ‎resistance and capacitor and also the noise information are ‎varying over the time. to solve ‎that, two separate on-line identification algorithms for parameters and noise ‎information are ‎introduced. in more details, a recursive least square (rls) algorithm is used to identify ‎the ‎resistance and capacitor values. moreover, the process and measurement noise covariance are ‎estimated based ‎on iterative noise information identification algorithm. then all of the ‎updated values are used in the ekf ‎algorithm. this paper aims to address the issue of uncertainty in soc estimation by proposing two algorithms. ‎the first algorithm focuses on identifying deterministic uncertainty, which refers to uncertainty in model ‎parameters. to address the challenge of uncertain model parameters, rls is introduced.