Reliability of battery energy storage systems (BESS) used for online applications, such as electric vehicles and smart grid, depends heavily on the accuracy and rapidness of the state of charge (SOC) estimation. Moreover, to achieve a robust SOC estimation, the battery model parameter identification process is of significant importance. This paper examines a combination of the adaptive unscented Kalman filter (AUKF) and the fast upper diagonal recursive least square (FUDRLS) for the parameter identification and SOC estimation processes, respectively. The analysis focuses on on-line applications and the results are compared with previous work. Experimental validation based on various setups and load conditions is conducted, whereas the advantages of the proposed combination are highlighted.