Online battery modeling for state-of-charge estimation using extended Kalman filter with Busse´s adaptive rule

State-of-charge estimation is vital to maximize battery performance and ensuring safe operation. The accuracy of a state-of-charge estimation technique is greatly influenced by the accuracy of the applied battery model. However, the battery model´s parameters are varying with several factors, such as temperature, charge/discharge rate, usage cycle, and age. Therefore, an online battery modeling approach is the key to update the battery model´s parameters continuously. In this paper, an equivalent circuit battery model is applied to capture dynamic behaviors of lithium titanate battery, while an online parameter estimation algorithm is proposed to update the model´s parameters. In this aspect, extended Kalman filter is applied for online parameter identification, while Busse´s adaptive rule is employed to update the process noise covariance and measurement noise covariance of the extended Kalman filter. The effectiveness of the proposed technique is verified experimentally.