The State-Of-Charge estimation constitutes an essential part of the battery management system. During the early phases, measurement-based methods were used for state-of-charge estimation, which depended on the current and voltage measurements. However, the accuracy of these methods is affected by the presence of noise, dc-bias, etc. In addition, the voltage measurement-based estimation approach is time-consuming due to the delay in battery terminal voltage settling and hence is unsuitable for online SOC computation. To subdue these drawbacks, model-based estimation techniques are employed. This work compares the performance of four commonly used model-based state-of-charge estimation techniques: the Kalman Filter, Extended Kalman Filter, Sigma Point Kalman Filter, and the H∞. The battery model selected for the estimation procedure is Thevenin's battery model with a single parallel RC branch. © 2022 IEEE.