The ability to precisely estimate the State of Charge (SoC) of a Lithium-ion battery is critical. BMS plays an important role in ensuring a safe and dependable operation. Due to inefficiency or a tempered algorithm, an inefficient BMS may display the incorrect SoC. As a result, a MasterSlave BMS configuration architecture has been proposed for identifying and correcting SoC estimation error. Following a comparative study on SoC estimation, a hybrid model was chosen for accurate SoC estimation in Master BMS. Six estimation methods were used in conjunction with a dynamic vehicle model. For further improvement, the data-driven methods (Neural Network and Nonlinear Auto-Regression Moving Average -2) are combined.