The battery is a critical component in Electric Vehicles (EV’s), and accurate State of Charge (SoC) estimation is essential for the efficient functioning of Battery Management Systems (BMS). Traditional methods like Coulomb Counting (CC) are prone to integration drift over time, while Open Circuit Voltage (OCV) methods offer limited accuracy under dynamic load conditions. To address these challenges, this study proposes a Switch Mode Kalman Filter (KF) approach, which intelligently switches between CC during load conditions and OCV during rest periods. To simulate the dynamic behavior of EV operations, a Hybrid Pulse Power Characterization (HPPC) test was conducted on a Lithium-ion (Li-ion) cell. Real-time validation using OPALRT confirms the improved performance of the proposed method. The traditional Kalman Filter achieved an RMSE of 0.1085 and R2 of 0.7077, whereas the proposed SM-KF achieved a lower RMSE of 0.0548 and a higher R2 of 0.9253, demonstrating superior estimation accuracy. By utilizing OCV data during rest phases, the switch mode KF effectively corrects CC drift, making it highly suitable for real-world EV applications that frequently alternate between driving and idle conditions. The results highlight the robustness and adaptability of the proposed approach for real-time SoC estimation.