Accurate State of Charge (SOC) estimation is essential for effective power management in Electric Vehicles (EVs), as it directly impacts battery performance, energy efficiency, and driving range. This paper presents an adaptive SOC estimation method using the Coulomb counting method, implemented in MATLAB, aimed at optimizing EV battery power management. The proposed method integrates real-time battery modeling and dynamic filter adaptation to account for varying operational conditions, such as load fluctuations, temperature changes, and battery degradation. A simplified battery model, incorporating current and voltage data, is used to simulate the SOC, with the Coulomb counting method employed to refine the estimation based on noisy measurements. The method adapts its parameters in response to environmental changes, enhancing the accuracy of SOC predictions. MATLAB simulations demonstrate the effectiveness of the adaptive Coulomb counting method, showing improved SOC estimation accuracy and more efficient power management compared to traditional techniques. This adaptive approach ensures better performance in real-world driving scenarios, contributing to extended battery life and optimized energy consumption in EVs.