Lithium-ion batteries are essential to modern electric vehicles, yet managing them safely and efficiently requires accurate real-time estimation of the State of Charge (SoC) and State of Health (SoH). Traditional estimation methods face significant limitations under dynamic conditions. This paper presents a hybrid estimation framework for combined SoC and SoH estimation using a second-order equivalent circuit (2RC) model and error correction via an Extended Kalman Filter (EKF). MATLAB-based simulations validate the model, demonstrating accurate SoC and SoH predictions under real-world conditions. The proposed method shows high precision, achieving R2 values of 0.997 and 0.999 for SoC and SoH estimates, respectively. The standard error deviations are also low, confirming its reliability. These findings indicate that the proposed framework significantly enhances the accuracy and robustness of battery monitoring, making it well-suited for integration into advanced battery management systems (BMS) in electric vehicles.