Amid energy crises and environmental pollution concerns, lithium-ion batteries have become essential energy storage solutions thanks to their high energy density, safety, and low environmental impact. Nowadays, a robust battery model and an accurate state of charge (SoC) estimation are indispensable for enhancing the performance of battery management systems, efficiency, and lifespan. The present study utilizes the CALCE dataset to develop a precise cell model and implement advanced SoC estimation techniques for the studied batteries. A 3rd order electrical equivalent circuit model offered greater flexibility in tuning the system's behavior. Key parameters like open-circuit voltage (OCV), internal resistance, polarization, and charge transfer resistance characteristics were identified using the Simulink Parameter Estimator, consequently ensuring a trustworthy optimization across various SoC levels. The presented model's accuracy was confirmed through terminal voltage comparisons, which showed reliable parameter estimation and a strong correlation with experimental data. Indeed, to estimate SoC, different methods, such as coulomb counting, OCV, and extended Kalman filter (EKF) are implemented in the simulated MATLAB Simulink model, as a result, SoC estimation using the EKF in Simulink demonstrates high accuracy, with a quadratic error of 1.44 × 10−5 for a measured covariance of 0.5. A second test yielded a quadratic error of 0.00036 for a measured covariance of 0.1, confirming the robustness of the model.