State of Charge Estimation of Lithium-Ion Batteries Using Kalman Filter

Accurate state of charge (SOC) estimation is vital for ensuring the safety, efficiency, and longevity of lithium-ion batteries, which serve as key components in clean energy systems such as electric vehicles and renewable-integrated smart grids. In this study, we propose a robust SOC estimation framework based on a second-order RC equivalent circuit model and the unscented Kalman filter (UKF). Experimental data from LG $\mathbf{1 8 6 5 0 H G 2}$ batteries under hybrid pulse power characterization (HPPC) are used for model parameter identification via exponential fitting. The UKF algorithm is implemented to track nonlinear battery dynamics with high precision. Results demonstrate that the proposed method achieves fast convergence and reliable accuracy across a broad range of SOC levels, highlighting its potential for real-time deployment in energy storage systems. This work contributes to advancing intelligent battery management for lowcarbon and sustainable energy applications.