A Comparative Study of Battery State of Charge Estimation Techniques

Effective energy management in electric vehicles and renewable energy storage systems requires State of Charge estimation of batteries. In this paper, the effectiveness and reliability of the Kalman Filter Method (KFM) and the Coulomb Counting Method (CCM) for State of Charge (SoC) estimation are evaluated. The current sensor drift and State of Charge initial dependency make Coulomb Counting a simple but error-prone approximation. A change monitoring and battery model are employed within the Kalman Filter to provide error correction in estimation. Simulation results show that the KMF is feasible for real-time battery monitoring and performs better under dynamic operating conditions.