Comparative Analysis of Advanced State of Charge Estimation Techniques in Lithium-Ion Batteries Powering Electric Vehicles

Precise estimation of State of Charge (SOC) for the batteries is vital for performance optimization, prolonging battery life, and ensuring its safe operations in electric vehicles (EVs). This study provides a detailed comparison of two advanced SOC estimation techniques- Extended Kalman Filter (EKF) and Unscented Kalman Filter (UKF) for SOC estimation of Li-ion batteries (LIBs). The performance of these algorithms is studied under noisy conditions. The findings demonstrate the advantages and limitations of two method, offering key insights into their effectiveness for reliable SOC estimation. This work offers an effective approach for improving battery management systems (BMSs) in EVs, offering a pathway for enhanced energy management and operational safety.