Hybrid Elephant Herding and Golden Eagle Optimization-Based Extended Kalman Filter for State of Charge Estimation of Energy Storage Batteries

The Extended Kalman Filter (EKF) serves as a widely utilized approach to evaluate the state of charge (SOC) of energy storage batteries. However, the conventional EKF is commonly adversely affected by ambient temperature variations, uncertain noise matrices, and inaccurate parameter estimation in practice. Therefore, hybrid elephant herding and golden eagle optimization based EKF (HEGO) is introduced to enhance the precision and effectiveness of battery SOC estimation. The local contraction capability of elephant herding optimization is utilized to narrow the search range within a predefined search space and accurately locate the region of the optimal solution. Within the narrowed search range provided by EHO, golden eagle optimization (GEO) is then employed to accurately identify the noise matrix and equivalent circuit parameters appropriate for the current state, thereby increasing the precision and resilience of SOC estimations against environmental disturbances. Data for an 18650-battery evaluated with the Federal Urban Driving Schedule (FUDS), Dynamic Stress Test (DST), and Hybrid Pulse Power Characterization (HPPC) conditions were collected using an experimental platform, and the proposed algorithm was experimentally validated. The results demonstrate that, across different temperatures and operating conditions, the proposed algorithm consistently achieves optimal performance, with a mean absolute error below 0.7% and strong generalization, thereby providing stable and reliable technical support for battery SOC estimation.