A strong tracking adaptive fading‐extended Kalman filter for the state of charge estimation of lithium‐ion batteries

Lithium-ion batteries are widely used as rechargeable energy and power storage system in smart devices electric vehicles because of their high specific energy, densities, etc. The state charge (SOC) serves a vital feature that is monitored by the battery management to optimize performance, safety, lifespan lithium-ion batteries. In this paper, strong tracking adaptive fading-extended Kalman filter (STAF-EKF) based on second-order resistor–capacitor equivalent circuit model (2RC-ECM) proposed for accurate SOC estimation under different working conditions ambient temperatures. characteristic parameters established 2RC-ECM identified offline using least-squares curve fitting method with an average R-squared value 0.99881. Experimental data from hybrid pulse characterization (HPPC) verification STAF-EKF complex Beijing bus dynamic stress test (BBDST) (DST) at varying results show tracks actual voltage maximum error 28.44 mV BBDST condition. For estimation, has mean absolute (MAE) root square (RMSE) values 1.7159% 1.8507%, while EKF 6.7358% 7.2564%, respectively, temperature −10°C delivers optimal performance improvement compared temperatures, serving basis robust quick convergence real-time applications