Real-time robust State-of-Charge estimation of lithium-ion batteries under unknown bounded disturbances

Accurate and reliable State-of-Charge (SOC) estimation is essential for the safe and efficient operation of lithium-ion batteries in modern energy storage systems. However, this task remains challenging due to measurement outliers and unknown bounded disturbances. This study proposes a real-time SOC interval estimation method to address these issues. First, a robust recursive least squares (RRLS) method is adopted for real-time parameter identification of a second-order equivalent circuit model (ECM), demonstrating greater accuracy and robustness against outliers than conventional approaches. Second, a structurally configurable robust interval observer (IO) is designed to handle unknown bounded disturbances. This configurable structure enhances design flexibility, which is a key innovation that improves estimation accuracy. The observer's optimal gain matrix is derived by solving a constrained optimization problem formulated with linear matrix inequalities (LMIs). Finally, experimental validation under the Dynamic Stress Test (DST) and Urban Dynamometer Driving Schedule (UDDS) profiles demonstrates the method's notable effectiveness. Under both operating conditions, the maximum Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) for model parameter identification are only 1.64% and 2.12%, respectively. For SOC estimation, the corresponding maximum MAE and RMSE are 0.49% and 0.52%, respectively.