Bias-Corrected State of Charge Estimation for Supercapacitors Integrating Open Circuit Voltage Slope-Weighted Adjustment and Sage–Husa Noise Adaptive Mechanism

High-precision state of charge (SOC) estimation is essential for reliable supercapacitor (SC) energy storage systems. However, irregular noise and sensor biases can degrade estimation accuracy. To address these issues, an estimated-value correction (EVC) strategy integrating forgetting factor recursive least squares (FFRLS) and Sage–Husa adaptive extended Kalman filtering (SHEKF) is developed. FFRLS enables online parameter identification, while the Sage–Husa mechanism improves noise adaptability. An ampere-hour integration (AHI)-assisted correction mechanism with open-circuit voltage (OCV) slope-dependent gain modulation is introduced to mitigate sensor-bias-induced estimation deviations. Experiments under dynamic stress test (DST) and urban dynamometer driving schedule (UDDS) conditions demonstrate that EVC-SHEKF achieves root mean square errors (RMSEs) of 1.096% and 1.113%, respectively, under coupled voltage and current sensor biases, outperforming the investigated filtering and bias-compensation methods. Parameter sensitivity analysis supports the selected correction gain, while temperature experiments from 10 °C to 40 °C yield RMSEs between 1.09% and 1.14%. These results demonstrate the effectiveness and robustness of the proposed framework under the investigated measurement uncertainties and operating conditions.