State of charge estimation for the vanadium redox flow battery based on the Sage-Husa adaptive extended Kalman filter

Focusing on improving the accuracy of vanadium redox flow battery (VRFB) state of charge (SOC) estimation, this paper will combine the extended Kalman filter (EKF) estimator with the Sage-Husa adaptive method, referred to as the SAEKF estimator. Firstly, a second-order Equivalent Circuit Model (ECM) is established to provide the correct parameterization and degrees of freedom. Next, under a hybrid pulse power characterization (HPPC) text, the parameter estimator in MATLAB/Simulink is utilized to identify the ECM parameters by using nonlinear least squares method with Trust-Region-Reflective (TRR) iterative algorithm. Then, during the HPPC test, the estimators implemented on the ARM-Cortex STM32F103 estimate the VRFB SOC under both the 0 & DEG;C and 5 & DEG;C conditions. Finally, the mean absolute error (MAE) of the IEKF estimator are 4.66% at the 0 & DEG;C and 0.895% at the 5 & DEG;C. In the meantime, the MAE of the SAEKF estimator are 4.15% at the 0 & DEG;C and 0.703% at the 5 & DEG;C. The evaluation factors of the 0 & DEG;C and 5 & DEG;C experiments in the SAEKF estimator are smaller than those of the IEKF and EKF estimators, indicating that the SAEKF estimator outperforms the IEKF estimator in VRFB SOC estimation.