State of charge estimation for the Vanadium Redox Flow Battery based on Extended Kalman filter using modified parameter identification

Qu, Dawei; Zixuan, Luo; Yang, Fan; Fan, Liyun; Mingyue, Zhu; Haoxuan, Li

doi:10.1080/15567036.2022.2136800

Cited by 8 publications

(1 citation statement)

References 35 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The physical model not only takes a low cost of complexity but also has fast convergence rate. Many various models in previous literature [27][28][29] are proposed. As shown in Figure 2, the second-order model has excellent modeling capability, which consists of an ohmic resistance and two RC branches that represent the electrochemical polarization and the concentration polarization.…”

Section: Ecm Establishmentmentioning

confidence: 99%

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

Zixuan

Fan

et al. 2023

Circuit Theory & Apps

Self Cite

View full text Add to dashboard Cite

SummaryFocusing 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°C and 5°C conditions. Finally, the mean absolute error (MAE) of the IEKF estimator are 4.66% at the 0°C and 0.895% at the 5°C. In the meantime, the MAE of the SAEKF estimator are 4.15% at the 0°C and 0.703% at the 5°C. The evaluation factors of the 0°C and 5°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.

show abstract

Section: Ecm Establishmentmentioning

confidence: 99%