4.7 Article

An equivalent circuit model for Vanadium Redox Batteries via hybrid extended Kalman filter and Particle filter methods

Journal

JOURNAL OF ENERGY STORAGE
Volume 39, Issue -, Pages -

Publisher

ELSEVIER
DOI: 10.1016/j.est.2021.102587

Keywords

Battery capacity estimation; Electrochemical battery model; Energy storage systems; Equivalent circuit model; Extended Kalman filter; Particle filter; State of charge; Vanadium redox flow battery

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This paper proposes a new parameter estimation model for Vanadium Redox Flow Battery that is applicable for various charging procedures and shows improved accuracy when compared to previous models. By using two Kalman filter based methods, the discrepancies in the constant voltage mode of the proposed circuit model are reduced significantly. The proposed method can also be extended to estimate the state of charge of the battery with improved accuracy.
This paper proposes a model for parameter estimation of Vanadium Redox Flow Battery based on both the electrochemical model and the Equivalent Circuit Model. The equivalent circuit elements are found by a newly proposed optimization to minimized the error between the Thevenin and KVL-based impedance of the equivalent circuit. In contrast to most previously proposed circuit models, which are only introduced for constant current charging, the proposed method is applicable for all charging procedures, i.e., constant current, constant voltage, and constant current-constant voltage charging procedures. The proposed model is verified on a nine-cell VRFB stack by a sample constant current-constant voltage charging. As observed, in constant current charging mode, the terminal voltage model matches the measured data closely with low deviation; however, the terminal voltage model shows discrepancies with the measured data of VRFB in constant voltage charging. To improve the proposed circuit model's discrepancies in constant voltage mode, two Kalman filters, i.e., hybrid extended Kalman filter and particle filter estimation algorithms, are used in this study. The results show the accuracy of the proposed equivalent with an average deviation of 0.88% for terminal voltage model estimation by the extended KF-based method and the average deviation of 0.79% for the particle filter-based estimation method, while the initial equivalent circuit has an error of 7.21%. Further, the proposed procedure extended to estimate the state of charge of the battery. The results show an average deviation of 4.2% in estimating the battery state of charge using the PF method and 4.4% using the hybrid extended KF method, while the electrochemical SoC estimation method is taken as the reference. These two Kalman Filter based methods are more accurate compared to the average deviation of state of charge using the Coulomb counting method, which is 7.4%.

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