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ABSTRACT
Vanadium Redox Flow Battery (VRFB) is widely utilized in energy storage due to its excellent characteristics. Credible knowledge of the state of charge (SOC) is a pre-condition for the effective health management of batteries. The SOC estimation depends on the second-order RC equivalent circuit model (ECM) parameters identified by the forgetting factor recursive least squares (FF-RLS). Considering the time-varying characteristics of the model parameters, an adaptive forgetting factor recursive least squares (AFF-RLS) based on the gradient descent method is proposed to identify the EMC parameters. The forgetting factor can be obtained based on the error between the terminal voltage measurement and terminal voltage estimation. The proposed joint estimator has been verified by performing charge and discharge experiments for VRFB single cell. The mean error, maximum estimated error and root mean square error of SOC under 6 A pulse discharging current are 1.38 × 10−3, 3.76 × 10−5, and 1.38 × 10−5. The result indicates that AFF-RLS is robust against outliers of model parameters and improve the anti-interference of EKF. In addition, this paper finds that the different value of learning rate can affect the sensitive and anti-interference of EKF.
Acknowledgements
The authors would like to thank the anonymous reviewers for their valuable comments and suggestions to improve the paper’s quality.
Disclosure statement
No potential conflict of interest was reported by the author(s).
Data availability statement
A data availability statement: The data that support the findings of this study are available on request from the corresponding author, Fan. The data are not publicly available due to their containing information that could compromise the privacy of research participants.
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Funding
Notes on contributors
Qu Dawei
Qu Dawei(1983-), adjunct professor; He graduated with Ph.D. degrees from Jilin University. His current research focus on Vanadium Redox Flow Batteries energy storage systems.