Modeling and State of Charge Estimation of Vanadium Redox Flow Batteries: A Review

Ruijie Feng, Zhenshuo Guo, Xuan Meng () and Chuanyu Sun ()
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Ruijie Feng: School of Electrical Engineering and Automation, Harbin Institute of Technology, Harbin 150006, China
Zhenshuo Guo: Faculty of Science and Technology, Beijing Normal-Hong Kong Baptist University, Zhuhai 519087, China
Xuan Meng: School of Electrical Engineering and Automation, Harbin Institute of Technology, Harbin 150006, China
Chuanyu Sun: School of Electrical Engineering and Automation, Harbin Institute of Technology, Harbin 150006, China

Energies, 2025, vol. 18, issue 17, 1-32

Abstract: As a type of electrochemical energy storage, the vanadium redox flow battery system (VRFB) is currently one of the most promising large-scale energy storage methods. Nevertheless, the ability to accurately estimate the state of charge (SOC) is one of the critical factors restricting the commercialization of VRFBs. This review summarizes the estimation methods for the SOCs of VRFBs used by scholars in the past 10 years, comprehensively discusses the main factors affecting the accuracy of SOC estimation, and discusses the direct measurement methods, combined with modeling filter estimation methods and data-driven SOC estimation approaches currently investigated by mainstream scholars. Although several recent literature reviews describe the current modeling and estimation methods for VRFBs, there has been relatively little attention paid to the more common equivalent circuit modeling methods and parameter identification approaches. This review mainly focuses on common equivalent circuit model (ECM) modeling methods and filter estimation algorithms using modeling, and it summarizes their advantages and disadvantages. Finally, a description of potential research directions for VRFB modeling and SOC estimation in the future is presented.

Keywords: vanadium redox flow battery (VRFB); state of charge (SOC); battery model; equivalent circuit model (ECM); parameter estimation; large-scale energy storage; parameter identification; renewable energy; long-duration energy storage; Kalman filter (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2025
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