A robust machine learning-based SOC estimation approach for vanadium redox flow battery

Chengyan Zheng, Wendong Feng, Zhongbao Wei, Yifeng Li, Herbert Ho Ching Iu, Tyrone Fernando, Xinan Zhang*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract

The vanadium redox flow battery (VRB) is recognized as an effective large-scale energy storage solution for mitigating the renewable intermittency and ensuring grid reliability. Accurate estimation of the state of charge (SOC) is crucial for the optimal operation of VRB. This paper presents a novel machine learning-based estimation algorithm to overcome the long-lasting problem of model dependency in the existing SOC estimation approaches for VRB. Compared to the conventional model based methods, such as Kalman filter and sliding mode observer, the proposed algorithm does not need any knowledge of the VRB model. In addition, the proposed algorithm employs recurrent equilibrium network (REN), which has “built in” behavioral guarantees of stability and robustness compared to the traditional machine learning algorithms. Furthermore, the proposed algorithm employs the nonlinear direct parameterization technique to substantially simplify the neural network training. Its efficacy is verified by experimental results.

Original languageEnglish
Article number237087
JournalJournal of Power Sources
Volume645
DOIs
Publication statusPublished - 30 Jul 2025

Keywords

  • Direct parameterization
  • Recurrent equilibrium network
  • SOC estimation
  • Vanadium redox flow battery

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