A novel U-Net based data-driven vanadium redox flow battery modelling approach
Li Ran, Binyu Xiong, Shaofeng Zhang, Xinan Zhang, Yifeng Li, Herbert Ho‐Ching Iu, Tyrone Fernando
Abstract
This research proposes a highly accurate data-driven vanadium redox flow battery (VRB) modelling approach for power engineering applications. The proposed approach addresses the common problem of excessive model dependency in the existing electrochemical principle or equivalent circuit based VRB modelling methods. Furthermore, an experimentally trained U-Net is applied to directly learn the behavioral relationship between VRB current, flow rate, state-of-charge, and voltage with excellent accuracy, avoiding the usage of model parameters that are subject to variations. Once trained, the U-Net based neural network becomes mathematically very simple and thus, can be easily implemented in simulation studies. This contributes to substantially simplify the analysis of electrical systems with VRB. The validity of the proposed approach is verified experimentally.