A novel one dimensional convolutional neural network based data-driven vanadium redox flow battery modelling algorithm

• Published in: Journal of energy storage Vol. 61; p. 106767
• Main Authors: Li, Ran, Xiong, Binyu, Zhang, Shaofeng, Zhang, Xinan, Li, Yifeng, Iu, Herbert, Fernando, Tyrone
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.05.2023
• Subjects: Data-driven; Modelling; One dimensional conventional neural network; Vanadium redox flow battery; Modelling; Vanadium redox flow battery; Data-driven; One dimensional conventional neural network
• ISSN: 2352-152X; 2352-1538
• DOI: 10.1016/j.est.2023.106767

This study proposes an innovative data-driven battery modelling algorithm for vanadium redox flow battery (VRB) in power systems. Unlike the existing battery modelling methods, the proposed algorithm employed the simple but computationally efficient one dimensional convolutional neural network (1D-CNN) technique to learn the nonlinear relationships between VRB current, flow rate, state-of-charge (SOC), and voltage. Compared to the two dimensional CNN, which is widely used in lithium-ion battery modelling and monitoring studies, 1D-CNN eliminates the tedious data re-structuring process and provides better accuracy. Thus, it is more suitable for battery modelling based on one dimensional time series data. Furthermore, 1D-CNN is independent of battery model parameters, allowing it to provide superior modelling performance over the existing electrochemical principle-based and equivalent circuit-based modelling methods that rely on the knowledge of accurate battery model. The validity of the proposed 1D-CNN is verified by experiments. •Data-trained 1D-CNN to model the nonlinear dynamics of VRB with high accuracy.•Parameter independent modelling method with high computational efficiency.•No electrochemical background required and thus suitable for electrical engineers.•Sophisticated charge and discharge operations are considered.