A novel vanadium redox flow battery modelling method using honey badger optimization assisted CNN-BiLSTM

Yulin Liu, Ran Li, Binyu Xiong, Shaofeng Zhang, Xinan Zhang, Herbert Iu, Tyrone Fernando

Research output: Contribution to journalArticlepeer-review

10 Citations (Scopus)

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, a honey badger algorithm optimized CNN-BiLSTM is applied to directly learn the behavioural 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. Besides, an outstanding modelling accuracy is obtained under variable current and flow rate. Once trained, the honey badger algorithm optimized CNN-BiLSTM 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.
Original languageEnglish
Article number232610
JournalJournal of Power Sources
Volume558
DOIs
Publication statusPublished - 28 Feb 2023

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