SOC Estimation of Vanadium Redox Flow Batteries Based on the ISCSO-ELM Algorithm.

作者信息

Xiao Dong, Li Boyan, Shan Jiawei, Yan Zelin, Huang Jie

机构信息

School of Information Science and Engineering, Northeastern University, 110819 Shenyang, China.

Liaoning Key Laboratory of Intelligent Diagnosis and Safety for Metallurgical Industry, Northeastern University, 110819 Shenyang, China.

出版信息

ACS Omega. 2023 Nov 22;8(48):45708-45714. doi: 10.1021/acsomega.3c06113. eCollection 2023 Dec 5.

DOI:10.1021/acsomega.3c06113

PMID:38075804

原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC10702314/

Abstract

This study focuses on the stage of charge (SOC) estimation for vanadium redox flow batteries (VFBs), establishing an electrochemical model that provides parameters, including ion concentration. Second, considering the capacity decay of VFBs, an extreme learning machine (ELM) combined with an improved sand cat swarm optimization algorithm, named ISCSO-ELM, is integrated with SOC estimation to predict the battery's SOC more effectively.

摘要