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

被引：5

作者：

Xiao, Dong ^{[1
,2
]}

Li, Boyan ^{[1
,2
]}

Shan, Jiawei ^{[1
,2
,3
]}

Yan, Zelin ^{[1
,2
]}

Huang, Jie ^{[1
,2
]}

机构：

[1] Northeastern Univ, Sch Informat Sci & Engn, Shenyang 110819, Peoples R China

[2] Northeastern Univ, Liaoning Key Lab Intelligent Diag & Safety Met Ind, Shenyang 110819, Peoples R China

[3] Semicond Mfg Int Corp, Tianjin 300385, Peoples R China

来源：

ACS OMEGA | 2023年 / 8卷 / 48期

基金：

中国国家自然科学基金;

关键词：

EXTREME LEARNING-MACHINE;

D O I：

10.1021/acsomega.3c06113

中图分类号：

O6 [化学];

学科分类号：

0703 ;

摘要：

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.

引用

页码：45708 / 45714

页数：7

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