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

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

doi:10.1021/acsomega.3c06113

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

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

Authors

Dong Xiao^{1

2}, Boyan Li^{1

2}, Jiawei Shan^{1

2

3}, Zelin Yan^{1

2}, Jie Huang^{1

2}

Affiliations

¹ School of Information Science and Engineering, Northeastern University, 110819 Shenyang, China.
² Liaoning Key Laboratory of Intelligent Diagnosis and Safety for Metallurgical Industry, Northeastern University, 110819 Shenyang, China.
³ Semiconductor Manufacturing International Corporation, 300385 Tianjin, China.

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.