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A model for predicting the state of health of redox flow batteries based on an extreme learning machine model and swarm intelligence algorithm
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Lei ZHANG1,2, Zebo HUANG3, Guo'an YANG2, Fengming CHU1,2. A model for predicting the state of health of redox flow batteries based on an extreme learning machine model and swarm intelligence algorithm[J]. Journal of Zhejiang University Science A, 1998, -1(-1): .
@article{title="A model for predicting the state of health of redox flow batteries based on an extreme learning machine model and swarm intelligence algorithm",
author="Lei ZHANG1,2, Zebo HUANG3, Guo'an YANG2, Fengming CHU1,2",
journal="Journal of Zhejiang University Science A",
volume="-1",
number="-1",
pages="",
year="1998",
publisher="Zhejiang University Press & Springer",
doi="10.1631/jzus.A2500461"
}
%0 Journal Article
%T A model for predicting the state of health of redox flow batteries based on an extreme learning machine model and swarm intelligence algorithm
%A Lei ZHANG1
%A 2
%A Zebo HUANG3
%A Guo'an YANG2
%A Fengming CHU1
%A 2
%J Journal of Zhejiang University SCIENCE A
%V -1
%N -1
%P
%@ 1673-565X
%D 1998
%I Zhejiang University Press & Springer
%DOI 10.1631/jzus.A2500461
TY - JOUR
T1 - A model for predicting the state of health of redox flow batteries based on an extreme learning machine model and swarm intelligence algorithm
A1 - Lei ZHANG1
A1 - 2
A1 - Zebo HUANG3
A1 - Guo'an YANG2
A1 - Fengming CHU1
A1 - 2
J0 - Journal of Zhejiang University Science A
VL - -1
IS - -1
SP -
EP -
%@ 1673-565X
Y1 - 1998
PB - Zhejiang University Press & Springer
ER -
DOI - 10.1631/jzus.A2500461
Abstract: Accurate assessment of the state of health (SOH) is essential for the safe, economical, and reliable operation of all-vanadium redox flow batteries (VRFBs). However, systems for monitoring their SOH have rarely been studied. In this study, capacity retention rate was used to characterize the SOH, and a series of charge/discharge cycling tests were conducted. Based on a series of operational parameters and grey relation analysis (GRA), the best health feature subset to characterize the SOH was determined. Extreme learning machine (ELM) combined with swarm intelligence algorithms were used to predict SOH, based on which the effects of different swarm intelligence algorithms and different activation functions on estimations of SOH performance were investigated. Results showed that all the models with poor prediction results had a sigmoid activation function, indicating that such functions are not capable of predicting SOH. Grey relational analysis based on LASSO (LGRA) Algorithm-ELM was more suitable for SOH prediction than the interaction measurement algorithm based on Spearman's correlation coefficient (SCIM) Algorithm-ELM model. It can not only estimate accurately but also predict recession characteristics. The most suitable swarm intelligence algorithm was particle swarm optimization (PSO), and the best activation function was linear, in which the maximum error was lower than 0.2%.
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