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Extreme learning machine model for state-of-charge estimation of lithium-ion battery using gravitational search algorithm

Backpropagation; Charging (batteries); Electric vehicles; Estimation; Ions; Knowledge acquisition; Learning algorithms; Lithium-ion batteries; Machine learning; Radial basis function networks; Back-propagation neural networks; Electric vehicle drive cycles; Extreme learning machine; Gravitational se...

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Main Authors: Hossain Lipu M.S., Hannan M.A., Hussain A., Saad M.H., Ayob A., Uddin M.N.
Other Authors: 36518949700
Format: Article
Published: Institute of Electrical and Electronics Engineers Inc. 2023
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spelling my.uniten.dspace-246162023-05-29T15:25:09Z Extreme learning machine model for state-of-charge estimation of lithium-ion battery using gravitational search algorithm Hossain Lipu M.S. Hannan M.A. Hussain A. Saad M.H. Ayob A. Uddin M.N. 36518949700 7103014445 57208481391 7202075525 26666566900 55663372800 Backpropagation; Charging (batteries); Electric vehicles; Estimation; Ions; Knowledge acquisition; Learning algorithms; Lithium-ion batteries; Machine learning; Radial basis function networks; Back-propagation neural networks; Electric vehicle drive cycles; Extreme learning machine; Gravitational search algorithm (GSA); Gravitational search algorithms; Lithium ions; Radial basis function neural networks; State of charge; Battery management systems This paper develops a state-of-charge (SOC) estimation model for a lithium-ion battery using an improved extreme learning machine (ELM) algorithm. ELM is suitable for an SOC estimation since the ELM algorithm has fast estimation speed, good generalization performance, and high accuracy. However, the performance of ELM is highly dependent on training accuracy and the number of neurons in a hidden layer. Hence, a gravitational search algorithm (GSA) is applied to improve the ELM computational intelligence by searching for the optimal value hidden layer neurons. The optimal ELM-based GSA model does not require internal battery knowledge and mathematical model for an SOC estimation. The model robustness is validated at different temperatures using different electric vehicle drive cycles. The performance of the ELM-GSA model is verified with two popular neural network methods: Back-propagation neural network (BPNN) and radial basis function neural network (RBFNN). The results are evaluated using different error rates and computation costs. The results demonstrate that the ELM-based GSA model offers a higher accuracy and lower SOC error rate than those of BPNN-based GSA and RBFNN-based GSA models. Furthermore, a detailed comparative study between the proposed model and existing SOC strategies is conducted, which also demonstrates the superiority of the proposed model. � 2019 IEEE. Final 2023-05-29T07:25:09Z 2023-05-29T07:25:09Z 2019 Article 10.1109/TIA.2019.2902532 2-s2.0-85067849198 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85067849198&doi=10.1109%2fTIA.2019.2902532&partnerID=40&md5=1b1eceaee17d81276b2de47e9fe3601b https://irepository.uniten.edu.my/handle/123456789/24616 55 4 8656510 4225 4234 Institute of Electrical and Electronics Engineers Inc. Scopus
institution Universiti Tenaga Nasional
building UNITEN Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Tenaga Nasional
content_source UNITEN Institutional Repository
url_provider http://dspace.uniten.edu.my/
description Backpropagation; Charging (batteries); Electric vehicles; Estimation; Ions; Knowledge acquisition; Learning algorithms; Lithium-ion batteries; Machine learning; Radial basis function networks; Back-propagation neural networks; Electric vehicle drive cycles; Extreme learning machine; Gravitational search algorithm (GSA); Gravitational search algorithms; Lithium ions; Radial basis function neural networks; State of charge; Battery management systems
author2 36518949700
author_facet 36518949700
Hossain Lipu M.S.
Hannan M.A.
Hussain A.
Saad M.H.
Ayob A.
Uddin M.N.
format Article
author Hossain Lipu M.S.
Hannan M.A.
Hussain A.
Saad M.H.
Ayob A.
Uddin M.N.
spellingShingle Hossain Lipu M.S.
Hannan M.A.
Hussain A.
Saad M.H.
Ayob A.
Uddin M.N.
Extreme learning machine model for state-of-charge estimation of lithium-ion battery using gravitational search algorithm
author_sort Hossain Lipu M.S.
title Extreme learning machine model for state-of-charge estimation of lithium-ion battery using gravitational search algorithm
title_short Extreme learning machine model for state-of-charge estimation of lithium-ion battery using gravitational search algorithm
title_full Extreme learning machine model for state-of-charge estimation of lithium-ion battery using gravitational search algorithm
title_fullStr Extreme learning machine model for state-of-charge estimation of lithium-ion battery using gravitational search algorithm
title_full_unstemmed Extreme learning machine model for state-of-charge estimation of lithium-ion battery using gravitational search algorithm
title_sort extreme learning machine model for state-of-charge estimation of lithium-ion battery using gravitational search algorithm
publisher Institute of Electrical and Electronics Engineers Inc.
publishDate 2023
_version_ 1806428435254345728
score 13.222552

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