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

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dc.citedby96
dc.contributor.authorHossain Lipu M.S.en_US
dc.contributor.authorHannan M.A.en_US
dc.contributor.authorHussain A.en_US
dc.contributor.authorSaad M.H.en_US
dc.contributor.authorAyob A.en_US
dc.contributor.authorUddin M.N.en_US
dc.contributor.authorid36518949700en_US
dc.contributor.authorid7103014445en_US
dc.contributor.authorid57208481391en_US
dc.contributor.authorid7202075525en_US
dc.contributor.authorid26666566900en_US
dc.contributor.authorid55663372800en_US
dc.date.accessioned2023-05-29T07:25:09Z
dc.date.available2023-05-29T07:25:09Z
dc.date.issued2019
dc.descriptionBackpropagation; 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 systemsen_US
dc.description.abstractThis 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.en_US
dc.description.natureFinalen_US
dc.identifier.ArtNo8656510
dc.identifier.doi10.1109/TIA.2019.2902532
dc.identifier.epage4234
dc.identifier.issue4
dc.identifier.scopus2-s2.0-85067849198
dc.identifier.spage4225
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85067849198&doi=10.1109%2fTIA.2019.2902532&partnerID=40&md5=1b1eceaee17d81276b2de47e9fe3601b
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/24616
dc.identifier.volume55
dc.publisherInstitute of Electrical and Electronics Engineers Inc.en_US
dc.sourceScopus
dc.sourcetitleIEEE Transactions on Industry Applications
dc.titleExtreme learning machine model for state-of-charge estimation of lithium-ion battery using gravitational search algorithmen_US
dc.typeArticleen_US
dspace.entity.typePublication
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