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Neural network approach for estimating state of charge of lithium-ion battery using backtracking search algorithm

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dc.citedby183
dc.contributor.authorHannan M.A.en_US
dc.contributor.authorLipu M.S.H.en_US
dc.contributor.authorHussain A.en_US
dc.contributor.authorSaad M.H.en_US
dc.contributor.authorAyob A.en_US
dc.contributor.authorid7103014445en_US
dc.contributor.authorid36518949700en_US
dc.contributor.authorid57208481391en_US
dc.contributor.authorid7202075525en_US
dc.contributor.authorid26666566900en_US
dc.date.accessioned2023-05-29T06:53:13Z
dc.date.available2023-05-29T06:53:13Z
dc.date.issued2018
dc.descriptionBackpropagation; Backpropagation algorithms; Charging (batteries); Electric batteries; Electric vehicles; Errors; Ions; Learning algorithms; Learning systems; Lithium; Lithium-ion batteries; Mean square error; Neural networks; Optimization; Radial basis function networks; Secondary batteries; Torsional stress; Back propagation neural networks; Backtracking search algorithms; Battery residual capacity; Extreme learning machine; Generalized Regression Neural Network(GRNN); Mean absolute percentage error; Radial basis function neural networks; State of charge; Battery management systemsen_US
dc.description.abstractThe state of charge (SOC) is a critical evaluation index of battery residual capacity. The significance of an accurate SOC estimation is great for a lithium-ion battery to ensure its safe operation and to prevent from over-charging or over-discharging. However, to estimate an accurate capacity of SOC of the lithium-ion battery has become a major concern for the electric vehicle (EV) industry. Therefore, numerous researches are being conducted to address the challenges and to enhance the battery performance. The main objective of this paper is to develop an accurate SOC estimation approach for a lithium-ion battery by improving back-propagation neural network (BPNN) capability using backtracking search algorithm (BSA). BSA optimization is utilized to improve the accuracy and robustness of BPNN model by finding the optimal value of hidden layer neurons and learning rate. In this paper, Dynamic Stress Test and Federal Urban Driving Schedule drive profiles are applied for testing the model at three different temperatures. The obtained results of the BPNN based BSA model are compared with the radial basis function neural network, generalized regression neural network and extreme learning machine model using statistical error values of root mean square error, mean absolute error, mean absolute percentage error, and SOC error to check and validate the model performance. The obtained results show that the BPNN based BSA model outperforms other neural network models in estimating SOC with high accuracy under different EV profiles and temperatures. � 2013 IEEE.en_US
dc.description.natureFinalen_US
dc.identifier.doi10.1109/ACCESS.2018.2797976
dc.identifier.epage10079
dc.identifier.scopus2-s2.0-85040974639
dc.identifier.spage10069
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85040974639&doi=10.1109%2fACCESS.2018.2797976&partnerID=40&md5=624fe570a4d3e072457b6386d1dd3917
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/23926
dc.identifier.volume6
dc.publisherInstitute of Electrical and Electronics Engineers Inc.en_US
dc.relation.ispartofAll Open Access, Gold
dc.sourceScopus
dc.sourcetitleIEEE Access
dc.titleNeural network approach for estimating state of charge of lithium-ion battery using backtracking search algorithmen_US
dc.typeArticleen_US
dspace.entity.typePublication
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