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Lithium-ion Battery State of Charge Estimation Method Using Optimized Deep Recurrent Neural Network Algorithm

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dc.citedby14
dc.contributor.authorLipu M.S.H.en_US
dc.contributor.authorHannan M.A.en_US
dc.contributor.authorHussain A.en_US
dc.contributor.authorSaad M.H.M.en_US
dc.contributor.authorAyob A.en_US
dc.contributor.authorMuttaqi K.M.en_US
dc.contributor.authorid36518949700en_US
dc.contributor.authorid7103014445en_US
dc.contributor.authorid57208481391en_US
dc.contributor.authorid7202075525en_US
dc.contributor.authorid26666566900en_US
dc.contributor.authorid55582332500en_US
dc.date.accessioned2023-05-29T07:23:44Z
dc.date.available2023-05-29T07:23:44Z
dc.date.issued2019
dc.descriptionAlumina; Aluminum oxide; Backpropagation; Battery management systems; Bioluminescence; Charging (batteries); Cobalt compounds; Deep neural networks; Genetic algorithms; Ions; Lithium compounds; Machine learning; Nickel oxide; Radial basis function networks; Recurrent neural networks; Back-propagation neural networks; Computation intelligences; Electrochemical batteries; Firefly algorithms; Manganese-cobalt oxides; Radial basis function neural networks; Self-learning capability; State of charge; Lithium-ion batteriesen_US
dc.description.abstractThis paper presents an enhanced machine learning based state of charge (SOC) estimation method for a lithium-ion battery using a deep recurrent neural network (DRNN) algorithm. DRNN is suitable for SOC evaluation due to strong computation intelligence and self-learning capabilities. Nevertheless, the performance of DRNN is constrained due to the training accuracy and duration which entirely depends on the appropriate selection of hyper-parameters including hidden layer and hidden neurons. Therefore, firefly algorithm (FA) is employed to find the optimal number for hyper-parameters of DRNN networks. The optimized DRNN based FA algorithm for SOC estimation does not require extensive knowledge about battery chemistry, electrochemical battery model and added filter, rather only needs battery test bench to measure current and voltage. The developed model is tested using two different types of lithium-ion batteries namely lithium nickel manganese cobalt oxide (LiNiMnCoO2) and lithium nickel cobalt aluminum oxide (LiNiCoAlO2). The proposed model is validated by two experimental tests; one with static discharge test and other with pulse discharge test at room temperature. The experimental results indicate the superiority of the DRNN based FA method in comparison with the back-propagation neural network (BPNN) and radial basis function neural network (RBFNN). � 2019 IEEE.en_US
dc.description.natureFinalen_US
dc.identifier.ArtNo8912322
dc.identifier.doi10.1109/IAS.2019.8912322
dc.identifier.scopus2-s2.0-85076785379
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85076785379&doi=10.1109%2fIAS.2019.8912322&partnerID=40&md5=a012f9e43104e8a75d475818bf70da2f
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/24465
dc.publisherInstitute of Electrical and Electronics Engineers Inc.en_US
dc.sourceScopus
dc.sourcetitle2019 IEEE Industry Applications Society Annual Meeting, IAS 2019
dc.titleLithium-ion Battery State of Charge Estimation Method Using Optimized Deep Recurrent Neural Network Algorithmen_US
dc.typeConference Paperen_US
dspace.entity.typePublication
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