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Feature selection and optimal neural network algorithm for the state of charge estimation of lithium-ion battery for electric vehicle application

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dc.citedby13
dc.contributor.authorHossain Lipu M.S.en_US
dc.contributor.authorHannan M.A.en_US
dc.contributor.authorHussain A.en_US
dc.contributor.authorid36518949700en_US
dc.contributor.authorid7103014445en_US
dc.contributor.authorid57208481391en_US
dc.date.accessioned2023-05-29T06:39:48Z
dc.date.available2023-05-29T06:39:48Z
dc.date.issued2017
dc.descriptionBattery management systems; Charging (batteries); Errors; Feature extraction; Ions; Lithium-ion batteries; Mean square error; Multilayer neural networks; Principal component analysis; Data training and testing; Mean squared error; Neural network model; Neural-networks; Principle components analysis; Root mean squared error; Root mean squared errors; States of charges; Training and testing; Neural network modelsen_US
dc.description.abstractThis paper presents the estimation of the state of charge (SOC) for a lithium-ion battery using feature selection and an optimal NN algorithm. Principle component analysis (PCA) is used to select the most influencing features. Out of nine variables, five input variables are selected based on the value of eigenvectors. An optimal neural network (NN) is developed by selecting the hidden layer neurons and learning rate since these parameters are the most critical factors in constructing a NN model. The model is tested and evaluated by using US06 driving cycle at 25�C and 45�C respectively. In order demonstrate the effectiveness and accuracy of the proposed model, a comparative study is performed between proposed NN model and two different NN models (NN1 and NN2). The proposed NN model estimates SOC with lower mean squared error (MSE) and root mean squared error (RMSE) compared to two NN models which proves that the proposed model is competent and robust in estimating SOC. The simulation results show an improvement in proposed NN model accuracy over NN1 and NN2 models in minimizing RMSE by 26% and 22% and MSE by 45% and 39% respectively at 25�C. � Renewable Energy Research, 2017.en_US
dc.description.natureFinalen_US
dc.identifier.epage1708
dc.identifier.issue4
dc.identifier.scopus2-s2.0-85043487236
dc.identifier.spage1701
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85043487236&partnerID=40&md5=1a00ac72c7500ef3380e6900789819b9
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/23364
dc.identifier.volume7
dc.publisherInternational Journal of Renewable Energy Researchen_US
dc.sourceScopus
dc.sourcetitleInternational Journal of Renewable Energy Research
dc.titleFeature selection and optimal neural network algorithm for the state of charge estimation of lithium-ion battery for electric vehicle applicationen_US
dc.typeArticleen_US
dspace.entity.typePublication
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