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Optimal BP neural network algorithm for state of charge estimation of lithium-ion battery using PSO with PCA feature selection

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dc.citedby56
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.M.en_US
dc.contributor.authorid36518949700en_US
dc.contributor.authorid7103014445en_US
dc.contributor.authorid57208481391en_US
dc.contributor.authorid7202075525en_US
dc.date.accessioned2023-05-29T06:37:36Z
dc.date.available2023-05-29T06:37:36Z
dc.date.issued2017
dc.descriptionBackpropagation; Battery management systems; Charging (batteries); Electric batteries; Errors; Feature extraction; Ions; Lithium; Lithium-ion batteries; Mean square error; Neural networks; Optimization; Particle swarm optimization (PSO); Principal component analysis; Radial basis function networks; Back-propagation neural networks; Electric vehicle drive cycles; Hidden layer neurons; Mean absolute error; Mean absolute percentage error; Radial basis function neural networks; Root mean square errors; State-of-charge estimation; Secondary batteriesen_US
dc.description.abstractThe state of charge (SOC) is the residual capacity of a battery, which indicates the available charge left inside a battery to drive a vehicle. Accurate SOC estimation is of great significance for a lithium-ion battery to ensure its safe operation and to prevent it from over-charging or over-discharging. However, it is difficult to get an accurate value of SOC since it is an inner state of a battery cell, which cannot be directly measured. This paper presents an improved SOC estimation strategy for a lithium-ion battery using the back-propagation neural network (BPNN). Two algorithms, principal component analysis (PCA) and particle swarm optimization (PSO), are used to enhance the accuracy and robustness. PCA is utilized to select the most significant input features. The PSO algorithm is developed to determine the optimal value of hidden layer neurons and the learning rate since these parameters are the most critical factors in constructing an optimal BPNN model. The proposed model is tested and evaluated by using three electric vehicle drive cycles. The performance of the proposed model is compared with common BPNN and radial basis function neural network (RBFNN) models and verified based on the root mean square error (RMSE), mean square error (MSE), mean absolute error (MAE), mean absolute percentage error (MAPE), and SOC error. The validation results are very effective in predicting SOC with very narrow SOC error which demonstrates the model robustness. The results indicate that the proposed model computes RMSE to be 0.58%, 0.72%, and 0.47% for the Beijing Dynamic Stress Test (BJDST), Federal Urban Drive Schedule (FUDS), and US06, cycle, respectively. � 2017 Author(s).en_US
dc.description.natureFinalen_US
dc.identifier.ArtNo64102
dc.identifier.doi10.1063/1.5008491
dc.identifier.issue6
dc.identifier.scopus2-s2.0-85038446309
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85038446309&doi=10.1063%2f1.5008491&partnerID=40&md5=e8e16fc8efaffcc5692af160fe4c4459
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/23055
dc.identifier.volume9
dc.publisherAmerican Institute of Physics Inc.en_US
dc.sourceScopus
dc.sourcetitleJournal of Renewable and Sustainable Energy
dc.titleOptimal BP neural network algorithm for state of charge estimation of lithium-ion battery using PSO with PCA feature selectionen_US
dc.typeArticleen_US
dspace.entity.typePublication
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