Publication:
Optimal neural network approach for estimating state of energy of lithium-ion battery using heuristic optimization techniques

Simple item page
dc.citedby6
dc.contributor.authorLipu M.S.H.en_US
dc.contributor.authorHussain A.en_US
dc.contributor.authorSaad M.H.M.en_US
dc.contributor.authorHannan M.A.en_US
dc.contributor.authorid36518949700en_US
dc.contributor.authorid57208481391en_US
dc.contributor.authorid7202075525en_US
dc.contributor.authorid7103014445en_US
dc.date.accessioned2023-05-29T06:52:39Z
dc.date.available2023-05-29T06:52:39Z
dc.date.issued2018
dc.descriptionBackpropagation algorithms; Errors; Learning algorithms; Mean square error; Neural networks; Particle swarm optimization (PSO); Torsional stress; Back propagation neural networks; Backtracking search algorithms; Heuristic optimization technique; Optimal neural network; Optimization algorithms; Particle swarm optimization algorithm; Root mean square errors; state of energy; Lithium-ion batteriesen_US
dc.description.abstractThis paper presents an optimal state of energy (SOE) estimation strategy of a lithium-ion battery using the back-propagation neural network (BPNN). Two heuristic optmization techniques named backtracketing search algorithm (BSA) and particle swarm optimization (PSO) algorithm are applied to improve the accuracy of BPNN model. Optimization algorithms are developed to determine the optimal value of hidden layer neurons and learning rate of BPNN model. Three most influencing factors including current, voltage and temperature are considered as the inputs to the optimal BPNN model. Federal Urban Driving Schedule (FUDS) is used to check the model robustness at 0�C, 25�C and 45�C. The model performance is evaluated based on the root mean square error (RMSE) and mean absolute error (MAE). The results show that the proposed model obtains good accuracy with an absolute error of �5%. The BPNN based BSA model improves the SOE estimation accuracy by reducing RMSE and MAE by 2.8% and 4.4% compared to BPNN based PSO model at 25�C. � 2017 IEEE.en_US
dc.description.natureFinalen_US
dc.identifier.doi10.1109/ICEEI.2017.8312418
dc.identifier.epage6
dc.identifier.scopus2-s2.0-85050757119
dc.identifier.spage1
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85050757119&doi=10.1109%2fICEEI.2017.8312418&partnerID=40&md5=d7fdbdec3d9b85fff0234b2a42c0d5f5
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/23874
dc.identifier.volume2017-November
dc.publisherInstitute of Electrical and Electronics Engineers Inc.en_US
dc.sourceScopus
dc.sourcetitleProceedings of the 2017 6th International Conference on Electrical Engineering and Informatics: Sustainable Society Through Digital Innovation, ICEEI 2017
dc.titleOptimal neural network approach for estimating state of energy of lithium-ion battery using heuristic optimization techniquesen_US
dc.typeConference Paperen_US
dspace.entity.typePublication
Files
Collections
SCOPUS