Publication:
A COMPARATIVE PERFORMANCE EVALUATION OF NEURAL NETWORK ALGORITHMS BASED STATE OF CHARGE ESTIMATION FOR LITHIUM-ION BATTERY

Simple item page
dc.contributor.authorLipu M.S.H.en_US
dc.contributor.authorAyob A.en_US
dc.contributor.authorHussain A.en_US
dc.contributor.authorHannan M.A.en_US
dc.contributor.authorSalam M.A.en_US
dc.contributor.authorid36518949700en_US
dc.contributor.authorid26666566900en_US
dc.contributor.authorid57208481391en_US
dc.contributor.authorid7103014445en_US
dc.contributor.authorid57220489228en_US
dc.date.accessioned2023-05-29T08:07:55Z
dc.date.available2023-05-29T08:07:55Z
dc.date.issued2020
dc.description.abstractThis work presents a comparative analysis of state of charge (SOC) estimation for lithium-ion battery using neural network algorithms. The lithium-ion battery has been operating successfully in the automotive industry due to the long-life cycles, low memory effect, high voltage, and high energy density. As such, numerous research works have been conducted on lithiumion battery towards estimating SOC. The conventional and model-based SOC estimation approaches have shortcomings including heavy computational calculation and inaccurate battery model parameters determination. Therefore, neural network algorithms based SOC estimation have received huge attention since they have the adaptively to adjust the network parameters automatically without battery model. Three prominent neural network algorithms including backpropagation neural network (BPNN), radial basis function neural network (RBFNN) and recurrent nonlinear autoregressive with exogenous inputs neural network (RNARXNN) are used to compare the SOC estimation results. The three methods are validated by battery experimental tests and electric vehicle (EV) drive cycles. The results demonstrate that RNARXNN is dominant to BPNN and RBFNN algorithms in obtaining high SOC accuracy with the low computational cost. � 2020. All rights reserved.en_US
dc.description.natureFinalen_US
dc.identifier.epage100
dc.identifier.issue2
dc.identifier.scopus2-s2.0-85097054057
dc.identifier.spage89
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85097054057&partnerID=40&md5=a7874f2297a6bc44d4a9cda1ffbdcc18
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/25291
dc.identifier.volume14
dc.publisherPenerbit Universiti Teknikal Malaysia Melakaen_US
dc.sourceScopus
dc.sourcetitleJournal of Advanced Manufacturing Technology
dc.titleA COMPARATIVE PERFORMANCE EVALUATION OF NEURAL NETWORK ALGORITHMS BASED STATE OF CHARGE ESTIMATION FOR LITHIUM-ION BATTERYen_US
dc.typeArticleen_US
dspace.entity.typePublication
Files
Collections
SCOPUS