Publication:
Machine Learning Based Optimal Design of On-Road Charging Lane for Smart Cities Applications

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dc.citedby3
dc.contributor.authorShanmugam Y.en_US
dc.contributor.authorNarayanamoorthi R.en_US
dc.contributor.authorRamachandaramurthy V.K.en_US
dc.contributor.authorBernat P.en_US
dc.contributor.authorShrestha N.en_US
dc.contributor.authorSon J.en_US
dc.contributor.authorWilliamson S.S.en_US
dc.contributor.authorid58153403000en_US
dc.contributor.authorid57095044400en_US
dc.contributor.authorid6602912020en_US
dc.contributor.authorid56119573400en_US
dc.contributor.authorid58318687800en_US
dc.contributor.authorid58539742600en_US
dc.contributor.authorid7203080313en_US
dc.date.accessioned2025-03-03T07:47:40Z
dc.date.available2025-03-03T07:47:40Z
dc.date.issued2024
dc.description.abstractThe rapid advancement of electric vehicle (EV) technology toward environmentally friendly transportation emphasizes the necessity of dynamic wireless charging. However, challenges, such as the initial charging infrastructure cost, power transfer efficiency, and output power pulsation, pose significant limitations to dynamic wireless charging. Overcoming these challenges requires optimizing the design of various functional elements in dynamic charging, including the magnetic coupler, spacing between couplers, high-frequency inverter, and compensators. Despite the nonlinear relationships among these elements, obtaining mathematical relations proves cumbersome. This article proposes an effective machine learning (ML) approach to achieve the optimal design of the charging track, considering the cross-coupling effect. The algorithm not only aids in estimating the infrastructure cost of the charging lane but also predicts optimal design parameters using trained data. The ML approach, which predicts optimal design parameters with a trained dataset, is more efficient with reduced duration than conventional finite element analysis (FEA) tools and stochastic methods. The learning algorithms consider variables such as core structure, cross-coupling effect, and coil flux pipe length. Simulation and experimental prototype validation for a 3.3 kW system demonstrated an impressive efficiency of 93.21%. ? 2013 IEEE.en_US
dc.description.natureFinalen_US
dc.identifier.doi10.1109/JESTPE.2024.3400292
dc.identifier.epage4309
dc.identifier.issue4
dc.identifier.scopus2-s2.0-85193258691
dc.identifier.spage4296
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85193258691&doi=10.1109%2fJESTPE.2024.3400292&partnerID=40&md5=d8405b83a81bf8a3e5b880ffc35b2279
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/37118
dc.identifier.volume12
dc.pagecount13
dc.publisherInstitute of Electrical and Electronics Engineers Inc.en_US
dc.sourceScopus
dc.sourcetitleIEEE Journal of Emerging and Selected Topics in Power Electronics
dc.subjectCharging (batteries)
dc.subjectCost benefit analysis
dc.subjectEfficiency
dc.subjectElectric vehicles
dc.subjectEnergy transfer
dc.subjectInductive power transmission
dc.subjectMachine learning
dc.subjectOptimal systems
dc.subjectStochastic systems
dc.subjectCoil
dc.subjectCost analysis
dc.subjectCoupler
dc.subjectDynamic charging
dc.subjectMachine-learning
dc.subjectOptimal design
dc.subjectPrediction algorithms
dc.subjectReceiver
dc.subjectWireless charging
dc.subjectLearning algorithms
dc.titleMachine Learning Based Optimal Design of On-Road Charging Lane for Smart Cities Applicationsen_US
dc.typeArticleen_US
dspace.entity.typePublication
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