Machine Learning Based Optimal Design of On-Road Charging Lane for Smart Cities Applications
The 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 significa...
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my.uniten.dspace-371182025-03-03T15:47:40Z Machine Learning Based Optimal Design of On-Road Charging Lane for Smart Cities Applications Shanmugam Y. Narayanamoorthi R. Ramachandaramurthy V.K. Bernat P. Shrestha N. Son J. Williamson S.S. 58153403000 57095044400 6602912020 56119573400 58318687800 58539742600 7203080313 Charging (batteries) Cost benefit analysis Efficiency Electric vehicles Energy transfer Inductive power transmission Machine learning Optimal systems Stochastic systems Coil Cost analysis Coupler Dynamic charging Machine-learning Optimal design Prediction algorithms Receiver Wireless charging Learning algorithms The 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. Final 2025-03-03T07:47:40Z 2025-03-03T07:47:40Z 2024 Article 10.1109/JESTPE.2024.3400292 2-s2.0-85193258691 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85193258691&doi=10.1109%2fJESTPE.2024.3400292&partnerID=40&md5=d8405b83a81bf8a3e5b880ffc35b2279 https://irepository.uniten.edu.my/handle/123456789/37118 12 4 4296 4309 Institute of Electrical and Electronics Engineers Inc. Scopus |
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Charging (batteries) Cost benefit analysis Efficiency Electric vehicles Energy transfer Inductive power transmission Machine learning Optimal systems Stochastic systems Coil Cost analysis Coupler Dynamic charging Machine-learning Optimal design Prediction algorithms Receiver Wireless charging Learning algorithms |
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Charging (batteries) Cost benefit analysis Efficiency Electric vehicles Energy transfer Inductive power transmission Machine learning Optimal systems Stochastic systems Coil Cost analysis Coupler Dynamic charging Machine-learning Optimal design Prediction algorithms Receiver Wireless charging Learning algorithms Shanmugam Y. Narayanamoorthi R. Ramachandaramurthy V.K. Bernat P. Shrestha N. Son J. Williamson S.S. Machine Learning Based Optimal Design of On-Road Charging Lane for Smart Cities Applications |
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The 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. |
| author2 |
58153403000 |
| author_facet |
58153403000 Shanmugam Y. Narayanamoorthi R. Ramachandaramurthy V.K. Bernat P. Shrestha N. Son J. Williamson S.S. |
| format |
Article |
| author |
Shanmugam Y. Narayanamoorthi R. Ramachandaramurthy V.K. Bernat P. Shrestha N. Son J. Williamson S.S. |
| author_sort |
Shanmugam Y. |
| title |
Machine Learning Based Optimal Design of On-Road Charging Lane for Smart Cities Applications |
| title_short |
Machine Learning Based Optimal Design of On-Road Charging Lane for Smart Cities Applications |
| title_full |
Machine Learning Based Optimal Design of On-Road Charging Lane for Smart Cities Applications |
| title_fullStr |
Machine Learning Based Optimal Design of On-Road Charging Lane for Smart Cities Applications |
| title_full_unstemmed |
Machine Learning Based Optimal Design of On-Road Charging Lane for Smart Cities Applications |
| title_sort |
machine learning based optimal design of on-road charging lane for smart cities applications |
| publisher |
Institute of Electrical and Electronics Engineers Inc. |
| publishDate |
2025 |
| _version_ |
1825816255043469312 |
| score |
13.244109 |