Machine Learning Based Optimal Design of On-Road Charging Lane for Smart Cities Applications
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F24%3A10257056" target="_blank" >RIV/61989100:27240/24:10257056 - isvavai.cz</a>
Result on the web
<a href="https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10530012" target="_blank" >https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10530012</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1109/JESTPE.2024.3400292" target="_blank" >10.1109/JESTPE.2024.3400292</a>
Alternative languages
Result language
angličtina
Original language name
Machine Learning Based Optimal Design of On-Road Charging Lane for Smart Cities Applications
Original language description
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%.
Czech name
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Czech description
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Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
20200 - Electrical engineering, Electronic engineering, Information engineering
Result continuities
Project
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Continuities
S - Specificky vyzkum na vysokych skolach
Others
Publication year
2024
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
IEEE Journal of Emerging and Selected Topics in Power Electronics
ISSN
2168-6777
e-ISSN
2168-6785
Volume of the periodical
12
Issue of the periodical within the volume
4
Country of publishing house
US - UNITED STATES
Number of pages
14
Pages from-to
4296-4309
UT code for WoS article
001293897400079
EID of the result in the Scopus database
2-s2.0-85193258691