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

Kód výsledku v 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>

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

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

Popis výsledku v původním jazyce

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%.

Název v anglickém jazyce

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

Popis výsledku anglicky

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%.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

20200 - Electrical engineering, Electronic engineering, Information engineering

Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2024

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

IEEE Journal of Emerging and Selected Topics in Power Electronics

ISSN

2168-6777

e-ISSN

2168-6785

Svazek periodika

12

Číslo periodika v rámci svazku

4

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

14

Strana od-do

4296-4309

Kód UT WoS článku

001293897400079

EID výsledku v databázi Scopus

2-s2.0-85193258691