N. Sivakumar;S. Charles Raja;Chelladurai Balasundar;M. Geethanjali
{"title":"基于猎鹿优化转换器控制(DHOCC)的电动汽车充电动态无线IPT系统","authors":"N. Sivakumar;S. Charles Raja;Chelladurai Balasundar;M. Geethanjali","doi":"10.1109/ICJECE.2024.3469390","DOIUrl":null,"url":null,"abstract":"Coil alignment plays a vital role in wireless charging systems which affects the transmission power and resonance coupling efficiency in electric vehicle (EV) charging. Also, the cutting-edge controlling model is used to improve the converter operations in the wireless inductive power transfer (IPT) system for EV charging. This work proposes a deer hunting optimized converter control (DHOCC) algorithm for buck dc–dc converter to effectively step down the desired voltage and reduce the system complexity such as misalignments and air gap. The coil’s misalignment and air gaps are changed through the buck dc–dc converter output. This algorithm aligns the coil by changing the ranges of misalignment and air gap to improve coupling efficiency. The EV is placed on its surface to charge the battery. The proposed work is designed in the MATLAB/Simulink platform and the experimental setup validation has been carried out through the laboratory test setup. The simulation output shows the high effective coupling between two coils for an 8 cm air gap with 89.7% power transfer efficiency (PTE) and the experimental output shows an 8 cm air gap with 84.77% of PTE. The obtained result demonstrates the performance of the DHOCC based on a wireless IPT system under less complexity.","PeriodicalId":100619,"journal":{"name":"IEEE Canadian Journal of Electrical and Computer Engineering","volume":"47 4","pages":"218-225"},"PeriodicalIF":2.1000,"publicationDate":"2024-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"A Cutting-Edge Deer Hunting Optimized Converter Control (DHOCC) Based Dynamic Wireless IPT System for EV Charging Applications\",\"authors\":\"N. Sivakumar;S. Charles Raja;Chelladurai Balasundar;M. Geethanjali\",\"doi\":\"10.1109/ICJECE.2024.3469390\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Coil alignment plays a vital role in wireless charging systems which affects the transmission power and resonance coupling efficiency in electric vehicle (EV) charging. Also, the cutting-edge controlling model is used to improve the converter operations in the wireless inductive power transfer (IPT) system for EV charging. This work proposes a deer hunting optimized converter control (DHOCC) algorithm for buck dc–dc converter to effectively step down the desired voltage and reduce the system complexity such as misalignments and air gap. The coil’s misalignment and air gaps are changed through the buck dc–dc converter output. This algorithm aligns the coil by changing the ranges of misalignment and air gap to improve coupling efficiency. The EV is placed on its surface to charge the battery. The proposed work is designed in the MATLAB/Simulink platform and the experimental setup validation has been carried out through the laboratory test setup. The simulation output shows the high effective coupling between two coils for an 8 cm air gap with 89.7% power transfer efficiency (PTE) and the experimental output shows an 8 cm air gap with 84.77% of PTE. The obtained result demonstrates the performance of the DHOCC based on a wireless IPT system under less complexity.\",\"PeriodicalId\":100619,\"journal\":{\"name\":\"IEEE Canadian Journal of Electrical and Computer Engineering\",\"volume\":\"47 4\",\"pages\":\"218-225\"},\"PeriodicalIF\":2.1000,\"publicationDate\":\"2024-10-31\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"IEEE Canadian Journal of Electrical and Computer Engineering\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://ieeexplore.ieee.org/document/10740173/\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"COMPUTER SCIENCE, HARDWARE & ARCHITECTURE\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Canadian Journal of Electrical and Computer Engineering","FirstCategoryId":"1085","ListUrlMain":"https://ieeexplore.ieee.org/document/10740173/","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"COMPUTER SCIENCE, HARDWARE & ARCHITECTURE","Score":null,"Total":0}
A Cutting-Edge Deer Hunting Optimized Converter Control (DHOCC) Based Dynamic Wireless IPT System for EV Charging Applications
Coil alignment plays a vital role in wireless charging systems which affects the transmission power and resonance coupling efficiency in electric vehicle (EV) charging. Also, the cutting-edge controlling model is used to improve the converter operations in the wireless inductive power transfer (IPT) system for EV charging. This work proposes a deer hunting optimized converter control (DHOCC) algorithm for buck dc–dc converter to effectively step down the desired voltage and reduce the system complexity such as misalignments and air gap. The coil’s misalignment and air gaps are changed through the buck dc–dc converter output. This algorithm aligns the coil by changing the ranges of misalignment and air gap to improve coupling efficiency. The EV is placed on its surface to charge the battery. The proposed work is designed in the MATLAB/Simulink platform and the experimental setup validation has been carried out through the laboratory test setup. The simulation output shows the high effective coupling between two coils for an 8 cm air gap with 89.7% power transfer efficiency (PTE) and the experimental output shows an 8 cm air gap with 84.77% of PTE. The obtained result demonstrates the performance of the DHOCC based on a wireless IPT system under less complexity.