Elektrikli Araç Şarj İstasyonları İçin Yüksek Verimli AA-DA Dönüştürücü Topolojileri ve Yapay Sinir Ağı Tabanlı Harmonik Bozunum Tahmini

Abstract

Elektrikli araçlardaki yüksek şarj süreleri ve uzun menzil sorunları, elektrikli araç kullanımını istenilen seviyeye ulaştırmada engel oluşturmaktadır. Şehirlerde ve yollarda şarj istasyonlarının yaygınlaşması, özellikle uzun yolculuklarda yaşanan menzil sorunlarını azaltacaktır. Bunun yanı sıra elektrikli araç kullanımını daha düşük şarj süreleri ile desteklemek için hızlı şarj cihazlarının geliştirilmesi ve verimlerinin artırılması da kritik öneme sahiptir. Literatürde, şarj işleminin etkinliğini artırmayı amaçlayan hızlı şarj istasyonları için geliştirilmiş, farklı dönüştürücü topolojilerinin kullanıldığı çeşitli çalışmalar bulunmaktadır. Bu makalede, öncelikle elektrikli araç şarj istasyonları için kullanılan AA-DA dönüştürücü topolojilerini incelenmiş, geleneksel AA-DA dönüştürücüler ve PFC (Power Factor Correction (Güç Faktörü Düzeltme)) içeren topolojilerin, avantajları, dezavantajları ve devre şemaları üzerinden karşılaştırılmıştır. Güç elektroniği devrelerinde daha hızlı ve daha düşük kayıplı olan Gallium Nitride (GaN) gibi yeni nesil yarı iletkenlerin şarj devresi verimliliğine katkıları ele alınmıştır. Ayrıca, makale Yapay Sinir Ağı (YSA) tabanlı bir model geliştirme sürecini tanıtılmış olup, bu modelin toplam harmonik bozunum (THD) tahminini nasıl gerçekleştirdiğini açıklanmıştır. Model ile elde edilen sonuçlar, MATLAB/Simulink ortamında gerçekleştirilmiş olan PFC şarj devresi üzerinde test edilmiş ve başarılı sonuçlar elde edilmiştir.

Keywords

• Elektrikli araç şarj istasyonları
• Güç faktörü düzeltme PFC Topolojileri
• Toplam Harmonik Bozunum (THB)
• AA-DA Dönüştürücüler
• Yapay Sinir Ağı (YSA)

High-Efficiency AC-DC Converter Topologies for Electric Vehicle Charging Stations and Artificial Neural Network-Based Harmonic Distortion Estimation

Abstract

The high charging times and range issues in electric vehicles are among the significant factors hindering their widespread adoption. The proliferation of charging stations in cities and along roads will effectively mitigate range issues, especially on long journeys. However, to increase the appeal of electric vehicles, it is necessary to shorten charging times and enhance energy efficiency. In this direction, the development of fast chargers not only enables quicker vehicle charging but also improves the efficiency of charging stations. In the literature, various fast charging station designs based on converter topologies aiming to improve efficiency have been examined to achieve these goals. One of the critical aspects for the efficient use of electric vehicles is controlling harmonic distortions and reactive power increases during charging processes. Drawing non-sinusoidal currents from the grid causes harmonic distortions and an increase in reactive power, thereby reducing power quality, leading to energy losses, and causing devices to operate inefficiently. Therefore, managing non-linear loads and limiting their effects is crucial in terms of Power Factor Correction (PFC). This paper examines AC-DC converter topologies used in electric vehicle charging stations, comparing traditional AC-DC converters with topologies that include PFC based on their advantages and disadvantages. The main goal of the study is to predict the Total Harmonic Distortion (THD) value using an Artificial Neural Network (ANN) in a Totem-Pole PFC Boost topology. The developed ANN-based model can accurately predict the THD value, which is a step toward improving efficiency in PFC topologies, thereby contributing to the improvement of power quality. The model's performance was tested in the MATLAB/Simulink environment, yielding successful results. This study presents a contribution toward the goal of enhancing efficiency in power converters through an ANN-based modeling approach.

Keywords

• Electric Vehicle Charging Stations
• Power Factor Correction PFC Topologies
• Total Harmonic Distortion (THD)
• AC-DC Converters
• Artificial Neural Network (ANN)

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