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

Murat Ünlü; Ahmet Büyükdağ

Makale detayları

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

Yazarlar:

Murat Ünlü | Ahmet Büyükdağ

Yayıncı:

Marmara University

Ana Başlıklar:

Derin Öğrenme | Elektrik Mühendisliği (Diğer)

Dergi:

International Journal of Advances in Engineering and Pure Sciences Vol.37 Issue 2, pp.124-132

Belge Tipi:

Makale

Yayın Tarihi:

6/2025

Belge Dili:

Türkçe

Document ID (Org No.):

2001350688

pISSN:

2636-8277

Anahtar kelimeler:

Özet:

 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 models performance was tested in the MATLABSimulink environment, yielding successful results. This study presents a contribution toward the goal of enhancing efficiency in power converters through an ANN-based modeling approach.

 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 models performance was tested in the MATLABSimulink environment, yielding successful results. This study presents a contribution toward the goal of enhancing efficiency in power converters through an ANN-based modeling approach.

 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 models performance was tested in the MATLABSimulink environment, yielding successful results. This study presents a contribution toward the goal of enhancing efficiency in power converters through an ANN-based modeling approach.

 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, MATLABSimulink ortamında gerçekleştirilmiş olan PFC şarj devresi üzerinde test edilmiş ve başarılı sonuçlar elde edilmiştir.

Makale detayları

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

Yazarlar:

Murat Ünlü | Ahmet Büyükdağ

Yayıncı:

Marmara University

Ana Başlıklar:

Derin Öğrenme | Elektrik Mühendisliği (Diğer)

Dergi:

International Journal of Advances in Engineering and Pure Sciences Vol.37 Issue 2, pp.124-132

Belge Tipi:

Makale

Yayın Tarihi:

6/2025

Belge Dili:

Türkçe

Document ID (Org No.):

2001350688

pISSN:

2636-8277

Electric Vehicle Charging Stations

Power Factor Correction PFC Topologies

Total Harmonic Distortion (THD)

AC-DC Converters

Artificial Neural Network (ANN)Elektrikli araç şarj istasyonları

Güç faktörü düzeltme PFC Topolojileri

Toplam Harmonik Bozunum (THB)

AA-DA Dönüştürücüler

Yapay Sinir Ağı (YSA)

Sayı

Makale detayları

Yazarlar: Murat Ünlü | Ahmet Büyükdağ

Yayıncı: Marmara University

Ana Başlıklar: Derin Öğrenme | Elektrik Mühendisliği (Diğer)

Dergi: International Journal of Advances in Engineering and Pure Sciences Vol.37 Issue 2, pp.124-132

Belge Tipi: Makale

Yayın Tarihi: 6/2025

Belge Dili: Türkçe

Document ID (Org No.): 2001350688

pISSN: 2636-8277

Sayı

Yazarlar:

Murat Ünlü | Ahmet Büyükdağ

Yayıncı:

Marmara University

Ana Başlıklar:

Derin Öğrenme | Elektrik Mühendisliği (Diğer)

Dergi:

International Journal of Advances in Engineering and Pure Sciences Vol.37 Issue 2, pp.124-132

Belge Tipi:

Makale

Yayın Tarihi:

6/2025

Belge Dili:

Türkçe

Document ID (Org No.):

2001350688

pISSN:

2636-8277