Optimization of automotive HVAC performance and passenger comfort through intelligent algorithms
Yazarlar (6)
Ali Hussein Abdulkarim
University of Kirkuk, Irak
University of Kirkuk, Irak
Doç. Dr. Andaç Batur Çolak Niğde Ömer Halisdemir University, Türkiye
Prof. Dr. Ali ATEŞ Sinop Üniversitesi, Türkiye
Mohammed Alharbi
Umm Al Qura University, College of Engineering And Islamic Architecture, Suudi Arabistan
Umm Al Qura University, College of Engineering And Islamic Architecture, Suudi Arabistan
Doç. Dr. Yakup Karakoyun Van Yüzüncü Yıl Üniversitesi, Türkiye
Prof. Dr. Ahmet Selim Dalkilic Yıldız Teknik Üniversitesi, Türkiye
| Makale Türü |
Özgün Makale
(SSCI, AHCI, SCI, SCI-Exp dergilerinde yayınlanan tam makale)
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| Dergi Adı | Journal of Thermal Analysis and Calorimetry (Q1) | ||
| Dergi ISSN | 1388-6150 Dergi Bilgileri (2026) | ||
| Dergi Tarandığı Indeksler | |||
| Makale Dili | İngilizce | Basım Tarihi | 01-2026 |
| Kabul Tarihi | 19-05-2026 | Yayınlanma Tarihi | 17-06-2026 |
| Cilt / Sayı / Sayfa | 151 / 12 / 9641–9657 | DOI | 10.1007/s10973-026-15752-5 |
| Makale Linki | https://link.springer.com/article/10.1007/s10973-026-15752-5 | ||
| UAK Araştırma Alanları |
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| Özet |
| Optimizing passenger thermal comfort while maintaining energy efficiency remains a critical challenge in automotive climate control. This study investigates the complex thermal interaction between a passenger and a car seat, introducing a novel technique for direct thermal management through the vehicle’s heating, ventilation, and air-conditioning system. An experimental setup was developed using evaporator coils integrated beneath seat surfaces, addressing a significant gap in the literature regarding two-way (heating and cooling) seat thermal management. Artificial Neural Networks were employed to model the ambiguous system parameters and to predict thermal performance. The results show an excellent predictive accuracy with Mean Squared Error of 1.29 E-02 and correlation coefficient of 0.99659. The average deviations were under− 0.21% for cooling capacity,− 0.02% for heating capacity and 0.38 … |
| Anahtar Kelimeler |
| Artificial neural networks | Automotive | HVAC | Predictive modeling | Thermal comfort |
BM Sürdürülebilir Kalkınma Amaçları
| Atıf Sayıları | |
| Google Scholar | 1 |