User-oriented fuzzy logic–based optimization of regional thermal comfort and energy consumption in vehicle HVAC systems
Journal of Thermal Analysis and Calorimetry, 2026 (SCI-Expanded, Scopus)
- Yayın Türü: Makale / Tam Makale
- Basım Tarihi: 2026
- Doi Numarası: 10.1007/s10973-026-16161-4
- Dergi Adı: Journal of Thermal Analysis and Calorimetry
- Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Aerospace Database, Chemical Abstracts Core, Chimica, Compendex, Index Islamicus, INSPEC, Academic Search Ultimate (EBSCO), Engineering Source (EBSCO), Materials Science & Engineering Collection (ProQuest), Technology Collection (ProQuest)
- Anahtar Kelimeler: CFD analysis, Energy consumption, Fuzzy logic, HVAC systems, KANO method, Thermal comfort
- Bursa Uludağ Üniversitesi Adresli: Evet
Özet
This paper presents an integrated, user-centric decision framework designed to simultaneously optimize thermal comfort and energy efficiency in automotive HVAC systems. Departing from conventional fixed-setpoint or single-objective strategies, this study introduces a novel approach that bridges the gap between subjective expectations and physical system performance by integrating KANO Model of Customer Satisfaction user profiles, CFD(Computational Fluid Dynamics)-based regional thermal assessments, and a 1D HVAC (Heating, Ventilation, and Air Conditioning) energy model into a unified optimization architecture. Transient CFD results reveal significant thermal non-uniformity within the cabin, with pronounced comfort imbalances localized in the head and upper-body regions. These regional deviations are correlated with system load via a 1D energy model that utilizes ambient-temperature-dependent efficiency coefficients demonstrating that HVAC performance is highly sensitive to the interplay between thermodynamic constraints and occupant preferences. A Mamdani-type fuzzy logic controller was developed to dynamically modulate supply temperature, airflow rate, and vent orientation, enabling superior regional comfort without increasing aggregate energy consumption. Under a balanced optimization scenario (C:0.5, E:0.5) (equal weighting of comfort and energy efficiency), the system successfully maintained cabin temperatures within the ideal 22–26 °C range while keeping PMV (Predicted Mean Vote) values within neutral limits. Ultimately, this research shifts the HVAC control paradigm from mere energy reduction to an adaptive optimization challenge focused on allocating energy to the right body region under the right occupant scenario. The proposed methodology provides a robust framework for improving the range–comfort balance, particularly in the context of electric vehicle thermal management.