改善驾驶员热舒适度的技术系统分析

IF 1.8 Q3 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS
Mert Duzgun, Bertug Ozarisoy, Hasim Altan, Sam Moshaver
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Analysis of Technological Systems for Improving Drivers' Thermal Comfort

Analysis of Technological Systems for Improving Drivers' Thermal Comfort

This study investigates the integration of adaptive thermal comfort and indoor air quality (IAQ) systems in modern vehicles, emphasizing their impact on occupant well-being and automotive design innovation. The research examines state-of-the-art advancements in vehicle climate control technologies, from past developments to emerging smart thermal management solutions. It explores how artificial intelligence (AI), biometric monitoring, and energy-efficient climate control systems are transforming in-car comfort, ensuring dynamic adaptation to occupant needs while improving energy efficiency. A qualitative research methodology was adopted, incorporating explanatory and descriptive analysis. Data was collected through an extensive review of archival records, technical specifications, industry reports, and photographic sources to assess the evolution and effectiveness of thermal comfort technologies. The findings indicate that innovative HVAC and IAQ systems significantly enhance both driver and passenger comfort, leading to higher consumer demand for vehicles equipped with these technologies. Furthermore, the study highlights that future thermal comfort solutions will align with evolving user expectations, regulatory standards (such as ASHRAE-55 and EN-16798), and global sustainability goals. By bridging automotive engineering, human-centered design, and energy-efficient climate control, this study provides valuable insights into the next generation of intelligent vehicle comfort systems. The results offer a framework for optimizing thermal regulation strategies and demonstrate the potential of integrating adaptive climate control with AI-driven environmental sensing to enhance in-car experiences. Future research should focus on expanding AI-based climate personalization models and evaluating user perceptions of smart comfort technologies in real-world driving conditions.

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