An Unobstructive Sensing Method for Indoor Air Quality Optimization and Metabolic Assessment within Vehicles

Area of Science:

• Environmental Science
• Human Physiology
• Automotive Engineering

Background:

• Elevated carbon dioxide (CO2) levels in vehicle cabins can impair cognitive function.
• CO2 accumulation rates correlate with human metabolic rates.
• Maintaining optimal indoor air quality is crucial for driver alertness and health.

Purpose of the Study:

• To develop an intelligent sensing technique for simultaneous assessment of driver metabolic rate and vehicle cabin air quality.
• To investigate the relationship between CO2 accumulation and metabolic rate in a vehicle environment.
• To optimize ventilation control based on real-time CO2 monitoring.

Main Methods:

• Utilized CO2 accumulation patterns to estimate driver metabolic rate.
• Implemented a ventilation control system that alternates air recirculation based on CO2 levels.
• Developed a model incorporating vehicle air exchange rate, dependent on speed (λ = 0.060 × speed - 0.88 for speeds > 17 MPH).

Main Results:

• Achieved 95% accuracy in assessing driver metabolic rate compared to indirect calorimetry (1620 ± 140 kcal/day vs. 1550 ± 150 kcal/day).
• Successfully maintained vehicle cabin air quality through intelligent ventilation control.
• Established a speed-dependent model for vehicle air exchange rate.

Conclusions:

• The novel sensing method offers an unobtrusive approach for assessing driver metabolic rate.
• This technique effectively manages indoor air quality in vehicles.
• The findings contribute to enhanced driver safety and personalized vehicle environments.