On-road heavy-duty diesel particulate matter emissions modeled using chassis dynamometer data.

Area of Science:

• Environmental Science
• Mechanical Engineering
• Transportation Science

Background:

• Standardized heavy-duty vehicle emissions testing may not accurately reflect real-world driving conditions.
• Accurate measurement of particle emissions (g/mi) is crucial for environmental and health impact assessments.
• Existing models may not fully capture the variability of on-road heavy-duty diesel emissions.

Purpose of the Study:

• To develop and validate a model for operational correction factors (OCFs).
• To adjust heavy-duty diesel particle emission rates measured on test cycles for real-world conditions.
• To improve the accuracy of predicting on-road heavy-duty diesel particle emission rates.

Main Methods:

• Utilized chassis dynamometer test data from 531 tests of 34 heavy-duty vehicles.
• Employed a random effects mixed regression model.
• Incorporated covariates such as high power transient driving, idling time, and average speed.

Main Results:

• Developed a model predicting operational correction factors (OCFs) for heavy-duty diesel particle emissions.
• Found negative correlations between particle emission rates (g/mi) and high power transient driving, average speed, and idling time.
• The model effectively predicts relative changes in on-road emission rates under real-world driving scenarios.

Conclusions:

• The developed model provides a more realistic estimation of heavy-duty diesel particle emissions.
• Operational correction factors (OCFs) are essential for bridging the gap between lab testing and real-world emissions.
• This model enhances the prediction of on-road heavy-duty diesel particle emission rates for diverse driving conditions.