Heavy-Duty Diesel Truck Emissions Modeling

Abstract

Heavy-duty vehicles are the second-largest source of greenhouse gas emissions and energy use within the transportation sector even though they represent only a small portion of on-road vehicles. Heavy-duty diesel vehicles (HDDVs) emit about half of all on-road emissions of nitrogen oxide (NOx). However, because of the limited amount of HDDV emissions data, research has focused on light-duty vehicle emissions. The majority of these microscopic models suffer from two major limitations: the models result in a bang-bang control system and calibration of the model parameters is not possible with publicly available data. This paper proposes to extend the Virginia Tech Comprehensive Power-Based Fuel Consumption Model (VT-CPFM) to overcome the two shortcomings in state-of-the-practice HDDV emissions models of carbon monoxide (CO), hydrocarbons (HCs), and NOx. Heavy-duty diesel truck (HDDT) data from the University of California, Riverside, were used for the calibration and validation processes. The study’s results were satisfying, especially for NOx, which was the main concern in HDDV emissions. Model validity and performance were evaluated by comparing the correlation of measured field data and estimated emissions between the VT-CPFM model and the comprehensive modal emissions model (CMEM). The results demonstrate the efficacy of the VT-CPFM model in replicating empirical observations producing better accuracy compared with other state-of-the-practice models (e.g., CMEM). Moreover, unlike the CMEM model, which requires extensive data collection for calibration purposes, the VT-CPFM model needs only GPS and publicly accessible data for calibration.