Quantification of Tailpipe Emissions during Engine Restarts from Vehicles with Idle-Reduction Technologies
Andrew May, PhD, assistant professor in the Department of Civil, Environmental, and Geodetic Engineering serves as PI for the project “Quantification of Tailpipe Emissions during Engine Restarts from Vehicles with Idle-Reduction Technologies.” With an increase in demand for reducing fuel consumption and greenhouse gas emissions, more vehicles have incorporated idle-reduction technologies like engines that turn off when the vehicle comes to a complete stop. May and his team are studying the tailpipe emissions that occur once the engine restarts and the significance of repeated starting and stopping throughout a real-world trip as well as throughout the vehicle’s lifetime.
By measuring the amount of gases emitted from restarting the engine and then creating simulations from these measurements, May has built models for a single vehicle’s emissions throughout its lifetime and also for a larger set of vehicles stopped at a red light. Preliminary results show that idle-reduction technology reduces the amount of carbon dioxide (CO2) produced by a single car by tenfold over its lifetime, and it also reduces carbon monoxide (CO), nitrogen oxides (NOx) and methane (CH4) emissions, showing that reducing fuel consumption does not add to tailpipe emissions of other air pollutants. The current model for multiple vehicles relates the percentage of vehicles with idle-reduction technology in an area to the percent reduction of air pollutants. As the percentage of idle-reduction vehicles increases, so does the percent of reduction.
“For example,” May said, “when 50% of vehicles are equipped with idle-reduction technology, we estimate that there will be about 40% reduction in CO2 emissions, 50% reduction in CO emissions, and 25% reduction in NOx emissions. Therefore, not only do these technologies reduce fuel consumption, they also may lead to improved local air quality near intersections.”
May’s main challenge has been training students from various engineering backgrounds to complete automotive research using tools and programming languages in ways the students hadn’t experienced before. While the study began in May 2016, the team is still incorporating new data collected over the summer in order to increase model performance as well as expand their analysis to more chemical compounds.