Studies on eco-driving have mostly taken an energy-centric view and considered driving performance, while less attention has been paid on emissions behavior. This work extends in an experimentally verified way our understanding of the trade-offs among fuel economy, driving aggressiveness, and, especially, emissions in connected automated diesel-powered vehicles. Experiments are performed with a 6.7-L Ford Powerstroke diesel engine, a urea-SCR based NOx aftertreatment system, and a full model for a Ford F250 medium-duty truck in the loop to provide realistic assessment of fuel consumption, tailpipe emissions, and driving style performances. An energy and emissions conscious speed planner is leveraged to follow the traffic. This planner offers flexibility in prioritizing energy or emissions while satisfying user-defined headway constraints, and thus allows exploration of different calibrations in a unified way. Results show how various calibrations of the flexible leader following policy yield 8%–14% decrease in total fuel consumption and 64%–70% decrease in tailpipe emissions compared with a strictly constrained following policy.
This content will become publicly available on June 13, 2024
Impact of Fog on Vehicular Emissions and Fuel Consumption in a Mixed Traffic Flow with Autonomous Vehicles (AVs) and Human-Driven Vehicles Using VISSIM Microsimulation Model
Driving in foggy conditions poses high risks to road users due to the reduction of visibility, affecting the drivers’ vision and perception, and making changes in driving behavior, which is one of the most important factors affecting vehicular emissions and fuel consumption. This study analyzes the PTV VISSIM traffic microsimulation outputs for exhaust emissions and fuel consumption of vehicles simulated under adverse weather conditions. This weather-dependent simulation is developed by using the advanced psychophysical car-following model “Wiedemann’s 99,” to flexibly control the driving behavior parameters in various driving conditions. Results show that vehicles under foggy conditions consume more fuel and produce more emissions in comparison with clear sky conditions and other scenarios. With the transition of current cities to smart sustainable cities and by introducing automated vehicles (AVs) to the traditional traffic network and gradually increasing their penetration rate, negative environmental impacts of driving under foggy conditions will be reduced, and improvement in overall mobility of a shared network of autonomous and human-driven is observable.
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- Award ID(s):
- 2119691
- NSF-PAR ID:
- 10424962
- Date Published:
- Journal Name:
- 2023 International Conference on Transportation and Development 2023
- Page Range / eLocation ID:
- 253 to 262
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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