NSF/USDOT: In-Vehicle Energy and Emissions Information System (EEIS)

North Carolina State University, Raleigh NC

Investigators

Abstract

Current mobile emissions estimation systems are typically aimed at predicted average emissions for a fleet of vehicles and for a large geographic area. However, on-road, measured vehicle emissions are highly episodic, with short-term events of a few seconds in duration producing high emission rates and substantially contributing to total trip emissions. Emissions are influenced by prevailing traffic control measures (TCMs), transportation infrastructure (e.g., roadway design), vehicle characteristics, driver behavior, traffic flow, and ambient conditions. Therefore, there is a critical need to develop a rigorous technical basis for predicting emissions for individual vehicles taking into account all these key factors. The goals of this project are to: (1) evaluate the feasibility of developing micro-scale energy and emissions models for individual vehicles, including validation of the model under conditions not used in model construction; (2) test a prototype IVEEIS that accepts inputs from the vehicle, transportation infrastructure (e.g., road grade), and other external sources (e.g., ambient conditions) to produce microscale energy and emissions predictions for that particular vehicle; and (3) define a path for large-scale implementation of IVEEIS for designing and evaluating the effectiveness of various transportation design and control measures and for future policy development. The approach that will be used in this project includes the following tasks: (1) design of on-road field experiments; (2) field data collection, for extended periods (e.g., 70 hours of second-by-second data) and for several vehicles covering the full range of expected variability in energy and emissions; (3) IVEEIS model development using engineering and statistical methods; (4) IVEEIS validation and application; and (5) specification of IVEEIS application paths to improve transportation design, control, and policy, including improved emission predictions in current microscopic traffic simulation models. The broader impact of this research is to enable a deeper and more robust understanding of the factors affecting high emissions and energy use episodes in the real world, which in turn will provide a strong scientific basis for addressing pressing societal issues such as seeking effective air quality and energy use management strategies at the driver, network and regional levels.