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Today's computer-based vehicle operation simulators use engine speed, engine torque, and lookup tables to predict emissions during a driving simulation [1]. This approach is used primarily for light and medium-duty vehicles, with large discrepancies inherently due to the lack of transient engine emissions data and inaccurate emissions prediction methods [2]. West Virginia University (WVU) has developed an artificial neural network (ANN) based emissions model for incorporation into the ADvanced VehIcle SimulatOR (ADVISOR) software package developed by the National Renewable Energy Laboratory (NREL). Transient engine dynamometer tests were conducted to obtain training data for the ANN. The ANN was trained to predict carbon dioxide (CO2) and oxides of nitrogen (NOx) emissions based on engine speed, torque, and their representative first and second derivatives over various time ranges.

The goal of the Diesel Emissions Control-Sulfur Effects (DECSE) program was to determine the impact of diesel fuel sulfur levels on emissions control devices that could lower emissions of oxides of nitrogen (NOX) and particulate matter (PM) from on-highway trucks and buses. West Virginia University (WVU) performed evaluations of lean-NOx catalysts to determine the effects of fuel sulfur content on emissions reduction efficiency and catalyst durability in the first 250 hours of operation. A Cummins ISM370 engine (10.8 liter, 370 horsepower), typical of heavy -duty truck applications, was utilized to evaluate high-temperature lean-NOX catalyst while a Navistar T444E (7.3 liter, 210 horsepower), typical of medium-duty applications, was used to evaluate low-temperature catalyst. Catalysts were evaluated periodically during the first 250 hours of exposure to exhaust from engines operated on 3ppm, 30ppm, 150ppm and 350ppm sulfur content diesel fuel.