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West Virginia University has conducted research to characterize the emissions from medium-duty vehicles operating on Fischer-Tropsch synthetic gas-to-liquid compression ignition fuel. The West Virginia University Transportable Heavy Vehicle Emissions Testing Laboratory was used to collect data for gaseous emissions (carbon dioxide, carbon monoxide, oxides of nitrogen, and total hydrocarbon) while the vehicles were exercised through a representative driving schedule, the New York City Bus Cycle (NYCB). Artificial neural networks were used to model emissions to enhance the capabilities of computer-based vehicle operation simulators. This modeling process is presented in this paper. Vehicle velocity, acceleration, torque at rear axel, and exhaust temperature were used as inputs to the neural networks. For each of the four gaseous emissions considered, one set of training data and one set of validating data were used, both based on the New York City Bus Cycle.

In characterizing the emissions from mobile sources, it is essential that the vehicle be exercised in a way that reasonably represents typical in-use behavior. A heavy-heavy duty diesel truck (HHDDT) test schedule was developed from speed-time data gathered during two Air Resources Board-sponsored truck activity programs. The data were divided into four modes, termed Idle, Creep, Transient and Cruise Modes, in order of increasing speed. For the last three modes, speed-time schedules were created that represented all the data in that mode. Statistical parameters such as average speed, stops per unit distance, kinetic energy, maximum speed and acceleration and deceleration values were considered in arriving at these schedules. The schedules were evaluated using two Class 8 over-the-road tractors on a chassis dynamometer. Emissions were measured using a full-scale dilution tunnel, filtration for particulate matter (PM), and research grade analyzers for the gases.