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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.

Inventory approaches for truck and bus emissions rely heavily on certification data, and no comprehensive results have been published to date. Two transportable chassis dynamometer laboratories developed and operated by West Virginia University (WVU) have been used extensively to gather realistic emission data from heavy-duty vehicles tested in the field, in controlled, simulated driving conditions. By default, a comprehensive database has been assembled, that comprises a wide variety of vehicles, engines, fuels, and driving scenarios. A subset of these data is analyzed in this paper for an illustration of practical utilization of such information, either for inventory assessments, or for comparative and correlation studies. General guidelines for data screening and analysis approaches are provided, along with examples of specific results and discussions for a selected cross-section of samples.