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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.

When preparing inventory models, it is desirable to obtain representative distance-specific emissions factors that truthfully represent the vehicle activity on a particular road (facility) type. Unfortunately, emissions values are often measured using only one test schedule, which represents a single average speed and a specific type of activity. This paper investigated the accuracy of predicting the emissions for a test schedule based on measurements from a different test schedule for the case of a medium heavy-duty truck. First, the traditional Speed Correction Factor (SCF) approach was examined, followed by the use of a power-based model derived from continuous data, followed by an artificial neural network (ANN) approach. The SCF modeling used distance-averaged emissions and cycle-averaged vehicle speed to predict distance-averaged NOx. The power-based modeling was based on linear and polynomial correlations between continuous axle power and NOx.

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 use of Artificial Neural Networks (ANNs) as a predictive tool has been shown to have a broad range of applications. Earlier work by the authors using ANN models to predict carbon dioxide (CO2), carbon monoxide (CO), oxides of nitrogen (NOx), and particulate matter (PM) from heavy-duty diesel engines and vehicles yielded marginal to excellent results. These ANN models can be a useful tool in inventory prediction, hybrid vehicle design optimization, and incorporated into a feedback loop of an on-board, active fuel injection management system. In this research, the ANN models were trained on continuous engine and emissions data. The engine data were used as inputs to the ANN models and consisted of engine speed, torque, and their respective first and second derivatives over a one, five, and ten second time range. The continuous emissions data were the desired output that the ANN models learned to predict through an iterative training process.

The 1998 Consent Decrees between the settling heavy-duty diesel engine manufacturers and the United States Government require the engine manufacturer to perform in-use emissions testing to evaluate their engine designs and emissions when the vehicle is placed into service. This additional requirement will oblige the manufacturer to account for real-world conditions when designing engines and engine control algorithms and include driving conditions, ambient conditions, and fuel properties in addition to the engine certification test procedures. Engine operation and ambient conditions can be designed into the engine control algorithm. However, there will most likely be no on-board determination of fuel properties or composition in the near future. Therefore, the engine manufacturer will need to account for varying fuel properties when developing the engine control algorithm for when in-use testing is performed.

Chassis based emissions characterization of heavy-duty vehicles has advanced over the last decade, but the understanding of the effect of test schedule on measured emissions is still poor. However, this is an important issue because the test schedule should closely mimic actual vehicle operation or vocation. A wide variety of test schedules was reviewed and these cycles were classified as cycles or routes and as geometric or realistic. With support from the U.S. Department of Energy Office of Transportation Technologies (DOE/OTT), a GMC box truck with a Caterpillar 3116 engine and a Peterbilt over the road tractor-trailer with a Caterpillar 3406 engine were exercised through a large number of cycles and routes. Test weight for the GMC was 9,980 kg and for the Peterbilt was 19,050 kg. Emissions characterization was performed using a heavy-duty chassis dynamometer, with a full-scale dilution tunnel, analyzers for gaseous emissions, and filters for PM emissions.

It is difficult to project the emissions performance of a vehicle on a route unless the test cycle used to gain the emissions data reasonably represents that route. A chassis dynamometer emissions measurement test schedule consisting of three modes (congested, non-congested and bus rapid transit (BRT) operation) was developed for use in a program to evaluate transit bus technologies in Mexico City. Existing buses were fitted with global positioning system (GPS) data loggers and, between September 2nd and 8th of 2004, 54 hours of speed-time data were collected while the buses were operated over several bus routes in Mexico City. The data set was then broken down into individual micro-trips, each consisting of an idle period followed by the bus traveling some distance, followed by a final deceleration to idle.

Most transient heavy duty diesel emissions data in the USA have been acquired using the Federal Test Procedure (FTP), a heavy-duty diesel engine transient test schedule described in the US Code of Federal Regulations. The FTP includes both urban and freeway operation and does not provide data separated by driving mode (such as rural, urban, freeway). Recently, a four-mode engine test schedule was created for use in the Advanced Collaborative Emission Study (ACES), and was demonstrated on a 2004 engine equipped with cooled Exhaust Gas Recirculation (EGR). In the present work, the authors examined emissions using these ACES modes (Creep, Cruise, Transient and High-speed Cruise) and the FTP from a Detroit Diesel Corporation (DDC) Series 60 1992 12.7 liter pre-EGR engine. The engine emissions were measured using full exhaust dilution, continuous measurement of gaseous species, and filter-based Particulate Matter (PM) measurement.