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A personal computer-based vehicle powertrain simulation (VPS) is developed to predict fuel economy and performance. This paper summarizes the governing equations used in the model. Then the different simulation techniques are described with emphasis on the more complicated time-dependent simulation. The simulation is validated against constant speed and variable cycle test track data obtained for a 5 ton army truck. Then the simulation is used to compare the performance of the 5 ton truck when powered by a cooled and natually aspirated engine, a cooled and turbocharged engine, and an uncooled and turbocharged engine. Studies of the effect of payload, tire efficiency, and drag coefficient on vehicle performance are also conducted, as well as a performance comparison between manual and automatic transmissions. It is concluded that the VPS code can provide good predictions of vehicle fuel economy, and thus is a useful tool in designing and evaluating vehicle powertrains.

Variable geometry turbines (VGT) are of particular interest to advanced diesel powertrains for future conventional trucks, since they can dramatically improve system transient response to sudden changes in speed and load, characteristic of automotive applications. VGT systems are also viewed as the key enabler for the application of the EGR system for reduction of heavy-duty diesel emissions. This paper applies an artificial neural network methodology to VGT modeling in order to enable representation of the VGT characteristics for any blade (nozzle) position. Following validation of the ANN model of the baseline, fixed geometry turbine, the VGT model is integrated with the diesel engine system. The latter is linked to the driveline and the vehicle dynamics module to form a complete, high-fidelity vehicle simulation.

As part of a multi-agency study concerning emissions and fuel consumption from heavy-duty diesel truck idling, Oak Ridge National Laboratory personnel measured CO, HC, NOx, CO2, O2, particulate matter (PM), aldehyde and ketone emissions from truck idle exhaust. Two methods of quantifying PM were employed: conventional filters and a Tapered Element Oscillating Microbalance (TEOM). A partial flow micro-dilution tunnel was used to dilute the sampled exhaust to make the PM and aldehyde measurements. The work was performed at the U.S. Army's Aberdeen Test Center's (ATC) climate controlled chamber. ATC performed 37 tests on five class-8 trucks (model years ranging from 1992 to 2001). One was equipped with an 11 hp diesel auxiliary power unit (APU), and another with a diesel direct-fired heater (DFH). The APU powers electrical accessories, heating, and air conditioning, whereas a DFH heats the cab in cold weather. Both devices offer an alternative to extended truck-engine idling.

Medium trucks constitute a large market segment of the commercial transportation sector, and are also used widely for military tactical operations. Recent technological advances in hybrid powertrains and fuel cell auxiliary power units have enabled design alternatives that can improve fuel economy and reduce emissions dramatically. However, deterministic design optimization of these configurations may yield designs that are optimal with respect to performance but raise concerns regarding the reliability of achieving that performance over lifetime. In this article we identify and quantify uncertainties due to modeling approximations or incomplete information. We then model their propagation using Monte Carlo simulation and perform sensitivity analysis to isolate statistically significant uncertainties. Finally, we formulate and solve a series of reliability-based optimization problems and quantify tradeoffs between optimality and reliability.