Modeling and Validation of an Over-the-Road Truck 2010-01-2001
Heavy-duty trucks are an important sector to evaluate when seeking fuel consumption savings and emissions reductions. With fuel costs on the rise and emissions regulations becoming stringent, vehicle manufacturers find themselves spending large amounts of capital improving their products in order to be compliant with regulations. The Powertrain System Analysis Toolkits (PSAT), developed by the Argonne National Laboratory (ANL), is a simulation tool that helps mitigate costs associated with research and automotive system design. While PSAT has been widely used to predict the fuel consumption and exhaust emissions of conventional and hybrid light-duty vehicles, it also may be employed to test heavy-duty vehicles. The intent of this study was to develop an accurate model that predicts emissions and fuel economy for heavy-duty vehicles for use within PSAT. The model developed in cooperation with ANL during this research, has been integrated into the PSAT model for its application to heavy-duty trucks. The truck modeled was a Peterbilt tractor truck with a 410 kW Caterpillar 3406 non-EGR engine. This is a conventional over-the-road truck for which test data sets are available. It is equipped with an 18-speed Roadranger manual transmission and a tandem axle drive. The vehicle model configuration and development process were described, along with the model validation processes. For the engine model, a NOx (Oxides of Nitrogen) emissions model and a fuel rate map for the Caterpillar 3406 engine was created based on the test data. For the gearbox model, the shifting strategy was elaborated and the transmission efficiency lookup tables were developed. In this paper, a power loss of mechanical accessory was considered as variable other than constant value and an approach to estimate fan power demand was implemented. The difference between tested data and PSAT simulated data pertaining to engine fuel rate, engine torque, engine speed, engine power and NOx was within 5% relative error.