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Recent regulations on greenhouse gas (GHG) emission standards for heavy duty vehicles have prompted government agencies to standardize procedures to assess aerodynamic performance of Class 8 tractor-trailers. The coastdown test procedure is the primary reference method to assess vehicle drag and other valid alternatives include wind tunnel testing and computational fluid dynamics (CFD) simulations. While there have been many published studies comparing results between simulations and wind tunnel testing, it is less well understood how to compare results with coastdown testing. Both the wind tunnel and simulation directly measure aerodynamic drag forces in controlled conditions, while coastdown testing is conducted in an open road environment, aerodynamic forces are calculated from a road load equation, and variable wind and vehicle speed introduce additional complexity.

Development of new, competitive vehicles in the context of stricter regulations to reduce greenhouse gas emissions and increase fuel economy is driving OEM of commercial vehicles to further explore options for reducing aerodynamic drag in a real-world setting. To facilitate this in regards to the aerodynamics of a vehicle, virtual design methods such as CFD are often used to compliment experiments to help reduce physical testing time and costs. Once validated against experiments, CFD models can then act as predictive models to help speed development. In this paper, a wind tunnel experiment of a Class 8 truck is compared to a CFD simulation which replicates said experiment, validating the CFD model as a predictive tool in this instance. CFD is then used to evaluate the drag and flow around the vehicle in an open road scenario, and the results between the open road and wind tunnel scenarios are compared.

Simulations used to estimate carbon dioxide (CO2) emissions and fuel consumption of medium- and heavy-duty vehicles over prescribed drive cycles often employ engine fuel maps consisting of engine measurements at numerous steady-state operating conditions. However, simulating the engine in this way has limitations as engine controls become more complex, particularly when attempting to use steady-state measurements to represent transient operation. This paper explores an alternative approach to vehicle simulation that uses a “cycle average” engine map rather than a steady state engine fuel map. The map contains engine CO2 values measured on an engine dynamometer on cycles derived from vehicle drive cycles for a range of generic vehicles. A similar cycle average mapping approach is developed for a powertrain (engine and transmission) in order to show the specific CO2 improvements due to powertrain optimization that would not be recognized in other approaches.