Fuel Economy Prediction Models for Vehicles Operating on Different Highway Scenario

The HERS VOCs fuel consumption models use dates and are based on an unrepresentative database of the vehicle fleet and technologies currently used in the U.S. Thus the models were updates through several steps. First, driving cycles were developed that represent the driving population and behavior in addition to representing the different road characteristics and the vehicle fleets driven nowadays on U.S roads. SHRP2 NDS complemented with SHRP RID and ATRI data were used to fulfill this task, forty four hundred (4,400) NDS trips collected at six (6) sites including around 12,500,000-seconds of truck data were used for the development of synthetically optimized (SO) driving cycles for different vehicle types, traffic conditions, and road properties for different highway types. A representative vehicle simulation was developed on thirty (30) different vehicle dynamics models representing a range of vehicles, technologies, and fuel types were developed and verified. Each, vehicle model was then simulated over the developed driving cycles and respective highway grades (upslope and downslope) to predict fuel economy for different scenarios of full access control and partial or no access control facilities. The results were processed by segmenting the driving cycles to obtain additional fuel economy data as a function of average vehicle speed (AS) and introducing average speed steady state fuel economy allowing extrapolation to higher speeds. The impact of the parameters used for developing the SO driving cycles was studied and used to exclude the non-significant parameters from the driving cycles development. Thus, additional SO driving cycles were added to fill the absent highway scenarios that were missed by the originally developed SO driving cycles. Multi-variable non-linear power-polynomial prediction models were fitted to the data, to estimate fuel economy for different scenarios of full access control (FAC) and partial or no access control (PNAC) facilities at different grades (upslope and downslope), averages speeds, speed limits and intersections densities for straight smooth roads. These prediction models were verified using NDS trip data. The effect of curvature on fuel economy was further investigated with the vehicle simulation data showing an interaction effect for curvature and grade level on fuel economy. Reduction in fuel economy due to road curvature (RFEC) prediction models were developed for each grade level A through F (separately for upslope and downslope) as a function of AS, vehicle type, and highway type (FAC and PNAC). The developed RFEC models were verified for FAC and PNAC highways against FE of respective simulated trip data.