, Associate Professor, Mechanical Engineering
This project quantified fuel economy improvements by implementing hybrid electric utility vehicles in municipal fleets. The research team analyzed utility vehicle data and built computer vehicle simulations of utility trucks with three powertrain types: conventional, charge-sustaining hybrid, and charge-depleting hybrid plug-in hybrid vehicle (PHEV). Driving cycles were recorded from three vehicle groups: 3/4-ton pickup trucks, 1/2-ton pickup trucks, and SUVs using portable onboard diagnostics loggers. Collected data were used in vehicle simulations to determine the fuel economy improvement possible when implementing hybrid powertrain architectures in municipal fleets. The magnitude of benefits from implementing hybrid vehicles was highly dependent on driving cycles and the electric motor/battery combination of the PHEV. The highest kinetic intensity (KI) values, representing urban driving, were found to lead to the greatest fuel economy improvements for hybrid vehicles over conventionally powered vehicles. The results depended heavily on the electric motor/battery combination, with the higher battery capacity plug-in hybrid vehicles yielding the highest levels of fuel economy improvement. It is recommended that fleets consider driving cycle as the primary factor for determining the economic benefits of purchasing alternative powertrain vehicles. Hybrid vehicles should be placed on routes that are more urban, while rural/highway routes would be better served by conventionally powered vehicles. Idling time was also calculated for all the drive cycles and needs to be separately accounted for when analyzing driving cycle data. Idling for over 50 percent of the driving cycle can lead to about a 10 percent reduction in fuel economy based on the modeling conducted for 3/4-ton pickup trucks in this study. The research team further recommends that aggressive driving be reduced as it will negate the fuel economy advantages possible from hybrid powertrain architectures.
- Project number: 2019011
- Start date: 06/2018
- Project status: Completed
- Research area: Planning and Economy