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This project analyzes the impact that idling and snowfall have on the fuel consumed by MnDOT’s snowplow fleet, with the underlying objective to determine and advise MnDOT on ways to reduce fuel usage of the fleet using vehicle telematics data. This is a significant problem to solve as fuel use reduction contributes to MnDOT’s sustainability goals of achieving a 30% reduction in fossil fuel use and greenhouse gas (GHG) emissions from 2005 levels by 2025. Furthermore, rising fuel costs are a future cause for concern due to an increase in business operational costs that increases the burden on taxpayers to keep roads safe in winter. This problem is challenging because existing on-board diagnostics (OBD) data do not contain mass information for the trucks’ fuel use, which can fluctuate significantly when they are applying deicing substances to the road. Taking a mean value for the vehicle mass, we observe a clear positive correlation between snowfall and average fuel use. For days with snowfall totaling 4 inches or more, fuel use rises more than 25% on average compared to days without snowfall. In addition, the results from the idling analysis indicate that the idling time associated with the fleet is about 23% of total recorded hours and constitutes about 4.3% of the total fuel used. Daily idling activity reports containing information about idling events and fuel economy are generated for the sampled vehicles and shared with MnDOT.

Appendices to Report 2023-03 "Reducing Winter Maintenance Equipment Fuel Consumption Using Advanced Vehicle Data Analytics."

This project will use a combination of laboratory experimentation and road demonstrations to better understand the reduction of LiDAR signal and object detection capability under adverse weather conditions found in Minnesota. It will also lead to concepts to improve LiDAR systems to adapt to such conditions through better signal processing image recognition software.

This research 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; ¾-ton pickup trucks; ½-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% of the driving cycle can lead to about a 10% reduction in fuel economy based on the modeling conducted for ¾ 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.