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Title: Exploring Telematics Big Data for Truck Platooning Opportunities

Conference ·
DOI:https://doi.org/10.4271/2018-01-1083· OSTI ID:1435701

NREL completed a temporal and geospatial analysis of telematics data to estimate the fraction of platoonable miles traveled by class 8 tractor trailers currently in operation. This paper discusses the value and limitations of very large but low time-resolution data sets, and the fuel consumption reduction opportunities from large scale adoption of platooning technology for class 8 highway vehicles in the US based on telematics data. The telematics data set consist of about 57,000 unique vehicles traveling over 210 million miles combined during a two-week period. 75% of the total fuel consumption result from vehicles operating in top gear, suggesting heavy highway utilization. The data is at a one-hour resolution, resulting in a significant fraction of data be uncategorizable, yet significant value can still be extracted from the remaining data. Multiple analysis methods to estimate platoonable miles are discussed. Results indicate that 63% of total miles driven at known hourly-average speeds happens at speeds amenable to platooning. When also considering availability of nearby partner vehicles, results indicate 55.7% of all classifiable miles driven were platoonable. Analysis also address the availability of numerous partners enabling platoons greater than 2 trucks and the percentage of trucks that would be required to be equipped with platooning equipment to realize more than 50% of the possible savings.

Research Organization:
National Renewable Energy Lab. (NREL), Golden, CO (United States)
Sponsoring Organization:
USDOE Office of Energy Efficiency and Renewable Energy (EERE), Vehicle Technologies Office (EE-3V)
DOE Contract Number:
AC36-08GO28308
OSTI ID:
1435701
Report Number(s):
NREL/CP-5400-70869
Resource Relation:
Conference: Presented at WCX 18: SAE World Congress Experience, 10-12 April 2018, Detroit, Michigan
Country of Publication:
United States
Language:
English

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