Truck Technology Efficiency Assessment (TTEA) Project Final Report

LaClair, Tim J.; Gao, Zhiming; Siekmann, Adam; Fu, Joshua S.; Calcagno, Jimmy; Yun, Jeongran

doi:10.2172/1062611

Cited by 7 publications

(4 citation statements)

References 4 publications

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“…To minimize this effect, filtering functions are available in the SVET and FFLEET tools to smooth raw speed and/or elevation data that are derived from on-vehicle measurements and GPS signals. The filtering methodologies employed were developed in a previous study [15]. A visual comparison of the raw and filtered speed data generally shows nearly identical results, as shown in Fig.…”

Section: Filtering Of Speed and Elevation Datamentioning

confidence: 63%

“…Models employing a similar approach to that of the An/Ross model have been used for vehicle fuel economy evaluations. While such models have often been used for light duty (LD) vehicles [11,12], the same approach is applicable to buses [13] and heavy duty (HD) vehicle configurations [14,15], although the powertrain, vehicle and drive cycle characteristics are very different for the different vehicle types and HD vehicles tend to encounter more diverse driving conditions, and are therefore more difficult to characterize [16].…”

Section: The An/ross Modelmentioning

confidence: 99%

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Development of Fleet Energy Savings Evaluation Tools for Smart Mobility: Smart Vehicle Energy Technology (SVET) Model for Passenger Fleets and Freight Fleet Level Energy Estimation Tool (FFLEET) for Freight Fleets

LaClair

Moore

2018

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Section: Filtering Of Speed and Elevation Datamentioning

confidence: 63%

Section: The An/ross Modelmentioning

confidence: 99%

Development of Fleet Energy Savings Evaluation Tools for Smart Mobility: Smart Vehicle Energy Technology (SVET) Model for Passenger Fleets and Freight Fleet Level Energy Estimation Tool (FFLEET) for Freight Fleets

LaClair

Moore

2018

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“…The dashed vertical lines in Figure 2 show the mass levels used to separate the data into low-, medium-, and high-mass cases; these three cases were used in developing the synthetic drive cycles. Additional details of the analysis followed can be found in the report by LaClair et al (13).…”

Section: Estimations Of Massmentioning

confidence: 99%

“…This approach yielded a drive cycle whose distribution of speeds and accelerations closely matched that of the original scaled drive cycle and included real grade variations that were also charac teristic of the measured drive cycle data. LaClair et al provide additional details on the method employed (13).…”

Section: Development Of the Synthetic Drive Cyclementioning

confidence: 99%

Development of a Short-Duration Drive Cycle to Represent Long-Term Measured Drive Cycle Data

LaClair

Gao

et al. 2014

Transportation Research Record: Journal of the Transportation R

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Quantifying the fuel savings and emissions reductions that can be achieved from truck fuel efficiency technologies for a fleet's specific usage allows the fleet to select a combination of technologies that will yield the greatest operational efficiency and profitability. An accurate characterization of usage for the fleet is critical for such an evaluation; however, short-term measured drive cycle data do not generally reflect overall usage very effectively. This paper presents a detailed analysis of vehicle usage in a commercial vehicle fleet and demonstrates the development of a short-duration synthetic drive cycle with measured drive cycle data collected over an extended period of time. The approach matched statistical measures of the vehicle speed with acceleration history and integrated measured grade data to develop a compressed drive cycle that accurately represents total usage. Drive cycle measurements obtained during a full year from six tractor trailers in normal operations in a less-than-truckload carrier were analyzed to develop a synthetic drive cycle. The vehicle mass was also estimated to account for the variation of loads that the fleet experienced. These drive cycle and mass data were analyzed with a tractive energy analysis to quantify the benefits in terms of fuel efficiency and reduced carbon dioxide emissions that can be achieved on Class 8 tractor trailers by using advanced efficiency technologies, either individually or in combination. Although differences exist between Class 8 tractor trailer fleets, this study provides valuable insight into the energy and emissions reduction potential that various technologies can bring in this important trucking application. The methodology employed for generating the synthetic drive cycle serves as a rigorous approach to develop an accurate usage characterization that can be used to effectively compress large quantities of drive cycle data.

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Comparison of Life Cycle Greenhouse Gases from Natural Gas Pathways for Medium and Heavy-Duty Vehicles

Tong

Jaramillo

Azevedo

2015

Environ. Sci. Technol.

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The low-cost and abundant supply of shale gas in the United States has increased the interest in using natural gas for transportation. We compare the life cycle greenhouse gas (GHG) emissions from different natural gas pathways for medium and heavy-duty vehicles (MHDVs). For Class 8 tractor-trailers and refuse trucks, none of the natural gas pathways provide emissions reductions per unit of freight-distance moved compared to diesel trucks. When compared to the petroleum-based fuels currently used in these vehicles, CNG and centrally produced LNG increase emissions by 0-3% and 2-13%, respectively, for Class 8 trucks. Battery electric vehicles (BEVs) powered with natural gas-produced electricity are the only fuel-technology combination that achieves emission reductions for Class 8 transit buses (31% reduction compared to the petroleum-fueled vehicles). For non-Class 8 trucks (pick-up trucks, parcel delivery trucks, and box trucks), BEVs reduce emissions significantly (31-40%) compared to their diesel or gasoline counterparts. CNG and propane achieve relatively smaller emissions reductions (0-6% and 19%, respectively, compared to the petroleum-based fuels), while other natural gas pathways increase emissions for non-Class 8 MHDVs. While using natural gas to fuel electric vehicles could achieve large emission reductions for medium-duty trucks, the results suggest there are no great opportunities to achieve large emission reductions for Class 8 trucks through natural gas pathways with current technologies. There are strategies to reduce the carbon footprint of using natural gas for MHDVs, ranging from increasing vehicle fuel efficiency, reducing life cycle methane leakage rate, to achieving the same payloads and cargo volumes as conventional diesel trucks.

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Truck Technology Efficiency Assessment (TTEA) Project Final Report

Cited by 7 publications

References 4 publications

Development of Fleet Energy Savings Evaluation Tools for Smart Mobility: Smart Vehicle Energy Technology (SVET) Model for Passenger Fleets and Freight Fleet Level Energy Estimation Tool (FFLEET) for Freight Fleets

Development of Fleet Energy Savings Evaluation Tools for Smart Mobility: Smart Vehicle Energy Technology (SVET) Model for Passenger Fleets and Freight Fleet Level Energy Estimation Tool (FFLEET) for Freight Fleets

Development of a Short-Duration Drive Cycle to Represent Long-Term Measured Drive Cycle Data

Comparison of Life Cycle Greenhouse Gases from Natural Gas Pathways for Medium and Heavy-Duty Vehicles

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