TripEnergy: Estimating Personal Vehicle Energy Consumption Given Limited Travel Survey Data

Abstract

Estimating personal vehicle energy consumption is important for nationwide climate policy, local and statewide environmental policy, and technology planning. Transportation energy use is complex, depending on vehicle performance and the driving behavior of individuals, as well as on travel patterns of cities and regions. Previous studies combine large samples of travel behavior with fixed estimates of per mile fuel economy or use detailed models of vehicles with limited samples of travel behavior. This paper presents a model for estimating privately operated vehicle energy consumption—TripEnergy—that accurately reconstructs detailed driving behavior across the United States and simulates vehicle performance for different driving conditions. The accuracy of this reconstruction was tested by using out-of-sample predictions, and the vehicle model was tested against microsimulation. TripEnergy consists of a demand model, linking GPS drive cycles to travel survey trips, and a vehicle model, efficiently simulating energy consumption across different types of driving. Because of its ability to link small-scale variation in vehicle technology and driver behavior with large-scale variation in travel patterns, it is expected to be useful for a variety of applications, including technology assessment, cost and energy savings from ecodriving, and the integration of electric vehicle technologies into the grid.

Get full access to this article

View all access options for this article.

References

Bureau of Transportation Statistics U.S. Department of Transportation. National Transportation Statistics. https://www.rita.dot.gov/bts/sites/rita.dot.gov.bts/files/publications/national_transportation_statistics/html/table_04_04.html. Accessed Aug. 2016.

FHWA U.S. Department of Transportation. 2009 National Household Travel Survey. http://nhts.ornl.gov/download.shtml. Accessed Sept. 2013.

Williams

J. H.

, Haley

, Kahrl

, Moore

, Jones

A. D.

, Torn

M. S.

, and McJeon

. Pathways to Deep Decarbonization in the United States. Energy and Environmental Economics, Inc., San Francisco, Calif., 2014.

Miotti

, Supran

G. J.

, Kim

E. J.

, and Trancik

J. E.

. Personal Vehicle Technologies Evaluated Against Climate Change Mitigation Targets. Environmental Science & Technology, Vol. 50, No. 20, 2016, pp. 10795–10804. https://doi.org/10.1021/acs.est.6b00177.

NHTSA U. S. Department of Transportation. Obama Administration Finalizes Historic 54.5 mpg Fuel Efficiency Standards. Press release, 2012. https://obamawhitehouse.archives.gov/the-press-office/2012/08/28/obama-administration-finalizes-historic-545-mpg-fuel-efficiency-standard.

Williams

J. H.

, Debenedictis

, Ghanadan

, Mahone

, Moore

, Morrow

W. R.

III , Price

, and Torn

M. S.

. The Technology Path to Deep Greenhouse Gas Emissions Cuts by 2050: The Pivotal Role of Electricity. Science, Vol. 335, No. 6064, 2012, pp. 53–59. https://doi.org/10.1126/science.1208365.

Creutzig

, Baiocchi

, Bierkandt

, Pichler

, and Seto

K. C.

. Global Typology of Urban Energy Use and Potentials for an Urbanization Mitigation Wedge. Proceedings of the National Academy of Sciences, Washington, D.C., Vol. 112, No. 20, 2015, pp. 6283–6288. https://dx-doi-org-s.web.bisu.edu.cn/10.1073/pnas.1315545112.

EPA. Certified Vehicle Test Results Report Data, 2015. http://www.epa.gov/otaq/crttst.htm.

Fuel Economy Labeling of Motor Vehicle Revisions to Improve Calculation of Fuel Economy Estimates. 71 FR 77871. Office of Transportation and Air Quality, EPA, 2006.

10.

Gonder

, Markel

, Thornton

, and Simpson

. Using Global Positioning System Travel Data to Assess the Real-World Driving Energy Use of Plug-In Hybrid Electric Vehicles. Transportation Research Record: Journal of the Transportation Research Board, No. 2017, 2007, pp. 26–32. http://dx.doig.org/10.3141/2017-04.

11.

Vyas

A. D.

, Santini

D. J.

, and Johnson

L. R.

. Potential of Plug-In Hybrid Electric Vehicles to Reduce Petroleum Use: Issues Involved in Developing Reliable Estimates. Transportation Research Record: Journal of the Transportation Research Board, No. 2139, 2009, pp. 55–63. https://dx-doi-org-s.web.bisu.edu.cn/10.3141/2139-07.

12.

Basso

L. J.

, and Oum

T. H.

. Automobile Fuel Demand: A Critical Assessment of Empirical Methodologies. Transport Reviews, Vol. 27, No. 4, 2007, pp. 449–484. https://doi.org/10.1080/01441640601119710.

13.

Donateo

, Ingrosso

, Licci

, and Laforgia

. A Method to Estimate the Environmental Impact of an Electric City Car During Six Months of Testing in an Italian City. Journal of Power Sources, Vol. 270, 2014, pp. 487–498. https://doi.org/10.1016/j.jpowsour.2014.07.124.

14.

Yuksel

, and Michalek

. Effects of Regional Temperature on Electric Vehicle Efficiency, Range, and Emissions in the United States. Environmental Science & Technology, Vol. 49, No. 6, 2015, pp. 3974–3980. https://doi.org/10.1021/es505621s.

15.

Office of Transportation and Air Quality, Advanced Light-Duty Powertrain and Hybrid Analysis (ALPHA) Tool User’s Guide for Off-Cycle Credit Evaluation. EPA-420-B-12-051. Assessment and Standards Division, EPA, 2012.

16.

Markel

, Brooker

, Hendricks

, Johnson

, Kelly

, Kramer

, O’Keefe

, Sprik

, and Wipke

. ADVISOR: A Systems Analysis Tool for Advanced Vehicle Modeling. Journal of Power Sources, Vol. 110, No. 2, 2002, pp. 255–266. https://doi.org/10.1016/S0378-7753(02)00189-1.

17.

Karabasoglu

, and Michalek

. Influence of Driving Patterns on Life Cycle Cost and Emissions of Hybrid and Plug-In Electric Vehicle Powertrains. Energy Policy, Vol. 60, 2013, pp. 445–461. https://doi.org/10.1016/j.enpol.2013.03.047.

18.

Berry

I. M.

The Effects of Driving Style and Vehicle Performance on the Real-World Fuel Consumption of U.S. Light-Duty Vehicles. PhD thesis. Massachusetts Institute of Technology, Cambridge, 2010.

19.

Carlson

, Duoba

, Bocci

, and Lohse-Busch

. On-Road Evaluation of Advanced Hybrid Electric Vehicles over a Wide Range of Ambient Temperatures. Presented at 23rd Electric Vehicle Symposium, Anaheim Calif., Vol. 275, Dec. 2–5, 2007.

20.

Bingham

, Walsh

, and Carroll

. Impact of Driving Characteristics on Electric Vehicle Energy Consumption and Range. Intelligent Transport Systems, IET, Vol. 6, No. 1, 2012, pp. 29–35. https://doi.org/10.1049/iet-its.2010.0137.

21.

Raykin

, Roorda

M. J.

, and MacLean

H. L.

. Impacts of Driving Patterns on Tank-to-Wheel Energy Use of Plug-in Hybrid Electric Vehicles. Transportation Research Part D: Transport and Environment, Vol. 17, No. 3, 2012, pp. 243–250. https://doi.org/10.1016/j.trd.2011.12.002.

22.

Office of Highway Policy Information, FHWA, U.S. Department of Transportation. Highway Statistics, 2014. https://www.fhwa.dot.gov/policyinformation/statistics/2014/.

23.

Annual Energy Outlook: With Projections to 2040. DOE/EIA-0383. Office of Integrated and International Energy Outlook, Energy Information Administration, U.S. Department of Energy, 2014.

24.

Gately

C. K.

, Hutyra

L. R.

, and Wing

I. S.

. Cities, Traffic, and CO₂: A Multidecadal Assessment of Trends, Drivers, and Scaling Relationships. Proceedings of the National Academy of Sciences, Washington, D.C., 2015.

25.

Ericsson

Independent Driving Pattern Factors and Their Influence on Fuel-Use and Exhaust Emission Factors. Transportation Research Part D: Transport and Environment, Vol. 6, No. 5, 2001, pp. 325–345. https://doi.org/10.1016/S1361-9209(01)00003-7.

26.

Population and Activity of On-Road Vehicles in MOVES2014. Draft Report EPA-420-D-15-001. Assessment and Standards Division, Office of Transportation and Air Quality, EPA, 2015.

27.

MOVES2014 Technical Guidance: Using MOVES to Prepare Emission Inventories in State Implementation Plans and Transportation Conformity. EPA-420-B-15-093. Transportation and Climate Division, Office of Transportation and Air Quality, EPA, 2015.

28.

Amirjamshidi

, and Roorda

M. J.

. Development of Simulated Driving Cycles: Case Study of the Toronto Waterfront Area. Presented at 92nd Annual Meeting of the Transportation Research Board, Washington, D.C., 2013.

29.

Zhang

, Yin

, and Chen

. Robust Signal Timing Optimization with Environmental Concerns. Transportation Research Part C: Emerging Technologies, Vol. 29, 2013, pp. 55–71. https://doi.org/10.1016/j.trc.2013.01.003.

30.

Int Panis

, Broekx

, and Liu

. Modelling Instantaneous Traffic Emission and the Influence of Traffic Speed Limits. Science of the Total Environment, Vol. 371, No. 1–3, 2006, pp. 270–285. https://doi.org/10.1016/j.scitotenv.2006.08.017.

31.

California Household Transportation Survey. DOE-019-9889603959. National Renewable Energy Laboratory, U.S. Department of Energy, 2012.

32.

Atlanta Regional Commission. Regional Travel Survey. Final Report, 2011. http://www.atlantaregional.com/transportation/modeling/household-travel-survey.

33.

Texas Department of Transportation. 2002–2011 Regional Travel Surveys with GPS Data. Transportation Secure Data Center, National Renewable Energy Laboratory. https://www.nrel.gov/transportation/transportationdata/index.cfm?loc=6011. Accessed April 2014.

34.

EPA. Fuel Economy Test Car List Database Files. http://www3.epa.gov/otaq/tcldata.htm. Accessed Aug. 2016.

35.

Wilcox

, and Marion

. Users Manual for TMY3 Data Sets. NREL/TP-581-43156. National Renewable Energy Laboratory, 2008. https://doi.org/10.2172/928611.

36.

Zhao

, Pereira

F. C.

, Ball

, Kim

, Han

, Zegras

, and Ben-Akiva

. Exploratory Analysis of a Smartphone-Based Travel Survey in Singapore. Transportation Research Record: Journal of the Transportation Research Board, No. 2494, 2015, pp. 45–56. https://dx-doi-org.web.bisu.edu.cn/10.3141/2494-06.

37.

Needell

Z. A.

, McNerney

, Chang

M. T.

, and Trancik

J. E.

. Potential for Widespread Electrification of Personal Vehicle Travel in the United States. Nature Energy, Vol. 1, No. 16112, 2016.

38.

Saerens

, Rakha

, Ahn

, and Van Den Bulck

. Assessment of Alternative Polynomial Fuel Consumption Models for Use in Intelligent Transportation Systems Applications. Journal of Intelligent Transport Systems, Vol. 17, No. 4, 2013, pp. 294–303. https://doi.org/10.1080/15472450.2013.764801.

39.

Rakha

H. A.

, Ahn

, Moran

, Saerens

, and Bulck

E. V.

. Virginia Tech Comprehensive Power-Based Fuel Consumption Model: Model Development and Testing. Transportation Research Part D: Transport and Environment, Vol. 16, No. 7, 2011, pp. 492–503. https://doi.org/10.1016/j.trd.2011.05.008.

40.

Lutsey

A Technical Analysis of Model Year 2011 US Automobile Efficiency. Transportation Research Part D: Transport and Environment, Vol. 17, No. 5, 2012, pp. 361–369. https://doi.org/10.1016/j.trd.2012.03.002.

41.

Kambly

K. R.

, and Bradley

T. H.

. Estimating the HVAC Energy Consumption of Plug-In Electric Vehicles. Journal of Power Sources, Vol. 259, 2014, pp. 117–124. https://doi.org/10.1016/j.jpowsour.2014.02.033.

42.

Geringer

, and Tober

W. K.

. BatterieelektrischeFahrzeuge in der Praxis: Kosten, Reichweite, Umwelt, Komfort. InstitutfürFahrzeugantriebe und Automobiltechnik, TechnischeUniversität Wien, 2012.

43.

Gaker

, Vautin

, Vij

, and Walker

J. L.

. The Power and Value of Green in Promoting Sustainable Transport Behavior. Environmental Research Letters, Vol. 6, No. 3, 2011, pp. 1–10. https://doi.org/10.1088/1748-9326/6/3/034010.