Estimation of Energy Use by Plug-In Hybrid Electric Vehicles: Validating Gamma Distribution for Representing Random Daily Driving Distance
Abstract
The fuel and electricity consumptions of plug-in hybrid electric vehicles (PHEVs) are sensitive to the variation of daily vehicle miles traveled (DVMT). Although some researchers have assumed that DVMT follows a gamma distribution, such an assumption has yet to be validated. On the basis of continuous travel data from the Global Positioning System for 382 vehicles, each tracked for at least 183 days, the authors of this study validated the gamma assumption in the context of PHEV energy analysis. Small prediction errors caused by the gamma assumption were found in PHEV fuel use, electricity use, and energy cost. Validating the reliability of the gamma distribution paves the way for its application in energy use analysis of PHEVs in the real world. The gamma distribution can be easily specified with few pieces of driver information and is relatively easy for mathematical manipulation. Validation with real world travel data enables confident use of the gamma distribution in a variety of applications, such as the development of vehicle consumer choice models, the quantification of range anxiety for battery electric vehicles, the investigation of the role of charging infrastructure, and the construction of online calculators that provide personal estimates of PHEV energy use.
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References
1. Committee on Assessment of Resource Needs for Fuel Cell and Hydrogen Technologies. Transition to Alternative Transportation Technologies—A Focus on Hydrogen. National Academies Press, Washington, D.C., 2008.
2. Committee on Assessment of Resource Needs for Fuel Cell and Hydrogen Technologies. Transitions to Alternative Transportation Technologies—Plug-In Hybrid Electric Vehicles. National Academies Press, Washington, D.C., 2009.
3. Axsen J., and Kurani K. S. Anticipating Plug-In Hybrid Vehicle Energy Impacts in California: Constructing Consumer-Informed Recharge Profiles. Transportation Research Part D: Transport and Environment, Vol. 15, No. 4, 2009, pp. 212–219.
4. Vyas A., Santini D., and Johnson L. Potential of Plug-In Hybrid Electric Vehicles to Reduce Petroleum Use: Issues Involved in Developing Reliable Estimates. In Transportation Research Record: Journal of the Transportation Research Board, No. 2139, Transportation Research Board of the National Academies, Washington, D.C., 2010, pp. 55–63.
5. Lin Z., and Greene D. L. Assessing Energy Impact of Plug-In Hybrid Electric Vehicles: Significance of Daily Distance Variation over Time and Among Drivers. In Transportation Research Record: Journal of the Transportation Research Board, No. 2252, Transportation Research Board of the National Academies, Washington, D.C., 2011, pp. 99–106.
6. Pas E.I., and Sundar S. Intrapersonal Variability in Daily Urban Travel Behavior: Some Additional Evidence. Transportation, Vol. 22, No. 2, 1995, pp. 135–150.
7. Pendyala R.M., and Pas E. I. Multi-Day and Multi-Period Data for Travel Demand Analysis and Modeling. In Transportation Research Circular E-C008: Transportation Surveys: Raising the Standard, TRB, National Research Council, 2000.
8. Elango V.V., Guensler R.L., and Ogle J. H. Day-to-Day Travel Variability in the Commute Atlanta, Georgia, Study. In Transportation Research Record: Journal of the Transportation Research Board, No. 2014, Transportation Research Board of the National Academies, Washington, D.C., 2007, pp. 39–49.
9. Hamilton W. Prospects for Electric Cars: Electric Vehicle Impact Assessment Study. Final report. Division of Transportation Energy Conservation, U.S. Department of Energy, 1978.
10. Kiselewich S.J., and Hamilton W. F. Electrification of Household Travel by Electric and Hybrid Vehicles. SAE technical paper series No. 820452, SAE International, Warrendale, Pa., 1982.
11. Gonder J., Markel T., Simpson A., and Thornton M. Using GPS Travel Data to Assess the Real World Driving Energy Use of Plug-In Hybrid Electric Vehicles (PHEVs). NREL/CP 540-40858. National Renewable Energy Laboratory Department of Energy, Golden, Colo., 2007.
12. Utility Factor Definitions for Plug-In Hybrid Electric Vehicles Using Travel Survey Data. Standard J2841. Hybrid Committee, SAE International, Warrendale, Pa. 2010.
13. Pearre N.S., Kempton W., Guensler R.L., and Elango V. V. Electric Vehicles: How Much Range Is Required for a Day's Driving? Transportation Research Part C: Emerging Technologies, Vol. 19, No. 6, 2011, pp. 1171–1184.
14. Traut E., Hendrickson C.T., Klampfl E., Liu Y., and Michalek J. J. Optimal Design and Allocation of Electrified Vehicles and Dedicated Charging Infrastructure for Minimum Greenhouse Gas Emissions. Presented at 90th Annual Meeting of the Transportation Research Board, Washington, D.C., 2011.
15. Greene D. L. Estimating Daily Vehicle Usage Distributions and the Implications for Limited-Range Vehicles. Transportation Research Part B: Methodological, Vol. 19, No. 4, 1985, pp. 347–358.
16. Lin Z., and Greene D. Promoting the Market for Plug-In Hybrid and Battery Electric Vehicles: Role of Recharge Availability. In Transportation Research Record: Journal of the Transportation Research Board, No. 2252, Transportation Research Board of the National Academies, Washington, D.C., 2011, pp. 49–56.
17. 2001 National Household Travel Survey. FHWA, U.S. Department of Transportation, 2001.
18. Traffic Choices Study: Summary Report. Puget Sound Regional Council, Seattle, Wash. 2008.
19. Fan Y., and Nie Y. Optimal Routing for Maximizing the Travel Time Reliability. Networks and Spatial Economics, Vol. 6, No. 3, 2006, pp. 333–344.
20. Davis S.C., Diegel S.W., and Boundy R. G. Transportation Energy Databook, 29th ed. Oak Ridge National Laboratory, U.S. Department of Energy, Oak Ridge, Tenn., 2011.
21. Annual Energy Outlook 2011: With Projections to 2035. Energy Information Administration, U.S. Department of Energy, 2011.
22. Plotkin S., Singh M., Patterson P., Ward J., Wood F., Kydes N., Holte J., Moore J., Miller G., Das S., Rousseau A., Sharer P., and Pagerit S. Multi-Path Study, Phase 2: Vehicle Characterization and Key Results of Scenario Analysis. Argonne National Laboratory, U.S. Department of Energy, Argonne, Ill., 2009.
23. Byrd R.H., Lu P., Nocedal J., and Zhu C. A Limited Memory Algorithm for Bound Constrained Optimization. SIAM Journal of Scientific Computing, Vol. 16, No. 5, 1995, pp. 1190–1208.
24. Lin Z., and Greene D. The MA3T Model: Projecting Plug-In Hybrid Vehicle Demand and Impact with Detailed Market Segmentation. Presented at 89th Annual Meeting of Transportation Research Board, Washington, D.C., 2010.
25. Lin Z., Greene D., and Leiby P. N. Range Optimization for Fuel Cell Vehicles. 2010 Annual Merit Review and Peer Evaluation Meeting, Hydrogen Fuels Cells Program, U.S. Department of Energy, Washington, D.C. June 7–11, 2010.
26. Lin Z., and Greene D. Predicting Individual Fuel Economy. International Journal of Fuels and Lubricants, Vol. 4, No. 1, 2011, pp. 84–95.
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