Stochastic Modeling of Battery Electric Vehicle Driver Behavior: Impact of Charging Infrastructure Deployment on the Feasibility of Battery Electric Vehicles
Abstract
A stochastic modeling approach is proposed to characterize battery electric vehicle (BEV) drivers’ behavior. The approach uses longitudinal travel data and thus allows more realistic analysis of the impact of the charging infrastructure on BEV feasibility. BEV feasibility is defined as the probability that the ratio of the distance traveled between charges to the BEV range is kept within a comfort level (i.e., drivers are comfortable with driving the BEV when the battery's state of charge is above a certain level). When the ratio exceeds the comfort level, travel adaptation is needed–-use of a substitute vehicle, choice of an alternative transportation mode, or cancellation of a trip. The proposed stochastic models are applied to quantify BEV feasibility at different charging infrastructure deployment levels with the use of GPS-based longitudinal travel data collected in the Seattle, Washington, metropolitan area. In the Seattle case study, the range of comfort level was found to be critical. If BEV drivers were comfortable with using all the nominal range, about 10% of the drivers needed no or little travel adaptation (i.e., they made changes on less than 0.5% of travel days), and almost 50% of the drivers needed travel adaptation on up to 5% of the sampled days. These percentages dropped by half when the drivers were only comfortable with using up to 80% of the range. In addition, offering opportunities for one within-day recharge can significantly increase BEV feasibility, provided that the drivers were willing to make some travel adaptation (e.g., up to 5% of drivers in the analysis).
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References
1. Melaina M., and Bremson J. Refueling Availability for Alternative Fuel Vehicle Markets: Sufficient Urban Station Coverage. Energy Policy, Vol. 36, No. 8, 2008, pp. 3233–3241.
2. Traut E., Hendrickson C., Klampfi E., Liu Y., and Michalek J. J. Optimal Design and Allocation of Electrified Vehicles and Dedicated Charging Infrastructure for Minimum Life Cycle Greenhouse Gas Emissions and Cost. Energy Policy, Vol. 51, 2012, pp. 524–534.
3. 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. In Transportation Research Record: Journal of the Transportation Research Board, No. 2139, Transportation Research Board of the National Academies, Washington, D.C., 2009, pp. 55–63.
4. 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, 2010, pp. 212–219.
5. Lin Z., and Greene D. L. 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.
6. Smith R., Shahidinejad S., Blair D., and Bibeau E. L. Characterization of Urban Commuter Driving Profiles to Optimize Battery Size in Light-Duty Plug-In Electric Vehicles. Transportation Research Part D: Transport and Environment, Vol. 16, 2011, pp. 218–224.
7. Pearre N., Kempton W., Guensler R., and Elango 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.
8. Smart J., and Schey S. Battery Electric Vehicle Driving and Charging Behavior Observed Early in the EV Project. SAE International Journal of Alternative Powertrains, Vol. 1, No. 1, 2012, pp. 27–33.
9. 2009 National Household Travel Survey. FHWA, U.S. Department of Transportation, 2009. http://nhts.ornl.gov.
10. 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.
11. Lin Z., Dong J., Liu C., and Greene D. Estimation of Energy Use by Plug-In Hybrid Electric Vehicles: Validating Gamma Distribution for Representing Random Daily Driving Distance. In Transportation Research Record: Journal of the Transportation Research Board, No. 2287, Transportation Research Board of the National Academies, Washington, D.C., 2012, pp. 37–43.
12. Kelton W. D., and Law A. M. Simulation Modeling and Analysis. McGraw-Hill, Boston, Mass., 2000.
13. Nadarajah S., and Kotz S. On the Product and Ratio of Gamma and Weibull Random Variables. Econometric Theory, Vol. 22, No. 2, 2006, pp. 338–344.
14. Fisher S. R. A., and Cornish E. A. The Percentile Points of Distributions Having Known Cumulants. Technometrics, Vol. 2, No. 2, 1960, pp. 209–225.
15. Withers C., and Nadarajah S. On the Compound Poisson–Gamma Distribution. Kybernetika, Vol. 47, No. 1, 2011, pp. 15–37.
16. Traffic Choices Study—Summary Report. Puget Sound Regional Council, Seattle, Wash., 2008. http://www.psrc.org/assets/37/summaryreport.pdf.
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Article first published online: January 1, 2014
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