The relative importance of price and driving range on electric vehicle adoption: Los Angeles case study

Abstract

Electric vehicles (EVs) are still a maturing technology. Barriers to their adoption include price and range anxiety. EV batteries are significant in determining both EV prices and costs. In this work, we focus on the impact of a high-capacity battery and EV rebates on an EV ecosystem. Using survey data from Los Angeles, California, we simulate different cases of battery costs and prices by means of an agent-based EV ecosystem model. We find that even in Los Angeles, a geographically spread out city, the price of EVs is a more significant barrier to adoption than EV range. In fact, even a quintupling of battery size at no additional costs improves EV adoption by only 5 %. Therefore, policy makers should focus more on affordability than range in promoting EV adoption.

This is a preview of subscription content, log in to check access.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14

Notes

  1. 1.

    The EV ecosystem model is also described in greater detail in our prior work (Adepetu et al. 2014).

References

  1. Acha, S., van Dam, K.H., Keirstead, J., Shah, N.: Integrated modelling of agent-based electric vehicles into optimal power flow studies. In: 21st International Conference on Electricity Distribution, pp. pp. 6–9. Frankfurt (2011)

  2. Adepetu, A., Arya, V., Keshav, S.: An agent-based electric vehicle ecosystem model: a San Francisco case study. Technical report, University of Waterloo (2014)

  3. Al-Alawi, B.M., Bradley, T.H.: Review of hybrid, plug-in hybrid, and electric vehicle market modeling studies. Renew. Sustain. Energy Rev. 21, 190–203 (2013)

    Article  Google Scholar 

  4. Alternative Fuels Data Center. Fuel properties comparison (2014). http://www.afdc.energy.gov/fuels/fuel_comparison_chart.pdf. Accessed 18 Mar 2014

  5. Arellano, B., Sena, S., Abdollahy, S., Lavrova, O., Stratton, S., Hawkins, J.: Analysis of electric vehicle impacts in New Mexico urban utility distribution infrastructure. In: IEEE Transportation Electrification Conference and Expo (ITEC), pp. 1–6

  6. Boulanger, A.G., Chu, A.C., Maxx, S., Waltz, D.L.: Vehicle electrification: status and issues. Proc. IEEE 99(6), 1116–1138 (2011)

    Article  Google Scholar 

  7. Brown, M.: Catching the PHEVer: simulating electric vehicle diffusion with an agent-based mixed logit model of vehicle choice. J. Artif. Soc. Soc. Simul. 16(2), 5 (2013)

    Google Scholar 

  8. Bureau of Labor Statistics.: Average Energy Prices, San Francisco-Oakland-San Jose, J 2014. http://www.bls.gov/ro9/cpisanf_energy.htm. Accessed on 17 March 2014

  9. California Center for Sustainable Energy.: Clean Vehicle Rebate Project (2014). http://energycenter.org/clean-vehicle-rebate-project. Accessed 17 Mar 2014

  10. California Department of Transportation.: 2010–2012 California household travel survey final report (2013)

  11. Cui, X., Liu, C., Kim, H.K., Kao, S.C., Tuttle, M.A., Bhaduri, B.L.: A multi agent-based framework for simulating household PHEV distribution and electric distribution network impact. TRB Committee on Transportation Energy (ADC70) (2010)

  12. Daziano, R.A.: Conditional-logit Bayes estimators for consumer valuation of electric vehicle driving range. Resour. Energy Econ. 35(3), 429–450 (2013)

    Article  Google Scholar 

  13. Electric Drive Transportation Association. Electric drive sales (2014). http://www.electricdrive.org/index.php?ht=d/sp/i/20952/pid/20952. Accessed 02 Mar 2014

  14. Electric Power Research Institute.: Total cost of ownership model for current plug-in electric vehicles. Technical report (2013)

  15. Eppstein, M.J., Grover, D.K., Marshall, J.S., Rizzo, D.M.: An agent-based model to study market penetration of plug-in hybrid electric vehicles. Energy Policy 39(6), 3789–3802 (2011)

    Article  Google Scholar 

  16. Girishkumar, G., McCloskey, B., Luntz, A., Swanson, S., Wilcke, W.: Lithium–air battery: promise and challenges. J. Phys. Chem. Lett. 1(14), 2193–2203 (2010)

    Article  Google Scholar 

  17. He, L., Wang, M., Chen, W., Conzelmann, G.: Incorporating social impact on new product adoption in choice modeling: a case study in green vehicles. Transp. Res. Part D 32, 421–434 (2014)

    Article  Google Scholar 

  18. IBM.: Battery 500 Project: 800 km range for electric vehicles (2014). http://www.zurich.ibm.com/news/12/battery500.html. Accessed 20 Oct 2014

  19. Kim, J.D., Rahimi, M.: Future energy loads for a large-scale adoption of electric vehicles in the city of Los Angeles: impacts on greenhouse gas emissions. Energy Policy 73, 620–630 (2014)

    Article  Google Scholar 

  20. MapQuest.: Directions Web Service—MapQuest Platform (2014). http://www.mapquestapi.com/directions/#matrix. Accessed 18 Mar 2014

  21. Neubauer, J., Wood, E.: The impact of range anxiety and home, workplace, and public charging infrastructure on simulated battery electric vehicle lifetime utility. J. Power Sour. 257, 12–20 (2014)

    Article  Google Scholar 

  22. O’Connor, L. San Francisco and Los Angeles account for 35 % of nation’s electric vehicle sales, data finds (2013). http://www.huffingtonpost.com/2013/09/03/california-electric-cars_n_3862972.html. Accessed 17 Jan 2014

  23. Paevere, P., Higgins, A., Ren, Z., Horn, M., Grozev, G., McNamara, C.: Spatio-temporal modelling of electric vehicle charging demand and impacts on peak household electrical load. Sustain. Sci. 9(1), 61–76 (2014)

    Article  Google Scholar 

  24. Pellon, M.B., Eppstein, M.J., Besaw, L.E., Grover, D.K., Rizzo, D.M., Marshall, J.S.: An agent-based model for estimating consumer adoption of PHEV technology. Transportation Research Board (TRB), pp. 10–3303 (2010)

  25. Recargo.: PlugShare—EV charging station map (2014). http://www.plugshare.com/. Accessed 09 Mar 2014

  26. Schwoon, M.: Simulating the adoption of fuel cell vehicles. J. Evol. Econ. 16(4), 435–472 (2006)

    Article  Google Scholar 

  27. Shafiei, E., Thorkelsson, H., Ásgeirsson, E.I., Davidsdottir, B., Raberto, M., Stefansson, H.: An agent-based modeling approach to predict the evolution of market share of electric vehicles: a case study from Iceland. Technol. Forecast. Soc. Change 79, 1638–1653 (2012)

    Article  Google Scholar 

  28. Sullivan, J., Salmeen, I., Simon, C.: PHEV marketplace penetration: an agent based simulation. Transportation Research Institute, University of Michigan, Ann Arbor (2009)

    Google Scholar 

  29. Sweda, T., Klabjan, D. An agent-based decision support system for electric vehicle charging infrastructure deployment. In IEEE Vehicle Power and Propulsion Conference (VPPC), pp. 1–5 (2011)

  30. USEPA (US Environmental Protection Agency).: Federal tax credits for electric vehicles purchased in or after 2010 (2014a). http://www.fueleconomy.gov/feg/taxevb.shtml. Accessed 17 Mar 2014

  31. USEPA (US Environmental Protection Agency).: Fuel economy (2014b). http://www.fueleconomy.gov/. Accessed 18 Mar 2014

  32. Voelcker, J.: Reduce, reuse, recycle: average vehicle now 11.4 years old, oldest since WW2 (2013). http://www.greencarreports.com/news/1086136_reduce-reuse-recycle-average-vehicle-now-11-4-years-old-oldest-since-ww2. Accessed 10 Mar 2014

  33. Wolf, I., Schröder, T., Neumann, J., de Haan, G.: Changing minds about electric cars: an empirically grounded agent-based modeling approach. Technol. Forecast. Soc. Change 94, 269–285 (2014)

    Article  Google Scholar 

Download references

Author information

Affiliations

Authors

Corresponding author

Correspondence to Adedamola Adepetu.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Adepetu, A., Keshav, S. The relative importance of price and driving range on electric vehicle adoption: Los Angeles case study. Transportation 44, 353–373 (2017). https://doi.org/10.1007/s11116-015-9641-y

Download citation

Keywords

  • Electric vehicles
  • Agent-based modeling
  • Electric vehicle adoption
  • Range anxiety