, Volume 44, Issue 2, pp 353–373 | Cite as

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

  • Adedamola AdepetuEmail author
  • Srinivasan Keshav


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.


Electric vehicles Agent-based modeling Electric vehicle adoption Range anxiety 


  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)Google Scholar
  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)Google Scholar
  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)CrossRefGoogle Scholar
  4. Alternative Fuels Data Center. Fuel properties comparison (2014). 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. 1–6Google Scholar
  6. Boulanger, A.G., Chu, A.C., Maxx, S., Waltz, D.L.: Vehicle electrification: status and issues. Proc. IEEE 99(6), 1116–1138 (2011)CrossRefGoogle 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. Accessed on 17 March 2014
  9. California Center for Sustainable Energy.: Clean Vehicle Rebate Project (2014). Accessed 17 Mar 2014
  10. California Department of Transportation.: 2010–2012 California household travel survey final report (2013)Google Scholar
  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)Google Scholar
  12. Daziano, R.A.: Conditional-logit Bayes estimators for consumer valuation of electric vehicle driving range. Resour. Energy Econ. 35(3), 429–450 (2013)CrossRefGoogle Scholar
  13. Electric Drive Transportation Association. Electric drive sales (2014). Accessed 02 Mar 2014
  14. Electric Power Research Institute.: Total cost of ownership model for current plug-in electric vehicles. Technical report (2013)Google Scholar
  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)CrossRefGoogle 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)CrossRefGoogle 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)CrossRefGoogle Scholar
  18. IBM.: Battery 500 Project: 800 km range for electric vehicles (2014). 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)CrossRefGoogle Scholar
  20. MapQuest.: Directions Web Service—MapQuest Platform (2014). 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)CrossRefGoogle Scholar
  22. O’Connor, L. San Francisco and Los Angeles account for 35 % of nation’s electric vehicle sales, data finds (2013). 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)CrossRefGoogle 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)Google Scholar
  25. Recargo.: PlugShare—EV charging station map (2014). Accessed 09 Mar 2014
  26. Schwoon, M.: Simulating the adoption of fuel cell vehicles. J. Evol. Econ. 16(4), 435–472 (2006)CrossRefGoogle 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)CrossRefGoogle 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)Google Scholar
  30. USEPA (US Environmental Protection Agency).: Federal tax credits for electric vehicles purchased in or after 2010 (2014a). Accessed 17 Mar 2014
  31. USEPA (US Environmental Protection Agency).: Fuel economy (2014b). Accessed 18 Mar 2014
  32. Voelcker, J.: Reduce, reuse, recycle: average vehicle now 11.4 years old, oldest since WW2 (2013). 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)CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2015

Authors and Affiliations

  1. 1.Cheriton School of Computer ScienceUniversity of WaterlooWaterlooCanada

Personalised recommendations