Skip to main content

Advertisement

Log in

Prioritisation of Barriers in the Implementation of Electric Mobility in Indian Context Using Fuzzy Analytical Hierarchical Process

  • Original Contribution
  • Published:
Journal of The Institution of Engineers (India): Series A Aims and scope Submit manuscript

Abstract

The deteriorating ecosystem and rising emission levels have demanded from the transportation sector to switch towards more sustainable technology like electric vehicles. Even after the visible benefits of e-mobility, government and users are reluctant to use such low-carbon mobility option. Thus, this study aims to identify and prioritise the barriers in the adoption of e-mobility. From the literature review, twenty-three barriers are finalised and then fuzzy analytical hierarchical process is applied to compute their weights and rank based on a pairwise comparison of barriers with the help of different groups of experts. Results demonstrated that socio-economic barriers got the maximum weight, followed by infrastructural barriers and technological barriers. It reveals that if negative perception towards the electric vehicle is not improved, then it would be challenging to make e-mobility a success story.

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

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6

Similar content being viewed by others

References

  1. S. Sachan, S. Deb, S. Singh, Different charging infrastructures along with smart charging strategies for electric vehicles. Sustain. Cities Soc. (2020). https://doi.org/10.1016/j.scs.2020.102238

    Article  Google Scholar 

  2. Hall, D., & Lutsey, N. (2017). Emerging Best Practices for Electric Vehicle Charging Infrastructure. Washington.

  3. Johnson, C., & Walker, J. (2016). Peak Car Ownership: The Market Opportunity of Electric Automated Mobility Services. Rocky Mountain Institute.

  4. Shaffer, L. (2016). CNBC. Retrieved Feb 12, 2020, from https://www.cnbc.com/2016/06/14/electric-vehicles-will-soon-be-cheaper-than-regular-cars-because-maintenance-costs-are-lower-says-tony-seba.html

  5. Rocky Mountain Institute. (2017). Enabling the Transition to Electric Mobility in India. New Delhi.

  6. US Department of Energy. (2019, August 05). All-Electric Vehicles. Retrieved December 07, 2019, from Energy Efficiency and Renewable Energy: https://fueleconomy.gov/feg/evtech.shtml

  7. G. Merhy, A. Nait-Sidi-Moh, N. Moubayed, Control, regulation and optimization of bidirectional energy flows for electric vehicles’ charging and discharging. Sustain. Cities Soc. (2020). https://doi.org/10.1016/j.scs.2020.102129

    Article  Google Scholar 

  8. R. Zhang, E. Yao, Mesoscopic model framework for estimating electric vehicles’ energy consumption. Sustain. Cities Soc. (2019). https://doi.org/10.1016/j.scs.2019.101478

    Article  Google Scholar 

  9. A. Awang, A.A. Ghani, L. Abdullah, M.F. Ahmad, Fuzzy analytic hierarchy process (FAHP) with cosine consistency index for coastal erosion problem: a case study of setiu wetlands. J. Comput. Sci. Comput. Math. 7(4), 107–118 (2017)

    Article  Google Scholar 

  10. H.K. El-Din, H.E. El Munim, H. Mahdi, Decision-making in fuzzy environment: a survey (IntechOpen, Cairo, 2019). https://doi.org/10.5772/intechopen.88736

    Book  Google Scholar 

  11. T. Larimian, Z.S. Zarabadi, A. Sadeghi, Developing a fuzzy AHP model to evaluate environmental sustainability from the perspective of Secured by design scheme: a case study. Sustain. Cities Soc. 7, 25–36 (2013). https://doi.org/10.1016/j.scs.2012.10.001

    Article  Google Scholar 

  12. Radionovs, A., & Uzhga-Rebrov, O. (2017). Comparison of Different Fuzzy AHP Methodologies in Risk Assessment. Methodologies in Risk Assessment (pp. 137–142). Rezekne: Rezekne Academy of Technologies.

  13. S.M. Saad, N. Kunhu, A.M. Mohamed, A fuzzy-AHP multi-criteria decision making model for procurement process. Int. J. Logistics Syst. Manag. 23(1), 1–24 (2016)

    Article  Google Scholar 

  14. D.-Y. Chang, Applications of the extent analysis method on fuzzy AHP. Eur. J. Oper. Res. 95, 649–655 (1996)

    Article  MATH  Google Scholar 

  15. Krumm, J. (2012). How People Use Their Vehicles: Statistics from the 2009 National Household Travel Survey. SAE International.

  16. M. Du, L. Cheng, X. Li, J. Yang, Acceptance of electric ride-hailing under the new policy in Shenzhen, China: Influence factors from the driver’s perspective. Sustain. Cities Soc. (2020). https://doi.org/10.1016/j.scs.2020.102307

    Article  Google Scholar 

  17. B.E. Lebrouhi, Y. Khattari, B. Lamrani, M. Maaroufi, Y. Zeraouli, T. Kousksou, Key challenges for a large-scale development of battery electric vehicles: a comprehensive review. J. Energy Storage 44, 103273 (2021). https://doi.org/10.1016/j.est.2021.103273

    Article  Google Scholar 

  18. N. Wang, L. Tang, H. Pan, A global comparison and assessment of incentive policy on electric vehicle promotion. Sustain. Cities Soc. 44, 597–603 (2019). https://doi.org/10.1016/j.scs.2018.10.024

    Article  Google Scholar 

  19. L. Noel, G.Z. Rubens, J. Kester, B.K. Sovacool, Understanding the socio-technical nexus of Nordic electric vehicle (EV) barriers: a qualitative discussion of range, price, charging and knowledge. Energy Policy (2020). https://doi.org/10.1016/j.enpol.2020.111292

    Article  Google Scholar 

  20. Shalender, K., & Yadav, R. K. (2017). Promoting e-mobility in India: challenges, framework, and future roadmap. Springer Science+Business Media.

  21. C. Kongklaew, K. Phoungthong, M. Chanwit Prabpayak, S. Chowdhury, I. Khan, N. Yuangyai, C. Yuangyai, K. Techato, Barriers to electric vehicle adoption in Thailand. Sustainability 13(22), 12839 (2021). https://doi.org/10.3390/su132212839

    Article  Google Scholar 

  22. S. Asadi, M. Nilashi, M. Iranmanesh, M. Ghobakhloo, S. Samad, A. Alghamdi, A. Almulihi, S. Mohd, Drivers and barriers of electric vehicle usage in Malaysia: A DEMATEL approach. Resour. Conserv. Recycl. 177, 105965 (2022)

    Article  Google Scholar 

  23. K. Laurischkat, A. Viertelhausen, D. Jandt, Business models for electric mobility. Procedia CIRP 47, 483–488 (2016). https://doi.org/10.1016/j.procir.2016.03.042

    Article  Google Scholar 

  24. J. Axsen, C. Orlebar, S. Skippon, Social influence and consumer preference formation for pro-environmental technology: the case of a UK workplace electric-vehicle study. Ecol. Econ. 95, 96–107 (2013). https://doi.org/10.1016/j.ecolecon.2013.08.009

    Article  Google Scholar 

  25. T. Franke, J.F. Krems, What drives range preferences in electric vehicle users? Transp. Policy 30, 56–62 (2013). https://doi.org/10.1016/j.tranpol.2013.07.005

    Article  Google Scholar 

  26. P. Weldon, P. Morrissey, M.O. Mahony, Long-term cost of ownership comparative analysis between electric vehicles and internal combustion engine vehicles. Sustain. Cities Soc. 39, 578–591 (2018). https://doi.org/10.1016/j.scs.2018.02.024

    Article  Google Scholar 

  27. Field, K. (2016, January 1). EV Charging—The Time For A Single Fast-Charging Standard Is Now! Retrieved from CleanTechnica: https://cleantechnica.com/2016/01/01/ev-charging-time-single-fast-charging-standard-now/

  28. Roberts, G. (2019). Charge point anxiety is new barrier to EV take-up, say fleets. Retrieved from Fleet News: https://www.fleetnews.co.uk/news/fleet-industry-news/2019/12/03/charge-point-anxiety-is-new-barrier-to-ev-take-up

  29. D. Newbery, G. Strbac, What is needed for battery electric vehicles to become socially cost competitive? Econ. Transp. (2015). https://doi.org/10.1016/j.ecotra.2015.09.002i

    Article  Google Scholar 

  30. Y. He, Z. Song, Z. Liu, Fast-charging station deployment for battery electric bus systems considering electricity demand charges. Sustain. Cities Soc. (2019). https://doi.org/10.1016/j.scs.2019.101530

    Article  Google Scholar 

  31. G. Ghatikar, A. Ahuja, R.K. Pillai, Battery electric vehicle global adoption practices and distribution grid impacts: a preliminary case study for Delhi, India. Technol. Econ. Smart Grids Sustain Energy 2, 19–29 (2017). https://doi.org/10.1007/s40866-017-0034-5

    Article  Google Scholar 

  32. S. Steinhilber, P. Wells, S. Thankappan, Socio-technical inertia: Understanding the barriers to electric vehicles. Energy Policy 60, 531–539 (2013). https://doi.org/10.1016/j.enpol.2013.04.076

    Article  Google Scholar 

  33. B. Lane, S. Potter, The adoption of cleaner vehicles in the UK: exploring the consumer attitude-action gap. J. Clean. Prod. 15, 1085–1092 (2007). https://doi.org/10.1016/j.jclepro.2006.05.026

    Article  Google Scholar 

  34. H.R. Sayarshad, V. Mahmoodian, H.O. Gao, Non-myopic dynamic routing of electric taxis with battery swapping stations. Sustain. Cities Soc. (2020). https://doi.org/10.1016/j.scs.2020.102113

    Article  Google Scholar 

  35. I.-Y.L. Hsieh, A. Nunes, M.S. Pan, W.H. Green, Recharging systems and business operations to improve the economics of electrified Taxi Fleets. Sustain. Cities Soc. (2020). https://doi.org/10.1016/j.scs.2020.102119

    Article  Google Scholar 

  36. T.L. Saaty, What is the analytic hierarchy process?, in Mathematical models for decision support. ed. by G. Mitra, H.J. Greenberg, F.A. Lootsma, M.J. Rijkaert, H.J. Zimmermann (Springer, Berlin, 1988), pp.109–121. https://doi.org/10.1007/978-3-642-83555-1_5

    Chapter  Google Scholar 

  37. T.L. Saaty, A scaling method for priorities in hierarchical structures. J. Math. Psychol. 15(3), 234–281 (1977). https://doi.org/10.1016/0022-2496(77)90033-5

    Article  MathSciNet  MATH  Google Scholar 

  38. S.J. Patel, C.R. Patel, A stakeholders perspective on improving barriers in implementation of public bicycle sharing system (PBSS). Transp. Res. Part A Policy Pract. 138, 353–366 (2020). https://doi.org/10.1016/j.tra.2020.06.007

    Article  Google Scholar 

  39. R. Chhikara, R. Garg, S. Chhabra, U. Karnatak, G. Agrawal, Factors affecting adoption of electric vehicles in India: an exploratory study. Transp. Res. Part D Transp. Environ. 100, 103084 (2021). https://doi.org/10.1016/j.trd.2021.103084

    Article  Google Scholar 

  40. International Energy Agency, Electric Vehicles Initiative & Clean Energy Ministerial. (2019). Global EV Outlook 2019: Scaling-up the transition to electric mobility. France: IEA.

  41. B. Junquera, B. Moreno, R. Álvarez, Analyzing consumer attitudes towards electric vehicle purchasing intentions in Spain: technological limitations and vehicle confidence. Technol. Forecast. Soc. Change 109, 6–14 (2016). https://doi.org/10.1016/j.techfore.2016.05.006

    Article  Google Scholar 

  42. K. Lebeau, J. Van Mierlo, P. Lebeau, O. Mairesse, C. Macharis, Consumer attitudes towards battery electric vehicles: a large-scale survey. Int. J. Electric and Hybrid Veh. 5(1), 28 (2013). https://doi.org/10.1504/IJEHV.2013.053466

    Article  Google Scholar 

  43. V.S. Patyal, R. Kumar, S. Kushwah, Modeling barriers to the adoption of electric vehicles: an Indian perspective. Energy 237, 121554 (2021). https://doi.org/10.1016/j.energy.2021.121554

    Article  Google Scholar 

  44. A. Rosales-Tristancho, R. Brey, A.F. Carazo, J. Javier Brey, Analysis of the barriers to the adoption of zero-emission vehicles in spain. Transp. Res. Part A Policy Pract. 158, 19–43 (2022). https://doi.org/10.1016/j.tra.2022.01.016

    Article  Google Scholar 

  45. Y. Zheng, Z. Shao, Y. Zhang, L. Jian, A systematic methodology for mid-and-long term electric vehicle charging load forecasting: the case study of Shenzhen, China. Sustain. Cities Soc. 56, 102084 (2020). https://doi.org/10.1016/j.scs.2020.102084

    Article  Google Scholar 

  46. S. Shah, Y. Solangi, M. Ikram, Analysis of barriers to the adoption of cleaner energy technologies in Pakistan using modified delphi and fuzzy analytical hierarchy process. J. Clean. Prod. (2019). https://doi.org/10.1016/j.jclepro.2019.07.020

    Article  Google Scholar 

Download references

Funding

No funding.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Chetan R. Patel.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Singhal, R., Patel, C.R. Prioritisation of Barriers in the Implementation of Electric Mobility in Indian Context Using Fuzzy Analytical Hierarchical Process. J. Inst. Eng. India Ser. A 103, 1225–1236 (2022). https://doi.org/10.1007/s40030-022-00676-8

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s40030-022-00676-8

Keywords

Navigation