Volume 3 , Issue 2 , PP: 72-90, 2021 | Cite this article as | XML | Html | PDF | Full Length Article
Lobna Osman 1 *
Doi: https://doi.org/10.54216/IJWAC.030203
The use of alternative energy sources rather than fossil fuels will be unavoidable in the nearish term due to rising levels of toxic residues that threaten natural life and human health. Furthermore, the use of fossil fuels puts subsequent generations in danger from environmental damage and climate change. Battery electric vehicles (BEVs), an environmentally friendly kind of vehicle, are important in light of transportation's significant contribution to the carbon footprint. In light of the recent fast growth of the BEV industry, it has become more important to consider all available BEV options from the perspective of the end-user. Each BEV's fundamental characteristics may be examined in order to make this evaluation. For the correct BEV buying choice, MCDM strategies are useful. As a result, eleven battery-electric vehicles (BEVs) are considered in this study. A variety of multi-criteria methodologies are used to rate these cars on the basis of their technical specifications, such as acceleration, pricing, battery life, and range. It is then used entropy weight and TOPSIS approaches to gather findings from different MCDM strategies. The entropy method is used to compute the weights of the criteria. Then the TOPSIS is used to rank the options. The 3 key considerations for BEV choosing are "price," "permitted load," and "energy usage," with Tesla Model S emphasized as the preferred route.
Battery electric vehicles , MCDM , TOPSIS , Entropy , decision support
[1] M. Knez, G. K. Zevnik, and M. Obrecht, “A review of available chargers for electric vehicles: United States of America, European Union, and Asia,” Renewable and Sustainable Energy Reviews, vol. 109, pp. 284–293, 2019.
[2] F. Khan, Y. Ali, and A. U. Khan, “Sustainable hybrid electric vehicle selection in the context of a developing country,” Air Quality, Atmosphere & Health, vol. 13, no. 4, pp. 489–499, 2020.
[3] R. R. Kumar and K. Alok, “Adoption of electric vehicle: A literature review and prospects for sustainability,” Journal of Cleaner Production, vol. 253, p. 119911, 2020.
[4] F. Ecer, “Sustainability assessment of existing onshore wind plants in the context of triple bottom line: a best-worst method (BWM) based MCDM framework,” Environmental Science and Pollution Research, vol. 28, no. 16, pp. 19677–19693, 2021.
[5] W. Wei, M. Cao, Q. Jiang, S.-J. Ou, and H. Zou, “What influences Chinese consumers’ adoption of battery electric vehicles? A preliminary study based on factor analysis,” Energies, vol. 13, no. 5, p. 1057, 2020.
[6] F. Samaie, H. Meyar-Naimi, S. Javadi, and H. Feshki-Farahani, “Comparison of sustainability models in development of electric vehicles in Tehran using fuzzy TOPSIS method,” Sustainable Cities and Society, vol. 53, p. 101912, 2020.
[7] A. H. K. Babar, Y. Ali, and A. U. Khan, “Moving toward green mobility: overview and analysis of electric vehicle selection, Pakistan a case in point,” Environment, Development and Sustainability, vol. 23, no. 7, pp. 10994–11011, 2021.
[8] D.-D. Tran, M. Vafaeipour, M. El Baghdadi, R. Barrero, J. Van Mierlo, and O. Hegazy, “Thorough state-of-the-art analysis of electric and hybrid vehicle powertrains: Topologies and integrated energy management strategies,” Renewable and Sustainable Energy Reviews, vol. 119, p. 109596, 2020.
[9] M. M. Hoque, M. A. Hannan, A. Mohamed, and A. Ayob, “Battery charge equalization controller in electric vehicle applications: A review,” Renewable and Sustainable Energy Reviews, vol. 75, pp. 1363–1385, 2017.
[10] S. A. Neves, A. C. Marques, and J. A. Fuinhas, “Technological progress and other factors behind the adoption of electric vehicles: Empirical evidence for EU countries,” Research in Transportation Economics, vol. 74, pp. 28–39, 2019.
[11] I. E. A. G. E. V Outlook, “Scaling-up the transition to electric mobility,” International Energy Agency: Paris, France, 2019.
[12] F. Egnér and L. Trosvik, “Electric vehicle adoption in Sweden and the impact of local policy instruments,” Energy policy, vol. 121, pp. 584–596, 2018.
[13] W. Wang, Q. Zhang, Z. Peng, Z. Shao, and X. Li, “An empirical evaluation of different usage pattern between car-sharing battery electric vehicles and private ones,” Transportation Research Part A: Policy and Practice, vol. 135, pp. 115–129, 2020.
[14] N. Adnan, S. M. Nordin, M. H. Amini, and N. Langove, “What make consumer sign up to PHEVs? Predicting Malaysian consumer behavior in adoption of PHEVs,” Transportation Research Part A: Policy and Practice, vol. 113, pp. 259–278, 2018.
[15] C. Chen, G. Z. de Rubens, L. Noel, J. Kester, and B. K. Sovacool, “Assessing the socio-demographic, technical, economic and behavioral factors of Nordic electric vehicle adoption and the influence of vehicle-to-grid preferences,” Renewable and Sustainable Energy Reviews, vol. 121, p. 109692, 2020.
[16] R. Jena, “An empirical case study on Indian consumers’ sentiment towards electric vehicles: A big data analytics approach,” Industrial Marketing Management, vol. 90, pp. 605–616, 2020.
[17] A. Emadi, K. Rajashekara, S. S. Williamson, and S. M. Lukic, “Topological overview of hybrid electric and fuel cell vehicular power system architectures and configurations,” IEEE Transactions on vehicular technology, vol. 54, no. 3, pp. 763–770, 2005.
[18] E. K. Zavadskas and V. Podvezko, “Integrated determination of objective criteria weights in MCDM,” International Journal of Information Technology & Decision Making, vol. 15, no. 02, pp. 267–283, 2016.
[19] W.-W. Wu, “Beyond Travel & Tourism competitiveness ranking using DEA, GST, ANN and Borda count,” Expert Systems with Applications, vol. 38, no. 10, pp. 12974–12982, 2011.
[20] M. Yavuz, B. Oztaysi, S. C. Onar, and C. Kahraman, “Multi-criteria evaluation of alternative-fuel vehicles via a hierarchical hesitant fuzzy linguistic model,” Expert Systems with Applications, vol. 42, no. 5, pp. 2835–2848, 2015.
[21] W. Sierzchula, S. Bakker, K. Maat, and B. Van Wee, “The influence of financial incentives and other socio-economic factors on electric vehicle adoption,” Energy policy, vol. 68, pp. 183–194, 2014.
[22] N. Permpool and S. H. Gheewala, “Environmental and energy assessment of alternative fuels for diesel in Thailand,” Journal of Cleaner Production, vol. 142, pp. 1176–1182, 2017.
[23] S. Pfoser, O. Schauer, and Y. Costa, “Acceptance of LNG as an alternative fuel: Determinants and policy implications,” Energy Policy, vol. 120, pp. 259–267, 2018.
[24] M.-H. Sehatpour, A. Kazemi, and H. Sehatpour, “Evaluation of alternative fuels for light-duty vehicles in Iran using a multi-criteria approach,” Renewable and Sustainable Energy Reviews, vol. 72, pp. 295–310, 2017.
[25] G.-H. Tzeng, C.-W. Lin, and S. Opricovic, “Multi-criteria analysis of alternative-fuel buses for public transportation,” Energy policy, vol. 33, no. 11, pp. 1373–1383, 2005.
[26] J. J. Brey, I. Contreras, A. F. Carazo, R. Brey, A. G. Hernández-Díaz, and A. Castro, “Evaluation of automobiles with alternative fuels utilizing multicriteria techniques,” Journal of Power Sources, vol. 169, no. 1, pp. 213–219, 2007.
[27] H. S. Mohamadabadi, G. Tichkowsky, and A. Kumar, “Development of a multi-criteria assessment model for ranking of renewable and non-renewable transportation fuel vehicles,” Energy, vol. 34, no. 1, pp. 112–125, 2009.
[28] B. Vahdani, M. Zandieh, and R. Tavakkoli-Moghaddam, “Two novel FMCDM methods for alternative-fuel buses selection,” Applied Mathematical Modelling, vol. 35, no. 3, pp. 1396–1412, 2011.
[29] K. G. Tsita and P. A. Pilavachi, “Evaluation of alternative fuels for the Greek road transport sector using the analytic hierarchy process,” Energy policy, vol. 48, pp. 677–686, 2012.
[30] P. B. Lanjewar, R. V. Rao, and A. V Kale, “Assessment of alternative fuels for transportation using a hybrid graph theory and analytic hierarchy process method,” Fuel, vol. 154, pp. 9–16, 2015.
[31] M. Maimoun, K. Madani, and D. Reinhart, “Multi-level multi-criteria analysis of alternative fuels for waste collection vehicles in the United States,” Science of the Total Environment, vol. 550, pp. 349–361, 2016.
[32] N. C. Onat, S. Gumus, M. Kucukvar, and O. Tatari, “Application of the TOPSIS and intuitionistic fuzzy set approaches for ranking the life cycle sustainability performance of alternative vehicle technologies,” Sustainable Production and Consumption, vol. 6, pp. 12–25, 2016.
[33] K. Ullah, S. Hamid, F. M. Mirza, and U. Shakoor, “Prioritizing the gaseous alternatives for the road transport sector of Pakistan: A multi criteria decision making analysis,” Energy, vol. 165, pp. 1072–1084, 2018.
[34] H. Liang, J. Ren, R. Lin, and Y. Liu, “Alternative-fuel based vehicles for sustainable transportation: A fuzzy group decision supporting framework for sustainability prioritization,” Technological Forecasting and Social Change, vol. 140, pp. 33–43, 2019.
[35] T. K. Biswas and M. C. Das, “Selection of commercially available electric vehicle using fuzzy AHP-MABAC,” Journal of The Institution of Engineers (India): Series C, vol. 100, no. 3, pp. 531–537, 2019.
