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Comparative performance of electric vehicles using evaluation of mixed data

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Abstract

The electric vehicle (EV) technology has been getting momentum due to rapid depletion of fossil fuels and also in taking care of environment. Many manufacturers are investing a lot in electric vehicles for a particular outcome coming from it which can show a sign for replacement of conventional I.C engines. They are taking interest about the customer findings in a car. There are various factors which affect the performance of an electric vehicle such as battery capacity, charging time, price, driving range etc. As we know there are many electric vehicle models that are present in market with different combinations and this study is based on the performance evaluation of electric vehicles using multiple criteria decision making tool from customer point of view. This study highlights the best electric vehicle model in Asian market so that findings of an EV buyer can be fulfilled. Fuzzy analytic hierarchy process has been used to determine criteria weight whereas evaluation of mixed data has been used for performance evaluation and ranking. According to the study BYD E6 becomes the best electric vehicle model in Asian market.

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References

  1. Barbosa, S.B., Ferreira, M.G.G., Nickel, E.M., Cruz, J.A., Forcellini, F.A., Garcia, J., Guerra, J.B.S.O.A.: Multi-criteria analysis model to evaluate transport systems: an application in Florianópolis, Brazil. Transp. Res. Part A Policy Pract. 96, 1–13 (2017)

    Article  Google Scholar 

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

    Article  Google Scholar 

  3. Celik, E., Bilisik, O.N., Erdogan, M., Gumus, A.T., Baracli, H.: An integrated novel interval type-2 fuzzy MCDM method to improve customer satisfaction in public transportation for Istanbul. Transp. Res. Part E Logist. Transp. Rev. 58, 28–51 (2013)

    Article  Google Scholar 

  4. Çolak, M., Kaya, İ.: Prioritization of renewable energy alternatives by using an integrated fuzzy MCDM model: a real case application for Turkey. Renew. Sustain. Energy Rev. 80, 840–853 (2017)

    Article  Google Scholar 

  5. Einhorn, M., Roessler, W., Fleig, J.: Improved performance of serially connected Li-ion batteries with active cell balancing in electric vehicles. In: IEEE Transactions on Vehicular Technology, vol. 60, no. 6, pp. 2448–2457 (2011)

  6. Gandoman, F.H., Ahmadi, A., Bossche, P.V., Mierlo, J.V., Omar, N., Nezhad, A.E., Mavalizadeh, H., Mayet, C.: Status and future perspectives of reliability assessment for electric vehicles. Reliab. Eng. Syst. Saf. 183, 1–16 (2019)

    Article  Google Scholar 

  7. Gu, X., Zhou, L., Ieromonachou, P.: ‘An imperfect information game in subsidising the electric vehicle supply chain. Int. J. Prod. Econ. (2019). https://doi.org/10.1016/j.ijpe.2019.01.021

    Google Scholar 

  8. Guo, S., Zhao, H.: Optimal site selection of electric vehicle charging station by using fuzzy TOPSIS based on sustainability perspective. Appl. Energy 158, 390–402 (2015)

    Article  Google Scholar 

  9. Huang, J., Leng, M., Liang, L., Liu, J.: Promoting electric automobiles: supply chain analysis under a governments subsidy incentive scheme. IIE Trans. 45, 826–844 (2013)

    Article  Google Scholar 

  10. Janiak, M.K., Żak, J.: Multiple criteria evaluation of different redesign variants of the public tram system. Transp. Res. Procedia 3, 690–699 (2014)

    Article  Google Scholar 

  11. Khan, M.I.: Evaluating the strategies of compressed natural gas industry using an integrated SWOT and MCDM approach. J. Clean. Prod. 172, 1035–1052 (2018)

    Article  Google Scholar 

  12. Kang, S.-C., Lee, H.: Economic appraisal of implementing electric vehicle taxis in Seoul. Res. Transp. Econ. (2019). https://doi.org/10.1016/j.retrec.2018.11.007

    Google Scholar 

  13. Luo, C., Leng, M., Huang, J., Liang, L.: ‘Supply chain analysis under a price-discount incentive scheme for electric vehicles. Eur. J. Oper. Res. 235, 329–333 (2014)

    Article  Google Scholar 

  14. Liao, F., Molin, E., Timmermans, H., Wee, B.: Consumer preferences for business models in electric vehicle adoption. Transp. Policy 23, 12–24 (2019)

    Article  Google Scholar 

  15. Liu, J., Wei, Q.: Risk evaluation of electric vehicle charging infrastructure public-private partnership projects in China using fuzzy TOPSIS. J. Clean. Prod. 189, 211–222 (2018)

    Article  Google Scholar 

  16. Liu, H.-C., You, X.-Y., Xue, Y.-X., Luan, X.: Exploring critical factors influencing the diffusion of electric vehicles in China: a multi-stakeholder perspective. Res. Transp. Econ. 66, 46–58 (2017)

    Article  Google Scholar 

  17. Martel, J.M., Matarazzo, B.: Other outranking Ap-proaches. In: Salvatore, F.J., Ehrgott, G.M. (eds.) Multiple Criteria Decision Analysis: State of the Art Surveys, pp. 197–262. Springer, New York (2005)

    Chapter  Google Scholar 

  18. Miller, P., Barros, A.G., Kattan, L., Wirasinghe, S.C.: Analyzing the sustainability performance of public transit. Transp. Res. Part D Transp. Environ. 44, 177–198 (2016)

    Article  Google Scholar 

  19. Nassereddine, M., Eskandari, H.: An integrated MCDM approach to evaluate public transportation systems in Tehran. Transp. Res. Part A Pol. Pract. 106, 427–439 (2017)

    Article  Google Scholar 

  20. Owczarzak, Ł., Żak, J.: Design of passenger public transportation solutions based on autonomous vehicles and their multiple criteria comparison with traditional forms of passenger transportation. Transp. Res. Procedia 10, 472–482 (2015)

    Article  Google Scholar 

  21. Onat, N.C., Gumus, S., Kucukvar, M., Tatari, O.: Application of the TOPSIS and intuitionistic fuzzy set approaches for ranking the life cycle sustainability performance of alternative vehicle technologies. Sustain. Prod. Consum. 6, 12–25 (2016)

    Article  Google Scholar 

  22. Opricovic, S., Tzeng, G.-H.: Compromise solution by MCDM methods: a comparative analysis of VIKOR and TOPSIS. Eur. J. Oper. Res. 156(2), 445–455 (2004)

    Article  Google Scholar 

  23. Oztaysi, B., Onar, S.C., Kahraman, C., Yavuz, M.: Multi-criteria alternative-fuel technology selection using interval-valued intuitionistic fuzzy sets. Transp. Res. Part D Transp. Environ. 53, 128–148 (2017)

    Article  Google Scholar 

  24. Sawik, B., Faulin, J., Pérez-Bernabeu, E.: Multi-criteria optimization for fleet size with environmental aspects. Transp. Res. Procedia 27, 61–68 (2017)

    Article  Google Scholar 

  25. Saaty, T.L.: The Analytic Hierarchy Process, 1st edn. McGraw-Hill, New York (1980)

    Google Scholar 

  26. Sivaraja, C.M., Sakthivel, G.: Compression ignition engine performance modelling using hybrid MCDM techniques for the selection of optimum fish oil biodiesel blend at different injection timings. Energy 139, 118–141 (2017)

    Article  Google Scholar 

  27. Sehatpour, M.-H., Kazemi, A.: Sustainable fuel portfolio optimization: integrated fuzzy multi-objective programming and multi-criteria decision making. J. Clean. Prod. 176, 304–319 (2018)

    Article  Google Scholar 

  28. Sovacool, B.K., Abrahamse, W., Zhang, L., Ren, J.: ‘Pleasure or profit? Surveying the purchasing intentions of potential electric vehicle adopters in China’. Transp. Res. Part A 124, 69–81 (2019)

    Google Scholar 

  29. Tranfield, D., Denyer, D., Smart, P.: Towards a methodology for developing evidence-informed management knowledge by means of systematic review. Br. J. Manag. 14, 207–222 (2003)

    Article  Google Scholar 

  30. Voogd, H.: Multicriteria Evaluation for Urban and Regional Planning. Pion, London (1983)

    Google Scholar 

  31. Wang, R., Chen, Y., Feng, D., Huang, X., Wang, J.: Development and performance characterization of an electric ground vehicle with independently actuated in-wheel motors. J. Power Sources 196, 3962–3971 (2011)

    Article  Google Scholar 

  32. Wee, S., Coffman, M., Croix, S.L.: Data on U.S. state-level electric vehicle policies 2010–2015. Data Brief (2019). https://doi.org/10.1016/j.dib.2019.01.006

    Google Scholar 

  33. Yadav, G., Desai, T.N.: Lean Six Sigma: a categorized review of the literature. Int. J. Lean Six Sigma 7(1), 2–24 (2016)

    Article  Google Scholar 

  34. Yadav, G., Seth, D., Desai, T.N.: Prioritising solutions for Lean Six Sigma adoption barriers through fuzzy AHP-modified TOPSIS framework. Int. J. Lean Six Sigma 9(3), 270–300 (2018)

    Article  Google Scholar 

  35. Żak, J.: Multiple-criteria and group-decision making in the fleet selection problem for a public transportation system. Transp. Res. Procedia 27, 43–52 (2017)

    Article  Google Scholar 

  36. Zarazua de Rubens, G.: Who will buy electric vehicles after early adopters? Using machine learning to identify the electric vehicle mainstream market. Energy 10, 20 (2019). https://doi.org/10.1016/j.energy.2019.01.114

    Google Scholar 

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Authors and Affiliations

  1. Automobile Engineering Department, MCKV Institute of Engineering, Liluah, Howrah, 711204, India

    Manik Chandra Das, Abanish Pandey, Arun Kumar Mahato & Rajnish Kumar Singh

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  1. Manik Chandra Das
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  2. Abanish Pandey
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  3. Arun Kumar Mahato
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  4. Rajnish Kumar Singh
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Correspondence to Manik Chandra Das.

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Das, M.C., Pandey, A., Mahato, A.K. et al. Comparative performance of electric vehicles using evaluation of mixed data. OPSEARCH 56, 1067–1090 (2019). https://doi.org/10.1007/s12597-019-00398-9

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