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A consolidated MCDM framework for performance assessment of battery electric vehicles based on ranking strategies

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  • Ecer, Fatih

Abstract

Due to the ever-increasing harmful emissions affecting natural life and health seriously, it is inevitable the usage of renewable energy sources instead of fossil resources in the near future. Another drawback of fossil fuels is several threats like environmental pollution and global warming, which are potential risks for future generations. Given that the transportation sector makes a huge contribution to carbon emissions, the importance of battery electric vehicles (BEVs), which are an eco-friendly form of vehicles is obvious. Because the BEV market has been rapidly expanding recently, it has become a significant issue to assess BEV alternatives comprehensively from the customer's point of view. This assessment can be made by addressing the basic features of each BEV. Further, multiple criteria decision making (MCDM) techniques are efficient instruments for the right BEV purchase decision. In this work, therefore, ten BEVs are chosen as alternatives. These vehicles are then ranked using SECA, MARCOS, MAIRCA, COCOSO, ARAS, and COPRAS multi-criteria techniques on the basis of technical specifications, such as acceleration, price, battery, range, and so on. Afterward, results from various MCDM techniques are aggregated by applying the Borda count and Copeland ranking methodologies. “Price”, “permitted load,” and “energy consumption” are determined as the most three significant factors for BEV selection, respectively, whereas Tesla Model S is highlighted as the best choice. Further, the robustness and reliability of the results are performed by applying a sensitivity analysis. The proposed framework can be utilized as a basis for more detailed purchasing decisions.

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  • Ecer, Fatih, 2021. "A consolidated MCDM framework for performance assessment of battery electric vehicles based on ranking strategies," Renewable and Sustainable Energy Reviews, Elsevier, vol. 143(C).
    • Handle: RePEc:eee:rensus:v:143:y:2021:i:c:s1364032121002094
    DOI: 10.1016/j.rser.2021.110916
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