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Application of the EDAS Technique for Selecting the Electric Motor Vehicles

Application of the EDAS Technique for Selecting the Electric Motor Vehicles

Abstract

The popularity and development of electric motorcycles are exploding. This is merely the beginning of what many predict will be the motorbike industry's fastest-growing sector.The next development in motorcycle technology is the electric motorcycle. High-point electric bikes that are lighter, economical, and enjoyable to ride may be easily offered to clients and partners globally by combining the superlative capabilities of a traditional motorbike with the most cutting-edge technologies now accessible. A future era of electric engines and regulators with "ultra-high efficiency, high power-to-weight, and lightweight" has been specifically created to advance a wide range of models. Due to the abundance of possibilities offered on the global market, conflicting situations can develop while choosing a certain motorcycle. There may not necessarily be a certain number of options available or there may be multiple alternatives to the original pick. The possibility of not having an acceptable option for the criterion exists as well. “Multiple Criteria Decision Making” is a technique designed for the optimization of problems with an “infinite or finite number of choices” and the MCDM technique “EDAS method” is used to optimize the process in this paper. The rank of “Revolt RV400 is fifth, Joy e-Bike Monster is ninth, Tork Kratos R is fourth, Komaki Ranger is third, Cyborg Bob is sixth, Odysse Evoqis is seventh, Oben Rorr is first, PURE EV eTryst 350 is eight, Pure ecoDryft is second”. The ranking order is “Oben Rorr> Pure ecoDryft> Komaki Ranger> Tork Kratos R> Revolt RV400> Cyborg Bob> Odysse Evoqis> PURE EV eTryst 350”. Depending on EDAS research in this paper, it was discovered that among all sample electric motor vehicles, “Oben Rorr had the best overall performance while PURE EV eTryst 350 had the poorest”.

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
4
Top 10%
Average
Top 10%
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