Assessment of Electric Two-Wheeler Ecosystem Using Novel Pareto Optimality and TOPSIS Methods for an Ideal Design Solution

Author:

Das Kaushik1ORCID,Kumar Roushan1

Affiliation:

1. Mechanical Cluster, School of Engineering, University of Petroleum and Energy Studies, Dehradun 248007, Uttarakhand, India

Abstract

The demand for electric two-wheelers as an efficient and environmentally friendly means of transportation has increased due to the rapid expansion in urbanization and growing environmental sustainability concerns. The electric two-wheeler ecosystem requires an ideal design solution that strikes a balance between numerous features, technologies, and specifications to meet these changing needs. In this study, we present an evaluation framework to find the best design for electric two-wheelers using novel Pareto optimality and TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution) approaches. These solutions are then thoroughly assessed against predetermined criteria, such as energy efficiency, manufacturing viability, and market viability. Additionally, we incorporate the TOPSIS approach to order the non-dominated options according to how closely they resemble the best design solution. The design solution that best meets the required objectives while minimizing departures from the ideal state is identified using this procedure. Combining these approaches, our framework provides a more dependable and rigorous tool for evaluating the electric two-wheeler ecosystem, empowering producers and policymakers to choose the best design options. The findings show that the Pareto optimality and TOPSIS approaches efficiently identify the non-dominated options and make it easier to choose an ideal design solution that is in line with customer preferences and environmental sustainability. The results of this study support the development of electric two-wheeler technology and promote the use of environmentally friendly transportation options, thereby promoting a more sustainable future.

Publisher

MDPI AG

Subject

Automotive Engineering

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