Issue |
E3S Web Conf.
Volume 455, 2023
First International Conference on Green Energy, Environmental Engineering and Sustainable Technologies 2023 (ICGEST 2023)
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Article Number | 02016 | |
Number of page(s) | 10 | |
Section | Renewable & Sustainable Energy Technology | |
DOI | https://doi.org/10.1051/e3sconf/202345502016 | |
Published online | 05 December 2023 |
Choosing The Right Photovoltaic Panel for Electric Vehicles: An Integrated Decision Support Model
1 O. P. Jindal Global University, Sonipat, India
2 College of Commerce and Business Administration, Dhofar University, Salalah, Oman
In the current era, global carbon emissions are on the rise and to achieve environmental sustainability, greenhouse gas emissions must be reduced to net zero levels with greater reliance on renewable energy sources. Due to the increasing demand for sustainable transportation options, the integration of photovoltaic (PV) panels in electric vehicles (EVs) is considered a promising solution to boost energy efficiency and reduce greenhouse gas emissions. However, selecting the most suitable photovoltaic panel for EVs is a complex process that involves multiple criteria and considerations. This research article presents an integrated decision support model using the Best-Worst Method (BWM) and the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) to assist in selecting the optimal module. The BWM is employed to compute the weights of eight identified criteria, reflecting the preferences and priorities of decision experts. Subsequently, the TOPSIS method is utilized to evaluate and rank a set of PV panel options based on their performance against the identified criteria. The results reveal that a mono-crystalline bulk silicon module is the best alternative followed by multi-silicon modules. This study proposes a structured decision approach for EV manufacturers to select the right PV panel, promoting energy-efficient transportation solutions.
© The Authors, published by EDP Sciences, 2023
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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