The results reveal that a mono-crystalline bulk silicon module is the best alternative followed by multi-silicon modules.
Authors
Ashish Trivedi, Associate Professor, Jindal Global Business School, O.P. Jindal Global University, Sonipat, Haryana, India.
Vibha Trivedi, Assistant Professor, Jindal Global Business School, O.P. Jindal Global University, Sonipat, Haryana, India.
Moaz Gharib, College of Commerce and Business Administration, Dhofar University, Salalah, Oman.
Summary
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.
Published in: E3S Web of Conferences, 1st International Conference on Green Energy, Environmental Engineering and Sustainable Technologies 2023, ICGEST 2023, Belagavi, 5 October 2023 through 6 October 2023.
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