A novel hybrid Fuzzy AHP-TOPSIS Approach towards Enhanced multi-criteria Feature-based EV Recommender System

Electric Vehicles (EVs) have gained immense attention in recent years due to their numerous advantages as a green alternative to their fuel-based counterparts. Four-wheeler EVs are often expensive and not affordable by many people, but a high demand for two-wheeler EVs is being witnessed in the Indian market segment. Due to the novelty of the technology, many buyers in emerging EV markets lack a clear understanding of EV selection compared to their fuel-based equivalents, which have been on the market for decades. Therefore, customers often face difficulties in selecting models for purchase. Moreover, multiple features in EV models further make it challenging to develop appropriate criteria for building a recommendation system. Thus, there is a present need for a robust recommendation system that can rank the best alternative EV. This paper presents a novel hybrid Fuzzy AHP-TOPSIS approach for the ideal selection of two-wheeler EVs, explicitly targeting the Indian Market Segment. In this study, six criteria are selected to judge among eight popular EV alternatives. The Analytical Hierarchy Process (AHP) is employed to find the Fuzzy relative weights of each criterion, while TOPSIS is used to select one of the best alternatives among various similar options. The study would also help to aid low-performing EVs in determining their benchmarks.