Objective Selection of Battery Electric Vehicles in Türkiye: An Entropy–Based TOPSIS Application
Abstract
Due to the growing popularity of battery-electric vehicles (BEVs) in Türkiye over the last five years, numerous alternatives have emerged for consumers. This makes selecting the most suitable alternative by considering criteria such as cost, range, and performance challenging. To overcome this challenge, in this study, we propose an objective multi-criteria decision-making (MCDM) method based on a data-driven approach, using Shannon entropy for criteria weighting and the TOPSIS method for alternative ranking. We evaluate five BEV models available on the Turkish market (Togg T10X, Tesla Model Y, Torres EVX, BMW iX1, and Mini Countryman Electric) based on eight quantitative criteria. Our evaluation criteria are purchase price, battery capacity, driving range, DC fast charging time, maximum speed, energy consumption (kWh/100 km), vehicle weight, and 0-100 km/h acceleration time. By deriving criteria weights solely from observed data, the entropy method eliminates the risk of subjective expert opinions used in the classical AHP approach. The TOPSIS results in our study indicate that the Torres EVX achieves the highest overall closeness coefficient, followed by the Tesla Model Y, Mini Countryman Electric, Togg T10X, and BMW iX1. Price and driving range appear to be the most important factors for BEV choice in Türkiye. Fast-charging time also strongly affects how attractive a BEV is. Our framework provides a clear and repeatable tool that can support consumer decisions and inform policy on BEV adoption and market growth.
Keywords
Electric Vehicles, Multi-criteria Decision Making, Entropy, TOPSIS, Vehicle Selection
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