A Fuzzy-Multi Attribute Decision Making Scheme for Efficient User-Centric EV Charging Station Selection

Abstract

With the increasing popularity of Electric Vehicles (EVs), the demand for electric vehicle charging stations (EVCS) has grown significantly. Selecting an appropriate charging station based on user preferences is essential for a satisfactory charging experience. Existing EVCS selection solutions often focus solely on distance or cost, neglecting other crucial factors such as charging time, waiting time, and available facilities. To address these challenges, we propose the Personalized Charging Station Selection Scheme (PC3S), which integrates Fuzzy Analytical Hierarchical Process (FAHP) and Multi-attribute Decision Making (MADM) theory. FAHP is used to handle imprecise and uncertain user preferences, interdependencies among criteria, and to determine the weights of each selection criterion. MADM, specifically TOPSIS, ranks alternative charging stations with respect to each criterion and calculates their weighted priorities. The charging stations are then ranked in ascending order by priority. We developed a mathematical model of our proposed scheme and implemented a numerical example based on a real dataset from the US Department of Energy. MATLAB was used to evaluate PC3S effectiveness using three different scenarios: travel distance-based, price-based, and a random scenario. The performance analysis of PC3S showed that it outperformed other approaches like ELECTRE, PROMETHEE, VIKOR, SAW, and BlockEv in terms of accuracy (over 97%, 95%, and 95.5%) and ranking abnormality (less than 5%, 8%, and 9%) across all three scenarios. By considering user preferences and properly ranking alternative charging stations, our scheme enhances user experience and facilitates a more customized and efficient charging station selection process, potentially leading to wider adoption of EVs.

Keywords

• Electric vehicles
• decision making approach
• MADM
• fuzzy concept