Estimating the driving range of electric vehicles in real-time using a fuzzy logic classifier

The study investigates estimating the real-time driving range of electric vehicles (EVs) by utilizing the Fuzzy Analytical Hierarchy Process (FAHP) and TOPSIS methodologies. It addresses the shortcomings of conventional range estimation models, which often oversimplify the diverse factors affecting EV range, such as battery health, driving habits, and environmental variables. By incorporating fuzzy logic, the approach better manages imprecise and uncertain data, leading to more accurate and dependable range predictions. The methodology involves using AHP, FAHP, and TOPSIS to assess and rank EV options based on criteria like range, cost, efficiency, top speed, and fast charging capabilities. Findings show that while traditional AHP places the highest importance on range, FAHP emphasizes price. The study highlights the potential of fuzzy logic to improve EV range estimations, helping to alleviate range anxiety and enhance energy efficiency, thereby supporting the wider adoption of electric vehicles.