Evaluating AI-Based Energy Management Strategies for Electric Vehicles using SWARA - weighted Pythagorean Fuzzy MULTIMOORA

The growing adoption of electric vehicles (EVs) has formed a pressing need for intelligent energy management systems to extend battery life, improve efficiency and encourage the use of sustainable energy sources. As the complexity of energy optimization increases, the integration of artificial intelligence (AI) has become essential for enabling real-time decision-making and adaptive control. However, a significant gap remains in the literature regarding the comprehensive evaluation and prioritization of AI-based energy management strategies for EVs. This study addresses this gap by developing a multi-criteria decision-making (MCDM) framework that combines the Stepwise Weight Assessment Ratio Analysis (SWARA) method to determine the importance of evaluation criteria with the Pythagorean Fuzzy MULTIMOORA method to rank alternative strategies. The results show that Smart Battery Management Systems is the most critical strategy, followed by Predictive Energy Optimization and AI-Enabled Smart Charging and Grid Integration. A sensitivity analysis involving 21 weight variation scenarios confirms the robustness and stability of the suggested model. The findings offer practical insights for policymakers and professionals in engineering and present a flexible methodological framework that can be applied to other complex decision-making problems in sustainable energy and transportation systems.