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

Yıl 2025, Cilt: 13 Sayı: 3

Gözde Bakioğlu

https://doi.org/10.29109/gujsc.1566197

Öz

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.

Anahtar Kelimeler

Energy Management, Electric Vehicles, Artificial Intelligence, Multi-Criteria Decision-Making, Pythagorean Fuzzy Sets

Elektrikli Araçlar için Yapay Zekâ Tabanlı Enerji Yönetim Stratejilerinin SWARA Ağırlıklı Pisagor Bulanık MULTIMOORA Yöntemi ile Değerlendirilmesi

Öz

Elektrikli araçların yaygınlaşması, verimliliği artırmak, batarya ömrünü uzatmak ve yenilenebilir enerji kaynaklarını entegre etmek amacıyla akıllı enerji yönetim sistemlerine olan ihtiyacı artırmıştır. Artan karmaşıklık karşısında, yapay zekâ entegrasyonu gerçek zamanlı karar verme ve uyarlanabilir kontrol açısından büyük önem taşımaktadır. Ancak literatürde, elektrikli araçlar için yapay zekâ tabanlı enerji yönetim stratejilerinin kapsamlı şekilde değerlendirilmesine yönelik sınırlı çalışma bulunmaktadır. Bu çalışmada, değerlendirme kriterlerinin önemini belirlemek için SWARA, stratejileri önceliklendirmek için Pisagor Bulanık MULTIMOORA yöntemlerinin entegre edildiği çok kriterli karar verme tabanlı bir model geliştirilmiştir. Bulgulara göre, “Akıllı Batarya Yönetim Sistemleri” en öncelikli strateji olarak belirlenmiş, ardından “Tahmine Dayalı Enerji Optimizasyonu” ve “Yapay Zekâ Tabanlı Akıllı Şarj ve Şebeke Entegrasyonu” gelmiştir. Yirmi bir senaryoda yapılan duyarlılık analizi, modelin sağlamlığını ortaya koymuştur. Elde edilen sonuçlar, politika yapıcılar ve mühendislik uzmanları için stratejik karar alma süreçlerinde yol gösterici niteliktedir.

Anahtar Kelimeler

Enerji Yönetimi, Elektrikli Araçlar, Yapay Zekâ, Çok Kriterli Karar Verme, Pisagor Bulanık Kümeler

Kaynakça

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