Belirsizlik Altında Araç Mimarisi Alternatiflerinin Değerlendirilmesi: Küresel Bulanık ÇKKV Çerçevesi

Öz

Araç mimarisi tasarımında; ağırlık, yakıt tüketimi, kontrol karmaşıklığı, maliyet, güç aktarma sistemi verimliliği, konfor ve emisyonlar gibi birbiriyle ilişkili çok sayıda parametrenin eş zamanlı olarak değerlendirilmesi gerekmektedir. Küresel ölçekte sera gazı emisyonlarının azaltılmasına yönelik çabaların artmasıyla birlikte, araç mimarilerinin dönüşümü hız kazanmıştır. Bu bağlamda, daha düşük emisyon değerleri ve daha yüksek enerji verimliliği sunmaları nedeniyle elektrikli araçlar (EA) uygulanabilir alternatifler olarak öne çıkmaktadır. Güç aktarma sistemleri, batarya teknolojileri ve şarj altyapısındaki son gelişmeler, elektrikli araçların geleneksel içten yanmalı motorlu (İYM) araçlara kıyasla yapısal üstünlüklerini daha da pekiştirmiştir. Bununla birlikte, mimari seçiminde etkili olan kriterler enerji kaynağına bağlı olarak önemli ölçüde farklılık göstermekte ve bu durum karar verme sürecine ciddi bir belirsizlik katmaktadır. Araç değerlendirmelerinde çeşitli bulanık çok kriterli karar verme (ÇKKV) yaklaşımları kullanılmış olsa da, belirsizlik ve muğlaklığı modelleme konusundaki üstün yeteneklerine rağmen küresel (spherical) bulanık kümelerin (SBK) uygulamaları sınırlı kalmıştır. Bu boşluğu gidermek amacıyla, bu çalışmada belirsizlik altında araç mimarisi alternatiflerinin değerlendirilmesine yönelik yeni bir Küresel Bulanık Çok Kriterli Karar Verme (KB-ÇKKV) çerçevesi önerilmektedir. Önerilen çerçeve; Geleneksel, Elektrikli, Seri Hibrit, Paralel Hibrit, Seri-Paralel Hibrit ve Karmaşık Hibrit mimarilerin teknik, ekonomik ve çevresel açılardan bütüncül ve gerçekçi bir şekilde karşılaştırılmasına olanak sağlamaktadır. Değerlendirme sonuçları, güncel koşullar altında Seri Hibrit, Elektrikli ve Geleneksel mimarilerin diğer konfigürasyonlara kıyasla daha optimize edilmiş bir performans sunduğunu göstermektedir. Bu çalışma, küresel bulanık kümeleri araç mimarisi değerlendirmesine dâhil ederek literatüre katkı sağlamakta ve daha sürdürülebilir, verimli ve yüksek performanslı araç sistemlerine yönelmek isteyen paydaşlar için sağlam ve güvenilir bir karar destek aracı sunmaktadır.

Anahtar Kelimeler

• Sera gazı emisyonları
• Araç mimarisi değerlendirmesi
• Küresel bulanık kümeler
• SF-AHP
• SF-TOPSIS
• Çok Kriterli Karar Verme

Evaluating vehicle architecture alternatives under uncertainty: a spherical fuzzy MCDM framework

Öz

Weight, fuel economy, control complexity, cost, powertrain efficiency, comfort, and emissions are some of the factors considered in the design of vehicle architecture. The global actions to reduce greenhouse gas emissions have accelerated the transformation of vehicle architecture. In this situation, electric vehicles (EVs) have emerged as alternatives because of their low emissions and increased energy efficiency. Also, recent advancements in powertrain systems, battery technologies, and charging infrastructure have reinforced the structural advantages of EVs compared to traditional internal combustion engine (ICE) vehicles. However, the factors influencing architectural design choices vary significantly depending on the energy source, creating considerable uncertainty in the decision-making process. Although many fuzzy multi-criteria decision-making (MCDM) methods have been used to evaluate vehicles, spherical fuzzy sets (SFS) which are even better at handling uncertainty and imprecision, haven't been widely adopted yet. This framework makes it easier to thoroughly and realistically compare different vehicle types, including Conventional, Electric, Series Hybrid, Parallel Hybrid, Series-Parallel Hybrid, and Complex Hybrid architectures by considering not only technical factors, but also economic and environmental aspects. The results reveal that Series Hybrid, Electric, and Conventional architectures rank as the top three alternatives, achieving overall scores of 3.418, 2.82, and 2.267, respectively. Series Hybrid architecture significantly outperforms lower-ranked configurations such as Parallel Hybrid (0.068) and Series-Parallel Hybrid (0.077), indicating a strong discriminative capability of the proposed SF framework. The integration of SFS provides a reliable decision support tool for sustainable and efficient vehicle design.

Anahtar Kelimeler

• Greenhouse gas emissions
• Vehicle architecture evaluation
• Spherical fuzzy sets
• SF-AHP
• SF-TOPSIS
• Multi-criteria decision-making

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