KRİTER AĞIRLIKLANDIRMA YÖNTEMLERİNİN İNOVATİF ARAÇ GÜÇ AKTARMA ORGAN MİMARİLERİ ÇKKV SIRALAMASI ÜZERİNDEKİ ETKİSİ

Abstract

Küresel otomotiv şirketleri, rekabet avantajı kazanmak için araç portföylerinin inovatif hibridizasyon ve elektrifikasyon projelerine büyük önem vermekte ve bu alandaki çalışmalarını hızla sürdürmektedirler. Sektör şirketlerinin geleceğini belirleyebilme potansiyeli taşıdığı için araç güç aktarma organı (GAO) mimarisinin başlangıçta doğru seçimi son derece önemlidir. Bu çalışmada, objektif verilerle; seri-paralel hibrit, paralel hibrit, plug-in hibrit, seri hibrit ve tam elektrikli araç gibi beş farklı araç GAO mimarisi; menzil, batarya ağırlığı, batarya enerji verimliliği, batarya enerji yoğunluğu, batarya maliyeti ve sürüş maliyeti kriterleri dikkate alınmak suretiyle değerlendirilerek SAW ÇKKV yöntemi ile en uygun mimariye ilişkin bir sıralama yapılmıştır. İlaveten kriter ağırlıklandırma yöntemlerinden CRITIC, Entropi, IDDWS, MEREC ve Standart Sapma yöntemleri kullanılarak, bu beş farklı kriter ağırlıklandırma tekniğinin SAW ÇKKV yöntemiyle elde edilen sıralama üzerindeki etkisi araştırılmıştır. Araştırma sonucunda, araçlara ait nesnel verilerle kullanılan beş farklı kriter ağırlıklandırma yöntemi ve SAW ÇKKV yöntemiyle elde edilen sonuçların birbirine yakın olduğu ve seçilen farklı kriter ağırlıklandırma yöntemlerinin alternatiflerin nihai sıralama sonuçları üzerinde sınırlı düzeyde etkisinin olduğu tespit edilmiştir. Aynı anda hem araç aktarma organı mimarilerinin objektif verilere göre ÇKKV yöntemi ile sıralanması hem de kullanılan farklı kriter ağırlıklandırma yöntemlerinin sonuca etkisinin araştırılması bilindiği kadarı ile bu alanda ilktir ve bu bağlamda çalışmanın literatüre katkı sunacağı düşünülmektedir.

Keywords

• Güç Aktarma Organı (GAO) Mimarileri
• ÇKKV
• Elektrikli ve Hibrit Araçlar
• Kriter Ağırlıklandırma
• SAW

Supporting Institution

Yok

EFFECT OF CRITERIA WEIGHTING METHODS ON VEHICLE INNOVATIVE POWERTRAIN ARCHITECTURES MCDM RANKING

Abstract

Global automotive companies place great importance on innovative hybridization and electrification projects for their vehicle portfolios to gain competitive advantage and are rapidly pursuing work in this area. Choosing the right vehicle powertrain architecture from the outset is crucial, as it has the potential to shape the future of companies in the sector. In this study, five different vehicle powertrain architectures -series-parallel hybrid, parallel hybrid, plug-in hybrid, series hybrid, and fully electric-were evaluated using objective data, taking into account range, battery weight, battery energy efficiency, battery energy density, battery cost, and driving cost. The SAW MCDM method was used to rank the most suitable architecture. Additionally, the impact of these five criteria weighting methods, including CRITIC, Entropy, IDDWS, MEREC, and Standard Deviation, on the ranking obtained by the SAW MCDM method was investigated. The study found that the results obtained using five different criterion weighting methods and the SAW MCDM method, using vehicle data, were similar, and that the different criterion weighting methods had limited impact on the final ranking results of the alternatives. To our knowledge, this is the first of its kind to simultaneously rank vehicle powertrain architectures using the MCDM method based on objective data and investigate the impact of the different criterion weighting methods on the results. In this context, the study is expected to contribute to the literature.

Keywords

• Powertrain Architectures (PTA)
• MCDM
• Electric and Hybrid Vehicles
• Criteria Weighting
• SAW

References

1. Adriyendi S. “Multi attribute decision making using simple additive weighting and weighted product in food choice”. Information Engineering and Electronic Business, 6, 8-14, 2015.
2. Akbulut OY, Şenol Z. “Bütünleşik SD ve PROMETHEE ÇKKV Yöntemleri ile Portföy Optimizasyonu: BİST Gıda, İçecek ve Tütün Sektöründe Ampirik Bir Uygulama”. Muhasebe ve Finansman Dergisi, (92), 161-182, 2021.
3. Alemi-Ardakani M, Milani AS, Yannacopoulos S, Shokouhi G. “On the effect of subjective, objective and combinative weighting in multiple criteria decision making: A case study on impact optimization of composites”. Expert Systems with Applications, 46, 426-438, 2016.
4. Anupama G, Kesava Rao VVS. “Some Objective Methods for Determining Relative Importance of Financial Ratios”. International Journal of Management, 10(4), 2020.
5. Büyükselçuk EÇ, Tozan H. “Elektrikli Araçların Performansının CRITIC-EATWIOS ile Değerlendirilmesi”. Düzce Üniversitesi Bilim ve Teknoloji Dergisi, 10(4), 1670-1688, 2022.
6. Cheng LH, Cao DQ, Guo HM. “Analysis of coal mine occupational disease hazard evaluation index based on AHP-DEMATEL”. Archives of Environmental & Occupational Health, 1-13, 2020.
7. Churchman CW, Ackoff RL. “An approximate measure of value”. Journal of Operations Research Society of America. 2(1), 172-187, 1954.
8. Du Y, Zheng Y, Wu G, Tang Y. “Decision-making method of heavy-duty machine tool remanufacturing based on AHP-entropy weight and extension theory”. Journal of Cleaner Production, 252, 119607, 2020.

9. Diakoulaki D, Mavrotas G, Papayannakis L. “Determining objective weights in multiple criteria problems: The critic method”. Computers & Operations Research, 22(7), 763-770, 1995.
10. Ehsani M, Singh KV, Bansal HO, Mehrjardi RT. “Statü of the art and trends in electric and hybrid electric vehicles”. Proceedings of the IEEE, 109(6), 967-984, 2021.
11. Ersoy N. “Kriter ağırlıklandırma yöntemlerinin ÇKKV sonuçları üzerindeki etkisine yönelik gerçek bir hayat uygulaması”. MANAS Sosyal Araştırmalar Dergisi, 11(4), 1449-1463, 2022.
12. Gavcar E, Kara N. “Elektrikli otomobil seçiminde ENTROPI ve TOPSIS yöntemlerinin uygulanması”. İş ve İnsan Dergisi, 7(2), 351-359, 2020.
13. Ho JC, Huang YHS. “Evaluation of electric vehicle power technologies: Integration of technological performance and market preference”. Cleaner and Responsible Consumption, 5, 100063, 2022.
14. Husain I. Electric and Hybrid Vehicles: Design Fundamentals. 3rd ed. Boca Raton, USA, CRC press, 2021.
15. Jahan A, Mustapha F, Sapuan SM, Ismail MY, Bahraminasab M. “A framework for weighting of criteria in ranking stage of material selection process”. The International Journal of Advanced Manufacturing Technology, 58, 411–420, 2012.
16. Jain V, Raj T. “Evaluation of flexibility in FMS using SAW and WPM”. Decision Science Letters, 2(4), 223-230, 2013.
17. Karamehmet B, Morgül E. “Tüketicilerin Elektrikli Araç Tercihleri: Literatür Taraması ve Türkiye'de Tanıtılmasına Yönelik Öneriler”. Karadeniz Uluslararası Bilim Dergisi, (40), 246-260, 2018.
18. Karki A, Phuyal S, Tuladhar D, Basnet S, Shrestha BP. “Status of Pure Electric Vehicle Power Train Technology and Future Prospects”. Applied System Innovation, 3(3), 35, 2020.
19. Keshavarz-Ghorabaee M, Amiri M, Zavadskas EK, Turskis Z, Antucheviciene J. “Determination of Objective Weights Using a New Method Based on the Removal Effects of Criteria (MEREC)”. Symmetry, 13(4), 525, 2021.
20. Li H, Wang W, Fan L, Li Q, Chen X. “A novel hybrid MCDM model for machine tool selection using fuzzy DEMATEL, entropy weighting and later defuzzification VIKOR”. Applied Soft Computing, 91, 106207, 2020.
21. Maheshwari N, Choudhar J, Rath A, Shinde D, Kalita K. “Finite Element Analysis and Multi-criteria Decision-Making (MCDM)-Based Optimal Design Parameter Selection of Solid Ventilated Brake Disc”. J. Inst. Eng. (India) Ser. C, 2021.
22. Memariani A, Amini B, Alinezhad A. “Sensitivity analysis of Simple Additive Weighting method (SAW): The results of change in the weight of one attribute on the 77 final ranking of alternatives”. Journal of Industrial Engineering, 2(4): 13-18, 2009.
23. Mukhametzyanov I. “Specific character of objective methods for determining weights of criteria in MCDM problems: Entropy, CRITIC and SD”. Decision Making: Applications in Management and Engineering, 4(2), 76-105, 2021.
24. Muratori M, Alexander M, Arent D, Bazilian M, Cazzola P, Dede EM, Ward J. “The rise of electric vehicles-2020 status and future expectations”. Progress in Energy, 3(2), 022002, 2021.
25. Pala O. “IDOCRIW ve MARCOS Temelli BIST Ulaştırma İşletmelerinin Finansal Performans Analizi”. Kafkas Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi, 12(23), 263-294, 2021.
26. Pala Ü. “Evaluating the competitive edge of electric and hybrid powertrain architectures through MCDM”. Journal of Energy Storage, 111, 115047, 2025.
27. Pradhan PS, Pradhan S. “Selection of electric vehicle using integrated Fuzzy-MCDM approach with analysis on challenges faced in hilly terrain”. Energy Sources, Part A: Recovery, Utilization, and Environmental Effects, 44(2), 2651-2673, 2022.
28. Paradowski B, Shekhovtsov A, Bączkiewicz A, Kizielewicz B, Sałabun W. “Similarity Analysis of Methods for Objective Determination of Weights in Multi-Criteria Decision Support Systems”. Symmetry, 13(10), 1874, 2021.
29. Peng X, Krishankumar R, Ravichandran KS. “A novel interval-valued fuzzy soft decision-making method based on CoCoSo and CRITIC for intelligent healthcare management evaluation”. Soft Computing, 25(6), 4213-4241, 2021.
30. Piasecki M, Kostyrko K. “Development of weighting scheme for indoor air quality model using a multi-attribute decision making method”. Energies, 13(12), 3120, 2020.
31. Recalde A, Cajo R, Velasquez W, Alvarez-Alvarado MS. “Machine Learning and Optimization in Energy Management Systems for Plug-In Hybrid Electric Vehicles: A Comprehensive Review”. Energies, 17(13), 3059, 2024.
32. Sahin M. “Hybrid Multi-attribute Decision Method for Material Selection”. Int. J. Pure Appl. Sci., 6, 107–117, 2020.
33. Salabun W, Atróbski J, Shekhovtsov A. “Are MCDA methods benchmarkable? A comparative study of TOPSIS, VIKOR, COPRAS, and PROMETHEE II Methods”. Symmetry, 12, 1549, 2020.
34. Singh KV, Bansal HO, Singh D. “A comprehensive review on hybrid electric vehicles: architectures and components”. J. Mod. Transport, 27, 77–107, 2019.
35. Singh A, Mishra S, Gautam A. “Architecture, Modeling and Simulation of Fuel Cell-Based Vehicles”. Arab J. Sci Eng, 2025.
36. Sisay G, Gebre SL, Getahun K. “GIS-based potential landfill site selection using MCDM-AHP modeling of Gondar Town, Ethiopia”. African Geographical Review, 40(2), 105-124, 2021.
37. Sivakumar VL, Krishnappa RR, Nallanathel M. “Drought vulnerability assessment and mapping using Multi-Criteria decision making (MCDM) and application of Analytic Hierarchy process (AHP) for Namakkal District, Tamilnadu, India”. Materials Today: Proceedings, 43, 1592-1599, 2021.
38. Sotoudeh-Anvari A, Sadjadi S, Molana S, Sadi-Nezhad S. “A new MCDM-based approach using BWM and SAW for optimal search model”. Decision Science Letters, 7(4), 395-404, 2018.
39. Souissi D, Zouhri L, Hammami S, Msaddek MH, Zghibi A, Dlala M. “GIS-based MCDM–AHP modeling for flood susceptibility mapping of arid areas, southeastern Tunisia”. Geocarto International, 35(9), 991-1017, 2020.
40. Torkayesh AE, Ecer F, Pamucar D, Karamaşa Ç. “Comparative assessment of social sustainability performance: Integrated data-driven weighting system and CoCoSo model”. Sustainable Cities and Society, 71, 102975, 2021.
41. Tran MK, Akinsanya M, Panchal S, Fraser R, Fowler M. “Design of a hybrid electric vehicle powertrain for performance optimization considering various powertrain components and configurations”. Vehicles, 3(1), 20-32, 2020.
42. Vavrek R, Becica J. “Efficiency evaluation of cultural services in the Czech Republic via multi-criteria decision analysis”. Sustainability, 12, 3409, 2020.
43. Wang TC, Lee HD. “Developing a Fuzzy TOPSIS Approach Based on Subjective Weights and Objective Weights”. Expert systems with applications, 36(5), 8980-8985, 2009.
44. Wang YM, Luo Y. “Integration of correlations with standard deviations for determining attribute weights in multiple attribute decision making”. Mathematical and Computer Modelling, 51(1-2), 1-12, 2010.
45. Yang C, Lu Z, Wang W, Li Y, Chen Y, Xu B. “Energy management of hybrid electric propulsion system: Recent progress and a flying car perspective under three-dimensional transportation networks”. Green Energy and Intelligent Transportation, 2(1), 100061, 2023.
46. Yeh CH, Willis RJ. “A validation procedure for multicriteria analysis: application to the selection of scholarship students”. Asia Pacific Management Review, 6(1), 39-52, 2001.
47. Zavadskas EK, Podvezko V. “Integrated determination of objective criteria weights in MCDM”. International Journal of Information Technology Decision Making, 15(02), 267-283, 2016.
48. Zhang H, Gu C, Gu L, Zhang Y. “The Evaluation of Tourism Destination Competitiveness by TOPSIS & Information Entropy - A Case in the Yangtze River Delta of China”. Tourism Management, 32(2), 443-451, 2011.