Troleybüs Enerji Hatları İçin Matlab Modeli

Öz

Bu çalışmada, Türkiye’nin Malatya kentinde hizmet veren troleybüs sisteminin elektriksel ve dinamik davranışları MATLAB/Simulink ortamında modellenmiş ve analiz edilmiştir. Modelde çekiş motorları, eviriciler, troley hattı, doğrultucu merkezleri ve gerilim düşümlerini temsil eden bileşenler ayrıntılı biçimde ele alınmıştır. Gerçek 36 km’lik hat verileri kullanılarak yapılan simülasyonlarda, hat boyunca oluşan gerilim ve akım değişimleri gözlemlenmiş, sistemin 750 V DC besleme altında nominal koşullarda çalıştığı doğrulanmıştır. Elde edilen sonuçlar, modelin gerçeğe yakın davranış sergilediğini ve troleybüs sistemlerinin enerji verimliliği analizlerinde güvenle kullanılabileceğini göstermektedir. Bu kapsamda çalışma, kentsel elektrikli ulaşım sistemlerinin tasarım, planlama ve optimizasyon süreçlerinde referans oluşturabilecek niteliktedir.

Anahtar Kelimeler

• Trolleybus
• MATLAB Simulink
• DC Çekiş Sistemi
• Elektrikli Ulaşım
• Güç Sistemleri
• Enerji Hatları

Matlab Model for Trolleybus Power Lines

Öz

This study presents a MATLAB-based simulation model for analyzing the electrical and operational characteristics of trolleybus systems, with a particular focus on the Malatya Trambus line in Turkey. The model was developed entirely in the MATLAB/Simulink environment using a detailed representation of traction motors, inverters, traction rectifier modules (TRMs), and the overhead catenary system. The real 36 km Malatya trolleybus line, supplied by nine transformer groups, was modeled as a segmented DC network to investigate the voltage and current dynamics during operation. A variable-step ODE45 Dormand–Prince solver and discrete Tustin/Backward Euler methods were employed for high-fidelity simulation. The results showed that simulated current and voltage waveforms closely matched real operational data, confirming the model’s accuracy. The trolleybus reached its nominal speed of 65 km/h, with maximum current limited to 600 A and minimum operating voltage never dropping below 220 V. The proposed model provides a reliable platform for analyzing electrical load distribution, optimizing power flow, and improving future energy-efficient public transportation systems in urban environments.

Anahtar Kelimeler

• Trolleybus
• MATLAB Simulink
• DC Traction System
• Electric Transportation
• Power Syestems
• Power Lines

Kaynakça

1. Labrujulaverde, “Electromote - the story of the first trolleybus, invented by the founder of Siemens,” 2023.
2. Wikipedia, “Electromote,” 2024. Online. Available: https://en.wikipedia.org/wiki/Electromote
3. Siemens AG, “Road transportation – Siemens History,” 2024.
4. Ekici YE., Karadağ T., Akdağ O., “Redefining urban mobility: Real-world regenerative braking optimization via bio-inspired AI for electric buses energy efficiency,” Energy, 338, 138854, 2025. https://doi.org/10.1016/j.energy.2025.138854
5. Ekici YE., Karadağ T., Akdağ O., Aydın AA., Tekin HO., “Tailoring energy efficiency for urban electric buses: The GTECM model for enhanced range and sustainable operation using real-time big data,” IEEE Trans. Intell. Transp. Syst., 26(8), 12600–12614, 2025. https://doi.org/10.1109/TITS.2025.3558147
6. Ekici YE., Dikmen İC., Nurmuhammed M., Karadağ T., “A review on electric vehicle charging systems and current status in Turkey,” Int. J. Automot. Sci. Technol., 5(4), 316–330, 2021. https://doi.org/10.30939/ijastech..958368
7. Ekici YE., Aydın AA., Karadağ T., Akdağ O., Ateş A., “Energy consumption model with real-time data for driving range extension of electric buses,” Sustain. Futur., 9, 100603, 2025. https://doi.org/10.1016/j.sftr.2025.100603
8. Ekici YE., Tan N., “Investigation of charging and discharging characteristics of different type batteries using trambus accelerator pedal data on hybrid electric vehicle model,” Int. J. Energy Eng. Sci., 3(3), 55–67, 2018.

9. Ekici YE., Karadağ T., Akdağ O., “A novel model to predict the energy consumption of electric buses,” SSRN Electron. J., 2023. https://doi.org/10.2139/ssrn.4427022
10. Ekici YE., Karadağ T., Akdağ O., Aydın AA., Tekin H. O., “Enhancing electric vehicle range through real-time failure prediction and optimization: Introduction to DHBA-FPM model with an artificial intelligence approach,” ICT Express, 2025. https://doi.org/10.1016/j.icte.2025.03.009
11. Ekici YE., Karadağ T., Aydın AA., Akdağ O., “Impact of outside temperature on driving range and energy consumption using real-time big data for electric buses,” in 8th Int. Artificial Intelligence and Data Processing Symp. (IDAP), 2024, 1–9. https://doi.org/10.1109/IDAP64064.2024.10710801
12. Ekici YE., Akdağ O., Aydın AA., Karadağ T., “A novel energy consumption prediction model of electric buses using real-time big data from route, environment, and vehicle parameters,” IEEE Access, 11, 104305–104322, 2023. https://doi.org/10.1109/ACCESS.2023.3316362
13. Ekici YE., Dikmen İC., Nurmuhammed M., Karadağ T., “Efficiency analysis of various batteries with real-time data on a hybrid electric vehicle,” Int. J. Automot. Sci. Technol., 5(3), 214–223, 2021. https://doi.org/10.30939/ijastech..946047
14. Ekici YE., Akdağ O., Aydın AA., Karadağ T., “Review and analysis of real-time big data of electric bus consumption data and optimum integration into the urban transportation routes,” Ankara Int. Congr. Sci. Res. VII, 2022.
15. Ekici YE., Ka T., Aydın AA., Akdağ O., “Driving range problem in electric vehicles and methods to increase,” in Article and Reviews in Engineering, 2024, 253–286. https://doi.org/10.5281/zenodo.13950544
16. Dikmen İC., Yıldıran N., Karadağ T., “Multi-chemistry battery management system for electric vehicles,” Eur. J. Res. Dev., 2022.
17. Dikmen İC., Karadağ T., “Elektrikli araç batarya yönetim sistemi ve yeni trendler,” in Mühendislik Alanında Gelişmeler, 2023, ch. 10, 152–169. https://doi.org/10.5281/zenodo.10021990
18. Karadağ T., Dikmen İC., “Yeni nesil, modüler ve akıllı batarya yönetim sistemi,” Eur. J. Sci. Technol., 2022.
19. Yıldıran N., Dikmen İC., Karadağ T., “State of health estimation of lithium titanate oxide batteries through data-driven techniques and machine learning,” in 8th Int. Artificial Intelligence and Data Processing Symp. (IDAP), 2024. https://doi.org/10.1109/IDAP64064.2024.10711165
20. Dikmen İC., Yıldıran N., Karadağ T., “Machine learning approaches for enhancing the SoH estimation of LTO batteries,” Int. J. Automot. Sci. Technol., 9(1), 48–59, 2024. https://doi.org/10.30939/IJASTECH..1522403
21. Dikmen İC., Yıldıran N., Karadağ T., “Efficient state of health estimation for LTO batteries using liquid time-constant neural networks,” in 3rd International Congress of Electrical and Computer Engineering, 2024.
22. Dikmen İC., Karadağ T., “Pil kimyasını otomatik belirleyebilen adaptif, modüler ve akıllı batarya yönetim sistemi,” Patent 2021/018973, 2021.
23. Dikmen İC., Karadağ T., “Onboard battery type determination,” in 2021 5th Int. Symp. Multidisciplinary Studies and Innovative Technologies (ISMSIT), 2021, 360–365. https://doi.org/10.1109/ISMSIT52890.2021.9604658
24. Dikmen İC, Seven S., Karadağ T., “A numerical study for automatic battery identification in hybrid battery packs for electric vehicles,” 2018.
25. Arı A., Ayaz F., Hanbay D., “EMG sinyallerinin kısa zamanlı Fourier dönüşüm özellikleri kullanılarak yapay sinir ağları ile sınıflandırılması,” Fırat Univ. Müh. Bil. Derg., 31(2), 443–451, 2019. https://doi.org/10.35234/FUMBD.545161
26. Arı B., “Alkolik ve normal EEG sinyallerinin zaman-alan tanımlayıcı analizi tabanlı otomatik sınıflandırılması,” Fırat Univ. Müh. Bil. Derg., 35(1), 291–300, 2023. https://doi.org/10.35234/FUMBD.1222526
27. Arı B., Ucuz İ., Ozdemir F., Sengur A., “Grafik tablet kullanılarak makine öğrenmesi yardımı ile el yazısından cinsiyet tespiti,” Fırat Univ. Müh. Bil. Derg., 32(1), 243–252, 2020. https://doi.org/10.35234/FUMBD.659610
28. Arı B. G., “Efficient ECG beat classification using SMOTE-enhanced SimCLR representations and a lightweight MLP,” Symmetry, 17(10), 1677, 2025. https://doi.org/10.3390/sym17101677
29. Nurmuhammed M., Akdağ O., Karadağ T., Kafafy M., “Multi-factor optimization for electric bus charging stations: Integrating electrical, social, and environmental perspectives,” Energy Nexus, 19, 100500, 2025. https://doi.org/10.1016/j.nexus.2025.100500
30. Kuzu B., Nurmuhammed M., Akdağ O., Karadağ T.,“V2G uygulamalarında elektrikli araçların dağıtım şebekesine optimum entegrasyonunun analizi için Newton-Raphson temelli yeni bir model,” Dicle Univ. Müh. Fak. Müh. Derg., 15(2), 375–385, 2024. https://doi.org/10.24012/DUMF.1441551
31. Nurmuhammed M., Karadağ T., “Cost/performance analysis of battery pack placed in spare tire area for extending the range of hybrid and electric vehicles,” Int. J. Automot. Sci. Technol., 8(2), 252–259, 2024. https://doi.org/10.30939/IJASTECH..1337867
32. Nurmuhammed M., Akdağ O., Karadağ T., “A novel modified Archimedes optimization algorithm for optimal placement of electric vehicle charging stations in distribution networks,” Alexandria Eng. J., 84, 81–92, 2023. https://doi.org/10.1016/j.aej.2023.10.055
33. Nurmuhammed M., Akdağ O., Karadağ T., “A novel Newton-Raphson-based method for integrating electric vehicle charging stations to distribution network,” ELECTRICA, 23(2), 310–317, 2023. https://doi.org/10.5152/ELECTRICA.2022.22112
34. UITP and Partners, “Take-up guide for the replacement of urban Diesel buses by trolleybuses,” 2011.
35. Wikipedia contributors, “Trolleybuses in Shanghai,” 2024.
36. Rodrigues, G. S., Reis, J. G. M. d., Orynycz, O., Tucki, K., Matijošius, J., & Machado, S. T., “Environmental impact reduction of a trolleybus system in the city of São Paulo, Brazil,” Energies, 17(6), 1377, 2024. https://doi.org/10.3390/en17061377
37. Co-Curate Newcastle University Project, “Trolleybus (preservation figures),” 2024.
38. Kültür İstanbul, “Tosun,” 2022.
39. T. C. İstanbul Valiliği, “İstanbul’da nostalji: Troleybüs Tosun,” 2019.
40. İETT Genel Müdürlüğü, “Troleybüs,” 2019.
41. Aycıl S., “İstanbul’un nostaljik toplu taşıma aracı 101 kapı numaralı Tosun üzerine betimsel bir inceleme,” İstanbul Kent Üniversitesi Siyasal, Sos. ve Strat. Araştırmalar Derg., 1(2), 143–165, 2025.
42. Nurmuhammed M., Akdağ O., Karadağ T., “Modified Archimedes optimization algorithm for global optimization problems: a comparative study,” Neural Comput. Appl., 36, 8007–8038, 2024. https://doi.org/10.1007/s00521-024-09497-1