Actual-time modeling of a subway vehicle and optimal driving management with GA and ABC algorithms

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Arikan Y., Şen T., Çam E.

Journal of Engineering Research (Kuwait), cilt.10, sa.4, ss.204-218, 2022 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 10 Sayı: 4
  • Basım Tarihi: 2022
  • Doi Numarası: 10.36909/jer.10205
  • Dergi Adı: Journal of Engineering Research (Kuwait)
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, Arab World Research Source, Directory of Open Access Journals
  • Sayfa Sayıları: ss.204-218
  • Anahtar Kelimeler: Dynamic model, Energy efficiency, Energy consumption, Artificial bee colony algorithm, Genetic algorithm, ENERGY-CONSUMPTION, OPTIMIZATION, OPERATION
  • Sivas Cumhuriyet Üniversitesi Adresli: Evet

Özet

© 2022 University of Kuwait. All rights reserved.The optimization of operations of subway systems has critical importance in terms of energy efficiency and costs. Therefore, driving management of subway vehicles has been gaining more importance day by day. Optimal Driving Management (ODM) is the optimization of the velocity trajectory of a subway vehicle by considering operating conditions and travel time. In this study, the driving of a subway vehicle has been modeled dynamically with all parameters that affect driving. So, a realistic model has been prepared. Then, a new objective function has been proposed to reduce energy consumption by using the subway vehicle's acceleration and braking forces parameters for ODM. The Artificial Bee Colony algorithm (ABC) and Genetic algorithm (GA) have been used on the prepared model to determine the driving dynamics of the subway vehicle. The performance of the algorithms has been evaluated in the real line network, which has multiple stations with different characteristics. The energy consumption has been reduced by 10.47% in GA and 8.92% in ABC compared to the actual driving values. Moreover, the results of the study have been analyzed in terms of passenger comfort, cost, and emission values.