Efficient Dynamic Driving Technique Modeling in Urban Rail Vehicles and Optimization with Continuous Time Ant Colony Algorithm (ACOR)

Year 2023, Volume: 13 Issue: 3, 1169 - 1191, 15.09.2023

Ramazan Güngüneş , Volkan Ateş , Ertuğrul Çam

https://doi.org/10.31466/kfbd.1311789

Abstract

The demand for rail systems (RS) transportation is constantly increasing in cities with high population density. Along with the growing demand, the efficient management of energy in these systems has become almost mandatory. Efficient energy management will reduce both carbon emissions and operational costs. In RS vehicles equipped with regenerative braking (RB) energy generation capability, the optimal integration of the producted RB energy into the system contributes to energy efficiency. For this purpose, this study aims to efficiently manage energy in RS using the energy-efficient dynamic driving technique (EEDDT) model supported by RB energy. The optimal design of the model aims to select the most suitable speed profiles and starting positions for coasting running along a horizontally curved track for RS vehicles, achieving maximum energy efficiency. The proposed model includes the optimization of single-objective functions such as minimum travel time (MTT), minimum traction energy consumption (MTEC), and maximum regenerative braking energy production (MRBEP). Additionally, the proposed model also encompasses the optimization of multi-objective functions such as MTEC/MRBEP, MTEC/MTT, MRBEP/MTT, and MTEC/MRBEP/MTT. The single-objective and multi-objective functions were optimized using the Continuous Time Ant Colony Optimization Algorithm (ACOR) in a scenario-based manner to explore the operational constraints and optimum working regions. As a result of the study, an efficiency of 53.459% was achieved in the MRBEP/MTEC ratio. For the proposed scenario-based model, a ratio of 32.832% was obtained for MTEC, and a ratio of 80.060% was achieved for MRBEP. As an alternative to the driving models in the literature, the effect of the curve structure on the system dynamics has been increased and a more realistic driving model has been developed. In addition, with the artificial intelligence optimization technique used, it has contributed to literature by offering a different perspective on driving model development.

Keywords

Continuous Time Ant Colony Algorithm, Energy Efficient Driving Technique, Regenerative Braking Energy, Nature Inspired Optimization, Rail System Energy Efficiency, Rail System Dynamic Driving Technique

Şehir İçi Raylı Sistem Araçlarında Verimli Dinamik Sürüş Tekniği Modellemesi ve Sürekli Zaman Karınca Kolonisi Algoritması (ACOR) ile Optimizasyonu

Abstract

Nüfus yoğunluğunun yüksek olduğu şehirlerde raylı sistem (RS) taşımacılığına olan talep sürekli artmaktadır. Artan taleple birlikte bu sistemlerde enerjinin verimli bir şekilde yönetilmesi neredeyse zorunlu hale gelmiştir. Verimli enerji yönetimi hem karbon emisyonlarını hem de işletme maliyetlerini azaltacaktır. Rejeneratif frenleme (RF) ile enerji üretme kabiliyetine sahip RS araçlarda, üretilen RF enerjisinin sisteme en uygun şekilde entegre edilmesi enerji verimliliğine katkı sağlamaktadır. Bu amaçla, bu çalışma RF enerjisi ile desteklenen enerji verimli dinamik sürüş tekniği (EVDST) modelini kullanarak raylı sistemlerde enerjinin verimli bir şekilde yönetilmesini amaçlamaktadır. Modelin optimum tasarımı, RS araçları için yatay kurplu bir hat boyunca boşta çalışma için en uygun hız profillerini ve başlangıç konumlarını seçmeyi ve maksimum enerji verimliliği elde etmeyi amaçlamaktadır. Önerilen model, minimum yolculuk süresi (MYS), minimum çekiş enerjisi tüketimi (MÇET) ve maksimum rejeneratif frenleme enerjisi üretimi (MRFEÜ) gibi tek amaçlı fonksiyonların optimizasyonunu içermektedir. Ayrıca, önerilen model MÇET/MRFEÜ, MÇET/MYS, MRFEÜ/MYS ve MÇET/MRFEÜ/MYS gibi çok amaçlı fonksiyonların optimizasyonunu da kapsamaktadır. Tek amaçlı ve çok amaçlı fonksiyonlar, operasyonel kısıtlamaları ve optimum çalışma bölgelerini keşfetmek için senaryo tabanlı bir şekilde Sürekli Zaman Karınca Kolonisi Optimizasyon Algoritması (ACOR) kullanılarak optimize edilmiştir. Çalışma sonucunda MRFEÜ/MÇET oranında %53,459'luk bir verimlilik elde edilmiştir. Önerilen senaryo tabanlı modelde MÇET için %32,832'lik bir oran elde edilirken, MRFEÜ için %80,060'lık bir oran elde edilmiştir. Gerçekleştirilen çalışma ile literatürdeki sürüş modellerine alternatif olarak kurp yapısının sistem dinamiğine etkisi artırılmış ve daha gerçekçi bir sürüş modeli geliştirilmesi sağlanmıştır. Ayrıca kullanılan yapay zeka optimizasyon tekniği ile literatüre sürüş modeli geliştirilmesi noktasında farklı bir bakış açısı sunarak katkıda bulunmuştur.

Keywords

Sürekli Zaman Karınca Kolonisi Algoritması, Enerji Verimli Sürüş Tekniği, Rejeneratif Frenleme Enerjisi, Doğa Esinli Optimizasyon, Raylı Sistem Enerji Verimliliği, Raylı Sistem Dinamik Sürüş Tekniği

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