Research Article
BibTex RIS Cite

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

References

  • Asnis, I. A., Dmitruk, A. V, & Osmolovskii, N. P. (1985). Solution of the problem of the energetically optimal control of the motion of a train by the maximum principle. USSR Computational Mathematics and Mathematical Physics, 25(6), 37–44.
  • Bae, C., Jang, D., Kim, Y., Chang, S., & Mok, J. (2007). Calculation of regenerative energy in DC 1500V electric railway substations. 2007 7th Internatonal Conference on Power Electronics, 801–805.
  • Certa, A., Galante, G., Lupo, T., & Passannanti, G. (2011). Determination of Pareto frontier in multi-objective maintenance optimization. Reliability Engineering & System Safety, 96(7), 861–867.
  • Chen, J.-F., Lin, R.-L., & Liu, Y.-C. (2005). Optimization of an MRT train schedule: reducing maximum traction power by using genetic algorithms. IEEE Transactions on power systems, 20(3), 1366–1372.
  • Corlu, C. G., de la Torre, R., Serrano-Hernandez, A., Juan, A. A., & Faulin, J. (2020). Optimizing energy consumption in transportation: Literature review, insights, and research opportunities. Energies, 13(5), 1115.
  • Dorigo, M. (1992). Optimization, learning and natural algorithms. Ph. D. Thesis, Politecnico di Milano.
  • Dorigo, M., & Gambardella, L. M. (1997). Ant colony system: a cooperative learning approach to the traveling salesman problem. IEEE Transactions on evolutionary computation, 1(1), 53–66.
  • Dorigo, M., Maniezzo, V., & Colorni, A. (1996). Ant system: optimization by a colony of cooperating agents. IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), 26(1), 29–41.
  • ERRAC. (2017). Rail 2050 Vision. Uic, 27.
  • Feng, J., Ye, Z., Wang, C., Xu, M., & Labi, S. (2018). An integrated optimization model for energy saving in metro operations. IEEE Transactions on Intelligent Transportation Systems, 20(8), 3059–3069.
  • Fernández, P. M., Sanchís, I. V., Yepes, V., & Franco, R. I. (2019). A review of modelling and optimisation methods applied to railways energy consumption. Journal of Cleaner Production, 222, 153–162.
  • González-Gil, A., Palacin, R., Batty, P., & Powell, J. P. (2014). A systems approach to reduce urban rail energy consumption. Energy Conversion and Management, 80, 509–524.
  • Gordon, S. P., & Lehrer, D. G. (1998). Coordinated train control and energy management control strategies. Proceedings of the 1998 ASME/IEEE Joint Railroad Conference, 165–176.
  • Guo, J., Dong, H., Sheng, W., TU, C., & YE, M. (2017). Research of the influence of braking conditions on regenerative braking energy recovery for electric vehicles. Proceedings of the International conference on Energy, Ecology and Environment (ICEEE), Stockholm, Sweden, 26–29.
  • Huang, Y., Yang, L., Tang, T., Gao, Z., Cao, F., & Li, K. (2018). Train speed profile optimization with on-board energy storage devices: A dynamic programming based approach. Computers & Industrial Engineering, 126, 149–164.
  • Khodaparastan, M., Mohamed, A. A., & Brandauer, W. (2019). Recuperation of regenerative braking energy in electric rail transit systems. IEEE Transactions on Intelligent Transportation Systems, 20(8), 2831–2847.
  • Liu, J., Anavatti, S., Garratt, M., & Abbass, H. A. (2022). Multi-operator continuous ant colony optimisation for real world problems. Swarm and Evolutionary Computation, 69, 100984.
  • Luan, X., Wang, Y., De Schutter, B., Meng, L., Lodewijks, G., & Corman, F. (2018). Integration of real-time traffic management and train control for rail networks-part 1: Optimization problems and solution approaches. Transportation Research Part B: Methodological, 115, 41–71.
  • Mohamed, A., Awad, S., Mohamed, A. A., Alkhalaf, S., Mohamed, M., Senjyu, T., & El-din, A. B. (2020). Nature-inspired algorithms for feed-forward neural network classifiers : A survey of one decade of research. Ain Shams Engineering Journal, xxxx. https://doi.org/10.1016/j.asej.2020.01.007
  • Morea, D., Elia, S., Boccaletti, C., & Buonadonna, P. (2021). Improvement of Energy Savings in Electric Railways Using Coasting Technique. Energies, 14(23), 8120.
  • Rodrigue, J.-P., Comtois, C., & Slack, B. (2012). The geography of transport systems. Langara College. Scheepmaker, G. M., Goverde, R. M. P., & Kroon, L. G. (2017). Review of energy-efficient train control and timetabling. European Journal of Operational Research, 257(2), 355–376. https://doi.org/10.1016/j.ejor.2016.09.044
  • Siefert, J., & Li, P. Y. (2021). Optimal control of the energy-saving hybrid hydraulic-electric architecture (HHEA) for off-highway mobile machines. IEEE Transactions on Control Systems Technology, 30(5), 2018–2029.
  • Socha, K., & Dorigo, M. (2008). Ant colony optimization for continuous domains. European journal of operational research, 185(3), 1155–1173.
  • Su, S., Li, X., Tang, T., & Gao, Z. (2013). A subway train timetable optimization approach based on energy-efficient operation strategy. IEEE Transactions on Intelligent Transportation Systems, 14(2), 883–893.
  • Su, S., Wang, X., Cao, Y., & Yin, J. (2019). An energy-efficient train operation approach by integrating the metro timetabling and eco-driving. IEEE Transactions on Intelligent Transportation Systems, 21(10), 4252–4268.
  • Wu, Y., Ma, W., Miao, Q., & Wang, S. (2019). Multimodal continuous ant colony optimization for multisensor remote sensing image registration with local search. Swarm and Evolutionary Computation, 47, 89–95.
  • Xing, Z., Zhang, Z., Guo, J., Qin, Y., & Jia, L. (2023). Rail train operation energy-saving optimization based on improved brute-force search. Applied Energy, 330(PA), 120345. https://doi.org/10.1016/j.apenergy.2022.120345
  • Yang, L., Li, K., & Gao, Z. (2008). Train timetable problem on a single-line railway with fuzzy passenger demand. IEEE Transactions on fuzzy systems, 17(3), 617–629.
  • Yang, X., Ning, B., Li, X., Tang, T., & Song, X. (2013). A Subway Timetable Optimization Model for Maximizing the Utilization of Recovery Energy. ASME/IEEE Joint Rail Conference, 55300, V001T07A002.
  • Zhang, H., Jia, L., Wang, L., & Xu, X. (2019). Energy consumption optimization of train operation for railway systems: Algorithm development and real-world case study. Journal of Cleaner Production, 214, 1024–1037.

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

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

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

References

  • Asnis, I. A., Dmitruk, A. V, & Osmolovskii, N. P. (1985). Solution of the problem of the energetically optimal control of the motion of a train by the maximum principle. USSR Computational Mathematics and Mathematical Physics, 25(6), 37–44.
  • Bae, C., Jang, D., Kim, Y., Chang, S., & Mok, J. (2007). Calculation of regenerative energy in DC 1500V electric railway substations. 2007 7th Internatonal Conference on Power Electronics, 801–805.
  • Certa, A., Galante, G., Lupo, T., & Passannanti, G. (2011). Determination of Pareto frontier in multi-objective maintenance optimization. Reliability Engineering & System Safety, 96(7), 861–867.
  • Chen, J.-F., Lin, R.-L., & Liu, Y.-C. (2005). Optimization of an MRT train schedule: reducing maximum traction power by using genetic algorithms. IEEE Transactions on power systems, 20(3), 1366–1372.
  • Corlu, C. G., de la Torre, R., Serrano-Hernandez, A., Juan, A. A., & Faulin, J. (2020). Optimizing energy consumption in transportation: Literature review, insights, and research opportunities. Energies, 13(5), 1115.
  • Dorigo, M. (1992). Optimization, learning and natural algorithms. Ph. D. Thesis, Politecnico di Milano.
  • Dorigo, M., & Gambardella, L. M. (1997). Ant colony system: a cooperative learning approach to the traveling salesman problem. IEEE Transactions on evolutionary computation, 1(1), 53–66.
  • Dorigo, M., Maniezzo, V., & Colorni, A. (1996). Ant system: optimization by a colony of cooperating agents. IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), 26(1), 29–41.
  • ERRAC. (2017). Rail 2050 Vision. Uic, 27.
  • Feng, J., Ye, Z., Wang, C., Xu, M., & Labi, S. (2018). An integrated optimization model for energy saving in metro operations. IEEE Transactions on Intelligent Transportation Systems, 20(8), 3059–3069.
  • Fernández, P. M., Sanchís, I. V., Yepes, V., & Franco, R. I. (2019). A review of modelling and optimisation methods applied to railways energy consumption. Journal of Cleaner Production, 222, 153–162.
  • González-Gil, A., Palacin, R., Batty, P., & Powell, J. P. (2014). A systems approach to reduce urban rail energy consumption. Energy Conversion and Management, 80, 509–524.
  • Gordon, S. P., & Lehrer, D. G. (1998). Coordinated train control and energy management control strategies. Proceedings of the 1998 ASME/IEEE Joint Railroad Conference, 165–176.
  • Guo, J., Dong, H., Sheng, W., TU, C., & YE, M. (2017). Research of the influence of braking conditions on regenerative braking energy recovery for electric vehicles. Proceedings of the International conference on Energy, Ecology and Environment (ICEEE), Stockholm, Sweden, 26–29.
  • Huang, Y., Yang, L., Tang, T., Gao, Z., Cao, F., & Li, K. (2018). Train speed profile optimization with on-board energy storage devices: A dynamic programming based approach. Computers & Industrial Engineering, 126, 149–164.
  • Khodaparastan, M., Mohamed, A. A., & Brandauer, W. (2019). Recuperation of regenerative braking energy in electric rail transit systems. IEEE Transactions on Intelligent Transportation Systems, 20(8), 2831–2847.
  • Liu, J., Anavatti, S., Garratt, M., & Abbass, H. A. (2022). Multi-operator continuous ant colony optimisation for real world problems. Swarm and Evolutionary Computation, 69, 100984.
  • Luan, X., Wang, Y., De Schutter, B., Meng, L., Lodewijks, G., & Corman, F. (2018). Integration of real-time traffic management and train control for rail networks-part 1: Optimization problems and solution approaches. Transportation Research Part B: Methodological, 115, 41–71.
  • Mohamed, A., Awad, S., Mohamed, A. A., Alkhalaf, S., Mohamed, M., Senjyu, T., & El-din, A. B. (2020). Nature-inspired algorithms for feed-forward neural network classifiers : A survey of one decade of research. Ain Shams Engineering Journal, xxxx. https://doi.org/10.1016/j.asej.2020.01.007
  • Morea, D., Elia, S., Boccaletti, C., & Buonadonna, P. (2021). Improvement of Energy Savings in Electric Railways Using Coasting Technique. Energies, 14(23), 8120.
  • Rodrigue, J.-P., Comtois, C., & Slack, B. (2012). The geography of transport systems. Langara College. Scheepmaker, G. M., Goverde, R. M. P., & Kroon, L. G. (2017). Review of energy-efficient train control and timetabling. European Journal of Operational Research, 257(2), 355–376. https://doi.org/10.1016/j.ejor.2016.09.044
  • Siefert, J., & Li, P. Y. (2021). Optimal control of the energy-saving hybrid hydraulic-electric architecture (HHEA) for off-highway mobile machines. IEEE Transactions on Control Systems Technology, 30(5), 2018–2029.
  • Socha, K., & Dorigo, M. (2008). Ant colony optimization for continuous domains. European journal of operational research, 185(3), 1155–1173.
  • Su, S., Li, X., Tang, T., & Gao, Z. (2013). A subway train timetable optimization approach based on energy-efficient operation strategy. IEEE Transactions on Intelligent Transportation Systems, 14(2), 883–893.
  • Su, S., Wang, X., Cao, Y., & Yin, J. (2019). An energy-efficient train operation approach by integrating the metro timetabling and eco-driving. IEEE Transactions on Intelligent Transportation Systems, 21(10), 4252–4268.
  • Wu, Y., Ma, W., Miao, Q., & Wang, S. (2019). Multimodal continuous ant colony optimization for multisensor remote sensing image registration with local search. Swarm and Evolutionary Computation, 47, 89–95.
  • Xing, Z., Zhang, Z., Guo, J., Qin, Y., & Jia, L. (2023). Rail train operation energy-saving optimization based on improved brute-force search. Applied Energy, 330(PA), 120345. https://doi.org/10.1016/j.apenergy.2022.120345
  • Yang, L., Li, K., & Gao, Z. (2008). Train timetable problem on a single-line railway with fuzzy passenger demand. IEEE Transactions on fuzzy systems, 17(3), 617–629.
  • Yang, X., Ning, B., Li, X., Tang, T., & Song, X. (2013). A Subway Timetable Optimization Model for Maximizing the Utilization of Recovery Energy. ASME/IEEE Joint Rail Conference, 55300, V001T07A002.
  • Zhang, H., Jia, L., Wang, L., & Xu, X. (2019). Energy consumption optimization of train operation for railway systems: Algorithm development and real-world case study. Journal of Cleaner Production, 214, 1024–1037.
There are 30 citations in total.

Details

Primary Language Turkish
Subjects Software Engineering (Other)
Journal Section Articles
Authors

Ramazan Güngüneş 0000-0001-6722-7275

Volkan Ateş 0000-0002-2349-0140

Ertuğrul Çam 0000-0001-6491-9225

Publication Date September 15, 2023
Published in Issue Year 2023 Volume: 13 Issue: 3

Cite

APA Güngüneş, R., Ateş, V., & Çam, E. (2023). Şehir İçi Raylı Sistem Araçlarında Verimli Dinamik Sürüş Tekniği Modellemesi ve Sürekli Zaman Karınca Kolonisi Algoritması (ACOR) ile Optimizasyonu. Karadeniz Fen Bilimleri Dergisi, 13(3), 1169-1191. https://doi.org/10.31466/kfbd.1311789
 Download Cover Image

by-nc-sa.png

This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.