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Improvement of Energy Savings in Electric Railways Using Coasting Technique

Author

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  • Donato Morea

    (Department of Mechanical, Chemical and Materials Engineering, University of Cagliari, Via Marengo, 2, 09123 Cagliari, Italy)

  • Stefano Elia

    (Department of Astronautical, Electrical and Energy Engineering, Sapienza University of Rome, Via Eudossiana, 18, 00184 Rome, Italy)

  • Chiara Boccaletti

    (Department of Astronautical, Electrical and Energy Engineering, Sapienza University of Rome, Via Eudossiana, 18, 00184 Rome, Italy)

  • Pasquale Buonadonna

    (Department of Mechanical, Chemical and Materials Engineering, University of Cagliari, Via Marengo, 2, 09123 Cagliari, Italy)

Abstract

The main goal of this work is the evaluation of the energy saving achievable in railway drive when using the coasting technique extensively, with reference to a practical case of the Italian railway network taken as an example. This technique consists in exploiting the kinetic energy accumulated by the running train whenever possible. To implement a driving style on purpose, the only driver contribution is not enough; indeed, it is necessary to provide an embedded automatic calculation control system. In the paper, an algorithm has been developed to evaluate the energy absorption of railway locomotives during the normal service and validated on a real railway line. The proposed hardware and software system could be implemented aboard the train, allowing motion data processing in real-time. Speed, time intervals and power absorption for a given path are calculated; then, the best coasting parameters are estimated to maximize the energy savings. In particular, the case study presented in the paper showed that the fast-run strategy, always adopted by the railway company to recover an unexpected delay, can lead to a negligible time recovery with respect to the coasting strategy, while determining a significantly larger energy consumption.

Suggested Citation

  • Donato Morea & Stefano Elia & Chiara Boccaletti & Pasquale Buonadonna, 2021. "Improvement of Energy Savings in Electric Railways Using Coasting Technique," Energies, MDPI, vol. 14(23), pages 1-15, December.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:23:p:8120-:d:694766
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    References listed on IDEAS

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    1. Mariano Gallo & Marilisa Botte & Antonio Ruggiero & Luca D’Acierno, 2020. "A Simulation Approach for Optimising Energy-Efficient Driving Speed Profiles in Metro Lines," Energies, MDPI, vol. 13(22), pages 1-17, November.
    2. Małgorzata Ćwil & Witold Bartnik & Sebastian Jarzębowski, 2021. "Railway Vehicle Energy Efficiency as a Key Factor in Creating Sustainable Transportation Systems," Energies, MDPI, vol. 14(16), pages 1-14, August.
    3. Stefano Elia & Massimo Battistin, 2015. "Energy consumption evaluation for sports centers: a method based on optimization classes and qualitative dashboards," ECONOMICS AND POLICY OF ENERGY AND THE ENVIRONMENT, FrancoAngeli Editore, vol. 2015(1), pages 133-144.
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    Cited by:

    1. Donato Morea & Mohamad El Mehtedi & Pasquale Buonadonna, 2023. "Energy Context: Analysis of Selected Studies and Future Research Developments," Energies, MDPI, vol. 16(3), pages 1-6, February.
    2. Mihaela Popescu, 2022. "Energy Efficiency in Electric Transportation Systems," Energies, MDPI, vol. 15(21), pages 1-5, November.
    3. Zhang, Zhenyu & Cheng, Xiaoqing & Xing, Zongyi & Gui, Xingdong, 2023. "Pareto multi-objective optimization of metro train energy-saving operation using improved NSGA-II algorithms," Chaos, Solitons & Fractals, Elsevier, vol. 176(C).
    4. Franciszek Restel & Szymon Mateusz Haładyn, 2022. "The Railway Timetable Evaluation Method in Terms of Operational Robustness against Overloads of the Power Supply System," Energies, MDPI, vol. 15(17), pages 1-17, September.
    5. Artur Kierzkowski & Szymon Haładyn, 2022. "Method for Reconfiguring Train Schedules Taking into Account the Global Reduction of Railway Energy Consumption," Energies, MDPI, vol. 15(5), pages 1-18, March.
    6. Xing, Zongyi & Zhang, Zhenyu & Guo, Jian & Qin, Yong & Jia, Limin, 2023. "Rail train operation energy-saving optimization based on improved brute-force search," Applied Energy, Elsevier, vol. 330(PA).

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