Improvement of Energy Savings in Electric Railways Using Coasting Technique

Donato Morea, Stefano Elia, Chiara Boccaletti and Pasquale Buonadonna
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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

Energies, 2021, vol. 14, issue 23, 1-15

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.

Keywords: coasting; electric locomotive; energy saving; optimization; railway; traction energy; train driving cycle simulation (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (6)

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