Application and improvement of a direct method optimization approach for battery electric railway vehicle operation
While the largely electrified rail network allows for direct utilization of renewable energy sources, there is still a considerable share of diesel-powered trains operating on non- and partly electrified tracks. To replace these, the more sustainable alternatives such as battery electric railway veh...
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Veröffentlicht in: | Proceedings of the Institution of Mechanical Engineers. Part F, Journal of rail and rapid transit Journal of rail and rapid transit, 2021-08, Vol.235 (7), p.854-865 |
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Format: | Artikel |
Sprache: | eng |
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Zusammenfassung: | While the largely electrified rail network allows for direct utilization of renewable energy sources, there is still a considerable share of diesel-powered trains operating on non- and partly electrified tracks. To replace these, the more sustainable alternatives such as battery electric railway vehicles need to present a viable option with sufficient range. This paper aims to adapt and improve an existing optimization algorithm, previously used with diesel-powered trains, for the operation of battery electric railway vehicles. In this new approach, battery control is optimized alongside train control, utilizing a direct method solver to find the minimum energy trajectory. Furthermore, a detailed train model is implemented that is designed for operation on partly electrified tracks. To yield a highly accurate, yet also sufficiently fast algorithm, a numerical analysis is conducted and the parameters of the algorithm are determined accordingly. Finally, the application of the adapted algorithm on a use case in Germany shows that both velocity profile and control adapt in a way that minimizes utilization of the battery. The results indicate that the proposed algorithm presents a reliable and robust method to obtain minimum energy controls for battery electric railway vehicles with any electrification pattern. |
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ISSN: | 0954-4097 2041-3017 |
DOI: | 10.1177/0954409720970002 |