Eco-Driving in Railway Lines Considering the Uncertainty Associated with Climatological Conditions

Manuel Blanco-Castillo, Adrián Fernández-Rodríguez, Antonio Fernández-Cardador and Asunción P. Cucala
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Manuel Blanco-Castillo: Institute for Research in Technology, Pontifical Comillas University, 23 Alberto Aguilera Street, 28015 Madrid, Spain
Adrián Fernández-Rodríguez: Institute for Research in Technology, Pontifical Comillas University, 23 Alberto Aguilera Street, 28015 Madrid, Spain
Antonio Fernández-Cardador: Institute for Research in Technology, Pontifical Comillas University, 23 Alberto Aguilera Street, 28015 Madrid, Spain
Asunción P. Cucala: Institute for Research in Technology, Pontifical Comillas University, 23 Alberto Aguilera Street, 28015 Madrid, Spain

Sustainability, 2022, vol. 14, issue 14, 1-26

Abstract: Eco-driving is a keystone in energy reduction in railways and a fundamental tool to contribute to the Sustainable Development Goals in the transport sector. However, its results in real applications are subject to uncertainties such as climatological factors that are not considered in the train driving optimisation. This paper aims to develop an eco-driving model to design efficient driving commands considering the uncertainty of climatological conditions. This uncertainty in temperature, pressure, and wind is modelled by means of fuzzy numbers, and the optimisation problem is solved using a Genetic Algorithm with fuzzy parameters making use of an accurate railway simulator. It has been applied to a realistic Spanish high-speed railway scenario, proving the importance of considering the uncertainty of climatological parameters to adapt driving commands to them. The results obtained show that the energy savings expected without considering climatological factors account for 29.76%, but if they are considered, savings can rise up to 34.7% in summer conditions. With the proposed model, a variation in energy of 5.32% is obtained when summer and winter scenarios are compared while punctuality constraints are fulfiled. In conclusion, the model allows the operator to estimate better energy by obtaining optimised driving adapted to the climate.

Keywords: eco-driving; energy efficiency; fuzzy logic; simulation; railway operation; high-speed railway (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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