A New Approach for Real Time Train Energy Efficiency Optimization

Agostinho Rocha, Armando Araújo, Adriano Carvalho and João Sepulveda
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Agostinho Rocha: Department of Electrical and Computer Engineering, Engineering Faculty, University of Porto, 4200-465 Porto, Portugal
Armando Araújo: Department of Electrical and Computer Engineering, Engineering Faculty, University of Porto, 4200-465 Porto, Portugal
Adriano Carvalho: Department of Electrical and Computer Engineering, Engineering Faculty, University of Porto, 4200-465 Porto, Portugal
João Sepulveda: Department of Industrial Electronics, University of Minho, 4800-058 Braga, Portugal

Energies, 2018, vol. 11, issue 10, 1-21

Abstract: Efficient use of energy is currently a very important issue. As conventional energy resources are limited, improving energy efficiency is, nowadays, present in any government policy. Railway systems consume a huge amount of energy, during normal operation, some routes working near maximum energy capacity. Therefore, maximizing energy efficiency in railway systems has, recently, received attention from railway operators, leading to research for new solutions that are able to reduce energy consumption without timetable constraints. In line with these goals, this paper proposes a Simulated Annealing optimization algorithm that minimizes train traction energy, constrained to existing timetable. For computational effort minimization, re-annealing is not used, the maximum number of iterations is one hundred, and generation of cruising and braking velocities is carefully made. A Matlab implementation of the Simulated Annealing optimization algorithm determines the best solution for the optimal speed profile between stations. It uses a dynamic model of the train for energy consumption calculations. Searching for optimal speed profile, as well as scheduling constraints, also uses line shape and velocity limits. As results are obtained in seconds, this new algorithm can be used as a real-time driver advisory system for energy saving and railway capacity increase. For now, a standalone version, with line data previously loaded, was developed. Comparison between algorithm results and real data, acquired in a railway line, proves its success. An implementation of the developed work as a connected driver advisory system, enabling scheduling and speed constraint updates in real time, is currently under development.

Keywords: railway; train; energy efficiency; driver advisory system; optimization algorithms; simulated annealing; scheduling (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: 2018
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (10)

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