Optimal driving strategy for traction energy saving on DC suburban railways

• Published in: IET electric power applications Vol. 1; no. 5; pp. 675 - 682
• Main Authors: BOCHARNIKOV, Y. V, TOBIAS, A. M, ROBERTS, C, HILLMANSEN, S, GOODMAN, C. J
• Format: Journal Article
• Language: English
• Published: London Institution of engineering and technology 01.09.2007; The Institution of Engineering & Technology
• Subjects: Applied sciences; Braking; Direct energy conversion and energy accumulation; Electrical engineering. Electrical power engineering; Electrical power engineering; Energy accumulation; Energy conservation; Energy consumption; Exact sciences and technology; Fitness; Operation. Load control. Reliability; Optimization; Power networks and lines; Railway engineering; Running; Trains; Formal method; Power system economics; Energy economy; Optimization method; Rail vehicle; Driver; Energy recovery; Energy storage system; Energy savings; Search algorithm; Learning; Trade; Power consumption; Weighting; Genetic algorithm; Optimal trajectory; Automatic system; System simulation; Power markets; Rail transportation; Train
• ISSN: 1751-8660; 1751-8679
• DOI: 10.1049/iet-epa:20070005

Energy saving on electrified railways has been studied for many years and the technical solution is usually provided by a combination of driving strategy (e.g. coasting), regenerative braking and energy storage systems. An alternative approach is for the driver (or automatic train operation system if fitted) to manage energy consumption more efficiently. A formal method for optimising traction energy consumption. during a single-train journey by trading-off reductions in energy against increases in running time, has been demonstrated. The balance between saving energy and running faster has been investigated by designing a fitness function with variable weightings. Energy savings were found, both qualitatively and quantitatively, to be affected by acceleration and braking rates, and, by running a series of simulations in parallel with a genetic algorithm search method, the fitness function was used to identify optimal train trajectories. The influence of the fitness function representation on the search results was also explored.