Abstract
A train operation optimization by minimizing its traction energy subject to various constraints is carried out using nature-inspired evolutionary algorithms. The optimization process results in switching points that initiate cruising and coasting phases of the driving. Due to nonlinear optimization formulation of the problem, nature-inspired evolutionary search methods, Genetic Simulated Annealing, Firefly, and Big Bang-Big Crunch algorithms were employed in this study. As a case study a real-like train and test track from a part of Eskisehir light rail network were modeled. Speed limitations, various track alignments, maximum allowable trip time, and changes in train mass were considered, and punctuality was put into objective function as a penalty factor. Results have shown that all three evolutionary methods generated effective and consistent solutions. However, it has also been shown that each one has different accuracy and convergence characteristics.
Highlights
Researchers have been focusing on new energy saving areas, as energy becomes more expensive besides scarce availability and negative environmental effects in the process of its generation and consumption
In this case, where the train has no passenger, train starts its motion from Osmangazi University station and travel ends at Stadyum station
Genetic Simulated Annealing (GSA), Firefly Algorithm (FA), and BB-BC searching methods were compared for finding the optimal speed trajectory
Summary
Researchers have been focusing on new energy saving areas, as energy becomes more expensive besides scarce availability and negative environmental effects in the process of its generation and consumption. One such area is the train operations process where there is a significant potential to reduce energy consumption by optimizing operation strategies. Energy savings through improvements in driving strategies do not require any hardware modification or additional manufacturing costs; it is the first choice in many cases. In this manuscript we develop energy-efficient driving strategies by choosing switching times among the motion modes of a railway vehicle. Finding the optimal switching times involves solving a nonlinear optimization problem with objective function containing an integral and constraints involving a differential equations set
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