In this paper, hybridizing a heavy vehicle is developed. A switcher locomotive is considered for hybridization. Due to their low operational speed, the switcher locomotives require much lower power when compared to other types of locomotives. Besides, switcher locomotives have higher loss of energy due to their frequent starting and stopping. Hybridpowered transit vehicles are considered to be excellent replacements for ordinary transit vehicles, since hybrid powered vehicles are equipped with more than one traction power sources. Therefore, a switcher locomotive's driving cycle is derived from the measured field data and used to calculate and design the hybrid vehicle's components. A ''fuzzy controller'' is used to plan a suitable controller for the designed hybrid locomotive. Comparisons show a substantial decrease, both in the fuel consumption and the pollutions of the designed hybrid switcher locomotive versus the conventional diesel-electric locomotives.
The paper deals with common concepts of modern methods of train speed determination with minimal errors. Balise locations depend on a variety of parameters. With genetic algorithm and particle swarm optimization, as two new intelligent algorithms, and Kalman filtering concept used, the best locations are determined to reduce tachometer errors.
Today, rail transport systems are widely used in the world. Because of the high consumption of energy in these systems, finding ways to optimize their energy consumption is important. One of the best ways to save more energy and prevent the losses of rail transportation is using the optimal speed profile. In this article, intelligent algorithms, involving ant colony optimization for continuous domain ðACO R Þ and genetic algorithm, are applied to the energy efficiency problem of electrical trains for various track gradients and curvatures. With proper determination of switching points in the moving strategies such as acceleration, cruising, and coasting, the optimal speed profile for the safe journey of trains will be obtained. For the simulation, real data from rail tracks of Tehran metro have been incorporated.
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