|Title:||Optimization of run time and energy consumption of train movement|
|Subject:||Railroads -- Trains -- Fuel consumption|
Hong Kong Polytechnic University -- Dissertations
Department of Electrical Engineering
|Pages:||103,  leaves : ill. (some col.) ; 30 cm|
|Abstract:||In general, the operation performance of a railway system is evaluated by the service quality, energy consumption, and passengers' riding comfort. From the viewpoint of the passengers, quality of service and riding comfort are the two major factors. The main concern for the operator is the energy consumption and hence cost. Conventionally, possible approach for the operator to adjust the time schedule is to alter the stop time at station, if other system constraints such as characteristic of traction equipment are not taken into account. Yet it is not an effective, flexible and economical method because the run-time of a train simply cannot be extended without limitation, and a balance between run-time and energy consumption has to be maintained. Modification or installation of a new signaling system not only increases the capital cost, but also affects the normal train service. Therefore, in order to procure a more effective, flexible and economical approach for controlling the railway system, optimization of train performance by coasting point identification has become more attractive and popular recently. In this project, the main objective is to optimize train performance with respect to inter-station run-time and energy consumption by coasting-point identification technique. In order to identify a new coasting point in each inter-station run, a genetic algorithm approach is proposed for the optimization process. Each coasting-point where coasting starts, is encoded into a binary chromosome. Each gene (coasting-point) generation is evolved by crossover and mutation. The output train performance in each generation is checked by fitness function, which is the indication of how good the solution is. In addition, modified methods of crossover and mutation have also been developed in an attempt to attain a better optimization. From the results, it has been shown that the application of a genetic algorithm in obtaining the optimal coasting control strategy is successful in the improvement of train performance in terms of run time and energy consumption. On the other hand, mutation seems to be more reliable than crossover on the optimization of train performance. Moreover, an optimal solution can also be obtained more quickly when a larger number of bits are used in mutation during generation evolution. In fact, a number of approaches are proposed for further investigation to attain a better optimization performance.|
|Rights:||All rights reserved|
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