Train Speed Profile Optimization for Energy Saving


Train speed profile
Energy saving
Brute force
Genetic algorithm




This study aims to optimize energy consumption by modifying the train’s maximal speed and coasting velocity. The methods used in the simulation are brute force and genetic algorithm (GA). The introduction briefly introduces the aim and objectives of the study, as well as the scope and the methodology. The following section gives an overview of the current rail transit development and the existing issues. Despite the rapid development of rail transit and its successful operation, energy consumption is a major issue. The methodology of brute force and genetic algorithm is then introduced. The exact algorithm of the two methods in MATLAB is explained so as to make preparations for the latter simulation optimization. The results from the brute force and genetic algorithm methods are obtained and compared for data analysis. The driving strategy for using STS (Single Train Simulator) is then optimized for an advanced modification. By inserting more values in the code, an optimal speed profile is obtained, and the energy saving target is achieved. Overall, the energy consumption of the studied line could be decreased by optimizing the maximal speed of different sections between the stations and the coasting velocity. However, influencing factors such as service and infrastructure, application of acceleration, and braking power should also be considered as improvements in future studies.


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