Design, Development and Test of a Practical Train Energy Optimization using GAPSO Algorithm

One of the strategies for reduction of energy consumption in railway systems is to execute efficient driving by presenting optimized speed profile considering running time, energy consumption and practical constraints. In this paper, by using real route data, an approach based on combination of Genetic and Particle swarm (GA-PSO) algorithms in order to optimize the fuel consumption is provided. The model of train takes into account the length and mass of train, running resistance, tractive effort curves for each notch, signaling system, variations of the motor efficiency with respect to speed and effort ratio, auxiliary equipment consumption and rotary inertia. The route characteristics included in the model are speed limits, gradients, gradient transitions (and its effect along the train) and curves. GA-PSO algorithm combining the benefits of both the original algorithms GA and PSO is validated by formulating the optimization problem. The GA-PSO performance is evaluated by comparing it with a GA algorithm. Further, it is used for obtaining the optimal speed profiles for a locomotive equipped with a GT26CW engine on Tehran- Tappe_sefid block.