Optimal Design of Automatic Train Operation Information with the Consideration of Regenerative Braking

Energy saving is a major consideration of train operation to realize environmentally-friendly urban railway systems. In this paper, train control information is studied with the consideration of regenerative braking to realize a better energy saving. The static and dynamic models of train are established firstly. Then the energy flow of the urban railway train system is analyzed as well as the train operation performance indexes are constructed. Performance indexes of energy consumption, running time, passenger comfort and stopping accuracy are taken into account. To get the optimized Pareto solutions of control information, multi-objective particle swarm optimization algorithm is used to solve the problem with the popular running styles. Through the case study, train control information can be obtained after the software simulation which validate our proposed method. The selected optimization algorithm MOPSO performs better than the NSGA-II algorithm. And the optimization results can saving 9.7% energy compared with the practice running data. Besides, the sensitive analysis of regenerative braking coefficient is conducted in the last to show the influence of regenerative braking factory on the train control information.