Adaptive Fuzzy Planning of Optimal Speed Profiles for High-Speed Train Operation on the Basis of a Pareto Set

Abstract

Today, one of the main concerns for railways administrations and operators is reducing energy consumption. Ecodriving design is one of the main approaches to reducing energy consumption with low levels of investment. In this paper, a Pareto set–based adaptive fuzzy approach to trajectory planning is proposed to generate energy-efficient speed profiles for high-speed train operation. First, the Pareto set for high-speed train operation is constructed by using a hybrid evolutionary algorithm based on differential evolution and simulated annealing. Second, by considering that the operational delays are variable because of the uncertainties of line conditions in practice, a system to control adaptive fuzzy predictions is proposed to regulate the coasting point dynamically so as to meet the requirements of punctuality and energy savings. The proposed approach decreases the energy consumption of high-speed trains mainly by changing the coasting point, a technique that make implementation easy and guarantees passenger comfort compared with frequent changes under different operating conditions. Finally, the proposed approach is analyzed by using real operational data from the Wuhan–Guangzhou high-speed railway line in China to assess energy savings and punctuality. The results of simulation illustrate the efficiency of the proposed approach.