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• W2896753429 abstract "This paper studies the optimal operating strategy of high-speed trains to reduce the total energy consumption in fixed time. Optimization of train running trajectory and efficient utilization of regenerative energy are two effective methods for energy-efficient operation. Based on the analysis of dynamic model of high-speed trains, this paper proposes a Genetic Algorithm-Particle Swarm Optimization(GA-PSO) fusion algorithm in the pursuit of a sequence of speed codes at each end of subsection, which shares the same geographical data, to minimize the energy consumption. First, we formulate a two-objective dynamic model with energy consumption and operating time. Second, we design a combined algorithm with real number encoding to obtain the optimal solution. Then, we conduct the proposed optimization method under an actual route condition to verify the effectiveness. The simulation results show that the proposed operating strategy saves 22.81% more than the minimum time strategy and 4.22% than the global optimization on energy consumption. Finally, the situation involving interval speed limit is simulated with proposed method. The results demonstrate that optimal train trajectory saves 6.76% more than the normal train control strategy." @default.
• W2896753429 created "2018-10-26" @default.
• W2896753429 creator A5019486267 @default.
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• W2896753429 date "2018-07-01" @default.
• W2896753429 modified "2023-09-26" @default.
• W2896753429 title "Trajectory Optimization for High-Speed Train Operation" @default.
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• W2896753429 doi "https://doi.org/10.23919/chicc.2018.8484135" @default.
• W2896753429 hasPublicationYear "2018" @default.
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