Journal
IEEE ACCESS
Volume 9, Issue -, Pages 25090-25100Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/ACCESS.2021.3056387
Keywords
Optimization; Energy efficiency; Rail transportation; Genetic algorithms; Heuristic algorithms; Dynamic programming; Optimal control; Neutral section constraint; high-speed railway; energy-efficient speed profile; optimization; dynamic programming
Categories
Funding
- National Key Research and Development Program of China [2018YFC0705000]
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This paper studies the characteristics of the neutral section to optimize the operating curves of high-speed trains. The proposed method is feasible and close to reality. Numerical results show that the proposed model and solution methodology are efficient and achieve an energy-efficient effect of 1.43%.
A neutral zone is a section of a railway line without traction current during high-speed train operations and is very common in high-speed railway lines. However, previous studies on energy-efficient speed profile (ESP) optimization did not consider the electric neutral section constraints. Thus, a risk of being unable to drive out of the neutral section because of a low initial speed and a deviation in the energy consumption effect occur. This paper studies the characteristics of the neutral section to optimize the operating curves of high-speed trains. It can aid in trains passing through the neutral section smoothly. The proposed method is feasible and close to reality. By analyzing the actual coasting distance of a train in the neutral section and building its mathematical model, we provide special double-speed limits and stability constraints. Subsequently, by simulating vehicle performance, operating conditions, and the location of the neutral section, a train ESP optimization model is constructed. Moreover, an improved dynamic programming algorithm is proposed, which uses the vehicle maximum traction and braking force to simplify the computation of state transitions. Finally, the numerical results of simulations demonstrate the efficiency of the proposed model and solution methodology. Compared with the solution without neutral section constraints, an energy-efficient effect of 1.43% is achieved. Further, the proposed method performs better with a stability and high-quality solution than the genetic algorithm.
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