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With the increasing awareness of carbon neutrality, the application of energy-efficient train control (EETC) to rail transportation systems continues to attract attention from industry and academia. In many classic EETC studies, train models are commonly simplified with pure regenerative braking and constant power characteristics during high speeds, to simplify the complexity of the model. In this paper, a realistic model incorporating hybrid braking characteristics combining regenerative and mechanical braking, and reduced-power characteristics at high speeds into the EETC problem is proposed to improve control precision of the train and the modeling precision of the energy consumption and time. This study addresses the minimum-time train control (MTTC) problem and EETC problem considering nonlinear traction characteristics based on the mixed-integer linear programming (MILP) method, and nonlinear traction and braking characteristics are approximated via a piecewise linear (PWL) modeling technique. Results indicate that the proposed alternative models achieves some deviations from the realistic model in terms of time, energy consumption, and control strategies. The deviations between models in energy consumption and time accumulate as the number of operating stations increases. Therefore, the choice of an appropriate model depends on the precision requirements of various scenarios. In scenarios demanding higher precision, selecting the proposed realistic model is crucial for more accurate computation of energy consumption and time and for obtaining more precise control strategies. • A novel MILP model addresses EETC and MTTC problems with varying nonlinear traction system characteristics, using PWL modeling for approximation. • The impact of realistic nonlinear traction system characteristics model on the EETC problem is considered, and the optimal train control strategy is derived. • Comprehensive case studies on urban rail transit and high-speed railways demonstrate the impact of realistic traction/braking forces on eco-driving strategies. • Sensitivity analysis of critical speed assesses energy consumption variations. [ABSTRACT FROM AUTHOR]

The train generates regenerative braking energy (RBE) during braking. When the RBE generated by the braking train is too large to be absorbed and utilized, the OCS voltage will rise rapidly. It may even exceed the rated voltage threshold, resulting in hidden dangers for driving safety. How to use RBE efficiently and reasonably has become a hot issue in recent years. This paper presents a metro timetable optimization method for multi-train operation to improve the RBE utilization efficiency of trains. Firstly, an energy-saving multi-objective model for multi-train operation is established, which takes the total energy consumption and travel time as the objective functions and the dwell time and departure interval as the constraints. Then, the genetic algorithm (GA) is used to generate the composition sequence of dwell time and departure interval, and the multi-node nonlinear model is used to calculate the total energy consumption of the metro line. Finally, Guangzhou metro line 7 is selected as an example to illustrate the effectiveness of the developed optimization model. The results show that the energy consumption of the whole line can be effectively reduced by adjusting the dwell time and departure interval. Meanwhile, the timetable optimization model with GA has better energy-saving effects by comparative analysis. • A metro timetable optimization method based on improved genetic algorithm is developed. • The multi-node nonlinear model is developed to calculate the total energy consumption. • The developed optimization model has better energy-saving effects than previous methods. [ABSTRACT FROM AUTHOR]

This paper investigates the nonlinear model predictive control to automatic train regulation in metro systems under dynamically disturbed environments. A coupled state-space model for the evolution of train traffic and passenger load dynamics is explicitly developed. With the safety constraints, a mixed-integer nonlinear optimal control model is formulated to generate train regulation strategies. An efficient solution algorithm, based on spatial branch and bound combined with generalized Benders decomposition, is particularly designed to obtain exact solutions quickly for the embedded applications. The original complex problem can be converted into several smaller convex quadratic subproblems with reduced domains to be solved quickly. The effectiveness of our train control model and approaches are verified in numerical simulations. Computational results illustrate that the proposed control method effectively improves the operational efficiency and meanwhile reduces energy consumption during operations. Besides, the efficient embedded solution algorithm performs satisfactory computational efficiency, facilitating a real-time implementation. [ABSTRACT FROM AUTHOR]