Energy-efficient train operation is regarded as an effective way to reduce the operational cost and carbon emissions in metro systems. Reduction of the traction energy and increasing of the regenerative energy are two important ways for saving energy, which is closely related to the train timetable and driving strategy. To minimize the systematic net energy consumption, i.e., the difference between the traction energy consumption and the reused regenerative energy, this paper proposes an integrated train operation approach by jointly optimizing the train timetable and driving strategy. A precise train driving strategy is presented and the timetable model considers the headway between successive trains, the distribution of the trip time, and passenger demand in this paper. In addition, a distributed regenerative braking energy model is proposed, based on which the integrated optimization model is formulated. Then, a two-level approach is proposed to solve the problem. At the driving strategy level, the train control problem is transferred into a multi-step decision problem and the Dynamic Programming method is introduced to calculate the energy-efficient driving strategy with the given trip time. As for the timetable level, the trip times and headway of trains are optimized by using the Simulated Annealing algorithm based on the results of dynamic programming method. The timetable optimization level balances the mechanical traction energy of multi-interstations and the amount of the reused regenerative energy such that the net mechanical energy consumption of the metro system is minimized. Furthermore, two numerical examples are conducted for train operations in the peak and off-peak hours separately based on the real-world data of a metro line. The simulation results illustrate that the proposed approach can produce a good performance on energy-saving.

This paper proposes a trajectory optimization approach for high-speed trains to reduce traction energy consumption and increase riding comfort. Besides, the proposed approach can also achieve energy-saving effects by optimizing the operation time between stations. First, an optimization model is developed by defining the objective function as a trade-off function of the traction energy consumption and riding comfort. In addition to constraints in the classic optimal train control model, three new factors–the discrete throttle settings, neutral zones, and sectionalized tunnel resistance–are considered. Then, the model is discretized and turned into a multi-step decision optimization problem. All the nonlinear constraints are approximated using piecewise affine (PWA) functions, and the trajectory optimization problem is turned into a mixed integer linear programming (MILP) problem which can be solved by existing solvers CPLEX and YALMIP. Finally, some case studies with real-world data sets are conducted to present the effectiveness of the proposed approach. The simulation results are compared with the practical running data of trains, which shows that the proposed model and the optimization approach save energy and improve the riding comfort.

The increasing demand for capacity in railway transportation has spawned the concept of virtual coupling (VC), which can further shorten the intertrain distance by applying communication technology. In this article, a centralized model predictive control (MPC) is developed for virtually coupled train set (VCTS) with a nonlinear safety equilibrium spacing policy over gradient rolling terrains. The tracking target of the controller in cruise phase is given based on the braking distance difference and speed difference of trains. A more precise braking distance model of trains explicitly considering the gradient is introduced for safety, based on which the state space applicable to the controller for VCTS is derived. To deal with the nonlinearities of the controller, a new optimization solving algorithm based on the continuation method and generalized minimum residual (GMRES) method is presented in this article. Finally, controller performances and empirical string stability of VCTS are verified by numerical simulations. It is proven that the proposed controller has a good effect for the safety and smoothness of the train operation on the railway line over gradient terrain through the test. The actual intertrain distance under the proposed controller is about 1 m closer to the equilibrium state than that under the same controller without considering the gradient at low speed (about 10 m/s) on the 0.3% downhill line, which verifies the safety of the controller. Besides, the influence of the parameters of the controller and optimization method on controller efficiency is analyzed, and for the latter, the simulation data show that the applied algorithm for the optimal control solver can save the calculation time more than seven times than the traditional algorithm.

As one of the important devices in railway signaling system, railway point machines have a great influence on train operation safety. To realize the fault diagnosis of railway point machines, this paper presents a vibration signal-based diagnosis method considering its advantages of easy-to-collect and anti-interference ability. First, the vibration signals are preprocessed using Variational Mode Decomposition (VMD) for stationary preprocessing. Then a multi-domain feature extraction method is developed, which is verified as a more effective feature extraction tool than single-domain feature extraction methods. An ensemble feature selection strategy is proposed for feature selection, superior to single feature selection method. Finally, Support Vector Machine (SVM) is used for diagnosis and analysis. The diagnosis accuracy using the presented method reaches 100%, and its superiority is verified by many experiment comparisons.