Energy-efficient Train Operation Research Articles

Improving energy‐efficient train operation in urban railways: employing the variation of regenerative energy recovery rate

Energy-efficient train operation is of high importance in urban railways. It takes into account both energy saving and punctuality at the same time, which is the goal of the most operators. In this study, the effect of employing variable regenerative energy recovery rate (RERR) for each inter-station was shown in energy-efficient operation improvement. For this purpose, a two-stage optimisation was proposed. In the first stage, which is a mechanical optimisation, optimal speed profiles for a single train were determined by a bi-objective optimisation through a non-dominated sorting algorithm. In this stage, a simulation model for determination of train's performance was developed and utilised. It was shown that variable RERRs eventually led to different energy-time Pareto fronts. In the second stage, the total input energy for a multi-train system was minimised by using obtained optimal speed profiles from the first stage and distributing total travelling time among inter-stations (electrical/electromechanical optimisation). It was shown that optimisation of the net energy – with different values of RERR – instead of consumed energy, can reduce the total input energy of the network. The simulation results, which are based on actual operating data of Mashhad urban railway system, confirm the feasibility and effectiveness of the proposed method.

The Energy-Efficient Operation Problem of a Freight Train Considering Long-Distance Steep Downhill Sections

With the energy consumption rising in rail transport, the railway sector is showing increasing interest in the energy-efficient operation of freight trains. Freight trains require more complicated driving strategies than ordinary passenger trains do due to their heavy loads, especially in the long-distance steep downhill (LDSD) sections that are very common in freight rail lines in China. This paper studies the energy-efficient operation of a freight train considering LDSD sections. An optimal control model including regenerative and pneumatic braking is developed for the freight train. Then, when a train leaves/enters the LDSD section, we verify the uniqueness of control transitions and discuss the speed profile linkage between LDSD and its adjacent sections, which indicates that the periodic braking should be applied on LDSD sections for optimality. Additionally, given the same running time for the entire journey, our analysis shows that electrical braking-full braking strategy is more energy-efficient than coasting-full braking strategy on LDSD sections. Finally, a numerical algorithm for the optimal driving solution is proposed. The simulation results demonstrate that the driving strategies generated by the proposed algorithm performs better than those from fuzzy predictive control and field operation regarding energy saving.

Driving strategy optimization for trains in subway systems

Energy-efficient operation of trains in subway systems is regarded as one of the most effective strategies to cut down the operational cost. Most of the studies about the energy-efficient operation of trains aim to minimize the consumption of mechanical energy of trains. In this paper, an energy-efficient train control model is proposed under the practical billing system by introducing the traction efficiency. In addition, a numerical solution approach is presented to solve the energy-efficient driving strategy, which consists of the control sequences and the corresponding switching points. First, the authors analyze the energy-efficient control problem by applying the maximum principle, from which the energy-efficient control regime is proved to include the maximum acceleration, coasting, cruising with partial braking, and maximum braking. Second, the control sequences for one section are fully analyzed and an algorithm is presented to calculate the switching points with the energy consumption constraint. To obtain the solution to an interval with variable speed limits and gradients, the interval is divided into several sections, where the gradient and speed limit of each section are constants. A primary energy-efficient solution, which costs a longer trip time, is given. Then, energy units are assigned into multiple sections to shorten the trip time of the primary solution. Specifically, each energy unit is added into the selected section to achieve the maximum time reduction. Finally, the energy-efficient driving strategy at all sections will be obtained when the scheduled trip time of the interval is delivered. Some examples based on the practical data are conducted to illustrate that the proposed approach has a good potential on saving energy of trains.

Multiple-phase train trajectory optimization with signalling and operational constraints

The train trajectory optimization problem aims at finding the optimal speed profiles and control regimes for a safe, punctual, comfortable, and energy-efficient train operation. This paper studies the train trajectory optimization problem with consideration of general operational constraints as well as signalling constraints. Operational constraints refer to time and speed restrictions from the actual timetable, while signalling constraints refer to the influences of signal aspects and automatic train protection on train operation. A railway timetable provides each train with a train path envelope, which consists of a set of positions on the route with a specified target time and speed point or window. The train trajectory optimization problem is formulated as a multiple-phase optimal control model and solved by a pseudospectral method. This model is able to capture varying gradients and speed limits, as well as time and speed constraints from the train path envelope. Train trajectory calculation methods under delay and no-delay situations are discussed. When the train follows the planned timetable, the train trajectory calculation aims at minimizing energy consumption, whereas in the case of delays the train trajectory is re-calculated to track the possibly adjusted timetable with the aim of minimizing delays as well as energy consumption. Moreover, the train operation could be affected by yellow or red signals, which is taken into account in the train speed regulation. For this purpose, two optimization policies are developed with either limited or full information of the train ahead. A local signal response policy ensures that the train makes correct and quick responses to different signalling aspects, while a global green wave policy aims at avoiding yellow signals and thus proceed with all green signals. The method is applied in a case study of two successive trains running on a corridor with various delays showing the benefit of accurate predictive information of the leading train on energy consumption and train delay of the following train.

Two-Train Trajectory Optimization with a Green-Wave Policy

The problem of train trajectory optimization of two successive trains on the same line is studied in this paper. The two-train optimal control problem is formulated by using the multiple-phase method of optimal control, in which the train schedules as well as train performance parameters and track gradients, curves and speed limits are taken into account. The objective is to ensure a smooth, safe, punctual, and energy-efficient train operation. A green-wave (GW) policy is adopted to ensure that the trains proceed safely under all green signals. Special objective functions and constraints are developed in case of train delays. The method is applied in a case study of two successive trains running on a railway line with various input delays showing the benefit of the GW policy and the ability of delay recovery.

Optimisation of recommended speed profile for train operation based on ant colony algorithm

An automatic train operation (ATO) system generally consists of the generation of recommended speed profile and the speed tracking strategy. It determines the tracked trajectory and the energy consumption of trains during the trip. Therefore, the optimisation of recommended speed profile and the ATO tracking strategy are regarded as two important means to achieve energy-efficient train operation between the successive stations. By considering the ATO tracking strategy, an optimisation method of the recommended speed profile is proposed in this paper. Based on the approximate calculation, a discrete combination optimisation model is formulated and a modified max-min ant system (MMAS) is taken as the core algorithm. With the integration speed tracking strategy, this method achieves the recommended speed profile with optimised energy consumption and a perfect running punctuality along the actual tracked trajectory. The computation time of the algorithm is shorter and the switching time of operation during the cruising phase is reduced by integrating the drivers' experience, which also reduces the energy consumption of train running between stations. The simulation results of a case study based on Beijing Subway verify the effectiveness of the proposed method, which has a good performance on energy-efficient train operation.

Energy optimisation of single train operation based on tabu search

Amid concerns about rising energy prices and environmental issues, the optimisation of automatic train operation (ATO) is significant for energy-efficient operation of trains. Different from the previous researches, this paper presents a more accurate model of the train energy consumption by considering the control strategy of ATO. It then proposes two modifications of tabu search (TS) algorithm, which are named as up-down modification (UDM) and left-right modification (LRM), to optimise train recommended speed curve based on the presented model. The simulation results based on the Beijing Subway illustrate that the proposed approaches can achieve a good performance on energy reduction. Sensitivities of the parameters are analysed to illustrate that the proposed approach is practical and meaningful. In addition, the computation time is within 2 s, which is short enough to be applied in the real-time control of trains.

Energy Efficient Motion Control for a Light Rail Vehicle Using The Big Bang Big Crunch Algorithm

Abstract: In this manuscript, an emerging evoluitonary optimization technique, Big Bang - Big Crunch algorithm, is used for an energy-efficient train operation in light rail network of Eskisehir is studied. Cruising and coasting, two basic motion phases of train, should be taken into consideration in order to decrease energy consumption. Determining the optimal switching points from one motion phase into another is key in the energy saving. Switching points for optimal train operation are modeled for the Big Bang Big Crunch algorithm platform and globally optimum set of switching points is obtained as its output, which has proved to be suitable for solving a wide class of global optimization problems. Obtained results are verified through simulations of the operation for five consecutive stations between Buyukdere and Stadyum. Test paths are created considering actual grade and track length. Also certain real life constraints are taken into account such as punctuality and maximum speed limit. It is shown that using the optimal switching points in the simulations results in a significant energy saving.

A Survey on Energy-Efficient Train Operation for Urban Rail Transit

Due to rising energy prices and environmental concerns, the energy efficiency of urban rail transit has attracted much attention from both researchers and practitioners in recent years. Timetable optimization and energy-efficient driving, as two mainly used train operation methods in relation to the tractive energy saving, make major contributions in reducing the energy consumption that has been studied for a long time. Generally speaking, timetable optimization synchronizes the accelerating and braking actions of trains to maximize the utilization of regenerative energy, and energy-efficient driving optimizes the speed profile at each section to minimize the tractive energy consumption. In this paper, we present a fully comprehensive survey on energy-efficient train operation for urban rail transit. First, a general energy consumption distribution of urban rail trains is described. Second, the current literature on timetable optimization and energy-efficient driving is reviewed. Finally, according to the review work, it is concluded that the integrated optimization method jointly optimizing the timetable and speed profile has become a new tendency and ought to be paid more attention in future research.

Optimization of Train Operation in Multiple Interstations with Multi-Population Genetic Algorithm

Subway systems consume a large amount of energy each year. How to reduce the energy consumption of subway systems has already become an issue of concern in recent years. This paper proposes an energy-efficient approach to reduce the traction energy by optimizing the train operation for multiple interstations. Both the trip time and driving strategy are considered in the proposed optimization approach. Firstly, a bi-level programming model of multiple interstations is developed for the energy-efficient train operation problem, which is then converted into an integrated model to calculate the driving strategy for multiple interstations. Additionally, the multi-population genetic algorithm (MPGA) is used to solve the problem, followed by calculating the energy-efficient trip times. Finally, the paper presents some examples based on the operation data of the Beijing Changping subway line. The simulation results show that the proposed approach presents a better energy-efficient performance than that with only optimizing the driving strategy for a single interstation.

The interplay between energy-efficient train control and scheduled running time supplements

Energy-efficient train operation is not yet included in the timetable design process in the Netherlands. Hence, running time supplements are not optimally distributed in the timetable. Therefore research has been conducted on the possibilities to better incorporate energy-efficient train operation into the railway timetable. This paper describes the developed EZR model (energy-efficient operation or in Dutch ‘EnergieZuinig Rijden’) based on optimal control theory and an algorithm that determines the joint optimal cruising speed and coasting point for individual train trips; taking into account a desired robustness, the possibilities for energy-efficient operation, and the desired punctuality during operations. The model is applied in a case study of a regional train line in the Netherlands between Utrecht Centraal and Rhenen. The results show that it is better to distribute the running time supplements evenly than concentrating it near the main stations.

2P24 Energy-efficient train operation for urban rail transit systems(Shotgun Session)

Application of energy-efficient train operation has been regarded as an effective way to reduce the energy consumption which benefits the cost of operation companies and emission to environment. Most conventional approaches only focused on optimizing the driving strategy for single train, which neglects the influence of timetable on the energy consumption and reuse of the regenerative energy among trains. Based on the optimal train control model, a timetable optimization approach is proposed to achieve the energy-efficient operation for multi-trains in two-track urban rail transit systems, together with a cooperative train control approach for efficient utilization of the regenerative energy by properly adjusting the train departure times. The proposed approaches in this paper extend the optimal train control method, which realizes the combination of the high-level transportation management and the low-level accurate train operation control. With the satisfaction of the passenger transport demand, the presented solution can provide decisions to the operators and drivers for energy-efficient train operations. A case study based on the data of Beijing Yizhuang urban rail transit line is presented and then the simulation result illustrates that the proposed approach can averagely achieve 9.4% energy reduction for one day than the approach only optimizing the single train operation. In addition, the computation time of the analytical algorithm is within 3 seconds, which satisfies the application of real-time train control system.

Energy-Efficient Train Operation in Urban Rail Transit Using Real-Time Traffic Information

Energy-efficient train operation represents an important issue for daily operational urban rail transit. Most energy-efficient train operation strategies are normally planned according to a timetable, which is designed by offline traffic information. In this paper, a new energy-efficient train operation model based on real-time traffic information is proposed from the geometric and topographic points of view through a nonlinear programming method, leading to an energy-efficient driving strategy with real-time interstation running time monitored by the automatic train supervision system. The novelty of this work lies not only in the establishment of a new model for energy-efficient train operation but also in the utilization of combining analytical and numerical methods for deriving energy-efficient train operation strategies. More specifically, the energy-efficient operation model is built based on trajectory analysis when the energy-efficient optimal controls are applied, from which an energy-efficient reference trajectory is obtained under the running time and distance constraints, in which the nonlinear programming method is utilized. In contrast to most existing methods, the proposed model turns out to be a small-scale problem, and the difficulties of solving partial differential equations or the process of predetermining and reiteratively calculating some key factors as traditionally involved are avoided. Thus, it is more feasible to implement the strategy and easier to make real-time adjustment if needed. The comparative analysis and the simulation verification with the actual operating data confirm the effectiveness of the proposed method. With the proposed method, some delayed trains are able to maintain punctuality at the next station and sometimes even reducing energy consumption.

Optimization of Multitrain Operations in a Subway System

Energy efficiency is paid more and more attention in railway systems for reducing the cost of operation companies and emissions to the environment. In subway systems, the optimizations on timetable and driving strategy are two important and closely dependent parts of energy-efficient operations. The former regulates the fleet size and the trip time at interstations, and the latter determines the control sequences of traction and braking force during the trip. Most conventional research optimized the timetable and the driving strategy separately such that global optimality cannot be achieved. In this paper, we analyze the hierarchy of energy-efficient train operation and then propose an integrated algorithm to generate the globally optimal operation schedule, which can get better energy-saving performance. Within the criteria of meeting the passenger demand, the integrated energy-efficient algorithm can simultaneously obtain the optimal timetable and driving strategy for trains, which realizes the combination of the high-level transportation management and the low-level train operation control. The simulation results based on the Beijing Yizhuang Subway Line illustrate that the integrated algorithm can achieve a 24.0% energy reduction for one day, on average. In addition, the computation time is within 2 s, which is short enough to be applied for real-time control system.

FA-Based Optimal Strategy Study of Train's Energy Saving Operation under Disturbed Condition

Different from changeable chromosome length genetic algorithm, a new method based on firefly optimization algorithm is put forward to search a trains energy saving operation strategy under running disturbance condition in railway network. The algorithm is designed and validated using a referenced simulation case. Compared with other methods, it demonstrates this new FA-based algorithm has a better result in computation efficiency and may be considered to use in other energy efficient train operation models.

Managing Rail Traffic on Commuter Lines Based on Dynamic Timetable Application

The increase of demand for transport service in rail commuter traffic stipulates higher ratio of consumed infrastructure capacity. In this method of traffic flow even minor deviations from the planned timetable can have negative influence on its stability, and this can result in major reduction of the quality of transport service. This research has defined the commuter rail traffic management system model with the application of real-time timetable rescheduling. It understands the application of the decision support system during the procedure of adjusting the timetable to the real condition in traffic in the form of genetic algorithm defined on the basis of the valid rules for the train and traffic control. Besides, this model in all the commuter trains understands the existence of the driver advisory system which is based on the algorithm for determination of the most favourable running regime with the aim of saving in energy consumption. The paper proves that by applying the proposed model the commuter rail traffic can be improved regarding the increase of the timetable stability and energy-efficient train operation. KEY WORDS: rail traffic management, genetic algorithm, energy efficient timetabling and train operation

Optimal Train Operation for Minimum Energy Consumption Considering Track Alignment, Speed Limit, and Schedule Adherence

Developing an optimal train speed profile for energy-efficient train operation is significant in both theory and applications but very difficult and complex to achieve. An optimization method that minimizes energy consumption by considering track alignment, speed limit, and schedule adherence is proposed. The objective function is total energy consumption, and the decision variables include the timing of train motion regimes. A simulated annealing algorithm (SA) is developed to search for the optimal train operation or “golden run.” The developed model is applied to a segment of the New Haven line of the Metro-North Commuter Railroad, which runs between Woodlawn, New York, and New Haven, Connecticut. A sensitivity analysis is conducted, and the relationship between model parameters and decision variables are explored.

A Train Performance Simulation using Simulink for Generating Energy- efficient Speed Profiles

In this research TPS (Train Performance Simulation) blocks are designed using Simulink and applied to generate speed profiles for energy-efficient train operation. With a train operation mode of maximum powering, coasting, and maximum breaking, a breaking point is calculated from forward-backward running profiles. Then, GA (Genetic Algorithm) is used to solve a running time constraint, and a coasting point is produced from the searching process of GA. With the breaking point and the coasting point a speed profile is plotted. Train performance under a speed limit and gradient variations is simulated and resultant speed profiles are analyzed.

Principles of railway transit, as they regard the force of traction, expense, and speed

Energy-efficient train operation is not yet included in the timetable design process in the Netherlands. Hence, running time supplements are not optimally distributed in the timetable. Therefore research has been conducted on the possibilities to better incorporate energy-efficient train operation into the railway timetable. This paper describes the developed EZR model (energy-efficient operation or in Dutch ‘EnergieZuinig Rijden’) based on optimal control theory and an algorithm that determines the joint optimal cruising speed and coasting point for individual train trips; taking into account a desired robustness, the possibilities for energy-efficient operation, and the desired punctuality during operations. The model is applied in a case study of a regional train line in the Netherlands between Utrecht Centraal and Rhenen. The results show that it is better to distribute the running time supplements evenly than concentrating it near the main stations.