Energy-efficient Train Research Articles

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.

An Energy-Efficient Train Control Framework for Smart Railway Transportation

Railway transportation systems are the backbone of smart cities. With the rapid increasing of railway mileage, the energy consumption of train becomes a major concern. The uniqueness of train operations is that the geographic characteristics of each route is known a priori. On the other hand, the parameters (e.g., loads) of a train varies from trip to trip. Such a specialty determines that an energy-optimal driving profile for each train operation has to be pursued by considering both the geographic information and the inherent train conditions. The solution of the optimization problem, however, is hard due to its high dimension, nonlinearity, complex constraints and time-varying characteristics of a control sequence. As a result, an energy-saving solution to the train control optimization problem has to address the dilemma of optimization quality and computing time. This work proposes an energy-efficient train control framework by integrating both offline and onboard optimization techniques. The offline processing builds a decision tree based sketchy solution through a complete flow of sequence mining, optimization and machine learning. The onboard system feeds the train parameters into the decision tree to derive an optimized control sequence. A key innovation of this work is the identification of optimal patterns of control sequence by data mining the driving behaviors of the experienced train drivers and then apply the patterns to online trip planning. The proposed framework efficiently find an optimized driving solution by leveraging the training results derived with a compute-intensive offline learning flow. The framework was already testified in a smart freight train system. It was demonstrated an average of $9.84$ percent energy-saving can be achieved.

Between two Advisors: Interconnecting Academic and Workplace Settings in an Emerging Field

This article examines a new training design for continuing professional development that aims to support the learning of the novel knowledge and skills needed in emerging professional fields by interconnecting academic and workplace settings. The training design is based on using two advisors, one from working life and the other from an academic context. The article examined whether participants’ personal orientation to adaptive expertise predicts the success of a guidance process. The interconnection of workplace and academic contexts was expected to occur through guidance practices. In addition, the features underlying the most successful guidance relationships were analysed. Data were collected by conducting repeated semi-structured interviews with 18 course participants, eight academic advisors and eight workplace advisors in the context of a 1-year energy efficiency training programme. The results indicated that a trainee’s personal orientation towards adaptive expertise is a significant component in successful guidance processes. An interconnection of workplace and academic knowledge and practices was hardly found in the guidance provided by each participant’s academic and workplace advisors. The feature underlying the most successful guidance relationships are related at the personal, dyad and context levels. An excellent match between the expert profiles of the learner and the advisor appears to be especially critical for successful guidance and powerful knowledge exchange in emerging fields. However, finding matching advisors is often challenging. Many problems are presumably solved if these ‘right persons’ can be found and if the trainees are themselves oriented to utilise the novel resources provided to them by the advisors.

Energy-Efficient Train Tracking Operation Based on Multiple Optimization Models

This paper studies the energy-efficient operation problem of two trains operating along the same line consecutively in urban trail transit (URT). In URT, a moving block signaling (MBS) system is utilized in which the following train's tracking target point is moving forward continuously with the leading train's running. In this case, the following train may be influenced by the leading train's exceptional situations, leading to energy wasting and arrival time delay. In order to reduce energy consumption and arrival delay for the following train, a multiple-optimization-model-based energy-efficient operation method is proposed and verified in this paper. The novelty of this paper lies on the establishment of a novel multiple-model-based switching optimization framework to reduce energy consumption while guaranteeing the punctuality during train tracking operation. More specifically, the operation status of the leading train is combined into the operation optimization for the following train, based on which eight energy-efficient tracking scenarios are discovered and classified into four optimization problems. Then, a general form of running phase sequence is designed to describe the energy-efficient train tracking operation strategy, and a new energy consumption function is proposed. Based on these, the four optimization problems are built into small-scale nonlinear programming models, which are computationally inexpensive and easy for online implementation. During the tracking process, the following train can obtain the energy-efficient strategies through switching among these four optimization models. In this framework, the following train is able to arrive at the next station by the fastest arrival time with lower energy consumption even when the arrival time is delayed by the leading train. Simulation results show the feasibility and effectiveness of the new method.

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.

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.

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.

Energy-efficient train control: The two-train separation problem on level track

When two trains travel in the same direction along the same track it is a common safety requirement that they must be separated by at least one signal. If the signals are located at fixed positions, they divide the track into separate sections and the safety requirement means that two trains cannot occupy the same section at the same time. Safe separation can be ensured by specifying supplementary section clearance times which define the latest allowed exit time for the leading train and the earliest possible entry time for the following train. The clearance times could initially be based on an existing timetable but we will show that adjusting these times can substantially decrease the total energy required by the trains.In this paper we find driving strategies that minimize total energy consumption and allow both trains to finish on time while adhering to the separation constraints imposed by the supplementary clearance times. We establish a new necessary condition to check whether a set of specified clearance times is optimal and discuss a heuristic procedure to find the optimal clearance times and the corresponding speed profiles. We illustrate our methods with a simplified but realistic case study.

Design of a methodology for impact assessment of energy efficiency programs: measuring indirect effects in the Chilean case

Today, many countries are promoting energy efficiency (EE) measures as part of their energy strategy. Among the goals sought with these actions are producing a decoupling between economic growth and energy consumption, reducing the dependence on fossil fuels as a primary energy source, and reducing greenhouse gas emissions. Measuring direct, indirect, and co-benefit effects of EE programs is crucial. However, in the current literature and practice, assessments of EE programs have focused on direct impacts (i.e., impacts whose energy savings can be directly and instantaneously quantified) due to their objectivity and simplicity to put evaluations in a cost-effectiveness framework. Moreover, several assessment methodologies studying the indirect effects of EE programs only focus on identifying the effects and quantifying a proxy of the effects in terms of the number of activities developed or the number of people attending EE training or dissemination events. Some few existing methodologies correctly assess the indirect effects of EE measures, but they often require a significant budget. We propose a new methodology to assess the impacts of EE programs, especially focusing on indirect effects (i.e., long-term effects on energy use), that is suitable for low-budget programs. We focus on those indirect effects having the capability of mobilizing long-term energy savings through transformations in energy markets. We attempt to measure the potential future energy savings that are sustainable in the long term due to a behavioral transformation of energy markets. In order to measure these indirect effects, we use three axes: presence, valuation, and mobilizing capacity. This methodology was applied to 12 EE programs (implemented during 2011 and 2012 in Chile) in order to obtain their indirect impact assessment.

局所的な省エネ運転操作を組み合わせた省エネ運転支援システムの開発

局所的な省エネ運転操作を組み合わせた省エネ運転支援システムの開発

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.

Working in partnership to develop engineering capability in energy efficiency

Energy efficiency is a complex topic to integrate into higher education curricula, with limited success internationally or in Australia. This paper discusses one of the successful initiatives within the Energy Efficiency Training Program, which was jointly managed and implemented by the New South Wales Office of Environment and Heritage and Department of Education and Communities. The state government initiative aimed to increase the knowledge and skills of the New South Wales workforce, help business to identify and implement energy efficiency projects, and provide professional development for the training providers. Key sectors targeted included property, construction, manufacturing and services. The Program was externally evaluated over the three years 2011–2013 and a range of insights were gained through these facilitated reflective opportunities, confirming and building upon literature on the topic to date. This paper presents lessons learned from the engineering part of the program (‘the project’), spanning government agencies, academic institutions, and academia. The paper begins with a contextual summary, followed by a synthesis of key learnings and implications for future training initiatives. It is intended that sharing these lessons will contribute to literature in the field, and assist other organisations in Australia and overseas planning similar initiatives.

Energy-efficient train control in urban rail transit systems

With the latest developments in technology, the Automatic Train Operation (ATO) has been widely used in urban rail transit systems over the past decade. The control process used by the ATO system generally consists of two levels. The high-level control calculates the target speed according to the moving authority of the trains and the low-level control implements precise tracking on the target speed by controlling the traction and braking force. Most of the literature has only focused on the high-level control to optimize the train trajectory, but did not practically combine the low-level control of the ATO system. When the optimized trajectory is applied as the target speed, it will cause frequent switches between acceleration and braking for precise tracking and waste a lot of energy. Hence, this previous research may not be applied to practical ATO systems. In this paper, a numerical algorithm is proposed to solve the energy-ecient train control problem with a given trip time by distributing the reverse time to dierent segments. Then a method is presented for optimization of target speeds based on the ATO control principles, which guides the train to output optimized control sequences. The proposed approach is capable of avoiding the unnecessary switching and then eciently reduces the traction energy consumption of the train switches. Furthermore, case studies have been undertaken based on infrastructure data from the Beijing Yizhuang rail transit line, and the simulation results illustrate that the proposed approach results in good performance with regards to energy saving.

Does academic apprenticeship increase networking ties among participants? A case study of an energy efficiency training program

In order to address the requirements of future education in different fields of academic professional activity, a model called Academic Apprenticeship Education was initiated in Finland in 2009. The aim of this article is to analyse the development of expert networks in the context of a 1-year Academic Apprenticeship Education model in the field of energy efficiency, which is a new and rapidly developing knowledge-intensive field. We examined the creation of networking ties among all course participants, the process of networking in small groups, and individual participants’ networking activity. Data was collected by administering a social networking questionnaire in the beginning and at the end of the training to all course participants (n = 87) and analysed using social network analysis and repeated measures ANOVA. In addition, semi-structured interviews were conducted with organizers of the training to examine how the operational practices of the training supported networking. The results indicated that there was little change in the networking ties among all course participants. However, those small groups that were able to communicate appeared to create internal linkages. At the individual level, more new ties emerged for private sector actors than for public sector actors. In conclusion, we propose that a consolidated educational model should be created for the Academic Apprenticeship Education model in general. The quality of education might be better assured if the current ad hoc networks were not the only way to organise knowledge exchange among participants.

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.

An Iterative Predictive Learning Control Approach With Application to Energy Efficient Train Trajectory Tracking

An approach of iterative predictive learning control (IPLC) is studied with consideration to both precise train trajectory tracking and energy efficient operation. Through designing the predictive cost function, the IPLC approach for input-affine nonlinear systems is formulated and solved in this paper. Its application to train operation is detailed to compromise between punctuality and energy consumption. Rigorous theoretical analysis confirms that the proposed approach can guarantee the asymptotic convergence of train speed and position to desired profiles along iteration axis. Simulation result shows its effectiveness and enegry efficiency.