1,720,952 research outputs found
Running time supplements: Energy-efficient train control versus robust timetables
No full textEnergy-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 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 on a case study on a sprinter/local train line in the Netherlands between Utrecht Centraal and Rhenen. The model results show that it is better to distribute the running time supplements evenly than concentrating it near the main stations.Transport & PlanningCivil Engineering and Geoscience
Effect of regenerative braking on energy-efficient train control
No full textAn important topic to reduce the energy consumption in railways is the use of energy-efficient train control (EETC). Modern trains allow regen-erative braking where the released kinetic energy can be reused. This regener-ative braking has an effect on the optimal driving regimes compared to trains which can only use mechanical braking. This paper compares the impact of regenerative braking on the optimal train control strategy on level track. The energy-efficient train control problem is modelled as an optimal control problem over distance and solved using a Pseudospectral Method. Three different braking strategies are compared: mechanical braking only, a combination of mechanical and regenerative braking, and regenerative braking only. The result of a case study show that energy savings of at least 28% are possible by including regenerative braking
Energy-efficient Train Timetabling
No full textRailways in Europe need to reduce CO2 emissions and energy usage to contribute to sustainability. One of the measures that railway undertakings can apply with low investment cost and both high reductions in CO2 emission and energy consumption, is energy-efficient train trajectory optimization. Another efficient measure is incorporating energy-efficiency in the timetable design. The aim of this thesis is to incorporate energy-efficient train trajectory optimization in the timetable design in order to improve the potential for energy-efficiency of railways. The thesis develops and applies models for the energy-efficient traincontrol problem for a single train over (multiple) stops with both mechanical and regenerative braking behavior. In addition, energy-efficient train trajectory optimization is incorporated in timetabling by formulating and developing algorithms for a multiple-objective optimization problem applied on a corridor with multiple interacting trains.TRAIL Thesis Series no. T2022/1, the Netherlands TRAIL Research SchoolTransport and Plannin
Rijtijdspeling in treindienstregelingen: Energiezuinig rijden versus robuustheid
No full textCommissioned by the Dutch Railways Operation (NS Reizigers) research has been conducted on the possibilities to better incorporate energy-efficient train operation into the timetable. The motivation for this research is based on the fact that energy-efficient operation is not yet optimally included in the timetable design. This is because of the fact that the running time supplement is not optimally divided. Running time supplement or slack time is the running time above the minimal running time between two timetable points. The objective of the research is to develop a model which determines the optimal coasting point and the optimal cruising speed for trains and the associated running time supplement distribution; taking into account the desired robustness, the possibilities for energy-efficient operation and the desired punctuality during the execution of the timetable. The behavior of a train is described by four driving regimes: acceleration, cruising, coasting and braking. With these driving regimes this study considers two driving strategies: time optimal and energy-efficient. The time optimal driving strategy requires a train to drive as fast as possible from A to B using the driving regimes acceleration, braking and possibly cruising. The energy-efficient driving strategy requires a train to drive also from A to B using as less as possible traction energy given the available time from the timetable. The coasting regime is an important part in this strategy. The energy-efficient driving strategy is determined by the optimal control theory. The algorithm which is applied in MATLAB determines the energy-efficient driving strategy by calculating the optimal coasting point and the optimal cruising speed given the time from the timetable. The model has been applied on the section Utrecht Central – Rhenen for sprinter train series 7400. The results of this research show that there are yearly energy savings possible of almost \u80 26.400 if the energy-efficient driving strategy is used instead of the UZI (‘Universeel Zuinig rijden Idee’) method of NS Reizigers. Moreover the results show that using a uniform distribution of the running time supplements leads to extra energy savings and an improvement for the punctuality compared to the method of tightening the timetable. Tightening the timetable means that the running time supplements are placed as much as possible short before stations where the punctuality is measured. These yearly extra savings which are possible by the uniform distribution instead of the current slack time are almost \u80 44.000 for the energy-efficient driving strategy and are almost \u80 27.500 for the UZI method on the total section.Transport & PlanningTransport & PlanningCivil Engineering and Geoscience
Energy-efficient train control using nonlinear bounded regenerative braking
No full textEnergy-efficient train control (EETC) has been studied a lot over the last decades, because it contributes to cost savings and reduction of CO2 emissions. The aim of EETC is to minimize total traction energy consumption of a train run given the running time in the timetable. Most research is focused to apply mechanical braking on this problem. However, current trains are able to use regenerative braking, which leads to another optimal driving strategy compared to mechanical braking. Research on EETC with a realistic nonlinear bounded model for regenerative braking or a combination between regenerative and mechanical braking is limited. The aim of this paper is to compare the difference between the EETC with regenerative and/or mechanical braking. First, we derive the optimal control structure for the problems with different braking combinations. Second, we apply the pseudospectral method on different scenarios where we investigate the effect of varying speed limits and gradients on the different driving strategies. Results indicate that compared to pure mechanical braking, combined regenerative and mechanical braking leads to a driving strategy with higher energy savings, a lower optimal cruising speed, a shorter coasting phase and a higher speed at the beginning of the braking phase. In addition, a nonlinear bounded regenerative braking curve leads to a different driving strategy compared to a constant braking rate that is commonly used in literature. We show that regenerative braking at a constant braking rate overestimates the total energy savings.Transport and Plannin
Ontwerpmethoden van Dienstregelingen, verkennend onderzoek naar de ontwerpmethodiek per fase in het planningsproces bij NS & ProRail.
No full textGraduation at faculty: Civil Engineering and Geosciences (CITG), Mechanical, Maritime and Materials Engineering (3mE), Technology, Policy and Management (TPM). This thesis deals with real-world plannings problems of timetable design. Improving the timetable design methodology results in a more reliable timetable and a higher quality for passengers. This research first identified the Dutch timetable design process. Afterwards the weaknesses of the Dutch timetable design methodology is identified. These identifications are made by conducting interviews within NS & ProRail. In the third section a case study is conducted. The Dutch 2014 timetable is simulated and compared with a conflict-free timetable which was constructed using microscopic methods (planning in seconds instead of minutes and headways calculated by blocking times instead of plan norms). The simulations resulted in 37% less conflicts and a decrease of 8% delay. Further research is recommended to validate the conclusions in this paper and to investigate the practical usability of a microscopic methodology in an actual design process.Civil Engineering and GeosciencesTransport & PlanningTI
Pseudospectral optimal train control
No full textIn the last decade, pseudospectral methods have become popular for solving optimal control problems. Pseudospectral methods do not need prior knowledge about the optimal control structure and are thus very flexible for problems with complex path constraints, which are common in optimal train control, or train trajectory optimization. Practical optimal train control problems are nonsmooth with discontinuities in the dynamic equations and path constraints corresponding to gradients and speed limits varying along the track. Moreover, optimal train control problems typically include singular solutions with a vanishing Hessian of the associated Hamiltonian. These characteristics make these problems hard to solve and also lead to convergence issues in pseudospectral methods. We propose a computational framework that connects pseudospectral methods with Pontryagin's Maximum Principle allowing flexible computations, verification and validation of the numerical approximations, and improvements of the continuous solution accuracy. We apply the framework to two basic problems in optimal train control: minimum-time train control and energy-efficient train control, and consider cases with short-distance regional trains and long-distance intercity trains for various scenarios including varying gradients, speed limits, and scheduled running time supplements. The framework confirms the flexibility of the pseudospectral method with regards to state, control and mixed algebraic inequality path constraints, and is able to identify conditions that lead to inconsistencies between the necessary optimality conditions and the numerical approximations of the states, costates, and controls. A new approach is proposed to correct the discrete approximations by incorporating implicit equations from the optimality conditions. In particular, the issue of oscillations in the singular solution for energy-efficient driving as computed by the pseudospectral method has been solved.Transport and Plannin
Review of energy-efficient train control and timetabling
No full textThe energy consumption of trains is highly efficient due to the low friction between steel wheels and rails, although the efficiency is also influenced largely by the driving strategy applied and the scheduled running times in the timetable. Optimal energy-efficient driving strategies can reduce operating costs significantly and contribute to a further increase of the sustainability of railway transportation. The railway sector hence shows an increasing interest in efficient algorithms for energy-efficient train control, which could be used in real-time Driver Advisory Systems (DAS) or Automatic Train Operation (ATO) systems. This paper gives an extensive literature review on energy-efficient train control (EETC) and the related topic of energy-efficient train timetabling (EETT), from the first simple models from the 1960s of a train running on a level track to the advanced models and algorithms of the last decade dealing with varying gradients and speed limits, and including regenerative braking. Pontryagin’s Maximum Principle (PMP) has been applied intensively to derive optimal driving regimes that make up the optimal energy-efficient driving strategy of a train under different conditions. Still, the optimal sequence and switching points of the optimal driving regimes are not trivial in general, which led to a wide range of optimization models and algorithms to compute the optimal train trajectories and more recently to use them to optimize timetables with a trade-off between energy efficiency and travel times
Multi-objective railway timetabling including energy-efficient train trajectory optimization
Full text linkEnergy-efficient train driving is an important topic to railway undertakings (RUs) for sustainability and cost reduction. The timetable affects the possibilities for energy-efficient train driving by the amount of running time supplements, which is the topic of energy-efficient train timetabling (EETT). The scientific literature on EETT focuses mainly on the balance between total running time and energy consumption. However, in practice RUs consider a trade-off between the total running time, the infrastructure occupation and the timetable robustness, while energy efficiency is not considered. In this paper we consider a multiple-objective timetabling problem at a microscopic infrastructure level that adds energy consumption to the other three objectives. We approach the multiple-objective problem by a brute force search algorithm, where we use two different methods to compute the optimal solution: A weighted sum method and a distance metric method. We apply the method to a Dutch case study on the corridor between the stations Arnhem Central and Nijmegen with alternating Intercity and Sprinter trains, without intermediate overtaking possibilities. The results indicate that there is a balancing relationship between the total running time and energy consumption, without influencing the infrastructure occupation and robustness. The results of the 10 Pareto-optimal solutions show a variation of 5% for the total running time, 18% for the energy consumption, 0.3% for the extended cycle time, and 0.8% for the buffer time. The shortest running time leads to 18% more energy consumption than the longest running time with 5% more running time supplement. In both cases the extended cycle time and buffer time are almost constant. On the other hand, reducing the infrastructure occupation leads to homogenization of the timetable. Therefore, including energy consumption in the multiple-objective can be used to balance the trade-off between total running time and capacity consumption.Transport and Plannin
Coasting advice based on the analytical solutions of the train motion model
No full textA large variety of supervision, data analysis and communication algorithms monitor trains, exploiting most of their available computational power. On-board eco-driving algorithms such as Driver Advisory Systems are no exception, as the computational power available can limit their complexity and features. This was the case of Roltijd, the in-house developed Driver Advisory System based on coasting advice of Nederlandse Spoorwegen (NS), the main Dutch passenger railway undertaking. This platform cal-culated the coasting curves at every second by integrating the equations of motion numerically, assuming that the track is fl at. However, the plans of NS regarding gener-ating more complex driving advice require replacing this coasting curve calculation by a more computationally-effi cient algorithm. In this article we propose a new coasting advice algorithm based on the analytical solutions of the train motion model’s diff er-ential equations that assumes the gradients and speed limits as piecewise constant functions of the train location. We analyze the qualitative properties of these solutions using the theory of dynamical systems, showing that bifurcations arise depending on the value of the gradient and the applied tractive eff ort. We validate the proposed algorithm by comparing its performance and accuracy against the previous method and a train trajectory optimizer based on a pseudospectral method, fi nding that our algorithm is accurate and can be 15 times faster than the previous method. This allows NS to implement the proposed method on the trains running in the Dutch railway network, contributing daily to the sustainable mobility of 1.3 million passengers.Transport and Plannin
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