Application and benchmarking of a direct method to optimize the fuel consumption of a diesel electric locomotive

[EN] This paper addresses the optimal control of a long-haul passenger train to deliver minimum-fuel operations. Contrary to the common Pontryagin minimum principle approach in railroad-related literature, this work addresses this optimal control problem with a direct method of optimization, the use of which is still marginal in this field. The implementation of a particular direct method based on the Euler collocation scheme and its transcription into a nonlinear problem are described in detail. In this paper, this optimization technique is benchmarked with well-known optimization methods in the literature, namely dynamic programming and the Pontryagin minimum principle, by simulating a real route. The results showed that the direct methods are on the same level of optimality compared with other algorithms while requiring reduced computational time and memory and being able to handle very complex dynamic systems. The performance of the direct method is also compared to the real trajectory followed by the train operator and exhibits up to 20% of fuel saving in the example route.The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: The authors acknowledge the support of Spanish Ministerio de Economı´a, Industria y Competitividad through project TRA2016-78717-R.Macian Martinez, V.; Guardiola, C.; Pla Moreno, B.; Reig, A. (2018). Application and benchmarking of a direct method to optimize the fuel consumption of a diesel electric locomotive. Proceedings of the Institution of Mechanical Engineers Part F Journal of Rail and Rapid Transit. 232(9):2272-2289. https://doi.org/10.1177/0954409718772133S22722289232

Leveraging Connected Highway Vehicle Platooning Technology to Improve the Efficiency and Effectiveness of Train Fleeting Under Moving Blocks

Future advanced Positive Train Control systems may allow North American railroads to introduce moving blocks with shorter train headways. This research examines how closely following trains respond to different throttle and brake inputs. Using insights from connected automobile and truck platooning technology, six different following train control algorithms were developed, analyzed for stability, and evaluated with simulated fleets of freight trains. While moving blocks require additional train spacing beyond minimum safe braking distance to account for train control actions, certain following train algorithms can help minimize this distance and balance fuel efficiency and train headway by changing control parameters

Braking penalized receding horizon control of heavy haul trains

Incorporated with a receding horizon control (RHC) approach, a penalty method is proposed to reduce energy wasted by braking in a heavy haul train’s operation. The train’s practical nonlinear model is linearized to design the RHC controller. This controller is then applied to the train practical nonlinear dynamics and its performances are analyzed. In particular, the main focus in this study is on the brake penalty’s impact on the train performances. Meantime, a fence method is presented to tackle two issues. The first one is that all the cars in a train cannot be controlled individually due to limit of available transmission channels for control systems in a long train. The other one is that the RHC approach suffers from heavy computation and memory load. Simulations verified that the brake penalty presented in the design can reduce a train’s energy consumption and intrain forces remarkably without sacrificing the train’s velocity tracking performance. Simulations also verified that the fence method is essential to reduce the related computation load when the RHC approach is applied to a long heavy haul train. Further, it is demonstrated that the fence method can effectively shorten computation time and reduce memoryhttp://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=6979hb2014ai201

Development of an optimal operation approach in the MPC framework for heavy-haul trains

An operation control approach for heavy haul trains to optimize their performance, including operation safety, service quality and energy consumption, is proposed. Following a model predictive control method, the controller is capable of scheduling a train to operate optimally during a long section of the rail track. In the cost function, two penalty factors are presented, one for the braking forces and one for coupler damping effects. The penalty for braking forces is employed to reduce energy waste incurred by braking. The penalty for coupler damping is introduced to alleviate the cyclic vibration of couplers, which link adjacent cars in the train. The damping penalty is also expected to reduce energy wasted by coupler damping and corresponding maintenance/replacement cost of the dampers. In addition, the weight of the velocity tracking term in the objective function is modified to vary dynamically according to the train’s velocity to improve the train’s overall performance. Simulations verify the effectiveness of the proposed control approach. Discussions over the impacts of the two penalty factors and dynamic weight method are provided together with some suggestions on their applications.http://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=6979hb201

Multi-agent Near Real-Time Simulation of Light Train Network Energy Sustainability Analysis

As an attractive transportation mode, rail transit consumes a lot of energy while transporting a large number of passengers annually. Most energy-aimed research in rail transit focuses on optimizing the train timetable and speed trajectory offline. However, some disturbances during travel will cause the train to fail to follow the offline optimized control strategy, thus invalids the offline optimization. In the typical rail transit control framework, the moving authority of trains is calculated by the zone controller based on the moving/fixed block system in the zone. The zone controller is used to ensure safety when the travel plan of trains changes due to disturbance. Safety is guaranteed during the process, but the change of travel plan leads to extra energy costs. The energy-aimed optimization problem in rail transit requires ensuring safety, pursuing punctuality with considering track slope, travel comfort, energy transferring efficiency, and speed limit, etc. The complex constraints lead to high computational pressure. Therefore, it is difficult for the regional controller to re-optimize the travel plan for all affected trains in near real-time. Multi-agent systems are widely used in many other fields, which show decent performance in solving complex problems by coordinating multiple agents. This study proposes a multi-agent system with multiple optimization algorithms to realize energy-aimed re-optimization in rail transit under different disturbances. The system includes three types of agents, train agents, station agents and central agents. Each agent exchanges information by following the time trigger mechanism (periodically) and the event trigger mechanism (occasionally). Trigger mechanism ensures that affected agents receive necessary information when interference occurs, and their embedded algorithms can achieve necessary optimization. Four types of cases 5 / 128 are tested, and each case has plenty of scenarios. The tested results show that the proposed system provides encouraging performance on energy savings and computational speed

DC railway power supply system reliability evaluation and optimal operation plan

With the continuous and rapid development of the economy and the acceleration of urbanisation, public transport in cities has entered a period of rapid development. Urban rail transit is characterised by high speed, large traffic volume, safety, reliability and punctuality, which are incomparable with those of other forms of public transport. The traction power supply system (TPSS) is an important part of an electrified railway, and its safety issues are increasingly prominent. Different from the substation in a general power system, the load of a TPSS has a great impact on the traction transformer; moreover, in order to ensure normal operation of the train in case of failure, the traction substation must be able to access a cross-district power supply, as it has a high demand for reliable operation. The safe and reliable operation of DC TPSSs is the basis of the whole urban railway transit system. Previous studies have investigated the reliability of the TPSS main electrical wiring system. However, the impact of traction load and the actual operation of trains on system reliability has not been considered when designing a DC railway power supply system. The purpose of the research for this thesis is to find an optimal system operation plan for urban railways, considering load characteristics. This thesis begins with a review of the main arrangements of DC railway power supply systems and the literature on railway reliability studies. A model of single train simulation and a power supply system is established in MATLAB. The developed simulator is then integrated with a TPSS reliability model to evaluate the energy and reliability performance of DC railway power systems. Based on the train traction load model and train schedule, a comprehensive method for evaluating a DC TPSS considering traction load is proposed. Through simulation of the actual operation of the train group, the system energy consumption and substation life loss generated under different train operation diagrams and schedules are compared to provide a reference for the reasonable design of the timetable. Taking the life loss and energy consumption of the whole TPSS as the objective function, a genetic algorithm is used to optimise the train speed, coasting velocity, station dwell time and headway to find the optimal operation strategy. This is illustrated with a case study of the Singapore East–West metro line. The study has addressed the following issues: development of a multi-train power simulator, evaluation of reliability performance, and finally the search for an optimal operation plan. The train running diagram and timetable are optimised jointly. This can help railway operators make decisions for an optimal operation plan and reduce the operation risk of the power system

Proceedings of the 3rd International Conference on Models and Technologies for Intelligent Transportation Systems 2013

Challenges arising from an increasing traffic demand, limited resource availability and growing quality expectations of the customers can only be met successfully, if each transport mode is regarded as an intelligent transportation system itself, but also as part of one intelligent transportation system with “intelligent” intramodal and intermodal interfaces. This topic is well reflected in the Third International Conference on “Models and Technologies for Intelligent Transportation Systems” which took place in Dresden 2013 (previous editions: Rome 2009, Leuven 2011). With its variety of traffic management problems that can be solved using similar methods and technologies, but with application specific models, objective functions and constraints the conference stands for an intensive exchange between theory and practice and the presentation of case studies for all transport modes and gives a discussion forum for control engineers, computer scientists, mathematicians and other researchers and practitioners. The present book comprises fifty short papers accepted for presentation at the Third Edition of the conference. All submissions have undergone intensive reviews by the organisers of the special sessions, the members of the scientific and technical advisory committees and further external experts in the field. Like the conference itself the proceedings are structured in twelve streams: the more model-oriented streams of Road-Bound Public Transport Management, Modelling and Control of Urban Traffic Flow, Railway Traffic Management in four different sessions, Air Traffic Management, Water Traffic and Traffic and Transit Assignment, as well as the technology-oriented streams of Floating Car Data, Localisation Technologies for Intelligent Transportation Systems and Image Processing in Transportation. With this broad range of topics this book will be of interest to a number of groups: ITS experts in research and industry, students of transport and control engineering, operations research and computer science. The case studies will also be of interest for transport operators and members of traffic administration