Speed profile optimization of an electrified train in Cat Linh-Ha Dong metro line based on pontryagin's maximum principle

An urban railway is a complex technical system that consumes large amounts of energy, but this means of transportation still has been obtained more and more popularity in densely populated cities because of its features of high-capacity transportation capability, high speed, security, punctuality, lower emission, reduction of traffic congestion. The improved energy consumption and environment are two of the main objectives for future transportation. Electrified trains can meet these objectives by the recuperation and reuse of regenerative braking energy and by the energy - efficient operation. Two methods are to enhance energy efficiency: one is to improve technology (e.g., using energy storage system, reversible or active substations to recuperate regenerative braking energy, replacing traction electric motors  by energy-efficient traction system as permanent magnet electrical motors; train's mass reduction by lightweight material mass...); the other is to improve operational procedures (e.g. energy efficient driving including: eco-driving; speed profile optimization; Driving Advice System (DAS); Automatic Train Operation (ATO); traffic management optimization...). Among a lot of above solutions for saving energy, which one is suitable for current conditions of metro lines in Vietnam. The paper proposes the optimization method based on Pontryagin's Maximum Principle (PMP) to find the optimal speed profile for electrified train of Cat Linh-Ha Dong metro line, Vietnam in an effort to minimize the train operation energy consumption

Automatic Train Operation Speed Profile Optimization and Tracking with Multi-Objective in Urban Railway

Besides energy-efficiency, people also want train operation to be comfortable, punctual and parking precise. In this paper, a multi-objective model for automatic train operation in urban railway is proposed by unifying dimensions of different objectives firstly. This model is built by applying multi-objective decision with the penalty function, based on the analysis of train performance and its operation environment. Then a genetic algorithm is developed to solve this model and obtain the optimal recommended speed profiles. Thirdly, fuzzy controller is designed to achieve track recommended speed profiles. Finally, with the help of Matlab software, control effect is verified based on simulation. From the simulation results, it can be seen this strategy can meet the requirement of multi-objective, which are energy-saving, parking precisely, running punctually and comfort

Microscopic disruption management: energy consumption and passenger compensation optimisation

Rail operations are often disrupted by accidents that cause traffic to diverge from the scheduled operations, rendering it difficult to run the schedule as planned. In such a case, the operator must change the schedule to return to the original schedule. If passengers are delayed, a train operator may have a policy of economically compensating them (e.g., refunding ticket fare). Compensation amounts are usually determined by the length of the delay. As a result, it is critical to have a smart way of determining whether to accelerate trains to absorb delays, thus increasing energy usage, or to compensate passengers. This paper presents a mathematical model for determining the speed profile while taking passenger usage into account. The model determines the best sequence of operating regimes and switching points between them for a variety of different situations and train types, all while accounting for delays and passenger compensation policies. The aim is to reduce both the amount of energy consumed and the amount of compensation paid to passengers. There are constraints on traction and braking forces, train velocity, forces induced by vertical and horizontal track profile, and passenger compensation policy. The results of computational tests performed on practical problem instances of the Spanish rail operator RENFE are showed. The suggested approach is capable of producing strategies that strike an excellent balance between different managerial objectives.Peer ReviewedPostprint (published version

Measuring attitudes towards VR technology simulation training of train drivers

This paper presents the results of measurements of attitudes towards a demonstrative virtual-reality railway simulator for eco-driving techniques training. This device has been prepared for InnoTrans 2016 railway trade fair, and during this exhibition 144 railway professionals from 35 countries took part in the simulation and filled a questionnaire about their experience and whether they found this type of simulator a viable choice for railway driver training. The results have been very positive with barely any negative answers. This shows that a VR-based railway simulator could potentially be used as a low-cost solution supporting energy efficient train driving

Energy-saving strategies for electric railway systems in the next generation by advanced control of onboard DC/DC converters and enhanced powering acceleration

学位の種別: 修士University of Tokyo(東京大学

A review on artificial intelligence in high-speed rail

High-speed rail (HSR) has brought a number of social and economic benefits, such as shorter trip times for journeys of between one and five hours; safety, security, comfort and on-time commuting for passengers; energy saving and environmental protection; job creation; and encouraging sustainable use of renewable energy and land. The recent development in HSR has seen the pervasive applications of artificial intelligence (AI). This paper first briefly reviews the related disciplines in HSR where AI may play an important role, such as civil engineering, mechanical engineering, electrical engineering and signalling and control. Then, an overview of current AI techniques is presented in the context of smart planning, intelligent control and intelligent maintenance of HSR systems. Finally, a framework of future HSR systems where AI is expected to play a key role is provided

Collaborative Eco-Drive of Railway Vehicles via Switched Nonlinear Model Predictive Control

A switched Nonlinear Model Predictive Control (NMPC) strategy for time efficient energy control of railway vehicles, while fulfilling constraints on velocity, journey time and driving style in a collaborative fashion (collaborative eco-drive) is proposed. More specifically, the train dynamics are modeled as discrete, switched and nonlinear, while the optimization variable is the handle position which modulates the available traction/braking force and has to belong to a set of discrete values and/or operating modes, which the human driver is able to implement. Hence the aim is to choose the optimal handle position that minimizes the cost, is implementable by the driver and also fulfills the eco-driving objective, such that the driving style is constrained by predefined driving sequences. A supervisor detects the states of the trains and subsequently modifies the weights of the cost by negotiating between constraint satisfaction and control aggressiveness, in order to share the available regenerated braking energy among the connected trains in a substation network. The efficiency of the proposed switched NMPC strategy is demonstrated using realistic simulation case study