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Efficient driving of CBTC ATO operated trains
KTH, School of Electrical Engineering (EES), Electric Power and Energy Systems. Railways Research Group. IIT. UPComillas.ORCID iD: 0000-0002-1219-9462
2017 (English)Doctoral thesis, monograph (Other academic)
Abstract [en]

Energy consumption reduction is one of the priorities of metro operators, due to financial cost and environmental impact. The new signalling system Communications-Based Train Control (CBTC) is being installed in new and upgraded metro lines to increase transportation capacity. But its continuous communication feature also permits to improve the energy performance of traffic operation, by updating the control command of the Automatic Train Operation (ATO) system at any point of the route. The present research addresses two main topics. The first is the design of efficient CBTC speed profiles for undisturbed train trajectory between two stations. The second takes into account the interaction between two consecutive trains under abnormal traffic conditions and proposes a tracking algorithm to save energy.

In the first part of the research an off-line methodology to design optimal speed profiles for CBTC-ATO controlled trains is proposed. The methodology is based on a new multi-objective optimisation algorithm named NSGA-II-F, which is used to design speed profiles in such a way that can cover all the possible efficient solutions in a pseudo-Pareto front. The pseudo–Pareto front is built by using dominated solutions to make available a complete set of feasible situations in a driving scenario. The uncertainty in the passenger load is modelled as a fuzzy parameter. Each of the resulting speed profiles is obtained as a set of parameters that can be sent to the ATO equipment to perform the driving during the operation.

The proposed optimisation algorithm makes use of detailed simulation of the train motion. Therefore, a simulator of the train motion has been developed, including detailed model of the specific ATO equipment, the ATP constraints, the traction equipment, the train dynamics and the track.

A subsequent analysis considers the effect in the design of considering the regenerative energy flow between the train and the surrounding railway system.

The second part of the research is focused on the proposal and validation of a fuzzy tracking algorithm for controlling the motion of two consecutive trains during disturbed conditions. A disturbed condition is understood as a change in the nominal driving command of a leading train and its consequences in the subsequent trains. When a train runs close enough to the preceding one, a tracking algorithm is triggered to control the distance between both trains. The following train receives the LMA (limit of movement authority) via radio, which is updated periodically as the preceding train runs. The aim of the proposed algorithm is to take actions in such a way that the following train could track the leading train meeting the safety requirements and applying an energy saving driving technique (coasting command). The uncertainty in the variations of the speed of the preceding train is modelled as a fuzzy quantity. The proposed algorithm is based on the application of coasting commands when possible, substituting traction/braking cycles by traction/coasting cycles, and hence saving energy. Both algorithms were tested and validated by using a detailed simulation program. The NSGA-II-F algorithm provided additional energy savings when compared to fixed block distance-to-go configurations, and giving a more even distribution of the solutions. The fuzzy tracking algorithm provides energy savings with a minor impact on running times while improving comfort, because of the reduction of the inefficient traction/braking cycles.

Place, publisher, year, edition, pages
Stockholm: KTH Royal Institute of Technology, 2017. , p. 112
Series
TRITA-EE, ISSN 1653-5146 ; TRITA-EE 2016:201
Keyword [en]
Energy efficiency, CBTC signalling system, ATO, Metro, Ecodriving, NSGA-II-F, pseudo-Pareto front, Tracking algorithm, Fuzzy parameters, Moving-block, Train Simulation.
National Category
Other Electrical Engineering, Electronic Engineering, Information Engineering
Research subject
Electrical Engineering; Energy Technology; Transport Science
Identifiers
URN: urn:nbn:se:kth:diva-201723ISBN: 978-84-617-7523-1 (electronic)OAI: oai:DiVA.org:kth-201723DiVA, id: diva2:1074658
Public defence
2017-03-07, Pérez del Pulgar, C/Alberto Aguilera 25, Madrid, 12:00 (English)
Opponent
Supervisors
Funder
EU, European Research Council
Note

QC 20170216

Available from: 2017-02-16 Created: 2017-02-15 Last updated: 2017-03-07Bibliographically approved

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