Optimal driving strategy for traction energy saving on DC suburban railways

Bocharnikov, Y.V.; Tobias, Andrew M.; Roberts, Clive; Hillmansen, Stuart; Goodman, C.J.

doi:10.1049/iet-epa:20070005

Cited by 227 publications

(154 citation statements)

References 7 publications

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“…Han et al [28] conclude that the performance of their genetic algorithm is better than the analytic solution obtained by Howlett and Pudney [4] in view of energy cost. A formal method combined genetic algorithm and fuzzy logic was proposed in [29] to optimize a weighted sum between energy consumption and running time. Bocharnikov et al [29] concluded that the energy saving was affected by the acceleration and deceleration rates by running a series of simulations in parallel by using genetic algorithm.…”

Section: A Fuzzy and Evolutionary Algorithmsmentioning

confidence: 99%

A survey on optimal trajectory planning for train operations

Wang

Ning

Cao

et al. 2011

Proceedings of 2011 IEEE International Conference on Service Operations, Logistics and Informatics

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Abstract-Because of the rising energy prices and environmental concerns, the calculation of energy-optimal reference trajectories for trains is significant for energy saving. On the other hand, with the development automatic train operation (ATO), the optimal trajectory planning is significant to the performance of train operation. In this paper, we present an integrated survey of this field. First, a nonlinear continuoustime train model and a continuous-space model of train operations are described, after which the optimal trajectory planning problem is formulated based on these two models. The various approaches in the literature to calculate the reference trajectory are reviewed and categorized into two groups: analytical solutions and numerical optimization. Finally, a short discussion of some open topics in the field of optimal trajectory planning for train operations are given.

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Section: A Fuzzy and Evolutionary Algorithmsmentioning

confidence: 99%

A survey on optimal trajectory planning for train operations

Wang

Ning

Cao

et al. 2011

Proceedings of 2011 IEEE International Conference on Service Operations, Logistics and Informatics

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“…In terms of energy efficiency, the ideal operation of a train is in a sequence of: maximum tractive effort up to a speed limit (Schmid 2007), constant speed, coasting from a pre-defined location, and braking at the precise moment to stop at the station (Bocharnikov 2007). The train driver can operate the train without any traction control error and brake in the correct sequence, which gives the ideal operation of the train.…”

Section: Modelling and Environmental Objectivesmentioning

confidence: 99%

“…The calculation of a coasting point is within the second and third regions because these are not necessary for the train to complete its journey (Bocharnikov 2007).…”

Section: Coasting Pointsmentioning

confidence: 99%

“…Research into developing technologies that improve the energy efficiency of railways include hybrid systems (Ogawa 2007, Hillmansen 2006, Energy Storage 2 Systems (ESS) (Fletcher 1991, Açikbas 2007, Flinders 1995, and driving style (Bocharnikov 2007, Wong 2004, Golovitcher 2001. However, there has not been extensive research into the proficiency of these energy efficient schemes to improve railways, nor into the quantification of their effect when used together.…”

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confidence: 99%

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Simulation of energy efficiency improvements on commuter railways

Hull¹,

Roberts²,

Hillmansen³

2010

IET Conference on Railway Traction Systems (RTS 2010)

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Railway networks consume large amounts of energy and, as a result, are the cause of a significant amount of CO2 and other harmful gas emissions. British railways strive to comply with objectives set by the European Rail Research Advisory Council and the Committee on Climate Change. Improvements in the operation of railway networks through the use of energy efficiency regimes can decrease the operational costs and CO2 emissions. Existing and industry proven methods include regeneration, coasting allowances and driver training. The use of a simulator allows railway organisations to understand and quantify the benefits of the various methods for their particular rolling stock and networks.This thesis describes the development of a multi-train simulator for the Merseyrail network. The simulator is based on a pre-existing single train simulator.Results from the simulator show that a combination of regeneration, introduction of coasting points and improved driving style have the potential to provide up to a 50% increase in energy efficiency. In particular, the study shows that a 23% reduction in energy consumption through regeneration; a 22% energy reduction with the strategic placement of coasting points; and a 4% reduction in energy when an improved driving style is adopted, are possible. These improvements have the potential to eliminate 12 kilotonnes of CO2 emissions from Merseyrail per year. ACKNOWLEDGEMENTS

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“…The operation strategy of eco-driving is to optimize the speed profile of each train, while the utilization of regenerative braking energy is for one train to re-use the energy generated by the braking of other trains. The first topic has been well studied by many researchers [3][4][5][6][7][8][9][10][11][12][13][14][15], while the latter is a rather new approach. It is based on the practice that the acceleration process consumes energy from the power supply network whereas the braking process returns energy to the supply network.…”

Section: Introductionmentioning

confidence: 99%

An Integrated Energy-Efficient Operation Methodology for Metro Systems Based on a Real Case of Shanghai Metro Line One

Gong

Zhang

et al. 2014

Energies

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Metro systems are one of the most important transportation systems in people's lives. Due to the huge amount of energy it consumes every day, highly-efficient operation of a metro system will lead to significant energy savings. In this paper, a new integrated Energy-efficient Operation Methodology (EOM) for metro systems is proposed and validated. Compared with other energy saving methods, EOM does not incur additional cost. In addition, it provides solutions to the frequent disturbance problems in the metro systems. EOM can be divided into two parts: Timetable Optimization (TO) and Compensational Driving Strategy Algorithm (CDSA). First, to get a basic energy-saving effect, a genetic algorithm is used to modify the dwell time of each stop to obtain the most optimal energy-efficient timetable. Then, in order to save additional energy when disturbances happen, a novel CDSA algorithm is formulated and proposed based on the foregoing method. To validate the correctness and effectiveness of the energy-savings possible with EOM, a real case of Shanghai Metro Line One (SMLO) is studied, where EOM was applied. The result shows that a significant amount of energy can be saved by using EOM. OPEN ACCESSEnergies 2014, 7 7306

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Optimal driving strategy for traction energy saving on DC suburban railways

Cited by 227 publications

References 7 publications

A survey on optimal trajectory planning for train operations

A survey on optimal trajectory planning for train operations

Simulation of energy efficiency improvements on commuter railways

An Integrated Energy-Efficient Operation Methodology for Metro Systems Based on a Real Case of Shanghai Metro Line One

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