Energy-efficient train control: From local convexity to global optimization and uniqueness

Albrecht, Amie; Howlett, Phil; Pudney, Peter; Vu, Xuan

doi:10.1016/j.automatica.2013.07.008

Cited by 186 publications

(105 citation statements)

References 8 publications

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“…Albrecht et al (2013a) proved that the switching points obtained from the local energy minimization principle are uniquely defined for each steep section of track and therefore also deduced that the global optimal strategy is unique. They now reported an implementation of the algorithm in a DAS called Energymiser, the follow up of Freightmiser.…”

Section: Exact Methods Without Regenerative Brakingmentioning

confidence: 99%

Review of energy-efficient train control and timetabling

Scheepmaker

Goverde

Kroon

2017

European Journal of Operational Research

333

149

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The 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.

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Section: Exact Methods Without Regenerative Brakingmentioning

confidence: 99%

Review of energy-efficient train control and timetabling

Scheepmaker

Goverde

Kroon

2017

European Journal of Operational Research

333

149

View full text Add to dashboard Cite

show abstract

“…On the other hand, many studies focus on the efficient driving of the train. Albrecht et al [15] proved that the optimal switching points are uniquely defined for each steep section, and the global optimal strategy is also unique. Carvajal et al [16] proposed an optimization algorithm to obtain the minimum energy consumption and the Pareto optimal curve for CBTC (communication-based train control).…”

Section: Introductionmentioning

confidence: 99%

Optimization of Train Operation in Multiple Interstations with Multi-Population Genetic Algorithm

Huang

et al. 2015

Energies

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Subway systems consume a large amount of energy each year. How to reduce the energy consumption of subway systems has already become an issue of concern in recent years. This paper proposes an energy-efficient approach to reduce the traction energy by optimizing the train operation for multiple interstations. Both the trip time and driving strategy are considered in the proposed optimization approach. Firstly, a bi-level programming model of multiple interstations is developed for the energy-efficient train operation problem, which is then converted into an integrated model to calculate the driving strategy for multiple interstations. Additionally, the multi-population genetic algorithm (MPGA) is used to solve the problem, followed by calculating the energy-efficient trip times. Finally, the paper presents some examples based on the operation data of the Beijing Changping subway line. The simulation results show that the proposed approach presents a better energy-efficient performance than that with only optimizing the driving strategy for a single interstation.

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“…First some energy-efficient operation strategies, which choose the proper switch points among the four driving phase (acceleration, cruising, coasting and braking) in order to minimize the energy cost for a single train, had been described [1,2]. On this basis some extended work have been presented, like the local to global energy-efficient control strategy with the local steep rail track in [3] and the multi-stage energy-efficient subway control approach for multi-trains [4]. Moreover, a number of advanced control methods had been used for train tacking problem, which can make sure not only the punctuality but also precise train stopping under the train overspeed protection, Linear Quadratic Regulation (LQR) [5], H2/H∞ control [6], robust adaptive control [7], iterative learning control [8].…”

Section: Introductionmentioning

confidence: 99%

“…All of the methods above, however, are either only considering the optimal speed trajectory planning [1][2][3] or closed-loop tracking problem [5][6][7][8]. Compared with the two-level approach above, an approach combining the calculation of optimization with implementation of tracking control together is more cost effective.…”

Section: Introductionmentioning

confidence: 99%