Optimal driving strategy for traction energy saving on DC suburban railways

Optimal driving strategy for traction energy saving on DC suburban railways

For access to this article, please select a purchase option:

Buy article PDF
(plus tax if applicable)
Buy Knowledge Pack
10 articles for £75.00
(plus taxes if applicable)

IET members benefit from discounts to all IET publications and free access to E&T Magazine. If you are an IET member, log in to your account and the discounts will automatically be applied.

Learn more about IET membership 

Recommend to library

You must fill out fields marked with: *

Librarian details
Your details
Why are you recommending this title?
Select reason:
IET Electric Power Applications — Recommend this title to your library

Thank you

Your recommendation has been sent to your librarian.

Energy saving on electrified railways has been studied for many years and the technical solution is usually provided by a combination of driving strategy (e.g. coasting), regenerative braking and energy storage systems. An alternative approach is for the driver (or automatic train operation system if fitted) to manage energy consumption more efficiently. A formal method for optimising traction energy consumption during a single-train journey by trading-off reductions in energy against increases in running time has been demonstrated. The balance between saving energy and running faster has been investigated by designing a fitness function with variable weightings. Energy savings were found, both qualitatively and quantitatively, to be affected by acceleration and braking rates, and, by running a series of simulations in parallel with a genetic algorithm search method, the fitness function was used to identify optimal train trajectories. The influence of the fitness function representation on the search results was also explored.


    1. 1)
      • C.D. Waters , M. Farell , R.P. Grainger , P.J. Leahy , B. Mellitt . Dublin area rapid transit. IEE Proc. Electr. Power Appl. , 3 , 134 - 150
    2. 2)
    3. 3)
      • `Review of the discount for using regenerative braking', A Report for the Office of Rail Regulations. AEA TECHNOLOGY, 27 June 2005.
    4. 4)
      • Richardson, M.B.: `Flywheel energy storage system for traction applications', IEE Int. Conf. Power Electronics, Machines and Drives, April 2002, p. 275–279.
    5. 5)
      • B. Mellitt , C.J. Goodman . Simulation study of DC transit systems with inverting substations. IEE Proc. , 2 , 38 - 50
    6. 6)
    7. 7)
    8. 8)
      • R. Liu , I.M. Golovitcher . (2003) Energy-efficient operation of rail vehicles, Transportation research Part A.
    9. 9)
      • Mellitt, B., Sujitjorn, S., Goodman, C.J., Rambukwella, N.B.: `Energy minimisation using an expert system for dynamic coast control in rapid transit trains', Presented at Conf. on Railway Engineering, 1987, p. 48–52.
    10. 10)
      • T. Albrecht , S. Oettich . (2002) A new integrated approach to dynamic schedule synchronization and energy saving train control, Computers in railways VIII.
    11. 11)
      • T. Albrecht . (2004) Reducing power peaks and energy consumption in rail transit systems by simultaneous train running time control, Computers in railways IX.
    12. 12)
      • P. Lukaszewicz . (2004) Energy-saving driving methods for freight trains, Computers in railways IX.
    13. 13)
      • P. Lukaszewicz . (2000) Driving techniques and strategies for freight trains, Computers in railways VII.
    14. 14)
      • Lukaszewicz, P.: `Energy consumption and running time for trains', 2001, PhD, Railway Technologies, Department of Vehicle Engineering, Royal Institute of Technology, Stockholm.
    15. 15)
    16. 16)
    17. 17)
      • H.S. Hwang . Control strategy for optimal compromise between trip time and energy consumption in a high-speed railway. IEEE Trans. Syst. Man Cybernet. , 6 , 791 - 802
    18. 18)
      • E. Cox . (2005) Fuzzy modeling and genetic algorithms for data mining and exploration.
    19. 19)
      • Chang, C.S., Wand, W., Liew, A.C., Wen, F.S., Srinivasan, D.: `Genetic algorithm based bicriterion optimisation for traction substations in DC railway system', IEEE Int. Conf. Computation, 1995, p. 11–16.
    20. 20)
      • Hetherington, D.: `Traction power and train performance', IET seminar on “Innovation in the railways: evolution or revolution?”, 14 September 2006, p. 47–64.
    21. 21)
      • S.I. Osipov , S.S. Osipov . (2000) Foundations of train traction.
    22. 22)
      • Mellitt, B., Goodman, C.J., Rambukwella, N.B.: `Optimisation of chopper equipment for minimising energy consumption in rapid transit systems', IEE Int. Conf. Railways in the Electronic Age, November 1981, London, p. 34–41.

Related content

This is a required field
Please enter a valid email address