TY - JOUR
T1 - Optimal driving strategy for traction energy saving on DC suburban railways
AU - Bocharnikov, Yury
AU - Roberts, Clive
AU - Hillmansen, Stuart
AU - Tobias, Andrew
AU - Goodman, Colin
PY - 2007/1/1
Y1 - 2007/1/1
N2 - 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.
AB - 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.
U2 - 10.1049/iet-epa:20070005
DO - 10.1049/iet-epa:20070005
M3 - Article
SN - 1751-8679
VL - 1
SP - 675
EP - 682
JO - IET Electric Power Applications
JF - IET Electric Power Applications
IS - 5
ER -