Standardised approach to energy consumption calculations for high‐speed rail

2016 ◽  
Vol 6 (3) ◽  
pp. 179-189 ◽  
Author(s):  
Daisuke Hasegawa ◽  
Gemma L. Nicholson ◽  
Clive Roberts ◽  
Felix Schmid
2019 ◽  
Vol 2019 ◽  
pp. 1-9
Author(s):  
Dingjun Chen ◽  
Sihan Li ◽  
Junjie Li ◽  
Shaoquan Ni ◽  
Xiaolong Liu

Timetable optimization techniques offer opportunity for saving energy and hence reducing operational costs for high-speed rail services. The existing energy-saving timetable optimization is mainly concentrated on the train running state adjustment and the running time redistribution between two stations. Not only the adjustment space of timetables is limited, but also it is hard for the train to reach the optimized running state in reality, and it is difficult to get feasible timetable with running time redistribution between two stations for energy-saving. This paper presents a high-speed railway energy-saving timetable based on stop schedule optimization. Under the constraints of safety interval and stop rate, with the objective of minimizing the increasing energy consumption of train stops and the shortest travel time of trains, the high-speed railway energy-saving timetable optimization model is established. The fuzzy mathematics programming method is used to design an efficient algorithm. The proposed model and algorithm are demonstrated in the actual operation data of Beijing-Shanghai high-speed railway. The results show that the total operating energy consumption of the train is reduced by 3.7%, and the total travel time of the train is reduced by 11 minutes.


2013 ◽  
Vol 711 ◽  
pp. 562-565
Author(s):  
Hui Hu

From operation management strategy perspective, a multi-objective time-space network optimization model of train energy consumption on a high speed rail line is proposed on the basis of train time table predetermined. The models objectives are to minimize circulation of rail stock and total energy consumption, and decision variables are number of train units in stations, while constraints include node flow conservation, passenger demand and capacity limitation. Finally, a simulation case is provided and solved for comparison and an optimization analysis is carried on via weighting method to illustrate the models feasibility and effectiveness.


CICTP 2020 ◽  
2020 ◽  
Author(s):  
Jing Shi ◽  
Qiyuan Peng ◽  
Ling Liu

2018 ◽  
Vol 8 (3) ◽  
pp. 515-530
Author(s):  
Massimo Zucchetti1,2 ◽  
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Keyword(s):  

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