Energy-Efficient Train Control

1995 ◽  
Author(s):  
Philip G. Howlett ◽  
Peter J. Pudney
Keyword(s):  
Energy Efficient ◽  
Train Control

scholarly journals The impact of wind on energy-efficient train control

2020 ◽  
pp. 100013
Author(s):  
Alessio Trivella ◽  
Pengling Wang ◽  
Francesco Corman
Keyword(s):  
Energy Efficient ◽  
Train Control ◽  
The Impact

Speed Tracking Based Energy-Efficient Freight Train Control Through Multi-Algorithms Combination

2017 ◽  
Vol 9 (2) ◽  
pp. 76-90 ◽  
Author(s):  
Jie Yang ◽  
Limin Jia ◽  
Yunxiao Fu ◽  
Shaofeng Lu
Keyword(s):  
Energy Efficient ◽  
Freight Train ◽  
Train Control ◽  
Speed Tracking

Energy-Efficient Train Control

1993 ◽  
Vol 26 (2) ◽  
pp. 1081-1088 ◽  
Author(s):  
P.G. Howlett ◽  
I.P. Milroy ◽  
P.J. Pudney
Keyword(s):  
Energy Efficient ◽  
Train Control

Energy-efficient train control method based on soft actor-critic algorithm

2021 ◽  
Author(s):  
Q. Zhu ◽  
S. Su ◽  
T. Tang ◽  
X. Xiao
Keyword(s):  
Energy Efficient ◽  
Control Method ◽  
Train Control

scholarly journals Integrated Optimization on Train Control and Timetable to Minimize Net Energy Consumption of Metro Lines

2018 ◽  
Vol 2018 ◽  
pp. 1-19 ◽  
Author(s):  
Yuhe Zhou ◽  
Yun Bai ◽  
Jiajie Li ◽  
Baohua Mao ◽  
Tang Li
Keyword(s):  
Energy Consumption ◽  
Energy Efficient ◽  
Net Energy ◽  
Brute Force ◽  
Control Approach ◽  
Integrated Optimization ◽  
Train Control ◽  
Improved Model ◽  
Regenerative Energy ◽  
Timetable Optimization

Energy-efficient metro operation has received increasing attention because of the energy cost and environmental concerns. This paper developed an integrated optimization model on train control and timetable to minimize the net energy consumption. The extents of train motoring and braking as well as timetable configurations such as train headway and interstation runtime are optimized to minimize the net energy consumption with consideration of utilizing regenerative energy. An improved model on train control is proposed to reduce traction energy by allowing coasting on downhill slopes as much as possible. Variations of train mass due to the change of onboard passengers are taken into account. The brute force algorithm is applied to attain energy-efficient speed profiles and an NS-GSA algorithm is designed to attain the optimal extents of motoring/braking and timetable configurations. Case studies on Beijing Metro Line 5 illustrate that the improved train control approach can save traction energy consumption by 20% in the sections with steep downhill slopes, in comparison with the commonly adopted train control sequence in timetable optimization. Moreover, the integrated model is able to significantly prolong the overlapping time between motoring and braking trains, and the net energy consumption is accordingly reduced by 4.97%.

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