Robust energy-efficient train speed profile optimization in a scenario-based position—time—speed network

2021 ◽  
Author(s):  
Yu Cheng ◽  
Jiateng Yin ◽  
Lixing Yang
Keyword(s):  
Energy Efficient ◽  
Speed Profile ◽  
Train Speed ◽  
Profile Optimization

scholarly journals Feedforward Tactical Optimization for Energy-Efficient Operation of Freight Trains: The Swiss Case

2018 ◽  
Vol 2672 (10) ◽  
pp. 278-288 ◽  
Author(s):  
Valerio De Martinis ◽  
Ambra Toletti ◽  
Francesco Corman ◽  
Ulrich A. Weidmann ◽  
Andrew Nash
Keyword(s):  
Energy Efficiency ◽  
Energy Efficient ◽  
Real Data ◽  
Efficient Solutions ◽  
Future Market ◽  
Speed Profile ◽  
Efficient Operation ◽  
The North ◽  
Simulation Based ◽  
Profile Optimization

The optimization of rail operation for improving energy efficiency plays an important role for the current and future market of rail freight services and helps rail compete with other transport modes. This paper presents a feedforward simulation-based model that performs speed profile optimization together with minor rescheduling actions. The model’s purpose is to provide railway operators and infrastructure managers with energy-efficient solutions that are tailored especially for freight trains. This work starts from the assumption that freight train characteristics are completely defined only a few hours before actual departure; therefore, small specific feedforward adjustments that do not affect the surrounding operation can still be considered. The model was tested in a numerical example. The example clearly shows how the optimized solutions can be evaluated with reference to energy saved and robustness within the rail traffic. The evaluation is based on real data from the North–South corridor crossing Switzerland from Germany to Italy.

scholarly journals Energy-Efficient Speed Profile Optimization and Sliding Mode Speed Tracking for Metros

Energies ◽  
2020 ◽  
Vol 13 (22) ◽  
pp. 6093
Author(s):  
Xiaowen Wang ◽  
Zhuang Xiao ◽  
Mo Chen ◽  
Pengfei Sun ◽  
Qingyuan Wang ◽  
...  
Keyword(s):  
Energy Saving ◽  
Energy Efficient ◽  
Artificial Bee Colony ◽  
Sliding Mode ◽  
Speed Profile ◽  
Terminal Sliding Mode ◽  
Bee Colony ◽  
Speed Tracking ◽  
Speed Error ◽  
Profile Optimization

Nowadays, most metro vehicles are equipped with an automatic train operation (ATO) system, and the speed control method, combining cruise speed planning and proportional-integral-derivative (PID) control, is widely used. The automation is achieved, and the energy-efficient can be improved. This paper presents an improved artificial bee colony algorithm for speed profile optimization with coast mode and an adaptive terminal sliding mode method for speed tracking. Specifically, a multi-objective optimization model is established, which considers energy consumption, comfortableness, and punctuality. Then, a novel artificial bee colony algorithm named regional reinforcement artificial bee colony (RR-ABC) is designed, to search the optimal speed profile with coast mode, in which some improvements are made to speed up convergence and to avoid local optimal solutions. For speed-tracking control, the adaptive terminal sliding mode controller (ATSMC) is used to improve the speed error, robustness, and energy saving. In addition, a disturbance observer (DOB) is designed to improve the anti-interference ability of the system and further improve the robustness and anti-disturbance, which are also conducive to speed error and energy saving. Finally, the line and train data of the Qingdao Metro Line 6 are used for simulation, which proves the effectiveness of the study. Specific to the energy saving rate, and compared with normal algorithms, RR-ABC with coast mode is approximately 9.55%, and ATSMC+DOB is 7.58%.

Energy-efficient speed profile optimization for medium-speed maglev trains

2020 ◽  
Vol 141 ◽  
pp. 102007
Author(s):  
Qingying Lai ◽  
Jun Liu ◽  
Ali Haghani ◽  
Lingyun Meng ◽  
Yihui Wang
Keyword(s):  
Energy Efficient ◽  
Speed Profile ◽  
Medium Speed ◽  
Profile Optimization

scholarly journals Discrete Train Speed Profile Optimization for Urban Rail Transit: A Data-Driven Model and Integrated Algorithms Based on Machine Learning

2019 ◽  
Vol 2019 ◽  
pp. 1-17 ◽  
Author(s):  
Kang Huang ◽  
Jianjun Wu ◽  
Xin Yang ◽  
Ziyou Gao ◽  
Feng Liu ◽  
...  
Keyword(s):  
Machine Learning ◽  
Energy Consumption ◽  
Urban Rail Transit ◽  
Data Driven ◽  
Support Vector ◽  
Rail Transit ◽  
Speed Profile ◽  
Urban Rail ◽  
Train Speed ◽  
Profile Optimization

Energy-efficient train speed profile optimization problem in urban rail transit systems has attracted much attention in recent years because of the requirement of reducing operation cost and protecting the environment. Traditional methods on this problem mainly focused on formulating kinematical equations to derive the speed profile and calculate the energy consumption, which caused the possible errors due to some assumptions used in the empirical equations. To fill this gap, according to the actual speed and energy data collected from the real-world urban rail system, this paper proposes a data-driven model and integrated heuristic algorithm based on machine learning to determine the optimal speed profile with minimum energy consumption. Firstly, a data-driven optimization model (DDOM) is proposed to describe the relationship between energy consumption and discrete speed profile processed from actual data. Then, two typical machine learning algorithms, random forest regression (RFR) algorithm and support vector machine regression (SVR) algorithm, are used to identify the importance degree of velocity in the different positions of profile and calculate the traction energy consumption. Results show that the calculation average error is less than 0.1 kwh, and the energy consumption can be reduced by about 2.84% in a case study of Beijing Changping Line.

Energy-Efficient Train Speed Profile Generator Combining Partial Energy-Oriented Driving Approaches

2016 ◽  
Vol 196 (1) ◽  
pp. 42-51
Author(s):  
KAZUMASA KUMAZAWA ◽  
KEISUKE SATO ◽  
TOMOYUKI OGAWA
Keyword(s):  
Energy Efficient ◽  
Speed Profile ◽  
Partial Energy ◽  
Train Speed

scholarly journals Energy-Efficient Speed Profile Optimization for High-Speed Railway Considering Neutral Sections

IEEE Access ◽  
2021 ◽  
Vol 9 ◽  
pp. 25090-25100
Author(s):  
Zhang Bin ◽  
You Shijun ◽  
Zhang Lanfang ◽  
Li Daming ◽  
Chen Yalan
Keyword(s):  
Energy Efficient ◽  
High Speed ◽  
Speed Profile ◽  
High Speed Railway ◽  
Profile Optimization
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