Real-Time Energy-Efficient Driver Advisory System for High-Speed Trains

Xiao, Zhuang; Wang, Hongtao; Sun, Pengfei; Zhao, Zining; Rao, Yu; Feng, Xiaoyun

doi:10.1109/tte.2021.3071251

Cited by 28 publications

(18 citation statements)

References 43 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For the most energy-saving driving strategy obtained, the full braking operation is rarely or never applied, instead the coasting operation is applied as much as possible. This is consistent with the conclusion drawn in [27]. As the iterations goes on, all solutions are guided to the Pareto front with lower sensitivities.…”

Section: B Casesupporting

confidence: 91%

“…With later advances in computer performance, more studies were focused on developing meta-heuristic algorithm based optimization models [18]- [25]. In [26], [27], multiple-phase trajectory controls are proposed and additional practical factors have been incorporated in the optimization models, such as passenger comfort and punctuality. In summary, studies on single-objective optimization of a train speed trajectory are mature.…”

Section: Romentioning

confidence: 99%

“…In particular, one driving strategy, which considers all possible operations including acceleration, cruising, coasting and braking, has been comprehensively adopted in the ATO system [4], [21], [34], [35], and it helps to explore the flexibility and superiority of different combinations of train operations. As stated in [27], [35], based on Pontryagin's maximum principle (PMP), if the train is aimed to reach the destination as quickly as possible, the train is supposed to reach the maximum allowable speed first with full acceleration and keep the maximum speed (cruising) until the final braking point. Then the full braking is performed to stop the train.…”

Section: B Train Driving Pattern and Speed Trajectory Controlmentioning

confidence: 99%

See 2 more Smart Citations

Robust Optimization of Energy-Saving Train Trajectories Under Passenger Load Uncertainty Based on p-NSGA-II

Xing

Zhang

et al. 2023

IEEE Trans. Transp. Electrific.

View full text Add to dashboard Cite

show abstract

Section: B Casesupporting

confidence: 91%

Section: Romentioning

confidence: 99%

Section: B Train Driving Pattern and Speed Trajectory Controlmentioning

confidence: 99%

See 1 more Smart Citation

Robust Optimization of Energy-Saving Train Trajectories Under Passenger Load Uncertainty Based on p-NSGA-II

Xing

Zhang

et al. 2023

IEEE Trans. Transp. Electrific.

View full text Add to dashboard Cite

show abstract

“…The reduction of energy consumption can be achieved by replacing the rolling stock with more energy efficiency and effectively organising onboard equipment. That was highlighted in the works of Luca Pugi et al and Zhuang Xiao et al [4,5].…”

Section: Introductionmentioning

confidence: 93%

Energy Efficiency of High-Speed Railways

Watson¹,

Ali²,

Bayyati³

2022

Adv Environ Eng Res

View full text Add to dashboard Cite

The world is becoming more dependent on energy resources, which translates into political dependency on energy-exported states. This will significantly impact the economy, transport, and the environment around the world. Railway transport is becoming an essential mode of transportation because it can operate on an electrified network and has zero carbon emissions. High-Speed Railways (HSR) is an energy-intensive transport system, and it is important economically and environmentally to reduce the amount of used energy. This study examined high-speed rail from an energy efficiency point of view and found factors that can significantly reduce the energy consumption of high-speed rail. This research aims to answer the question that motivated this research: what fundamental factors affect the energy consumption of the selected HSR systems? It estimated the dynamic changes in the total energy consumption for seven HSR systems from 2010-2017 and benchmarked the most energy efficient HSR systems. The non-radial Data Envelopment Analyses methodology has been used to fulfil this research. To conduct complex statistical analyses, IBM SPSS has been applied. The main findings have shown that the decrease in vehicle mass, improved design of high-speed rolling stock and increased occupancy of trains will support the reduction of energy consumption by high-speed railways. The changes in energy consumption strongly correlate with the type of high-speed rolling stock and operational strategies. The expected outcomes of this research will contribute to developing and advancing more sustainable HSR systems. This research will support train operators in making decisions when acquiring new trains and assess the benefits of acceleration in modernising the current rolling stock.

show abstract

“…References [6,7] gives an idea of using learning based methods. Furthermore, references [8,9] provides an on-line or real-time approach for train speed profile scheduling. Especially [9], which uses model predictive control, introduces a prediction and adjustment concepts.…”

Section: Introductionmentioning

confidence: 99%

Simultaneous DC Railway Power System Analysis Method Using Model-Based TPS

et al. 2022

View full text Add to dashboard Cite

This paper focuses on the development of a model-based train performance simulation with the practical train operation data using MATLAB Simulink. The developed program uses input operation data of a DC 1500 V metro line. And the simultaneous and multi-rate simulation for DC metro was performed to interface train schedule data with actual railway electrification system and vehicle. In the simulation, the voltage and power measured at each substation are provided during the traction, coasting and breaking mode. From this study, railroad vehicle load can be virtually modeled in the system internally and contribute to verifying new parts of the overall railway electrification systems.

show abstract

Real-Time Energy-Efficient Driver Advisory System for High-Speed Trains

Cited by 28 publications

References 43 publications

Robust Optimization of Energy-Saving Train Trajectories Under Passenger Load Uncertainty Based on p-NSGA-II

Robust Optimization of Energy-Saving Train Trajectories Under Passenger Load Uncertainty Based on p-NSGA-II

Energy Efficiency of High-Speed Railways

Simultaneous DC Railway Power System Analysis Method Using Model-Based TPS

Contact Info

Product

Resources

About