Applying Collaborative Co-Simulation to Railway Traction Energy Consumption

被引:0
作者
Golightly, David [1 ]
Bhattacharyya, Anirban [2 ]
Pierce, Ken [2 ]
Tian, Zhongbei [3 ]
Lin, Zhiyuan [4 ]
Liu, Ronghui [4 ]
Lyu, Xinnan [3 ]
Jiang, Kangrui [3 ]
Liu, Xiao [5 ]
机构
[1] Newcastle Univ, Sch Engn, Future Mobil Grp, Newcastle NE1 7RU, England
[2] Newcastle Univ, Sch Comp, Newcastle NE4 5TG, England
[3] Univ Birmingham, Sch Engn, Dept Elect Elect & Syst Engn, Birmingham B15 2TT, England
[4] Univ Leeds, Inst Transport Studies, Leeds LS2 9JT, England
[5] Univ Liverpool, Dept Elect Engn & Elect, Liverpool L69 3GJ, England
来源
ELECTRONICS | 2025年 / 14卷 / 07期
关键词
railway; energy consumption; timetabling; simulation; co-simulation; human performance; TRAIN; OPTIMIZATION; MODEL;
D O I
10.3390/electronics14071467
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Simulation is a vital tool for understanding rail traction energy consumption. Simulating such energy consumption requires an understanding of the interactions between timetable, infrastructure, and driver behavior to be encapsulated within a multi-train system model. This is critical to simulating systemic interactions that affect energy consumption on a rail network. However, building and executing such a system simulation is challenging because of diverse models, stakeholders, and knowledge, as well as a lack of tools to support flexible and scalable simulation. This paper presents a demonstration of co-simulation-an approach originating in the automotive industry and now being used in other sectors-that enables a system model to be assessed for different configurations of timetable, rolling stock, infrastructure, and driver behavior. This paper describes the co-simulation approach before outlining the development process that allowed three research institutes, each with diverse models, to collaborate and deliver an integrated, holistic modeling approach. The results of this work are presented and discussed, both in terms of the quantified outputs and findings for energy consumption, and the lessons learned through collaborative co-simulation. Future avenues to build on this work are identified.
引用
收藏
页数:26
相关论文
共 56 条
[41]   Investigations into the skills of modern and traditional train driving [J].
Naweed, A. .
APPLIED ERGONOMICS, 2014, 45 (03) :462-470
[42]   Early-stage analysis of cyber-physical production systems through collaborative modelling [J].
Neghina, Mihai ;
Zamfirescu, Constantin-Bala ;
Pierce, Ken .
SOFTWARE AND SYSTEMS MODELING, 2020, 19 (03) :581-600
[43]   Predicting Locomotive Crew Performance in Rail Operations with Human and Automation Assistance [J].
Nneji, Victoria Chibuogu ;
Cummings, Mary L. ;
Stimpson, Alexander J. .
IEEE TRANSACTIONS ON HUMAN-MACHINE SYSTEMS, 2019, 49 (03) :250-258
[44]  
ovariancancernz, about us
[45]  
Pieper T., 2018, P 2018 7 MED C EMB C, P1
[46]   USING CO-SIMULATION AND TIME SIGNAL AT RED (TSAR) TO DETERMINE IMPACT OF DRIVER BEHAVIOR ON RAIL NETWORK PERFORMANCE [J].
Pierce, Ken ;
Bhattacharyya, Anirban ;
Golightly, David ;
da Silva, Pedro Pinto ;
Merricks, Seb ;
Palacin, Roberto ;
Guo, Ziqi .
2024 ANNUAL MODELING AND SIMULATION CONFERENCE, ANNSIM 2024, 2024,
[47]   A comparison of modelled and real-life driving profiles for the simulation of railway vehicle operation [J].
Powell, J. P. ;
Palacin, R. .
TRANSPORTATION PLANNING AND TECHNOLOGY, 2015, 38 (01) :78-93
[48]   An FMI-based co-simulation framework for simulations of wave energy converter systems [J].
Shao, Xinyuan ;
Ringsberg, Jonas W. ;
Johnson, Erland ;
Li, Zhiyuan ;
Yao, Hua-Dong ;
Skjoldhammer, Jan G. ;
Bjorklund, Stefan .
ENERGY CONVERSION AND MANAGEMENT, 2025, 323
[49]   The Minimum Viable Product (MVP): Theory and Practice [J].
Stevenson, Regan ;
Burnell, Devin ;
Fisher, Greg .
JOURNAL OF MANAGEMENT, 2024, 50 (08) :3202-3231
[50]  
Szpigel B., 1973, Proceedings of the IFORS Conference Operational Research '72, P343