Increasing realism in modelling energy losses in railway vehicles and their impact to energy-efficient train control

被引:2
|
作者
Nold, Michael [1 ]
Corman, Francesco [1 ]
机构
[1] Swiss Fed Inst Technol, Inst Transport Planning & Syst, Zurich, Switzerland
关键词
Train trajectory optimization; Energy-efficient train control (EETC); Dynamic efficiency; Power losses in railway vehicles;
D O I
10.1007/s40534-023-00322-4
中图分类号
U [交通运输];
学科分类号
08 ; 0823 ;
摘要
The reduction of energy consumption is an increasingly important topic of the railway system. Energy-efficient train control (EETC) is one solution, which refers to mathematically computing when to accelerate, which cruising speed to hold, how long one should coast over a suitable space, and when to brake. Most approaches in literature and industry greatly simplify a lot of nonlinear effects, such that they ignore mostly the losses due to energy conversion in traction components and auxiliaries. To fill this research gap, a series of increasingly detailed nonlinear losses is described and modelled. We categorize an increasing detail in this representation as four levels. We study the impact of those levels of detail on the energy optimal speed trajectory. To do this, a standard approach based on dynamic programming is used, given constraints on total travel time. This evaluation of multiple test cases highlights the influence of the dynamic losses and the power consumption of auxiliary components on railway trajectories, also compared to multiple benchmarks. The results show how the losses can make up 50% of the total energy consumption for an exemplary trip. Ignoring them would though result in consistent but limited errors in the optimal trajectory. Overall, more complex trajectories can result in less energy consumption when including the complexity of nonlinear losses than when a simpler model is considered. Those effects are stronger when the trajectory includes many acceleration and braking phases.
引用
收藏
页码:257 / 285
页数:29
相关论文
共 50 条
  • [31] Online Energy-Efficient Train Traffic Adjustments
    Davydov, Boris
    Gopkalo, Vadim
    [J]. INTERNATIONAL SCIENTIFIC CONFERENCE ENERGY MANAGEMENT OF MUNICIPAL TRANSPORTATION FACILITIES AND TRANSPORT, EMMFT 2017, 2018, 692 : 236 - 244
  • [32] Superinsulation Materials for Energy-Efficient Train Envelopes
    Wernery, Jannis
    Brunner, Samuel
    Weber, Benedikt
    Knuth, Christopher
    Koebel, Matthias M.
    [J]. APPLIED SCIENCES-BASEL, 2021, 11 (07):
  • [33] Energy-efficient Train Control for Maglev Train Using Mixed-integer Linear Programming
    Feng, Minling
    Wang, Junjie
    Lu, Shaofeng
    Wang, Yihui
    [J]. 2022 IEEE INTELLIGENT VEHICLES SYMPOSIUM (IV), 2022, : 64 - 70
  • [34] A fuzzy control system for energy-efficient wireless devices in the Internet of vehicles
    Collotta, Mario
    Ferrero, Renato
    Giusto, Edoardo
    Ghazi Vakili, Mohammad
    Grecuccio, Jacopo
    Kong, Xiangjie
    You, Ilsun
    [J]. INTERNATIONAL JOURNAL OF INTELLIGENT SYSTEMS, 2021, 36 (04) : 1595 - 1618
  • [35] Energy-Efficient Adaptive Cruise Control for Electric Connected and Autonomous Vehicles
    Lu, Chaoru
    Dong, Jing
    Hu, Liang
    [J]. IEEE INTELLIGENT TRANSPORTATION SYSTEMS MAGAZINE, 2019, 11 (03) : 42 - 55
  • [36] Energy-Efficient Connected Cruise Control With Lean Penetration of Connected Vehicles
    Shen, Minghao
    He, Chaozhe R. R.
    Molnar, Tamas G.
    Bell, A. Harvey
    Orosz, Gabor
    [J]. IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS, 2023, 24 (04) : 4320 - 4332
  • [37] The interplay between energy-efficient train control and scheduled running time supplements
    Scheepmaker, Gerben M.
    Goverde, Rob M. P.
    [J]. JOURNAL OF RAIL TRANSPORT PLANNING & MANAGEMENT, 2015, 5 (04) : 225 - 239
  • [38] Simulation study on optimal energy-efficient control of high speed train considering regenerative brake energy
    Wang, Qingyuan
    Feng, Xiaoyun
    Zhu, Jinling
    Liang, Zhicheng
    [J]. Zhongguo Tiedao Kexue/China Railway Science, 2015, 36 (01): : 96 - 103
  • [39] Shrinking horizon parametrized predictive control with application to energy-efficient train operation
    Farooqi, Hafsa
    Fagiano, Lorenzo
    Colaneri, Patrizio
    Barlini, Davide
    [J]. AUTOMATICA, 2020, 112
  • [40] Energy-efficient train control: From local convexity to global optimization and uniqueness
    Albrecht, Amie R.
    Howlett, Phil G.
    Pudney, Peter J.
    Vu, Xuan
    [J]. AUTOMATICA, 2013, 49 (10) : 3072 - 3078