A feasibility assessment of multi-modelling approaches for rail decarbonisation systems simulation

Simulation is an important tool to support rail decarbonisation but can be challenging due to heterogeneous models, simulation tools and skill sets, and concerns around intellectual property. Multi-modelling, a proven methodology in sectors such as aerospace and automotive, uses Functional Mock-up Interface (FMI) and co-simulation to potentially overcome these problems. This paper presents a feasibility study of multi-modelling for rail decarbonisation, using a combination of audit of current state of the art, technical implementation and stakeholder consultation. The audit showed that while current uptake of FMI in rail is low, there is potential to repurpose models from pre-existing tools and apply them within multi-modelling. The technical feasibility assessment demonstrated how multi-modelling could generate flexible simulation outputs to identify decarbonisation systems effects both for urban and mainline rail, including rapid integration of pre-existing MATLAB Simulink models. Work with industry stakeholders identified use cases where multi-modelling would benefit rail decarbonisation, as well as barriers and enablers to adoption. Overall, the study demonstrates the feasibility and considerations for multi-modelling to support rail decarbonisation efforts, and the future developments necessary for wider rollout.

[1]  Paul Batty,et al.  Sustainable urban rail systems: strategies and technologies for optimal management of regenerative braking energy , 2013 .

[2]  Xiongwen Zhang,et al.  Unbalanced discharging and aging due to temperature differences among the cells in a lithium-ion battery pack with parallel combination , 2016 .

[3]  Paul Batty,et al.  A systems approach to reduce urban rail energy consumption , 2014 .

[4]  Roberto Palacin,et al.  Multi-modelling for Decarbonisation in Urban Rail Systems , 2019, Urban Rail Transit.

[5]  Julian Stow,et al.  Modelling and simulation of the train brake system in low adhesion conditions , 2020 .

[6]  Greg F. Naterer,et al.  Rail transportation by hydrogen vs. electrification – Case study for Ontario Canada, I: Propulsion and storage , 2010 .

[7]  Jonathan Powell,et al.  A comparison of modelled and real-life driving profiles for the simulation of railway vehicle operation , 2015 .

[8]  Luca D’Acierno,et al.  Dispatching and Rescheduling Tasks and Their Interactions with Travel Demand and the Energy Domain: Models and Algorithms , 2018, Urban Rail Transit.

[9]  Philippe Ladoux,et al.  25 kV–50 Hz railway power supply system emulation for power-hardware-in-the-loop testings , 2019 .

[10]  Osman Hasan,et al.  Formal Verification of Cyber-Physical Systems: Coping with Continuous Elements , 2013, ICCSA.

[11]  Frédéric Boulanger,et al.  Exploring Multi-Paradigm Modeling Techniques , 2009, Simul..

[12]  Casper Thule,et al.  Maestro: The INTO-CPS co-simulation framework , 2019, Simul. Model. Pract. Theory.

[13]  Hitoshi Hayashiya.  Recent Trend of Regenerative Energy Utilization in Traction Power Supply System in Japan , 2017 .

[14]  Iain Staffell,et al.  Measuring the progress and impacts of decarbonising British electricity , 2017 .

[15]  David Broman,et al.  Co-Simulation , 2018, ACM Comput. Surv..

[16]  David W. Coit,et al.  Multi-objective optimization using genetic algorithms: A tutorial , 2006, Reliab. Eng. Syst. Saf..

[17]  Hamed Jafari Kaleybar,et al.  The Evolution of Railway Power Supply Systems Toward Smart Microgrids: The concept of the energy hub and integration of distributed energy resources , 2020, IEEE Electrification Magazine.

[18]  Stuart Hillmansen,et al.  Energy consumption and carbon dioxide emissions analysis for a concept design of a hydrogen hybrid railway vehicle , 2017, IET Electrical Systems in Transportation.

[19]  Felix Schmid,et al.  Feasibility of discontinuous electrification on the Great Western Main Line determined by train simulation , 2013 .

[20]  Sarah Sharples,et al.  Applications, value and barriers of common data frameworks in the rail industry of Great Britain , 2013 .

[21]  Nastaran Dadashi,et al.  Evaluating the impact of rail research: Principles to maximise innovation uptake , 2016 .

[22]  Julian M. Allwood,et al.  Assessing the CO2 impact of current and future rail track in the UK , 2010 .

[23]  Ken Pierce,et al.  Applying ergonomics within the multi-modelling paradigm with an example from multiple UAV control , 2020, Ergonomics.

[24]  Jan F. Broenink,et al.  20-SIM software for hierarchical bond-graph/block-diagram models , 1999, Simul. Pract. Theory.

[25]  Bruno Sareni,et al.  Storage Device Sizing for a Hybrid Railway Traction System by Means of Bicausal Bond Graphs , 2006 .

[26]  G Malavasi,et al.  Driving and operation strategies for traction-energy saving in mass rapid transit systems , 2011 .

[27]  Simon Blainey,et al.  Assessing Whole-Life Carbon Footprint of Under Sleeper Pad Installation for Ballasted Track , 2018, Journal of Transportation Engineering, Part A: Systems.

[28]  Massimo Ceraolo,et al.  Use of Modelica language to simulate electrified railway lines and trains , 2019, Softw. Pract. Exp..

[29]  Sarah Sharples,et al.  Manufacturing in the cloud: A human factors perspective , 2016 .

[30]  J. P. Powell,et al.  Passenger Stability Within Moving Railway Vehicles: Limits on Maximum Longitudinal Acceleration , 2015 .

[31]  Gerald Schweiger,et al.  author-version of: An empirical survey on co-simulation: Promising standards, challenges and research needs , 2022 .