Vehicle system dynamics in digital twin studies in rail and road domains

References

• Grieves M, Vickers J. Digital twin: mitigating unpredictable, undesirable emergent behavior in complex systems. In: FJ Kahlen, S Flumerfelt, A Alves, editor. Transdisciplinary perspectives on complex systems. Cham: Springer-Verlag; 2017. p. 85–113.
   Google Scholar
• Shafto M, et al. Modeling, simulation, information technology & processing roadmap. Washington (DC): National Aeronautics and Space Administration; 2012. Available from: https://www.nasa.gov/sites/default/files/501321main_TA11-ID_rev4_NRC-wTASR.pdf.
   Google Scholar
• Haag S, Anderl R. Digital twin – proof of concept. Manuf Lett. 2018;15:64–66.
   Google Scholar
• Liu M, Fang S, Dong H, et al. Review of digital twin about concepts, technologies, and industrial applications. J Manuf Syst. 2021;58(Part B):346–361.
   Web of Science ®Google Scholar
• Schluse M, Priggemeyer M, Atorf L, et al. Experimentable digital twins—streamlining simulation-based systems engineering for industry 4.0. IEEE Trans Ind Inf. 2018;14(4):1722–1731.
   Web of Science ®Google Scholar
• Xu Y, Sun Y, Liu X, et al. A digital-twin-assisted fault diagnosis using deep transfer learning. IEEE Access. 2019;7:19990–19999.
   Web of Science ®Google Scholar
• Luo W, Hu T, Zhang C, et al. Digital twin for CNC machine tool: modeling and using strategy. J Ambient Intell Humaniz Comput. 2019;10:1129–1140.
   Web of Science ®Google Scholar
• Wang P, Yang M, Peng Y, et al. Sensor control in anti-submarine warfare—a digital twin and random finite sets based approach. Entropy. 2019;21(8):767.
   PubMed Web of Science ®Google Scholar
• Spiryagin M, Wu Q, Polach O, et al. Problems, assumptions and solutions in locomotive design, traction and operational studies. Railw Eng Sci. 2022. DOI: 10.1007/s40534-021-00263-w.
   Google Scholar
• Wright L, Davidson S. How to tell the difference between a model and a digital twin. Adv Model Simul Eng Sci. 2020;7(13):1–13.
   Google Scholar
• West T, Blackburn M. Is digital thread/digital twin affordable? A systemic assessment of the cost of DoD’s latest manhattan project. Procedia Comput Sci. 2017;114:47–56.
   Google Scholar
• Defense Acquisition University. Digital twin. DAU Glossary of Defense Acquisition Acronyms and Terms. [cited 2022 May 25]. Available from: https://www.dau.edu/glossary/Pages/Glossary.aspx#!both|D|27349.
   Google Scholar
• Schwarz C, Wang Z. The role of digital twins in connected and automated vehicles. IEEE Intell Transp Syst Mag. 2022. DOI: 10.1109/MITS.2021.3129524
   Web of Science ®Google Scholar
• Hughes A. Forging the digital twin in discrete manufacturing: a vision for unity in the virtual and real worlds. LNS Research’ 2018.
   Google Scholar
• Tao F, Qi Q. Make more digital twins. Nature. 2019;573:490–491.
   PubMed Web of Science ®Google Scholar
• Computer Simulation technical Committee on Model Credibility. Terminology for model credibility, reports of SCS technical committees. Simulation. 1979;32(3):103–104.
   Google Scholar
• Cameron WD. Terminology for model credibility, technical notes and comments. Simulation. 1980;35(6):206–207.
   Google Scholar
• Zeigler BP, Muzy A, Kofman E. Chapter 1 - Introduction to systems modeling concepts. In: Zeigler BP, Muzy A, Kofman E, editor. Theory of modeling and simulation: discrete event and iterative system computational foundations. 3rd ed. London: Academic Press; 2019. p. 3–25.
   Google Scholar
• Kortüm W. Introduction to system dynamics of ground vehicles. Veh Syst Dyn. 1987;16(S1):1–36.
   Google Scholar
• Jaschinski A. Application of MEDYNA in vehicle technology. Veh Syst Dyn. 1987;16(S1):189–214.
   Google Scholar
• Arnold M, Carrarini A, Heckman A, et al. Simulation techniques for multidisciplinary problems in vehicle system dynamics. In: M Valášek, editor. Computational mechanics in vehicle system dynamics, supplement to vehicle system dynamics, Vol. 40. London: Taylor & Francis; 2004. p. 17–36.
   Google Scholar
• Kortüm W, Schiehlen W, Hoffmann M. Progress in integrated system analysis and design software for controlled vehicles. Veh Syst Dyn. 1994;23(S1):274–296.
   Web of Science ®Google Scholar
• Kortüm W, Vaculín O. Is multibody simulation software suitable for mechatronic systems? Proceedings of the 5th World Congress on Computational Mechanics, Vienna, Austria; 2012 Jul 7–12.
   Google Scholar
• Arnold M, Burgermeister B, Führer C, et al. Numerical methods in vehicle system dynamics: state of the art and current developments. Veh Syst Dyn. 2011;49(7):1159–1207.
   Web of Science ®Google Scholar
• Spiryagin M, Cole C, Sun YQ, et al. Design and simulation of rail vehicles, ground vehicle engineering series. Boca Raton (FL): CRC Press; 2014.
   Google Scholar
• Belanger J, Venne P, Paquin JN. The what, where and why of real-time simulation, OPAL-RT Technologies, October, 2010, p. 37–49. [cited 2022 June 29]. Available from: https://blob.opal-rt.com/medias/L00161_0436.pdf.
   Google Scholar
• MODELISAR. Functional mock-up interface for model exchange and co-simulation. Document version 2.0 Beta 4; 2012 Aug 10.
   Google Scholar
• Gausemeier J, Moehringer S. New guideline VDI 2206 – a flexible procedure model for the design of mechatronic systems. In: A Folkeson, K Gralen, M Norell, etal, editors. Proceedings of 14th International Conference on Engineering Design (ICED 03), Aug 19–21. Stockholm: The Design Society; 2003. p. 599–608.
   Google Scholar
• Verein Deutscher Ingenieure. VDI 2206 - entwicklungsmethodik für mechatronische systeme [Design methodology for mechatronic systems]. Berlin: Beuth Verlag; 2004.
   Google Scholar
• Liu-Henke X, Lückel J, Jäker K-P. Development of an active suspension/tilt system for a mechatronic railway carriage. IFAC Conference on Mechatronic Systems, Darmstadt, Germany, 18-20 September 2000. IFAC Proc. 2000;33(26):283–288.
   Google Scholar
• Spiryagin M, Bruni S, Bosomworth C, et al. Rail vehicle mechatronics. Boca Raton (FL): CRC Press; 2022.
   Google Scholar
• Woolfson MM, Pert GJ. An introduction to computer simulation. Oxford: Oxford University Press; 1999.
   Google Scholar
• Raczynski S. Modeling and simulation: the computer science of illusion. West Sussex: Wiley; 2006.
   Google Scholar
• Chang CA. Simulation modelling: handbook: a practical approach. Boca Raton (FL): CRC Press; 2014.
   Google Scholar
• Gibson RR. Chapter 5 - Planning and plant layout. In: DA Snow, editor. Plant engineer's reference book. 2nd ed. Oxford: Butterworth-Heinemann; 2002. p. 5-1–5-18.
   Google Scholar
• Durán JM. What is a simulation model? Minds Mach. 2020;30:301–323.
   Web of Science ®Google Scholar
• Boschert S, Rosen R. Digital twin—the simulation aspect. In: Hehenberger P, Bradley D, editor. Mechatronic futures. Cham: Springer; 2016. p. 59–73.
   Google Scholar
• Marte C, Six K. Optimising the railway system by virtualisation. Vol. 4. Virtual Vehicle Magazine. Special Edition: Digital Rail Solutions; 2019, p. 5–7.
   Google Scholar
• Minerva R, Lee GM, Crespi N. Digital twin in the IoT context: a survey on technical features, scenarios, and architectural models. Proc IEEE. 2020;108(10):1785–1824.
   Web of Science ®Google Scholar
• Segovia M, Garcia-Alfaro J. Design, modeling and implementation of digital twins. Sensors. 2022;22(14):5396.
   PubMed Web of Science ®Google Scholar
• Steindl G, Stagl M, Kasper L, et al. Generic digital twin architecture for industrial energy systems. Appl Sci. 2020;10(24):8903.
   Google Scholar
• Arena F, Collotta M, Luca L, et al. Predictive maintenance in the automotive sector: a literature review. Math Comput Appl. 2022;27(2):21.
   Google Scholar
• Shao G. Use case scenarios for digital twin implementation based on Iso 23247. Gaithersburg (MD): National Institute of Standards; 2021.
   Google Scholar
• Grieves M. Digital twin: manufacturing excellence through virtual factory replication. Whitepaper; 2014.
   Google Scholar
• Lee J, Bagheri B, Kao H-A. A cyber-physical systems architecture for industry 4.0-based manufacturing systems. Manuf Lett. 2015;3:18–23.
   Google Scholar
• Biesinger F, Weyrich M. The facets of digital twins in production and the automotive industry. 2019 23rd International Conference on Mechatronics Technology (ICMT); 2019 Oct 23–26, 6 p.
   Google Scholar
• Harper KE, Ganz C, Malakuti S. Digital twin architecture and standards. IIC J Innov. 2019;12:72–83.
   Google Scholar
• Bass L. Software architecture in practice. 4th ed. (Clements P, Kazman R, editors). Boston (MA): Addison-Wesley; 2021.
   Google Scholar
• Bauer T, Antonino PO, Kuhn T. Towards architecting digital twin-pervaded systems. 2019 IEEE/ACM 7th International Workshop on Software Engineering for Systems-of-Systems (SESoS) and 13th Workshop on Distributed Software Development, Software Ecosystems and Systems-of-Systems (WDES); 2019 May 28.
   Google Scholar
• Caporuscio M, Edrisi F, Hallberg M, et al. Architectural concerns for digital twin of the organization 2020. Cham: Springer International Publishing; 2020.
   Google Scholar
• International Standard ISO 23247-2. Automation systems and integration — digital twin framework for manufacturing — Part 2: reference architecture; 2021.
   Google Scholar
• Lei Z, Zhou H, Hu W, et al. Toward a Web-based digital twin thermal power plant. IEEE Trans Ind Inf. 2022;18(3):1716–1725.
   Web of Science ®Google Scholar
• Hu S, Wang S, Su N, et al. Digital twin based reference architecture for petrochemical monitoring and fault diagnosis. Oil Gas Sci Technol. 2021;76:9.
   Google Scholar
• Lim KYH, Zheng P, Chen C-H, et al. A digital twin-enhanced system for engineering product family design and optimization. J Manuf Syst. 2020;57:82–93.
   Web of Science ®Google Scholar
• Alam KM, El Saddik A. C2PS: a digital twin architecture reference model for the cloud-based cyber-physical systems. IEEE Access. 2017;5:2062.
   Web of Science ®Google Scholar
• Josifovska K, Yigitbas E, Engels G. Reference framework for digital twins within cyber-physical systems. 2019 IEEE/ACM 5th International Workshop on Software Engineering for Smart Cyber-Physical Systems (SEsCPS); 2019 May 28.
   Google Scholar
• Wang Z, Gupta R, Han K, et al. Mobility digital twin: concept, architecture, case study, and future challenges. IEEE Internet Things J. 2022;1:17452–17467.
   Web of Science ®Google Scholar
• Hankel M, Rexroth B. The reference architectural model industries 4.0 (RAMI 4.0). Zvei, Frankfurt, Germany; 2015.
   Google Scholar
• Jacoby M, Usländer T. Digital twin and internet of things—current standards landscape. Appl Sci. 2020;10(18):6519.
   Google Scholar
• Contreras-Castillo J, Zeadally S, Guerrero-Ibañez JA. Internet of vehicles: architecture, protocols, and security. IEEE Internet Things J. 2018;5(5):3701–3709.
   Web of Science ®Google Scholar
• Wang FY, Zheng NN, Cao D, et al. Parallel driving in CPSS: a unified approach for transport automation and vehicle intelligence. IEEE/CAA JAutom Sin. 2017;4(4):577–587.
   Google Scholar
• Tao F, Zhang M, Nee AYC. Chapter 3 - Five-dimension digital twin modeling and its key technologies. In: Tao F, Zhang M, Nee AYC, editors. Digital twin driven smart manufacturing. London, UK: Academic Press; 2019. p. 63–81.
   Google Scholar
• James SS, Anderson SR, Lio MD. Longitudinal vehicle dynamics: a comparison of physical and data-driven models under large-scale real-world driving conditions. IEEE Access. 2020;8:73714–73729.
   Web of Science ®Google Scholar
• Maulik S, Riordan D, Walsh J. Dynamic reduction-based virtual models for digital twins - a comparative study. Appl Sci. 2022;12:12147154.
   Google Scholar
• Viehweger M, Vaseur C, van Aalst S, et al. Vehicle state and tyre force estimation: demonstrations and guidelines. Veh Syst Dyn. 2021;59(5):675–702.
   Web of Science ®Google Scholar
• Tsunashima H, Hirose R. Condition monitoring of railway track from car-body vibration using time-frequency analysis. Veh Syst Dyn. 2022;60(4):1170–1187.
   Web of Science ®Google Scholar
• Makke O, Gusikhin O. Robust IoT based parking information system. communications in computer and information science 1475. Cham: Springer International Publishing; 2021, p. 227.
   Google Scholar
• Milošević MDG, Pålsson BA, Nissen A, et al. Demonstration of a digital twin framework for model-based operational condition monitoring of crossing panels. In: Orlova A, Cole D, editors. Advances in dynamics of vehicles on roads and tracks II. Cham: Springer International Publishing; 2022. p. 95–105.
   Google Scholar
• Barricelli BR, Casiraghi E, Fogli D. A survey on digital twin: definitions, characteristics, applications, and design implications. IEEE Access. 2019;7:167653–167671.
   Web of Science ®Google Scholar
• Shao G, Helu M. Framework for a digital twin in manufacturing: scope and requirements. Manuf Lett. 2020;24:105–107.
   Google Scholar
• Ruba M, Nemes RO, Ciornei SM, et al. Digital twin real-time FPGA implementation for light electric vehicle propulsion system using EMR organization. 2019 IEEE Vehicle Power and Propulsion Conference, VPPC; 2019.
   Google Scholar
• Heber D, Groll M. Towards a digital twin: how the blockchain can foster E/E-traceability in consideration of model-based systems engineering. 21st International Conference on Engineering Design, ICED 2017; Vancouver, BC, Canada, 2017 Aug 21–25.
   Google Scholar
• Chen M, Mao S, Liu Y. Big data: a survey. Mobile Netw Appl. 2014;19(2):171–209.
   Web of Science ®Google Scholar
• Rathore MM, Shah SA, Shukla D, et al. The role of AI, machine learning, and big data in digital twinning: a systematic literature review, challenges, and opportunities. IEEE Access. 2021;9:32030–32052.
   Web of Science ®Google Scholar
• Prytz R. Machine learning methods for vehicle predictive maintenance using off-board and on-board data [dissertation]. Halmstad University Press; 2014.
   Google Scholar
• Kiela K, Barzdenas V, Jurgo M, et al. Review of V2X–IoT standards and frameworks for ITS applications. Appl Sci. 2020;10(12):4314.
   Google Scholar
• Wang H, Liu T, Kim B, et al. Architectural design alternatives based on cloud/edge/fog computing for connected vehicles. IEEE Commun Surv Tutor. 2020;22(4):2349–2377.
   Web of Science ®Google Scholar
• Wagg D, Worden K, Barthorpe R, et al. Digital twins: state-of-the-art future directions for modelling and simulation in engineering dynamics applications. ASCE-ASME J Risk Uncertain Eng Syst B: Mech Eng. 2020;6:030901.
   Google Scholar
• Worden K, Cross EJ, Barthorpe RJ, et al. On digital twins, mirrors, and virtualizations: frameworks for model verification and validation. ASCE-ASME J Risk Uncertain Eng Syst B: Mech Eng. 2020;6:030902.
   Google Scholar
• Kortüm W, Valasek M. The 4th IAVSD-Herbertov workshop: modelling and simulation of mechatronic vehicles: tools, standards and industry demands - objectives, issues and summary of results. Veh Syst Dyn. 1999;33(Suppl 1):191–201.
   Web of Science ®Google Scholar
• Veitl A, Gordon T, Van De Sand A, et al. Methodologies for coupling simulation models and codes in mechatronic system analysis and design. Veh Syst Dyn. 1999;33(Suppl 1):231–243.
   Web of Science ®Google Scholar
• Palensky P, Cvetkovic M, Gusain D, et al. Digital twins and their use in future power systems. Digital Twin Sep 1: paper 4; 2021. [cited 2022 August 4]. Available from: https://digitaltwin1.org/articles/1-4.
   Google Scholar
• Vaculín O, Krüger WR, Valášek M. Overview of coupling of multibody and control engineering tools. Veh Syst Dyn. 2004;41(5):415–429.
   Web of Science ®Google Scholar
• Arnold M, Burgermeister B, Eichberger A. Linearly implicit time integration methods in real-time applications: DAEs and stiff ODEs. Multibody Sys Dyn. 2007;17:99–117.
   Web of Science ®Google Scholar
• Khaled N, Pattel B, Siddiqui A. Digital twin development and deployment on the cloud. London: Academic Press, Elsevier; 2020.
   Google Scholar
• Polach O, Berg M, Iwnicki S. Chapter 17: simulation of railway vehicle dynamics. In: Iwnicki S, Spiryagin M, Cole C, McSweeney T, editor. Handbook of railway vehicle dynamics - 2nd ed. Boca Raton (FL): CRC Taylor & Francis; 2020.
   Google Scholar
• Bhatti G, Mohan H, Singh RR. Towards the future of smart electric vehicles: digital twin technology. Renewable Sustainable Energy Rev. 2021;141:110801.
   Web of Science ®Google Scholar
• Wang Z, Liao X, Zhao X, et al. A digital twin paradigm: vehicle-to-cloud based advanced driver assistance systems. 2020 IEEE 91st Vehicular Technology Conference (VTC2020-Spring), 2020, p. 1–6.
   Google Scholar
• Rassõlkin A, Vaimann T, Kallaste A, et al. Digital twin for propulsion drive of autonomous electric vehicle. 2019 IEEE 60th International Scientific Conference on Power and Electrical Engineering of Riga Technical University (RTUCON); 2019, p. 1–4.
   Google Scholar
• Schluse M, Rossmann J. From simulation to experimentable digital twins: simulation-based development and operation of complex technical systems. 2016 IEEE International Symposium on Systems Engineering (ISSE); 2016, p. 1–6.
   Google Scholar
• Jones D, Snider C, Nassehi A, et al. Characterising the digital twin: a systematic literature review. CIRP J Manuf Sci Technol. 2020;29:36–52.
   Web of Science ®Google Scholar
• Al-Rubaye AS, Tsourdos A, Avdelidis NP. Digital twin analysis to promote safety and security in autonomous vehicles. IEEE Commun Mag. 2021;5(1):40–46.
   Google Scholar
• Lampropoulos G, Siakas K. Enhancing and securing cyber-physical systems and industry 4.0 through digital twins: a critical review. J Softw Eval Process. 2022;2494:1–12.
   Google Scholar
• IPC-2551. International standard for digital twins; 2020, p. 1–70.
   Google Scholar
• Flamigni F, Pileggi P, Barrowclough O, et al. First report on standards relevant for digital twins. Change2Twin project: Deliverable D1.2. Version 1.0. Horizon 2020, EU; 16-03-2021, p. 1–75.
   Google Scholar
• Moya B, Badías A, Alfaro I, et al. Digital twins that learn and correct themselves. Int J Numer Methods Eng. 2022;123:3034–3044.
   Web of Science ®Google Scholar
• Kapteyn M, Knezevic D, Huynh D, et al. Data-driven physics-based digital twins via a library of component-based reduced-order models. Int J Numer Methods Eng. 2022;123:2986–3003.
   Web of Science ®Google Scholar
• Funfschilling C, Perrin G. Uncertainty quantification in vehicle dynamics. Veh Syst Dyn. 2019;57(7):1062–1086.
   Web of Science ®Google Scholar
• Kitanoski F. Digital solutions for railway systems. Vol. 4. Virtual Vehicle Magazine. Special Edition: Digital Rail Solutions; 2019, p. 3–4.
   Google Scholar
• Shevchenko DV. Methodology for constructing digital twins in railway transport. VNIIZHT Sci J. 2021;80(2):91–99. (in Russian). DOI:10.21780/2223-9731-2021-80-2-91-99.
   Google Scholar
• Kritzinger W, Karner M, Traar G, et al. Digital twin in manufacturing: a categorical literature review and classification. IFAC-PapersOnLine. 2018;51(11):1016–1022.
   Google Scholar
• Dirnfeld R. Digital twins in railways: state of the art, opportunities, and guidelines. VT2022. Bachelor Degree Project, Linnaeus University, Faculty of Technology, Department of Computer Science and Media Technology; 2022. Available from: http://www.diva-portal.org/smash/get/diva2:1674046/FULLTEXT01.pdf.
   Google Scholar
• Pappaterra MJ. A review of literature and public datasets for the application of artificial intelligence in the railway industry [master’s thesis]. Department of Information Technology, Uppsala Universitet; 2022. Available from: https://www.diva-portal.org/smash/get/diva2:1647149/FULLTEXT01.pdf.
   Google Scholar
• Cole C, Spiryagin M, Wu Q, et al. Modelling, simulation and applications of longitudinal train dynamics. Veh Syst Dyn. 2017;55:1498–1571.
   Web of Science ®Google Scholar
• Spiryagin M, Wolfs P, Cole C, et al. Design and simulation of heavy haul locomotives and trains. Ground vehicle engineering series. Boca Raton (FL): CRC Press; 2017.
   Google Scholar
• Spiryagin M, Persson I, Wu Q, et al. A co-simulation approach for heavy haul long distance locomotive-track simulation studies. Veh Syst Dyn. 2019;57(9):1363–1380.
   Web of Science ®Google Scholar
• Wu Q, Spiryagin M, Cole C, et al. Parallel computing in railway research. Int J Rail Transp. 2020;8(2):111–134.
   Web of Science ®Google Scholar
• Wu Q, Spiryagin M, Sun Y, et al. Parallel co-simulation of locomotive wheel wear and rolling contact fatigue in a heavy haul train operational environment. Proc Inst Mech Eng Part F J Rail Rapid Transit. 2021;235(2):166–178.
   Web of Science ®Google Scholar
• Spiryagin M, Wu Q, Bosomworth C, et al. Understanding the impact of high traction hybrid locomotive designs on heavy haul train performance. Proceedings of AusRAIL PLUS 2019, delivering growth; creating opportunity; embracing technology, 3-5 Dec 2019, Sydney, NSW, Australia, p. 1–12.
   Google Scholar
• Uzuka T. Preface. RTRI simulation technologies. Railway Technical research Institute. Ascent 10 (March); 2022, p. 2–5. Available from: https://bunken.rtri.or.jp/doc/fileDown.jsp?RairacID= 0048000073.
   Google Scholar
• Parbst B, Dolmans E, Rasmussen TF. Digital twin and railway system safety. Project Group 7, Report. Safety by Design, University of Twente; February 2022, p. 1–20. Available from: https://safety.productions/wp-content/uploads/2020/02/Group_07.pdf.
   Google Scholar
• Sun YQ, Nielsen D, Wu Q, et al. Design and safety analysis of a 11-waggon consist for transporting rails. Aust J Mech Eng. 2022: 1–15. DOI: 10.1080/14484846.2021.2022576
   Web of Science ®Google Scholar
• Nia SH, Stichel S. Multibody simulation as virtual twin to predict the wheel life for iron-ore locomotive wheels. Proceedings of International Heavy Haul Association Conference, IHHA 2019, Narvik, Norway, 2019 Jun 12–14.
   Google Scholar
• Stichel S, Nia SH, Krishna V, et al. Wheel and rail maintenance planning with support of digital twins. Presentation at William Hay Railroad Engineering Seminar, Railtec, University of Illinois at Urbana-Champaign, 2021 Spring Semester, 16 Apr 2021. Available from: https://railtec.illinois.edu/wp/wp-content/uploads/Stichel_Sebastian_Hay_Seminar_2021_April.pdf.
   Google Scholar
• Krishna VV, Wu Q, Nia SH, et al. Long freight trains & long-term rail surface damage – a systems perspective. Veh Syst Dyn. 2022: 1–24. DOI: 10.1080/00423114.2022.2085584
   Web of Science ®Google Scholar
• Krishna VV, Wu Q, Nia SH, et al. Long freight trains & long-term rail surface damage: towards digital twins that enable predictive maintenance of track. World Congress of Railway Research, WCRR 2022, Birmingham, UK, 2022 Jun 6–10, p. 1–6.
   Google Scholar
• Krishna VV. Long freight trains & long-term rail surface damage [doctoral thesis in Engineering Mechanics]. KTH, Stockholm, Sweden; 2022.
   Google Scholar
• Maglio MM. Influence of wheel thread damage on wheelset and track loading – field tests and numerical simulations. Thesis for the degree of Licentiate, Department of Mechanics and Maritime Sciences, Chalmers University of Technology, Gothenburg, Sweden; 2020.
   Google Scholar
• Maglio M, Pieringer A, Nielsen J, et al. Wheel–rail impact loads and axle bending stress simulated for generic distributions and shapes of discrete wheel tread damage. J Sound Vib. 2021;502: 116085.
   Web of Science ®Google Scholar
• Bernal E, Spiryagin M, Vollebregt E, et al. Prediction of rail surface damage in locomotive traction operations using laboratory-field measured and calibrated data. Eng Fail Anal. 2022;135:106165. DOI: 10.1016/j.engfailanal.2022.106165.
   Web of Science ®Google Scholar
• Zhang T, Du W, Zhang G, et al. PHM of rail vehicle based on digital twin global reliability and Prognostics and Health Management (PHM-Nanjing), 15–17 Oct 2021, Nanjing, China; 2021, p. 1–5. DOI: 10.1109/PHM-Nanjing52125.2021.9613068.
   Google Scholar
• Grunert G. Data and evaluation model for the description of the static–dynamic interface between trains and railway bridges. Eng Struct. 2022;262:114335.
   Web of Science ®Google Scholar
• Pillai N, Shih JY, Roberts C. Sensor selection and placement for track switch condition monitoring through validated structural digital twin models of train–track interactions. Eng Proc. 2021;10:49.
   Google Scholar
• Bustos A, Rubio H, Soriano-Heras E, et al. Methodology for the integration of a high-speed train in maintenance 4.0. J Comput Design Eng. 2021;8(6):1605–1621.
   Web of Science ®Google Scholar
• Spiryagin M, Szanto F, Oldknow K, et al. Advanced modelling and performance evaluation of hydrogen-powered heavy haul locomotive. Proceedings of the 2022 Joint Rail Conference, ASME. Virtual, Online, 2022 Apr 20–21, V001T05A001, p. 1–8.
   Google Scholar
• Spiryagin M, Persson I, Wu Q, et al. A co-simulation approach for heavy haul long distance locomotive-track simulation studies. Veh Syst Dyn. 2019;57(9):1363–1380.
   Web of Science ®Google Scholar
• Meng Z, Tang T, Wei G, et al. Digital twin based comfort scenario modeling of ATO controlled train. J Phys Conf Ser. 2020;1654:012071.
   Google Scholar
• Chu W, Wuniri Q, Du X, et al. Cloud control system architectures, technologies and applications on intelligent and connected vehicles: a review. Chin J Mech Eng. 2021;34:139.
   Web of Science ®Google Scholar
• Hu Z, Lou S, Xing Y, et al. Review and perspectives on driver digital twin and its enabling technologies for intelligent vehicles. IEEE Transactions on Intelligent Vehicles; 2022, 24p.
   Google Scholar
• Liu Y, Zhang K, Li Z. Application of digital twin and parallel system in automated driving testing. Proceedings 2021 IEEE 1st International Conference on Digital Twins and Parallel Intelligence, DTPI; 2021, 4p.
   Google Scholar
• Piromalis D, Kantaros A. Digital twins in the automotive industry: The road toward physical-digital convergence. Appl Syst Innov. 2022;5:65.
   Google Scholar
• Ezhilarasu CM, Skaf Z, Jennions IK. Understanding the role of a digital twin in Integrated Vehicle Health Management (IVHM). 2019 IEEE International Conference on Systems, Man and Cybernetics (SMC); 2019 Oct 6–9, 8p.
   Google Scholar
• Algin V, Ishin M, Paddubka S. Models and approaches in design and diagnostics of vehicles planetary transmissions. IOP Conf Ser: Mater Sci Eng. 2018;393:10.
   Google Scholar
• Hiwase SD, Jagtap P. Predictive maintenance of automotive component using digital twin model. SAE Technical Paper 2022-28-0075; 2022, 7p.
   Google Scholar
• Magargle R, Johnson L, Mandloi P, et al. A simulation-based digital twin for model-driven health monitoring and predictive maintenance of an automotive braking system. Proceedings of the 12th International Modelica Conference, Prague, Czech Republic; 2017 May 15–17, p. 35–46.
   Google Scholar
• Venkatesan S, Manickavasagam K, Tengenkai N, et al. Health monitoring and prognosis of electric vehicle motor using intelligent-digital twin. IET Electr Power Appl. 2019;13(9):1328–1335.
   Web of Science ®Google Scholar
• Scurria L, Risaliti E, Buss D, et al. Executable digital twin - prevent the early failure of a truck anchorage using smart virtual sensors. SAE Technical Paper 2022-01-0767; 2022, 9p.
   Google Scholar
• Ghosh N, Garg A, Li W, et al. Engineering design of battery module for electric vehicles: comprehensive framework development based on density functional theory, topology optimization, machine learning, multidisciplinary design optimization, and digital twins. J Electrochem Energy Convers Storage. 2022;19:030902-1-19.
   Web of Science ®Google Scholar
• Li B, Bei S. Estimation algorithm research for lithium battery SOC in electric vehicles based on adaptive unscented kalman filter. Neural Comput Appl. 2019;31:8171–8183.
   Web of Science ®Google Scholar
• Merkle L, Pöthig M, Schmid F. Estimate e-golf battery state using diagnostic data and a digital twin. Batteries. 2021;7(15):22.
   Google Scholar
• Ersal T, Kolmanovsky I, Masoud N, et al. Connected and automated road vehicles: state of the art and future challenges. Veh Syst Dyn. 2020;58(5):672–704.
   Web of Science ®Google Scholar
• Guo J, Bilal M, Qiu Y, et al. Survey on digital twins for internet of vehicles: fundamentals, challenges, and opportunities. Digital Communications and Networks; 2022, 17p.
   Google Scholar
• Peng H. MCity Abc Test. Whitepaper; 20222. [cited 2022 December]. Available: https://mcity.umich.edu/wpcontent/uploads/2019/01/mcity-whitepaper-ABC-test.pdf.
   Google Scholar
• Eichberger A, Liu H, Fellendorf M, et al. Advances in automated driving systems. Basel: MDPI - Multidisciplinary Digital Publishing Institute; 2022.
   Google Scholar
• Tihanyi V, Rövid A, Remeli V, et al. Towards cooperative perception services for ITS: digital twin in the automotive edge cloud. Energies. 2021;14:5930.
   Web of Science ®Google Scholar
• Wang Z, Han K, Tiwari P. Digital twin-assisted cooperative driving at non-signalized intersections. IEEE Trans Intell Veh. 2022;7(2):198–209.
   Google Scholar
• Liao X, Wang Z, Zhao X, et al. Cooperative ramp merging design and field implementation: a digital twin approach based on vehicle-to-cloud communication. IEEE Trans Intell Transp Syst. 2021;23(5):4490–4500.
   Web of Science ®Google Scholar
• Halin A, Verly JG, Van Droogenbroeck M. Survey and synthesis of state of the art in driver monitoring. Sensors. 2021;21(16):48.
   Web of Science ®Google Scholar
• Hasenjäger M, Heckmann M, Wersing H. A survey of personalization for advanced driver assistance systems. IEEE Trans Intell Veh. 2020;5(2):335–344.
   Google Scholar
• Best MC. Real-time characterisation of driver steering behaviour. Veh Syst Dyn. 2019;57(1):64–85.
   Web of Science ®Google Scholar
• Chen X, Kang E, Shiraishi S, et al. Digital behavioral twins for safe connected cars. Proceedings of the 21th ACM/IEEE International Conference on Model Driven Engineering Languages and Systems; Copenhagen, Denmark: Association for Computing Machinery; 2018, p. 144–153.
   Google Scholar
• Stellet JE, Zofka MR, Schumacher J, et al. Testing of advanced driver assistance towards automated driving: a survey and taxonomy on existing approaches and open questions. 2015 IEEE 18th International Conference on Intelligent Transportation Systems; 2015 Sept 15–18, 8p.
   Google Scholar
• Batsch F, Kanarachos S, Cheah M, et al. A taxonomy of validation strategies to ensure the safe operation of highly automated vehicles. J Intell Transp Syst. 2022;26(1):14–33.
   Web of Science ®Google Scholar
• Hallerbach S, Xia Y, Eberle U, et al. Simulation-based identification of critical scenarios for cooperative and automated vehicles. SAE Int J Connect Autom Veh. 2018;1(2):93–106.
   Google Scholar
• Brunner P, Denk F, Huber W, et al. Virtual safety performance assessment for automated driving in complex urban traffic scenarios. IEEE Intelligent Transportation Systems Conference (ITSC); October 27–30, 2019, 7p.
   Google Scholar
• Shadrin SS, Makarova DA, Ivanov AM, et al. Safety assessment of highly automated vehicles using digital twin technology. 2021 Intelligent Technologies and Electronic Devices in Vehicle and Road Transport Complex, TIRVED 2021 - Conference Proceedings; 2021, 5p.
   Google Scholar
• Xue D, Cheng J, Zhao X, et al. A vehicle-in-the-loop simulation test based digital-twin for intelligent vehicles. 2021 IEEE DASC/PiCom/CBDCom/CyberSciTech Congress; 2021 October 25–28, 5p.
   Google Scholar
• Yang L, Wang R, Zhao X, et al. CAVTest: a closed connected and automated vehicles test field of chang’an university in China. SAE Int J Connect Autom Veh. 2021;4(4):423–435.
   Google Scholar
• Szalay Z. Next generation X-in-the-loop validation methodology for automated vehicle systems. IEEE Access. 2021;9:35616–35632.
   Web of Science ®Google Scholar
• Zhou J, Schmied R, Sandalek A, et al. A framework for virtual testing of ADAS. SAE International J Passeng Cars Electron Electr Syst. 2016;9(1):66–73.
   Google Scholar
• Templeton B. Tesla's “shadow” testing offers a useful advantage on the biggest problem in robocars; 2019. [cited 2022 December]. Available from: https://www.forbes.com/sites/bradtempleton/2019/04/29/teslas-shadow-testing-offers-a-useful-advantage-on-the-biggest-problem-in-robocars/.
   Google Scholar
• Zofka MR, Kohlhaas R, Schamm T, et al. Semivirtual simulations for the evaluation of vision-based ADAS. 2014 IEEE Intelligent Vehicles Symposium Proceedings; 2014 June 8–11, 6p.
   Google Scholar
• Demiyanushko I. Modern methods of the digital virtual analysis for design and tests of road vehicle restraint systems. IOP Conf Ser: Mater Sci Eng. 2021;1159:13.
   Google Scholar
• Liu R, Wang J, Zhang B. High definition map for automated driving: overview and analysis. J Navig. 2020;73(2):324–341.
   Web of Science ®Google Scholar
• Zhang J, Letaief KB. Mobile edge intelligence and computing for the internet of vehicles. Proc IEEE. 2020;108(2):246–261.
   Web of Science ®Google Scholar
• Marai O E, Taleb T, Song L. Roads infrastructure digital twin: a step toward smarter cities realization. IEEE Network. 2021;35(2):136–143.
   Web of Science ®Google Scholar
• Kaliske M, Behnke R, Wollny I. Vision on a digital twin of the road-tire-vehicle system for future mobility. Tire Sci Technol. 2021;49(1):2–18.
   Web of Science ®Google Scholar
• Pomoni M. Exploring smart tires as a tool to assist safe driving and monitor tire-road friction. Vehicles. 2022;4(3):744–765.
   Google Scholar
• Rasol M, Schmidt F, Ientile S, et al. Progress and monitoring opportunities of skid resistance in road transport: a critical review and road sensors. Remote Sens. 2021;13(18):26.
   Web of Science ®Google Scholar
• Naets F, Geysen J, Desmet W. An approach for combined vertical vehicle model and road profile identification from heterogeneous fleet data. 2019 8th IEEE International Conference on Connected Vehicles and Expo, ICCVE 2019 – Proceedings; 2019, 5p.
   Google Scholar
• Fietkau P, Kistner B, Munier J. Virtual powertrain development. Proc Inst Mech Eng D: J Automobile Eng. 2020;234(14):3288–33301.
   Web of Science ®Google Scholar
• Rahate SL, Gupta N, Umbarkar Y, et al. Robust approach for development and analysis of electric three wheeler vehicle using digital twin. SAE Technical Paper 2021-26-0323; 2021, 8p.
   Google Scholar
• Foldager FF, Thule C, Balling O, et al. Towards a digital twin - modelling an agricultural vehicle. Lecture notes in computer science. 12479. Cham: Springer International Publishing; 2021; p. 109–123.
   Google Scholar