Article | Proceedings of the 8th International Modelica Conference; March 20th-22nd; Technical Univeristy; Dresden; Germany | The Functional Mockup Interface for Tool independent Exchange of Simulation Models Linköping University Electronic Press Conference Proceedings
Göm menyn

The Functional Mockup Interface for Tool independent Exchange of Simulation Models
T. Blochwitz: ITI GmbH, Dresden, Germany M. Otter: DLR Oberpfaffenhofen, Germany M. Arnold: University of Halle, Germany C. Bausch: Atego Systems GmbH, Wolfsburg, Germany H. Elmqvist: Dassault Systèmes, Lund, Sweden A. Junghanns: QTronic, Berlin, Germany J. Mauß: QTronic, Berlin, Germany M. Monteiro: Atego Systems GmbH, Wolfsburg, Germany T. Neidhold: ITI GmbH, Dresden, Germany D. Neumerkel: Daimler AG, Stuttgart, Germany H. Olsson: Dassault Systèmes, Lund, Sweden J.-V. Peetz: Fraunhofer SCAI, St. Augustin, Germany S. Wolf: Fraunhofer IIS EAS, Dresden, Germany C. Clauß: Fraunhofer IIS EAS, Dresden, Germany
Full text (pdf)
Proceedings of the 8th International Modelica Conference; March 20th-22nd; Technical Univeristy; Dresden; Germany
Article no.:
No. of pages:
Publication type:
Abstract and Fulltext
Linköping Electronic Conference Proceedings
ISSN (print):
ISSN (online):
Linköping University Electronic Press; Linköpings universitet

Export in BibTex, RIS or text

The Functional Mockup Interface (FMI) is a tool independent standard for the exchange of dynamic models and for co-simulation. The development of FMI was initiated and organized by Daimler AG within the ITEA2 project MODELISAR. The primary goal is to support the exchange of simulation models between suppliers and OEMs even if a large variety of different tools are used. The FMI was developed in a close collaboration between simulation tool vendors and research institutes. In this article an overview about FMI is given and technical details about the solution are discussed.

Keywords: Simulation; Co-Simulation; Model Exchange; MODELISAR; Functional Mockup Interface (FMI); Functional Mockup Unit (FMU)

Proceedings of the 8th International Modelica Conference; March 20th-22nd; Technical Univeristy; Dresden; Germany

T. Blochwitz, M. Otter, M. Arnold, C. Bausch, H. Elmqvist, A. Junghanns, J. Mauß, M. Monteiro, T. Neidhold, D. Neumerkel, H. Olsson, J.-V. Peetz, S. Wolf, C. Clauß
The Functional Mockup Interface for Tool independent Exchange of Simulation Models

[1] Modelica Association: Modelica – A Unified Object-Oriented Language for Physical Systems Modeling. Language Specification; Version 3.2. March 24; 2010. Download:

[2] VHDL-AMS: IEEE Std 1076.1-2007. Nov. 15; 2007. VHDL-AMS web page:

[3] The Mathworks: Manual: Writing S-Functions; 2002

[4] Using ADAMS/Solver Subroutines. Mechanical Dynamics; Inc.; 1998.

[5] A. Junghanns: Virtual integration of Automotive Hard- and Software with Silver. ITI-Symposium; 24.-25.11.2010; Dresden.


[7] Blochwitz T.; Kurzbach G.; Neidhold T. An External Model Interface for Modelica. 6th International Modelica Conference; Bielefeld 2008.

[8] MODELISAR Consortium: Functional Mock-up Interface for Model Exchange. Version 1.0;

[9] MODELISAR Consortium: Functional Mock-up Interface for Co-Simulation. Version 1.0; October 2010;

[10] OPENPROD - Open Model-Driven Whole-ProductDevelopment and Simulation Environment;

[11] Ch. Noll; T. Blochwitz; Th. Neidhold; Ch. Kehrer: Implementation of Modelisar Functional Mock-up Interfaces in SimulationX. 8th International Modelica Conference. Dresden 2011.

[12] J. Bastian; Ch. Clauß; S. Wolf; P. Schneider: Master for Co-Simulation Using FMI. 8th International Modelica Conference. Dresden 2011.

Proceedings of the 8th International Modelica Conference; March 20th-22nd; Technical Univeristy; Dresden; Germany

T. Blochwitz, M. Otter, M. Arnold, C. Bausch, H. Elmqvist, A. Junghanns, J. Mauß, M. Monteiro, T. Neidhold, D. Neumerkel, H. Olsson, J.-V. Peetz, S. Wolf, C. Clauß
The Functional Mockup Interface for Tool independent Exchange of Simulation Models
Note: the following are taken directly from CrossRef
  • Christopher Schölzel, Valeria Blesius, Gernot Erns & Andreas Dominik (2021). Characteristics of mathematical modeling languages that facilitate model reuse in systems biology: a software engineering perspective. npj Systems Biology and Applications, 7(1): . DOI: 10.1038/s41540-021-00182-w
  • Arnab Bhattacharya, Soumya Vasisht, Veronica Adetola, Sen Huang, Himanshu Sharm & Draguna L. Vrabie (2021). Control co-design of commercial building chiller plant using Bayesian optimization. Energy and Buildings, 246: 111077. DOI: 10.1016/j.enbuild.2021.111077
  • A.W.M. (Jos) van Schijndel (2014). A review of the application of SimuLink S-functions to multi domain modelling and building simulation. Journal of Building Performance Simulation, 7(3): 165. DOI: 10.1080/19401493.2013.804122
  • Torben Meye & Christoph Krause (2018). Using Open Standards for Behavior Models to Gain Performance in the Virtual Commissioning Simulation. IFAC-PapersOnLine, 51(11): 815. DOI: 10.1016/j.ifacol.2018.08.437
  • Andreas Himmler, Lars Stockman & Dominik Holler (2016). Communication Infrastructure for Hybrid Test Systems - Demands, Options, and Current Discussions. SAE International Journal of Aerospace, 9(1): 134. DOI: 10.4271/2016-01-2051
  • Tobias Jun & Michael Weyrich (2019). Synchronization of a “Plug-and-Simulate”-capable Co-Simulation of Internet-of-Things-Components. Procedia CIRP, 79: 367. DOI: 10.1016/j.procir.2019.02.090
  • Neeraj Kumar Singh, Mark Lawford, Thomas S. E. Maibau & Alan Wassyng (2021). A formal approach to rigorous development of critical systems. Journal of Software: Evolution and Process, 33(4): . DOI: 10.1002/smr.2334
  • Rodrigo Cortés Porto, Daniela Geniu & Ludovic Apvrille (2021). Handling causality and schedulability when designing and prototyping cyber-physical systems. Software and Systems Modeling, 20(3): 667. DOI: 10.1007/s10270-021-00866-1
  • Günter Kunze (2013). Mobile Baumaschinen Entwicklungen und Forschungsschwerpunkte. ATZoffhighway, 6(1): 4. DOI: 10.1365/s35746-013-0054-6
  • Thomas Anstötz, Christian Haupt, Thomas Ill & Carsten Intra (2017). Längsdynamiksimulation zur Effizienzsteigerung von Nutzfahrzeugen. ATZ - Automobiltechnische Zeitschrift, 119(11): 16. DOI: 10.1007/s35148-017-0126-5
  • Abdulrahman Dahash, Fabian Ochs, Michele Bianchi Janett & Wolfgang Streicher (2019). Advances in seasonal thermal energy storage for solar district heating applications: A critical review on large-scale hot-water tank and pit thermal energy storage systems. Applied Energy, 239: 296. DOI: 10.1016/j.apenergy.2019.01.189
  • Matthias Bartelt, Adrian Schyj & Bernd Kuhlenkötter (2014). More than a Mockup. Production Engineering, 8(6): 727. DOI: 10.1007/s11740-014-0575-6
  • F. De Filippo, A. Stork, H. Schmed & F. Bruno (2014). A modular architecture for a driving simulator based on the FDMU approach. International Journal on Interactive Design and Manufacturing (IJIDeM), 8(2): 139. DOI: 10.1007/s12008-013-0182-3
  • Peter Palensky, Edmund Widl, Matthias Stifte & Atiyah Elsheikh (2013). Modeling Intelligent Energy Systems: Co-Simulation Platform for Validating Flexible-Demand EV Charging Management. IEEE Transactions on Smart Grid, 4(4): 1939. DOI: 10.1109/TSG.2013.2258050
  • Cherifa Dad, Jean-Philippe Tavell & Stéphane Vialle (2021). Synthesis and feedback on the distribution and parallelization of FMI-CS-based co-simulations with the DACCOSIM platform. Parallel Computing, 106: 102802. DOI: 10.1016/j.parco.2021.102802
  • Jaclyn M. Branscomb, Christiaan J.J. Paredis, Judy Ch & Mark J. Jennings (2013). Supporting Multidisciplinary Vehicle Analysis Using a Vehicle Reference Architecture Model in SysML. Procedia Computer Science, 16: 79. DOI: 10.1016/j.procs.2013.01.009
  • Chiara Cimino, Alberto Leva, Elisa Negr & Marco Macchi (2020). An integrated simulation paradigm for lifecycle-covering maintenance in the Industry 4.0 context. IFAC-PapersOnLine, 53(3): 307. DOI: 10.1016/j.ifacol.2020.11.049
  • Gray Bachelor, Eugenio Brusa, Davide Ferrett & Andreas Mitschke (2020). Model-Based Design of Complex Aeronautical Systems Through Digital Twin and Thread Concepts. IEEE Systems Journal, 14(2): 1568. DOI: 10.1109/JSYST.2019.2925627
  • Ruoyang Yuan, Tom Fletcher, Ahmed Ahmedov, Nikolaos Kalantzis, Antonios Pezouvanis, Nilabza Dutta, Andrew Watso & Kambiz Ebrahimi (2020). Modelling and Co-simulation of hybrid vehicles: A thermal management perspective. Applied Thermal Engineering, 180: 115883. DOI: 10.1016/j.applthermaleng.2020.115883
  • Severin Sadjina, Lars Tandle Kyllingstad, Martin Rindarøy, Stian Skjong, Vilmar Æsø & Eilif Pedersen (2019). Distributed Co-simulation of Maritime Systems and Operations. Journal of Offshore Mechanics and Arctic Engineering, 141(1): . DOI: 10.1115/1.4040473
  • S. Paulick, C. Schroth, S. Guddusc & K. Rühling (2018). Resulting Effects On Decentralized Feed-In Into District Heating Networks – A Simulation Study. Energy Procedia, 149: 49. DOI: 10.1016/j.egypro.2018.08.168
  • David Blum, Javier Arroyo, Sen Huang, Ján Drgoňa, Filip Jorissen, Harald Taxt Walnum, Yan Chen, Kyle Benne, Draguna Vrabie, Michael Wette & Lieve Helsen (2021). Building optimization testing framework (BOPTEST) for simulation-based benchmarking of control strategies in buildings. Journal of Building Performance Simulation, 14(5): 586. DOI: 10.1080/19401493.2021.1986574
  • Manan Sing & Ryan Sharston (2022). A literature review of building energy simulation and computational fluid dynamics co-simulation strategies and its implications on the accuracy of energy predictions. Building Services Engineering Research and Technology, 43(1): 113. DOI: 10.1177/01436244211020465
  • Suthida Thongnuch, Alexander Fa & Rainer Drath (2018). Semi-automatic generation of a virtual representation of a production cell. at - Automatisierungstechnik, 66(5): 372. DOI: 10.1515/auto-2017-0108
  • M Wetter, C van Treeck, L Helsen, A Maccarini, D Saelens, D Robinso & G Schweiger (2019). IBPSA Project 1: BIM/GIS and Modelica framework for building and community energy system design and operation – ongoing developments, lessons learned and challenges. IOP Conference Series: Earth and Environmental Science, 323(1): 012114. DOI: 10.1088/1755-1315/323/1/012114
  • Marwan Abugabbara, Saqib Javed, Hans Bagg & Dennis Johansson (2020). Bibliographic analysis of the recent advancements in modeling and co-simulating the fifth-generation district heating and cooling systems. Energy and Buildings, 224: 110260. DOI: 10.1016/j.enbuild.2020.110260
  • David Golightly, Carl Gamble, Roberto Palaci & Ken Pierce (2019). Multi-modelling for Decarbonisation in Urban Rail Systems. Urban Rail Transit, 5(4): 254. DOI: 10.1007/s40864-019-00114-2
  • Janis Sebastian HÄSEKER, Niklas AKSTEINE & Annika OFENLOCH (2021). From Behavioural to In-Depth Modelling of a Scalable Spacecraft Power System in SystemC-AMS using Continuous Integration Techniques. TRANSACTIONS OF THE JAPAN SOCIETY FOR AERONAUTICAL AND SPACE SCIENCES, AEROSPACE TECHNOLOGY JAPAN, 19(5): 709. DOI: 10.2322/tastj.19.709
  • Fabio Cremona, Marten Lohstroh, David Broman, Edward A. Lee, Michael Masi & Stavros Tripakis (2019). Hybrid co-simulation: it’s about time. Software & Systems Modeling, 18(3): 1655. DOI: 10.1007/s10270-017-0633-6
  • Simon Bliudze, Sébastien Furic, Joseph Sifaki & Antoine Viel (2019). Rigorous design of cyber-physical systems. Software & Systems Modeling, 18(3): 1613. DOI: 10.1007/s10270-017-0642-5
  • Adil Rasheed, Omer Sa & Trond Kvamsdal (2020). Digital Twin: Values, Challenges and Enablers From a Modeling Perspective. IEEE Access, 8: 21980. DOI: 10.1109/ACCESS.2020.2970143
  • Gregorio Lopez, Javier Matanza, David De La Vega, Marta Castro, Amaia Arrinda, Jose Ignacio Moren & Alberto Sendin (2019). The Role of Power Line Communications in the Smart Grid Revisited: Applications, Challenges, and Research Initiatives. IEEE Access, 7: 117346. DOI: 10.1109/ACCESS.2019.2928391
  • Imco van Gent, Gianfranco La Rocc & Maurice F. M. Hoogreef (2018). CMDOWS: a proposed new standard to store and exchange MDO systems. CEAS Aeronautical Journal, 9(4): 607. DOI: 10.1007/s13272-018-0307-2
  • Stian Skjong, Martin Rindarøy, Lars T. Kyllingstad, Vilmar Æsø & Eilif Pedersen (2018). Virtual prototyping of maritime systems and operations: applications of distributed co-simulations. Journal of Marine Science and Technology, 23(4): 835. DOI: 10.1007/s00773-017-0514-2
  • Georg Rill, Florian Baue & Mathias Kirchbeck (2021). VTT – a virtual test truck for modern simulation tasks. Vehicle System Dynamics, 59(4): 635. DOI: 10.1080/00423114.2019.1705356
  • Markus Tranninger, Georg Stettinger, Martin Benedik & Martin Horn (2018). Diagnosis of Interconnected Systems via well tuned Model-Based Coupling Algorithms. IFAC-PapersOnLine, 51(24): 1271. DOI: 10.1016/j.ifacol.2018.09.571
  • Annika Ofenloc & Fabian Greif (2018). A Flexible Distributed Simulation Environment for Cyber-Physical Systems Using ZeroMQ. Journal of Communications, : 333. DOI: 10.12720/jcm.13.6.333-337
  • Stefan-Alexander Schneide & Kmeid Saad (2018). Camera behavioral model and testbed setups for image-based ADAS functions. e & i Elektrotechnik und Informationstechnik, 135(4-5): 328. DOI: 10.1007/s00502-018-0622-7
  • Salah Eddine Saidi, Nicolas Pernet, Yves Sorel, A. Anciaux-Sedrakia & Q. H. Tran (2019). A method for parallel scheduling of multi-rate co-simulation on multi-core platforms. Oil & Gas Science and Technology – Revue d’IFP Energies nouvelles, 74: 49. DOI: 10.2516/ogst/2019009
  • Daniel Fuhrländer-Völker, Martin Lindne & Matthias Weigold (2021). Design Method for Building Automation Control Programs to Enable the Energetic Optimization of Industrial Supply Systems. Procedia CIRP, 104: 229. DOI: 10.1016/j.procir.2021.11.039
  • Tobias Küster, Philipp Rayling, Robin Wiersi & Francisco Denis Pozo Pardo (2021). Multi-objective optimization of energy-efficient production schedules using genetic algorithms. Optimization and Engineering, : . DOI: 10.1007/s11081-021-09691-3
  • Martin Krammer, Clemens Schiffe & Martin Benedikt (2021). ProMECoS: A Process Model for Efficient Standard-Driven Distributed Co-Simulation. Electronics, 10(5): 633. DOI: 10.3390/electronics10050633
  • Philipp Petr, Wilhelm Tegethof & Jürgen Köhler (2017). Method for designing waste heat recovery systems (WHRS) in vehicles considering optimal control. Energy Procedia, 129: 232. DOI: 10.1016/j.egypro.2017.09.147
  • Jens Holtkötter, Jan Michael, Christian Henke, Ansgar Trächtler, Marcos Bockholt, Andreas Möhlenkam & Michael Katter (2018). Rapid-Control-Prototyping as part of Model-Based Development of Heat Pump Dryers. Procedia Manufacturing, 24: 235. DOI: 10.1016/j.promfg.2018.06.033
  • Mohammad Hassan Fathollahzade & Paulo Cesar Tabares-Velasco (2020). Building control virtual test bed and functional mock-up interface standard: comparison in the context of campus energy modelling and control. Journal of Building Performance Simulation, 13(4): 456. DOI: 10.1080/19401493.2020.1769191
  • A. Holmqvist, F. Magnusso & S. Stenström (2014). Scale-up analysis of continuous cross-flow atomic layer deposition reactor designs. Chemical Engineering Science, 117: 301. DOI: 10.1016/j.ces.2014.07.002
  • Vincent Reinbold, Christina Protopapadaki, Jean-Philippe Tavell & Dirk Saelens (2019). Assessing scalability of a low-voltage distribution grid co-simulation through functional mock-up interface. Journal of Building Performance Simulation, 12(5): 637. DOI: 10.1080/19401493.2019.1597923
  • Javier Bonilla, Lidia Roca, Diego López, Eduardo Cerrajero, Santiago Mirabal, Silvia Padilla, Luis E. Díez, Alberto R. Roch & Lucía González (2016). Operation and Training Tool for a Gas - Molten Salt Heat Recovery Demonstrator Facility. Procedia Computer Science, 83: 1118. DOI: 10.1016/j.procs.2016.04.232
  • Pengfei Li, Yaoyu Li, John E. Seem, Hongtao Qiao, Xiao L & Jon Winkler (2014). Recent advances in dynamic modeling of HVAC equipment. Part 2: Modelica-based modeling. HVAC&R Research, 20(1): 150. DOI: 10.1080/10789669.2013.836876
  • Awad Mukbil, Umut Dura & Sven Hartmann (2019). Conformance testing of FMI calling sequence for simulation environments. International Journal of Modeling, Simulation, and Scientific Computing, 10(02): 1950008. DOI: 10.1142/S1793962319500089
  • Severin Sadjina, Lars T. Kyllingstad, Stian Skjon & Eilif Pedersen (2017). Energy conservation and power bonds in co-simulations: non-iterative adaptive step size control and error estimation. Engineering with Computers, 33(3): 607. DOI: 10.1007/s00366-016-0492-8
  • Tiantian Dou, Yuri Kaszubowski Lopes, Peter Rockett, Elizabeth A. Hathwa & Esmail Saber (2020). GPML: an XML-based standard for the interchange of genetic programming trees. Genetic Programming and Evolvable Machines, 21(4): 605. DOI: 10.1007/s10710-019-09370-4
  • Thibaut Pierre Richert, Tue Vissing Jensen, Oliver Gehrk & Henrik WIlliam Bindner (2020). Operation of supermarket refrigeration units: a coupled district heating and electric network approach. IET Energy Systems Integration, 2(2): 80. DOI: 10.1049/iet-esi.2019.0059
  • Roel De Coninck, Fredrik Magnusson, Johan Åkesso & Lieve Helsen (2016). Toolbox for development and validation of grey-box building models for forecasting and control. Journal of Building Performance Simulation, 9(3): 288. DOI: 10.1080/19401493.2015.1046933

  • Responsible for this page: Peter Berkesand
    Last updated: 2019-11-06