# Automated Identification of Valid Model Networks Using Model-Based Systems Engineering

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## Abstract

**:**

## 1. Introduction

## 2. State of the Art

#### 2.1. Motego Method

#### 2.2. Model Classification

#### 2.3. Model Signatures

#### 2.4. Integration of Domain Models into the System Model Based on Model Classification and Model Signatures

## 3. Research Need

- How can valid model networks in system models be identified automatically based on model classification and model signatures during development?
- How can the determined model networks be automatically updated when parameters or models are changed?

## 4. Solution Approach to Determining Valid Model Networks

_{T}, can be calculated using the product of the number of models per purpose, N

_{M}:

## 5. Demonstration and Discussion of the Solution Approach Using a Battery System

## 6. Conclusions and Outlook

## Author Contributions

## Funding

## Institutional Review Board Statement

## Informed Consent Statement

## Data Availability Statement

## Conflicts of Interest

## References

- European Commission. Fit for 55: Delivering the EU’s 2030 Climate Target on the Way to Climate Neutrality. Available online: https://eur-lex.europa.eu/legal-content/EN/TXT/?uri=CELEX:52021DC0550 (accessed on 19 April 2022).
- Zhu, J.; Mathews, I.; Ren, D.; Li, W.; Cogswell, D.; Xing, B.; Sedlatschek, T.; Kantareddy, S.N.R.; Yi, M.; Gao, T.; et al. End-of-life or second-life options for retired electric vehicle batteries. Cell Rep. Phys. Sci.
**2021**, 2, 100537. [Google Scholar] [CrossRef] - Rallo, H.; Canals Casals, L.; de La Torre, D.; Reinhardt, R.; Marchante, C.; Amante, B. Lithium-ion battery 2nd life used as a stationary energy storage system: Ageing and economic analysis in two real cases. J. Clean. Prod.
**2020**, 272, 122584. [Google Scholar] [CrossRef] - Hossain, E.; Murtaugh, D.; Mody, J.; Faruque, H.M.R.; Haque Sunny, M.S.; Mohammad, N. A Comprehensive Review on Second-Life Batteries: Current State, Manufacturing Considerations, Applications, Impacts, Barriers & Potential Solutions, Business Strategies, and Policies. IEEE Access
**2019**, 7, 73215–73252. [Google Scholar] [CrossRef] - Sanguesa, J.A.; Torres-Sanz, V.; Garrido, P.; Martinez, F.J.; Marquez-Barja, J.M. A Review on Electric Vehicles: Technologies and Challenges. Smart Cities
**2021**, 4, 372–404. [Google Scholar] [CrossRef] - Drave, I.; Rumpe, B.; Wortmann, A.; Berroth, J.; Hoepfner, G.; Jacobs, G.; Spuetz, K.; Zerwas, T.; Guist, C.; Kohl, J. Modeling mechanical functional architectures in SysML. In Proceedings of the 23rd ACM/IEEE International Conference on Model Driven Engineering Languages and Systems, Montreal, QC, Canada, 16–23 October 2020; Syriani, E., Sahraoui, H., Eds.; ACM: New York, NY, USA, 2020; pp. 79–89, ISBN 9781450370196. [Google Scholar]
- Jacobs, G.; Konrad, C.; Berroth, J.; Zerwas, T.; Höpfner, G.; Spütz, K. Function-Oriented Model-Based Product Development. In Design Methodology for Future Products; Krause, D., Heyden, E., Eds.; Springer International Publishing: Cham, Switzerland, 2022; pp. 243–263. ISBN 978-3-030-78367-9. [Google Scholar]
- Eigner, M. Modellbasierte Virtuelle Produktentwicklung; Springer Vieweg: Berlin, Germany, 2014; ISBN 978-3-662-43816-9. [Google Scholar]
- Kerr, C.; Jaradat, R.; Ibne Hossain, N.U. Battlefield Mapping by an Unmanned Aerial Vehicle Swarm: Applied Systems Engineering Processes and Architectural Considerations From System of Systems. IEEE Access
**2020**, 8, 20892–20903. [Google Scholar] [CrossRef] - Berges, J.M.; Höpfner, G.; Zhang, Y.; Berroth, J.; Jacobs, G. Vernetzung von Simulationsmodellen und Model- Based Systems Engineering zur Virtuellen Produktentwicklung; NAFEMS Regionalconference: Bamberg, Germany, 2022. [Google Scholar]
- Elakramine, F.; Jaradat, R.; Ullah Ibne Hossain, N.; Banghart, M.; Kerr, C.; El Amrani, S. Applying Systems Modeling Language in an Aviation Maintenance System. IEEE Trans. Eng. Manage.
**2022**, 69, 4006–4018. [Google Scholar] [CrossRef] - Zhang, Y.; Roeder, J.; Jacobs, G.; Berroth, J.; Hoepfner, G. Virtual Testing Workflows Based on the Function-Oriented System Architecture in SysML: A Case Study in Wind Turbine Systems. Wind
**2022**, 2, 599–616. [Google Scholar] [CrossRef] - Höpfner, G.; Jacobs, G.; Zerwas, T.; Drave, I.; Berroth, J.; Guist, C.; Rumpe, B.; Kohl, J. Model-Based Design Workflows for Cyber-Physical Systems Applied to an Electric-Mechanical Coolant Pump. In Proceedings of the IOP Conference Series: Materials Science and Engineering, Surakarta, Indonesia, 24–25 August 2021; Volume 1097, p. 12004. [Google Scholar] [CrossRef]
- Torres, W.; van den Brand, M.; Serebrenik, A. Model Management Tools for Models of Different Domains: A Systematic Literature Review. In Proceedings of the 2019 IEEE International Systems Conference (SysCon), Orlando, FL, USA, 8–11 April 2019; IEEE: Piscataway, NJ, USA, 2019; pp. 1–8, ISBN 978-1-5386-8396-5. [Google Scholar]
- Torres, W.; van den Brand, M.G.J.; Serebrenik, A. A systematic literature review of cross-domain model consistency checking by model management tools. Softw. Syst. Model.
**2021**, 20, 897–916. [Google Scholar] [CrossRef] - Zerwas, T.; Jacobs, G.; Kowalski, J.; Husung, S.; Gerhard, D.; Rumpe, B.; Zeman, K.; Vafaei, S.; König, F.; Höpfner, G. Model Signatures for the Integration of Simulation Models into System Models. Systems
**2022**, 10, 199. [Google Scholar] [CrossRef] - Berges, J.M.; Jacobs, G.; Berroth, J. A Numerical Approach for the Efficient Concept Design of Laser-Based Hybrid Joints. Appl. Sci.
**2022**, 12, 10649. [Google Scholar] [CrossRef] - Berges, J.M.; Jacobs, G.; Stein, S.; Sprehe, J. Methodology for the Concept Design of Locally Reinforced Composites. Appl. Sci.
**2021**, 11, 7246. [Google Scholar] [CrossRef] - Piperni, P.; Rahman, S.M.M. Singlepoint and Multipoint Robust Design of Airfoils using CST Functions. In AIAA Scitech 2021 Forum; AIAA Scitech 2021 Forum, VIRTUAL EVENT; American Institute of Aeronautics and Astronautics: Reston, VA, USA, 2021; ISBN 978-1-62410-609-5. [Google Scholar]
- Hust, F.E. Physikalisch-Chemisch Motivierte Parametrierung und Modellierung von Echtzeitfähigen Lithium-Ionen Batteriemodellen—eine Fallstudie zur Tesla Model S Batterie; RWTH Aachen University: Aachen, Germany, 2018. [Google Scholar]
- Besbes, K.; Jimenez Mena, D.; Soto, A. Sizing of an Electric Powertrain Using a Hybrid Model Combining Simcenter Amesim and Modelica Components; 2020. [Google Scholar]
- Johnson, T.; Kerzhner, A.; Paredis, C.J.J.; Burkhart, R. Integrating Models and Simulations of Continuous Dynamics Into SysML. J. Comput. Inf. Sci. Eng.
**2012**, 12, 011002. [Google Scholar] [CrossRef][Green Version] - Reichwein, A.; Paredis, C.J.J.; Canedo, A.; Witschel, P.; Stelzig, P.E.; Votintseva, A.; Wasgint, R. Maintaining consistency between system architecture and dynamic system models with SysML4Modelica. In Proceedings of the 6th International Workshop on Multi-Paradigm Modeling—MPM ’12. the 6th International Workshop, Innsbruck, Austria, 1 October 2012; Hardebolle, C., Syriani, E., Sprinkle, J., Mészáros, T., Eds.; ACM Press: New York, NY, USA, 2012; pp. 43–48, ISBN 9781450318051. [Google Scholar]
- Qamar, A.; Paredis, C.J.J.; Wikander, J.; During, C. Dependency Modeling and Model Management in Mechatronic Design. J. Comput. Inf. Sci. Eng.
**2012**, 12, 041009. [Google Scholar] [CrossRef][Green Version] - Spütz, K.; Berges, J.M.; Jacobs, G.; Berroth, J.; Konrad, C. Classification of Simulation Models for the Model-based Design of Plastic-Metal Hybrid Joints. Procedia CIRP
**2022**, 109, 37–42. [Google Scholar] [CrossRef] - Husung, S.; Gerhard, D.; Jacobs, G.; Kowalski, J.; Rumpe, B.; Zeman, K.; Zerwas, T. Model signatures for design and usage of simulation-capable model networks in MBSE. In Proceedings of the IFIP 19th International Conference on Product Lifecycle Management, Grenoble, France, 10–13 July 2022. [Google Scholar]
- Börner, M.F.; Frieges, M.H.; Späth, B.; Spütz, K.; Heimes, H.H.; Sauer, D.U.; Li, W. Challenges of second-life concepts for retired electric vehicle batteries. Cell Rep. Phys. Sci.
**2022**, 3, 101095. [Google Scholar] [CrossRef] - Weilkiens, T. SYSMOD—The Systems Modeling Toolbox: Pragmatic MBSE with SysML, 3rd ed.; Auflage, revidierte Ausgabe; MBSE4U—Tim Weilkiens: Fredesdorf, Germany, 2020; ISBN 978-3981852981. [Google Scholar]
- Moeser, G.; Grundel, M.; Weilkiens, T.; Kümpel, S.; Kramer, C.; Albers, A. Modellbasierter mechanischer Konzeptentwurf: Ergebnisse des FAS4M-Projekts. In Tag des Systems Eingineering; 2016; p. 419. [Google Scholar]
- Zerwas, T.; Jacobs, G.; Spütz, K.; Höpfner, G.; Drave, I.; Berroth, J.; Guist, C.; Konrad, C.; Rumpe, B.; Kohl, J. Mechanical concept development using principle solution models. In Proceedings of the IOP Conference Series: Materials Science and Engineering, Surakarta, Indonesia, 24–25 August 2021; Volume 1097, p. 12001. [Google Scholar] [CrossRef]
- Spütz, K.; Jacobs, G.; Konrad, C.; Wyrwich, C. Integration of Production and Cost Models in Model-Based Product Development. JSS
**2021**, 09, 53–64. [Google Scholar] [CrossRef] - Dassault Systèmes. Cameo Systems Modeler. Available online: https://www.3ds.com/products-services/catia/products/no-magic/cameo-systems-modeler/ (accessed on 27 September 2022).
- Vereinigung zur Förderung des Instituts für Maschinenelemente und Systementwicklung der Rheinisch-Westfälischen Technischen Hochschule Aachen e.V. motego: Future Product Development. Available online: www.motego.info (accessed on 27 September 2022).
- Faraz, A.; Ambikapathy, A.; Thangavel, S.; Logavani, K.; Arun Prasad, G. Battery Electric Vehicles (BEVs). In Electric Vehicles; Patel, N., Bhoi, A.K., Padmanaban, S., Holm-Nielsen, J.B., Eds.; Springer: Singapore, 2021; pp. 137–160. ISBN 978-981-15-9250-8. [Google Scholar]
- Abada, S.; Marlair, G.; Lecocq, A.; Petit, M.; Sauvant-Moynot, V.; Huet, F. Safety focused modeling of lithium-ion batteries: A review. J. Power Sources
**2016**, 306, 178–192. [Google Scholar] [CrossRef] - Chen, Y.; Kang, Y.; Zhao, Y.; Wang, L.; Liu, J.; Li, Y.; Liang, Z.; He, X.; Li, X.; Tavajohi, N.; et al. A review of lithium-ion battery safety concerns: The issues, strategies, and testing standards. J. Energy Chem.
**2021**, 59, 83–99. [Google Scholar] [CrossRef] - Zhou, W.; Zheng, Y.; Pan, Z.; Lu, Q. Review on the Battery Model and SOC Estimation Method. Processes
**2021**, 9, 1685. [Google Scholar] [CrossRef] - Brady, N.W.; Gould, C.A.; West, A.C. Quantitative Parameter Estimation, Model Selection, and Variable Selection in Battery Science. J. Electrochem. Soc.
**2020**, 167, 13501. [Google Scholar] [CrossRef] - Keil, P.; Jossen, A. Aufbau und parametrierung von batteriemodellen. In 19. DESIGN&ELEKTRONIK-Entwicklerforum Batterien & Ladekonzepte; 2012. [Google Scholar]
- Lin, X.; Perez, H.E.; Mohan, S.; Siegel, J.B.; Stefanopoulou, A.G.; Ding, Y.; Castanier, M.P. A lumped-parameter electro-thermal model for cylindrical batteries. J. Power Sources
**2014**, 257, 1–11. [Google Scholar] [CrossRef] - Wu, J.; Wei, Z.; Li, W.; Wang, Y.; Li, Y.; Sauer, D.U. Battery Thermal- and Health-Constrained Energy Management for Hybrid Electric Bus Based on Soft Actor-Critic DRL Algorithm. IEEE Trans. Ind. Inf.
**2021**, 17, 3751–3761. [Google Scholar] [CrossRef] - COMSOL. Thermal Distribution in a Pack of Cylindrical Batteries. Available online: https://www.comsol.de/model/download/906591/models.battery.lumped_li_battery_pack_6s2p.pdf (accessed on 22 April 2022).

**Figure 1.**Seamless linkage of elements at a system level with the motego method illustrated for a battery system.

**Figure 3.**Model signatures with input, output and internal parameters for two exemplary models of battery system development (according to [26]).

**Figure 4.**Structure of motego system models and integration of domain models into the SystemSolution of a battery system based on model classification and model signatures according to [25].

**Figure 5.**Flowchart of a model network algorithm for the automated analysis of valid model networks using system models built with the motego method.

**Figure 6.**Reading elements and relationships between SysML system models using JavaScript to determine valid model networks.

**Figure 7.**Implementation of the model network algorithm with profile mechanisms in the SysML modeling environment Cameo Systems Modeler.

**Figure 8.**Resulting valid model networks for the specification of two versions of the SystemSolution battery pack with the applied ModelNetworkProfile without cell positions (

**a**) and with cell positions (

**b**).

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**MDPI and ACS Style**

Berges, J.M.; Spütz, K.; Jacobs, G.; Kowalski, J.; Zerwas, T.; Berroth, J.; Konrad, C. Automated Identification of Valid Model Networks Using Model-Based Systems Engineering. *Systems* **2022**, *10*, 250.
https://doi.org/10.3390/systems10060250

**AMA Style**

Berges JM, Spütz K, Jacobs G, Kowalski J, Zerwas T, Berroth J, Konrad C. Automated Identification of Valid Model Networks Using Model-Based Systems Engineering. *Systems*. 2022; 10(6):250.
https://doi.org/10.3390/systems10060250

**Chicago/Turabian Style**

Berges, Julius Moritz, Kathrin Spütz, Georg Jacobs, Julia Kowalski, Thilo Zerwas, Jörg Berroth, and Christian Konrad. 2022. "Automated Identification of Valid Model Networks Using Model-Based Systems Engineering" *Systems* 10, no. 6: 250.
https://doi.org/10.3390/systems10060250