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Multiscale simulation approach for production systems

Application to the production of lithium-ion battery cells

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Abstract

Planning support for industrial production systems aims at reducing production-related costs and environmental impacts while creating required products with a desired quality. However, the increasing complexity of modern products and production technology makes planning and the provision of appropriate methods and tools a challenging task. Isolated measures for improving quality, cost or eco-efficiency, often result in problem shifting causing negative effects regarding other goal criteria. For this reason, suitable methods and tools are required for the planning and evaluation of specific improvement measures and for obtaining an interdisciplinary system understanding. This paper presents an approach, which is capable of analyzing production systems considering multiple scales based on coupled simulation models. The approach enables the evaluation of interactions between product units, processes, machines, technical building services, and the building structure. The approach contains a generic framework for the simulation structure, detailed model concepts for relevant production system elements, and a definition of interfaces between models for co-simulation. A case study demonstrates an exemplary application of the simulation approach for the production of battery cells. The study shows how the simulation enables evaluating the influences of different process configurations on intermediate product characteristics as well as of different factory scenarios and seasonal effects on the energy demands. More specifically, on product and process scale, the study revealed how different process routes and process parameters in electrode production affect the characteristics of battery slurries and coated electrode foils along with production lead-times. On process chain and factory scale, the study illustrates how the energy demands of machines and building services are influenced by machine operation and outside weather conditions. Thus, the study provides insight into the capabilities of a multiscale simulation and how such simulation may be applied to evaluate different producton system configurations, operation strategies, or facility locations.

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References

  1. Gutowski TG, Allwood JM, Herrmann C, Sahni S (2013) A global assessment of manufacturing: economic development, energy use, carbon emissions, and the potential for energy efficiency and materials recycling. Annu Rev Environ Resour 38(1):81–106. https://doi.org/10.1146/annurev-environ-041112-110510

    Article  Google Scholar 

  2. Heinemann T, Thiede S, Herrmann C, Kara S (2012) A hierarchical evaluation scheme for industrial process chains : aluminum die casting. In: 19th CIRP international conference on life cycle engineering

  3. Herrmann C, Thiede S (2009) Process chain simulation to foster energy efficiency in manufacturing. CIRP J Manuf Sci Technol 1(4):221–229. https://doi.org/10.1016/j.cirpj.2009.06.005

    Article  Google Scholar 

  4. Landherr M, Neumann M, Volkmann J, Constantinescu C (2013) Digitale fabrik. In: Westkämper E, Spath D, Constantinescu C, Lentes J (eds) Digitale produktion. Springer, Berlin, pp 107–131

  5. Negahban A, Smith JS (2014) Simulation for manufacturing system design and operation: literature review and analysis. J Manuf Syst 33(2):241–261. https://doi.org/10.1016/j.jmsy.2013.12.007

    Article  Google Scholar 

  6. Schönemann M (2017) Multiscale simulation approach for battery production systems. Sustainable production, life cycle engineering and management. Springer International Publishing, Cham. https://doi.org/10.1007/978-3-319-49367-1

    Book  Google Scholar 

  7. Bergmann S (2014) Automatische generierung adaptiver modelle zur simulation von produktionssystemen. Universitätsverlag Ilmenau, Ilmenau

    Google Scholar 

  8. Bullinger HJ, Spath D, Warnecke HJ, Westkämper, E (eds) (2009) Handbuch unternehmensorganisation – strategien, planung, umsetzung, 3rd edn. 1 (Springer). https://doi.org/10.1007/978-3-540-87595-6

  9. März L, Krug W, Rose O, Weigert G (2011) Simulation und optimierung in produktion und logistik. https://doi.org/10.1007/978-3-642-14536-0

  10. Borshchev A, Filippov A (2004) From system dynamics to agent based modeling:. In: The 22nd international conference of the system dynamics society. Oxford

  11. Jahangirian M, Eldabi T, Naseer A, Stergioulas LK, Young T (2010) Simulation in manufacturing and business: a review. Eur J Oper Res 203(1):1–13. https://doi.org/10.1016/j.ejor.2009.06.004

    Article  Google Scholar 

  12. Liang S, Yao X (2008) Multi-level modeling for hybrid manufacturing systems using arena and MATLAB. 2008 International Workshop on Modelling, Simulation and Optimization, pp 155–159. https://doi.org/10.1109/WMSO.2008.79

  13. Väyrynen A, Salminen J (2012) Lithium ion battery production. J Chem Thermodyn 46:80–85. https://doi.org/10.1016/j.jct.2011.09.005

    Article  Google Scholar 

  14. Yuan C, Deng Y, Li T, Yang F (2017) CIRP annals - manufacturing technology manufacturing energy analysis of lithium ion battery pack for electric vehicles. CIRP Annals - Manufacturing Technology, pp 8–11. https://doi.org/10.1016/j.cirp.2017.04.109

  15. Fowler JW (2004) Grand challenges in modeling and simulation of complex manufacturing systems. Simulation 80(9):469–476. https://doi.org/10.1177/0037549704044324

    Article  Google Scholar 

  16. Wieser C, Prill T, Schladitz K (2015) Multiscale simulation process and application to additives in porous composite battery electrodes. J Power Sources 277:64–75. https://doi.org/10.1016/j.jpowsour.2014.11.090

    Article  Google Scholar 

  17. Gates T, Odegard G, Frankland S, Clancy T (2005) Computational materials: multi-scale modeling and simulation of nanostructured materials. Compos Sci Technol 65(15-16):2416–2434. https://doi.org/10.1016/j.compscitech.2005.06.009

    Article  Google Scholar 

  18. Hoekstra A, Lorenz E, Falcone JL, Chopard B (2007) Towards a complex automata framework for multi-scale modeling: formalism and the scale separation map. Proc ICCS 2007, pp 1–9

  19. Boras BW, Hirakis SP, Votapka LW, Malmstrom RD, Amaro RE, McCulloch AD (2015) Bridging scales through multiscale modeling: a case study on protein kinase A. Front Physiol 6(SEP):1–15. https://doi.org/10.3389/fphys.2015.00250

    Google Scholar 

  20. Wiendahl HP, ElMaraghy, HA, Nyhuis P, Zäh MF, Wiendahl HH, Duffie N, Brieke M (2007) Changeable manufacturing - classification, design and operation. CIRP Ann - Manuf Technol 56(2):783–809. https://doi.org/10.1016/j.cirp.2007.10.003

    Article  Google Scholar 

  21. Verl A, Westkämper E, Abele E, Dietmair A, Schlechtendahl J, Friedrich J, Haag H, Schrems S (2011) Architecture for multilevel monitoring and control of energy consumption. In: 18th CIRP international conference on life cycle engineering. Braunschweig

  22. Herrmann C, Kara S, Thiede S, Luger T (2010) Energy efficiency in manufacturing – perspectives from Australia and Europe. In: 17th CIRP international conference on life cycle engineering

  23. Schenk M, Wirth S, Müller E (2014) Fabrikplanung und fabrikbetrieb. Springer, Berlin. https://doi.org/10.1007/978-3-642-05459-4

    Book  Google Scholar 

  24. McDowell DL, Olson GB (2009) Concurrent design of hierarchical materials and structures. Lect Notes Comput Sci Eng 68 LNCSE:207–240

    Google Scholar 

  25. Sengupta D, Abraham JP, Ceja M, Gonzalez MA, Ingwersen WW, Ruiz-Mercado GJ, Smith RL (2015) Industrial process system assessment: bridging process engineering and life cycle assessment through multiscale modeling. J Clean Prod 90:142–152. https://doi.org/10.1016/j.jclepro.2014.11.073

    Article  Google Scholar 

  26. Panchal JH, Kalidindi SR, McDowell DL (2013) Key computational modeling issues in integrated computational materials engineering. Comput Aided Des 45(1):4–25. https://doi.org/10.1016/j.cad.2012.06.006

    Article  Google Scholar 

  27. Allison J (2011) Integrated computational materials engineering: A perspective on progress and future steps. Jom 63(4):15–18. https://doi.org/10.1007/s11837-011-0053-y

    Article  Google Scholar 

  28. Horstemeyer MF (2012) Integrated computational materials engineering (ICME) for metals. Wiley, Hoboken. https://doi.org/10.1002/9781118342664

    Book  Google Scholar 

  29. Brecher C, Esser M, Witt S (2009) Interaction of manufacturing process and machine tool. CIRP Ann Manuf Technol 58(2):588–607. https://doi.org/10.1016/j.cirp.2009.09.005

    Article  Google Scholar 

  30. Aurich JC, Biermann D, Blum H, Brecher C, Carstensen C, Denkena B, Klocke F, Kröger M, Steinmann P, Weinert K (2009) Modelling and simulation of process: Machine interaction in grinding. Prod Eng 3(1):111–120. https://doi.org/10.1007/s11740-008-0137-x

    Article  Google Scholar 

  31. Abele E, Braun S, Schraml P (2015) Holistic simulation environment for energy consumption prediction of machine tools. Procedia CIRP 29:251–256. https://doi.org/10.1016/j.procir.2015.02.059

    Article  Google Scholar 

  32. Eisele C (2014) Simulationsgestützte optimierung des elektrischen energiebedarfs spanender werkzeugmaschinen, Shaker

  33. Schrems S (2014) Methode zur modellbasierten integration des maschinenbezogenen energiebedarfs in die produktionsplanung, Shaker

  34. Weinert N, Chiotellis S, Seliger G (2011) Methodology for planning and operating energy-efficient production systems. CIRP Ann Manuf Technol 60(1):41–44. https://doi.org/10.1016/j.cirp.2011.03.015

    Article  Google Scholar 

  35. Colledani M, Tolio T (2013) Integrated process and system modelling for the design of material recycling systems. CIRP Ann Manuf Technol 62(1):447–452. https://doi.org/10.1016/j.cirp.2013.03.046

    Article  Google Scholar 

  36. Cho S (2005) A distributed time-driven simulation method for enabling real-time manufacturing shop floor control. Comput Ind Eng 49(4):572–590. https://doi.org/10.1016/j.cie.2005.08.003

    Article  Google Scholar 

  37. Heilala J, Vatanen S, Tonteri H, Montonen J, Lind S, Johansson B, Stahre J (2008) Simulation-based sustainable manufacturing system design. In: Winter simulation conference, pp 1922–1930

  38. Sproedt A (2013) Decision-support for eco-efficiency improvements in production systems based on discrete-event simulation. Dissertation, ETH Zürich. https://doi.org/10.3929/ethz-a-010112017

    Google Scholar 

  39. Thiede S, Schönemann M, Kurle D, Herrmann C (2016) Multi-level simulation in manufacturing companies: the Water-Energy Nexus case. J Clean Prod 139:1118–1127. https://doi.org/10.1016/j.jclepro.2016.08.144

    Article  Google Scholar 

  40. Thiede S (2012) Energy efficiency in manufacturing systems. Sustainable production, life cycle engineering and management. Springer, Berlin. https://doi.org/10.1007/978-3-642-25914-2

    Book  Google Scholar 

  41. Mousavi S, Thiede S, Li W, Kara S, Herrmann C (2015) An integrated approach for improving energy efficiency of manufacturing process chains. Int J Sustain Eng 7038(January):1–14. https://doi.org/10.1080/19397038.2014.1001470

    Google Scholar 

  42. Seow Y, Rahimifard S (2011) A framework for modelling energy consumption within manufacturing systems. CIRP J Manuf Sci Technol 4(3):258–264. https://doi.org/10.1016/j.cirpj.2011.03.007

    Article  Google Scholar 

  43. Hesselbach J (2012) Energie-und klimaeffiziente produktion. Springer Vieweg, p. 367. https://doi.org/10.1007/978-3-8348-9956-9

  44. Dür F, Flatz T, Kovacic I, Waltenberger L, Wiegand D, Emrich S, Leobner I, Bednar T, Eder K, Kastner W, Kastner W, Heinzl B, Kiesel K, Liesel K (2013) INFO – Interdisziplinäre Forschung zur Energieoptimierung in Fertigungsbetrieben. Technical report, TU Wien, Wien

  45. Heinzl B, Rossler M, Popper N, Leobner I, Ponweiser K, Kastner W, Dur F, Bleicher F, Breitenecker F (2013) Interdisciplinary strategies for simulation-based optimization of energy efficiency in production facilities. In: 2013 UKSim 15th international conference on computer modelling and simulation. IEEE, pp 304–309. https://doi.org/10.1109/UKSim.2013.115

  46. Hafner I, Rößler M, Heinzl B, Körner A, Landsiedl M, Breitenecker F (2014) Investigating communication and step-size behaviour for co-simulation of hybrid physical systems. J Comput Sci 5(3):427–438. https://doi.org/10.1016/j.jocs.2013.08.007

    Article  Google Scholar 

  47. Leobner I, Ponweiser K, Neugschwandtner G, Kastner W (2011) Energy efficient production – a holistic modeling approach. In: 2011 World Congress on Sustainable Technologies (WCST), London. IEEE, pp 62–67

  48. Bleicher F, Duer F, Leobner I, Kovacic I, Heinzl B, Kastner W (2014) Co-simulation environment for optimizing energy efficiency in production systems. CIRP Ann Manuf Technol 63(1):441–444. https://doi.org/10.1016/j.cirp.2014.03.122

    Article  Google Scholar 

  49. Wright AJ, Oates MR, Greenough R (2013) Concepts for dynamic modelling of energy-related flows in manufacturing. Appl Energy 112:1342–1348. https://doi.org/10.1016/j.apenergy.2013.01.056

    Article  Google Scholar 

  50. Oates M, Wright A, Greenough R, Shao L (2011) A new modelling approach which combines energy flows in manufacturing with those in a factory building. In: Proceedings of Building Simulation 2011: 12th Conference of International Building Performance Simulation Association. Sydney, pp 14–16

  51. Trčka M, Hensen JL, Wetter M (2010) Co-simulation for performance prediction of integrated building and HVAC systems – an analysis of solution characteristics using a two-body system. Simul Modell Pract Theory 18(7):957–970. https://doi.org/10.1016/j.simpat.2010.02.011

    Article  Google Scholar 

  52. Wetter M (2011) A view on future building system modeling and simulation. Building Performance Simulation for Design and Operation (i):1–28. https://doi.org/10.4324/9780203891612

  53. Schönemann M, Greschke P, Herrmann C, Thiede S (2015) Simulation of matrix-structured manufacturing systems. J Manuf Syst 37:104–112. https://doi.org/10.1016/j.jmsy.2015.09.002

    Article  Google Scholar 

  54. Greschke P (2016) Matrix-produktion – taktunabhängige fließfertigung. Dissertation, Technische Universität Braunschweig

  55. Schönemann M, Kurle D, Herrmann C, Thiede S (2016) Multi-product EVSM simulation. Procedia CIRP 41:334–339. https://doi.org/10.1016/j.procir.2015.10.012

    Article  Google Scholar 

  56. Winter M (2016) Eco-efficiency of grinding processes and systems. Springer International Publishing. https://doi.org/10.1007/978-3-319-25205-6

  57. Mousavi S, Kara S, Kornfeld B (2014) Energy efficiency of compressed air systems. Procedia CIRP 15:313–318. https://doi.org/10.1016/j.procir.2014.06.026

    Article  Google Scholar 

  58. Crawley DB, Hand JW, Kummert M, Griffith BT (2008) Contrasting the capabilities of building energy performance simulation programs. Build Environ 43(4):661–673. https://doi.org/10.1016/j.buildenv.2006.10.027

    Article  Google Scholar 

  59. Acatech (2010) Wie Deutschland zum Leitanbieter für Elektromobilität werden kann

  60. Schünemann JH, Dreger H, Bockholt H, Kwade A (2016) Smart electrode processing for battery cost reduction. ECS Trans 73(1):153–159. https://doi.org/10.1149/07301.0153ecst

    Article  Google Scholar 

  61. Bockholt H, Indrikova M, Netz A, Golks F, Kwade A (2016) The interaction of consecutive process steps in the manufacturing of lithium-ion battery electrodes with regard to structural and electrochemical properties. J Power Sources 325:140–151. https://doi.org/10.1016/j.jpowsour.2016.05.127

    Article  Google Scholar 

  62. Haselrieder W, Ivanov S, Tran HY, Theil S, Froböse L, Westphal B, Wohlfahrt-Mehrens M, Kwade A (2014) Influence of formulation method and related processes on structural, electrical and electrochemical properties of LMS/NCA-blend electrodes. Prog Solid State Chem 42(4):157–174. https://doi.org/10.1016/j.progsolidstchem.2014.04.009

    Article  Google Scholar 

  63. Bockholt H, Haselrieder W, Kwade A (2016) Intensive powder mixing for dry dispersing of carbon black and its relevance for lithium-ion battery cathodes. Powder Technol 297:266–274. https://doi.org/10.1016/j.powtec.2016.04.011

    Article  Google Scholar 

  64. Dreger H, Bockholt H, Haselrieder W, Kwade A (2015) Discontinuous and continuous processing of low-solvent battery slurries for lithium nickel cobalt manganese oxide electrodes. J Elec Materi 44(11):4434–4443. https://doi.org/10.1007/s11664-015-3981-4

    Article  Google Scholar 

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Funding

The data collection and model development for the exemplary case study was greatly supported by many researchers from the Battey LabFactory Braunschweig (BLB) in the context of the research project “DaLion” funded by the German Federal Ministry for Economic Affairs and Energy (BMWi; 03ET6089). The specific data for the electrode production are results of the research project “iFaaB” funded by the German Federal Ministry of Education and Research (BMBF; 02PJ2511).

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Schönemann, M., Bockholt, H., Thiede, S. et al. Multiscale simulation approach for production systems. Int J Adv Manuf Technol 102, 1373–1390 (2019). https://doi.org/10.1007/s00170-018-3054-y

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