DOI QR코드

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Towards a reduced order model of battery systems: Approximation of the cooling plate

  • 투고 : 2021.06.06
  • 심사 : 2021.11.11
  • 발행 : 2022.02.25

초록

In order to analyse the thermal performance of battery systems in electric vehicles complex simulation models with high computational cost are necessary. Using reduced order methods, real-time applicable model can be developed and used for on-board monitoring. In this work a data driven model of the cooling plate as part of the battery system is built and derived from a computational fluid dynamics (CFD) model. The aim of this paper is to create a meta model of the cooling plate that estimates the temperature at the boundary for different heat flow rates, mass flows and inlet temperatures of the cooling fluid. In order to do so, the cooling plate is simulated in a CFD software (ANSYS Fluent ®). A data driven model is built using the design of experiment (DOE) and various approximation methods in Optimus ®. The model can later be combined with a reduced model of the thermal battery system. The assumption and simplification introduced in this paper enable an accurate representation of the cooling plate with a real-time applicable model.

키워드

참고문헌

  1. Annaratone, D. (2010), Engineering Heat Transfer, Springer Science & Business Media.
  2. Baehr, H.D. and Stephan, K. (2006), Waerme-und Stoffuebertragung, Volume 5, Springer.
  3. Buhmann, M.D. (2000), "Radial basis functions", Acta Numerica, 9, 1-38. https://doi.org/10.1017/S0962492900000015.
  4. Giunta, A. and Watson, L. (1998), "A comparison of approximation modeling techniques-polynomial versus interpolating models", 7th AIAA/USAF/NASA/ISSMO Symposium on Multidisciplinary Analysis and Optimization, 4758.
  5. Goodfellow, I., Bengio, Y., Courville, A. and Bengio, Y. (2016), Deep Learning, Volume 1, MIT Press Cambridge.
  6. Hadzalic, E., Ibrahimbegovic, A. and Dolarevic, S. (2020), "3d thermo-hydro-mechanical coupled discrete beam lattice model of saturated poro-plastic medium", Couple. Syst. Mech., 9(2), 125-145. https://doi.org/10.12989/csm.2020.9.2.125.
  7. Islamoglu, Y. (2003), "A new approach for the prediction of the heat transfer rate of the wire-on-tube type heat exchanger-Use of an artificial neural network model", Appl. Therm. Eng., 23(2), 243-249. https://doi.org/10.1016/S1359-4311(02)00155-2.
  8. Jambunathan, K., Hartle, S., Ashforth-Frost, S. and Fontama, V. (1996), "Evaluating convective heat transfer coefficients using neural networks", Int. J. Heat Mass Transf., 39(11), 2329-2332. https://doi.org/10.1016/0017-9310(95)00332-0.
  9. Jeong, S., Murayama, M. and Yamamoto, K. (2005), "Efficient optimization design method using kriging model", J. Aircraft, 42(2), 413-420. https://doi.org/10.2514/1.6386.
  10. Julien, C., Mauger, A., Vijh, A. and Zaghib, K. (2016), "Lithium batteries", Lithium Batteries: Science and Technology, Springer International Publishing, Cham.
  11. Kleijnen, J.P. (2009), "Kriging metamodeling in simulation: A review", Eur. J. Oper. Res., 192(3), 707-716. https://doi.org/10.1016/j.ejor.2007.10.013
  12. Kutz, J.N. (2017), "Deep learning in fluid dynamics", J. Fluid Mech., 814, 1-4. https://doi.org/10.1016/j.ejor.2007.10.013.
  13. Moreno-Navarro, P., Ibrahimbegovich, A. and Perez-Aparicio, J.L. (2018), "Linear elastic mechanical system interacting with coupled thermo-electro-magnetic fields", Couple. Syst. Mech., 7(1), 5-25. https://doi.org/10.12989/csm.2018.7.1.005.
  14. Noesis Solutions (2019), OPTIMUS REV 2019.2 - USERS MANUAL, Noesis Solutions, Gaston Geenslaan 11, 3001 Leuven, Belgium, 2019.2nd Edition.
  15. Noesis Solutions, Gaston Geenslaan 11, 3001 Leuven, Belgium (2020), Optimus 2020.1 Theoretical Background, 1st Edition.
  16. Park, K., Oh, P.K. and Lim, H.J. (2006), "The application of the cfd and kriging method to an optimization of heat sink", Int. J. Heat Mass Transf., 49(19-20), 3439-3447. https://doi.org/10.1016/j.ijheatmasstransfer.2006.03.009.
  17. Peng, J.Z., Liu, X., Aubry, N., Chen, Z. and Wu, W.T. (2020), "Data-driven modeling of geometry- adaptive steady heat transfer based on convolutional neural networks: Heat conduction", arXiv preprint arXiv:2010.03854.
  18. Pesaran, A.A. (2001), "Battery thermal management in ev and hevs: issues and solutions", Battery Man, 43(5), 34-49.
  19. Prasad, V. and Bequette, B.W. (2003), "Nonlinear system identification and model reduction using artificial neural networks", Comput. Chem. Eng., 27(12), 1741-1754. https://doi.org/10.1016/S0098- 1354(03)00137-6.
  20. Rao, Z. and Wang, S. (2011), "A review of power battery thermal energy management", Renew. Sustain. Energy Rev., 15, 4554-4571. https://doi.org/10.1016/j.rser.2011.07.096.
  21. Ryu, J.S., Kim, M.S., Cha, K.J., Lee, T.H. and Choi, D.H. (2002), "Kriging interpolation methods in geostatistics and dace model", KSME Int. J., 16(5), 619-632. https://doi.org/10.1007/BF03184811.
  22. Sancarlos, A., Cameron, M., Abel, A., Cueto, E., Duval, J.L. and Chinesta, F. (2020), "From rom of electro-chemistry to ai-based battery digital and hybrid twin", Arch. Comput. Meth. Eng., 28(3), 979-1015. https://doi.org/10.1007/s11831-020-09404-6.
  23. Santner, T.J., Williams, B.J., Notz, W.I. and Williams, B.J. (2003), The Design and Analysis of Computer Experiments, Volume 1, Springer.
  24. Schenck, C. and Fox, D. (2018), "Spnets: Differentiable fluid dynamics for deep neural networks", arXiv preprint arXiv:1806.06094.
  25. Simpson, T.W., Poplinski, J.D., Koch, P.N. and Allen, J.K. (2001), "Metamodels for computer-based engineering design: survey and recommendations", Eng. Comput., 17(2), 129-150. https://doi.org/10.1007/PL00007198.
  26. Skala, V. (2017), "Rbf interpolation with csrbf of large data sets", Procedia Comput. Sci., 108, 2433-2437. https://doi.org/10.1016/j.procs.2017.05.081.
  27. Szardenings, A. "Verfahren und vorrichtung zum ueberwachen eines elektrischen energiespeichers, computerprogramm produkt", German Patent DE 10 2020 203 004 A1, to be published in 2022.
  28. Szardenings, A., Petersen, N. and Fassbender, H. (2020), "Concept for thermal analysis of batteries using reduced order modeling", AIP Conference Proceedings, 2293, AIP Publishing LLC.
  29. Thibault, J. and Grandjean, B.P. (1991), "A neural network methodology for heat transfer data analysis", Int. J. Heat Mass Transf., 34(8), 2063-2070. https://doi.org/10.1016/0017-9310(91)90217-3.
  30. Volpi, S., Diez, M., Gaul, N.J., Song, H., Iemma, U., Choi, K., Campana, E.F. and Stern, F. (2015), "Development and validation of a dynamic metamodel based on stochastic radial basis functions and uncertainty quantification", Struct. Multidisc. Optim., 51, 347-368. https://doi.org/10.1007/s00158-014-1128-5.
  31. Weigand, B., Koehler, J. and von Wolfersdorf, J. (2013), Thermodynamik Kompakt, Springer Berlin Heidel berg, 3rd Edition.
  32. White, C., Ushizima, D. and Farhat, C. (2019), "Neural networks predict fluid dynamics solutions from tiny datasets", arXiv preprint arXiv:1902.00091.
  33. Xie, G., Wang, Q., Zeng, M. and Luo, L. (2007), "Heat transfer analysis for shell-and-tube heat exchangers with experimental data by artificial neural networks approach", Appl. Therm. Eng., 27(5-6), 1096-1104. https://doi.org/10.1016/j.applthermaleng.2006.07.036.
  34. Xie, X., Zhang, G. and Webster, C.G. (2019), "Non-intrusive inference reduced order model for fluids using deep multistep neural network", Math., 7(8), 757. https://doi.org/10.3390/math7080757.