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http://dx.doi.org/10.12989/csm.2022.11.1.043

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

Szardenings, Anna (Battery System Development, Volkswagen Group Components)
Hoefer, Nathalie (Battery System Development, Volkswagen Group Components)
Fassbender, Heike (Institute for Numerical Mathematics, TU Braunschweig)
Publication Information
Coupled systems mechanics / v.11, no.1, 2022 , pp. 43-54 More about this Journal
Abstract
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.
Keywords
battery cooling; CFD simulation; data driven model; data sampling; multiple input multiple output system;
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1 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.   DOI
2 White, C., Ushizima, D. and Farhat, C. (2019), "Neural networks predict fluid dynamics solutions from tiny datasets", arXiv preprint arXiv:1902.00091.
3 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.   DOI
4 Weigand, B., Koehler, J. and von Wolfersdorf, J. (2013), Thermodynamik Kompakt, Springer Berlin Heidel berg, 3rd Edition.
5 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.   DOI
6 Annaratone, D. (2010), Engineering Heat Transfer, Springer Science & Business Media.
7 Baehr, H.D. and Stephan, K. (2006), Waerme-und Stoffuebertragung, Volume 5, Springer.
8 Buhmann, M.D. (2000), "Radial basis functions", Acta Numerica, 9, 1-38. https://doi.org/10.1017/S0962492900000015.   DOI
9 Noesis Solutions (2019), OPTIMUS REV 2019.2 - USERS MANUAL, Noesis Solutions, Gaston Geenslaan 11, 3001 Leuven, Belgium, 2019.2nd Edition.
10 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.
11 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.   DOI
12 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.   DOI
13 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.   DOI
14 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.   DOI
15 Goodfellow, I., Bengio, Y., Courville, A. and Bengio, Y. (2016), Deep Learning, Volume 1, MIT Press Cambridge.
16 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.   DOI
17 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.   DOI
18 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.   DOI
19 Julien, C., Mauger, A., Vijh, A. and Zaghib, K. (2016), "Lithium batteries", Lithium Batteries: Science and Technology, Springer International Publishing, Cham.
20 Kleijnen, J.P. (2009), "Kriging metamodeling in simulation: A review", Eur. J. Oper. Res., 192(3), 707-716.   DOI
21 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.   DOI
22 Noesis Solutions, Gaston Geenslaan 11, 3001 Leuven, Belgium (2020), Optimus 2020.1 Theoretical Background, 1st Edition.
23 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.   DOI
24 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.   DOI
25 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.
26 Pesaran, A.A. (2001), "Battery thermal management in ev and hevs: issues and solutions", Battery Man, 43(5), 34-49.
27 Schenck, C. and Fox, D. (2018), "Spnets: Differentiable fluid dynamics for deep neural networks", arXiv preprint arXiv:1806.06094.
28 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.   DOI
29 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.   DOI
30 Santner, T.J., Williams, B.J., Notz, W.I. and Williams, B.J. (2003), The Design and Analysis of Computer Experiments, Volume 1, Springer.
31 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.   DOI
32 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.   DOI
33 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.
34 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.