A Model Reduction Framework for Efficient Simulation of Li-Ion Batteries
In order to achieve a better understanding of degradation processes in lithium-ion batteries, the modelling of cell dynamics at the mircometer scale is an important focus of current mathematical research. These models lead to large-dimensional, highly nonlinear finite volume discretizations which, due to their complexity, cannot be solved at cell scale on current hardware. Model order reduction strategies are therefore necessary to reduce the computational complexity while retaining the features of the model. The application of such strategies to specialized high performance solvers asks for new software designs allowing flexible control of the solvers by the reduction algorithms. In this contribution we discuss the reduction of microscale battery models with the reduced basis method and report on our new software approach on integrating the model order reduction software pyMOR with third-party solvers. Finally, we present numerical results for the reduction of a 3D microscale battery model with porous electrode geometry.
This work has been supported by the German Federal Ministry of Education and Research (BMBF) under contract number 05M13PMA.
- 1.pyMOR—Model Order Reduction with Python. http://www.pymor.org
- 3.Drohmann, M., Haasdonk, B., Kaulmann, S., Ohlberger, M.: A software framework for reduced basis methods using Dune-RB and RBmatlab. In: Dedner, A., Flemisch, B., Klöfkorn, R. (eds.) Advances in DUNE, pp. 77–88. Springer, Berlin Heidelberg (2012)Google Scholar
- 6.Iliev, O., Latz, A., Zausch, J., Zhang, S.: On some model reduction approaches for simulations of processes in Li-ion battery. In: Proceedings of Algoritmy 2012, Conference on Scientific Computing, Vysoké Tatry, Podbanské, Slovakia, pp. 161–171. Slovak University of Technology in Bratislava (2012)Google Scholar
- 9.Popov, P., Vutov, Y., Margenov, S., Iliev, O.: Finite volume discretization of equations describing nonlinear diffusion in li-ion batteries. In: Dimov, I., Dimova, S., Kolkovska, N. (eds.) Numerical Methods and Applications. Lecture Notes in Computer Science, vol. 6046, pp. 338–346. Springer, Berlin Heidelberg (2011)Google Scholar