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A model reduction framework for efficient simulation of Li-ion batteries

: Ohlberger, M.; Rave, S.; Schmidt, S.; Zhang, S.


Fuhrmann, J.:
Finite Volumes for Complex Applications VII - Elliptic, Parabolic and Hyperbolic Problems : FVCA 7, Berlin, June 2014
Cham: Springer International Publishing, 2014 (Springer Proceedings in Mathematics & Statistics 78)
ISBN: 978-3-319-05590-9 (Print)
ISBN: 978-3-319-05591-6 (Online)
Conference on "Finite Volumes for Complex Applications" (FVCA) <7, 2014, Berlin>
Conference Paper
Fraunhofer ITWM ()

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.