5 research outputs found
Model Reduction for Multiscale Lithium-Ion Battery Simulation
In this contribution we are concerned with efficient model reduction for
multiscale problems arising in lithium-ion battery modeling with spatially
resolved porous electrodes. We present new results on the application of the
reduced basis method to the resulting instationary 3D battery model that
involves strong non-linearities due to Buttler-Volmer kinetics. Empirical
operator interpolation is used to efficiently deal with this issue.
Furthermore, we present the localized reduced basis multiscale method for
parabolic problems applied to a thermal model of batteries with resolved porous
electrodes. Numerical experiments are given that demonstrate the reduction
capabilities of the presented approaches for these real world applications
Adaptive Parameter Optimization For An Elliptic-Parabolic System Using The Reduced-Basis Method With Hierarchical A-Posteriori Error Analysis
In this paper the authors study a non-linear elliptic-parabolic system, which
is motivated by mathematical models for lithium-ion batteries. One state
satisfies a parabolic reaction diffusion equation and the other one an elliptic
equation. The goal is to determine several scalar parameters in the coupled
model in an optimal manner by utilizing a reliable reduced-order approach based
on the reduced basis (RB) method. However, the states are coupled through a
strongly non-linear function, and this makes the evaluation of online-efficient
error estimates difficult. First the well-posedness of the system is proved.
Then a Galerkin finite element and RB discretization is described for the
coupled system. To certify the RB scheme hierarchical a-posteriori error
estimators are utilized in an adaptive trust-region optimization method.
Numerical experiments illustrate good approximation properties and efficiencies
by using only a relatively small number of reduced bases functions.Comment: 24 pages, 3 figure