Model Reduction for Multiscale Lithium-Ion Battery Simulation

Abstract

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.

References in corpus (4)

• pyMOR - Generic Algorithms and Interfaces for Model Order Reduction
• Error control for the localized reduced basis multi-scale method with adaptive on-line enrichment
• A-posteriori error estimates for the localized reduced basis multi-scale method
• A Model Reduction Framework for Efficient Simulation of Li-Ion Batteries

Cited by in corpus (4)

• MULTIBAT: Unified workflow for fast electrochemical 3D simulations of lithium-ion cells combining virtual stochastic microstructures, electrochemical degradation models and model order reduction
• True Error Control for the Localized Reduced Basis Method for Parabolic Problems
• Localized Reduced Basis Approximation of a Nonlinear Finite Volume Battery Model with Resolved Electrode Geometry
• A Modeling Framework for Efficient Reduced Order Simulations of Parametrized Lithium-Ion Battery Cells