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ERC

COMBAT Report Summary

Project ID: 615132
Funded under: FP7-IDEAS-ERC
Country: Germany

Mid-Term Report Summary - COMBAT (Computational Modeling and Design of Lithium-Ion Batteries)

The key objective of this project was to develop a computational framework for the design of novel materials, i.e. materials with applications in batteries. This framework requires models at different length scales and efficient computational methods to analyse these models, multiscale methods to transfer information from the finer scale to the coarser scale or in some cases -- such as fracture -- vice versa, error estimators for adaptivity as well as efficient methods for uncertainty analysis and optimization in order to quantify the influence of all uncertain input parameters on an output of interest and finally identify the key input parameters.

In the first phase of this project, excellent progress has been made in each direction. Several models with potential for battery applications have been developed at different length scales: From the nano-scale based on DFT simulations (see e.g. our contributions in [1,2]) up to homogenized (continuum) models at the macro-scale. The latter models have been implemented meanwhile in our open-source software based on advanced computational methods including meshfree methods, finite element methods or IGA, to name a few. The models of the macro-scale also account for the multiphysics (electro-chemical, thermo-mechanical coupling finite strain elasticity with the Cahn Hilllard equation) nature of the underlying problem, see e.g. the contribution in [3]. Furthermore efficient multiscale methods are now available which allow the transfer of length scales in the case of fracture. We are furthermore also able to bridge several length scales (not only two). Through a complementary project, we were also able to provide a general and simple software framework for uncertainty analysis which is publicly available and can be used also for other applications [4]. This framework employs sampling methods and different techniques to perform sensitivity analysis accounting for correlated (and uncorrelated) input parameters. A variety of surrogate models such as polynomial regression, Kriging interpolation or Moving Least Squares are provided in order to drastically decrease the computational cost of the sensitivity analysis. Good progress was also made in developing efficient methods for optimization. They are based on computational methods which we exploited for fracture in the past and therefore, the methodology can be adopted for a wider range of applications. In conclusion, the computational framework has been widely established and needs to be specified to a certain material which will be one of the tasks in the second phase of the project.

[1] Ramahan O., Mortazavi B., Rabczuk T.: A first-principles study on the effect of oxygen content on the structural and electronic properties of silicon suboxide as anode material for Lithium Ion Batteries, Journal of Power Sources, 2016, 307, 657 - 664
[2] Mortazavi B., Dianat A., Rahaman O., Cuniberti G., Rabczuk T.: Borophene as an anode material for Ca, Mg, Na or Li ion storage: A first-principle study, Journal of Power Sources, 2016, 329, 456-461, 10.1016/j.jpowsour.2016.08.109
[3] Areias P., Samaniego E., Rabczuk T.: A staggered approach for the coupling of Cahn-Hilliard type diffusion and finite strain elasticity, Computational Mechanics, 2016, 57(2), 339-351
[4] https://sourceforge.net/projects/sensitivity-analysis/

Reported by

BAUHAUS-UNIVERSITAET WEIMAR
Germany
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