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Contributions to Mean-Cluster Modeling of Structured Materials - Applications to Lithium-Ion Batteries

One of the questions arising as regards to structured materials is how one can compute
their cluster concentrations. Specifically, we are interested in deriving the concentrations of the micro-structures in the NMC (Nickel-Manganese-Cobalt) layer of the cathodes of Li-ion batteries. A simulated annealing approach has been used lately for detecting the structure of the whole lattice which is computationally heavy. Here we propose
a mathematical model, called cluster approximation model, in the form of a dynamical
system for describing the concentrations of different clusters inside the lattice. However, the dynamical system is hierarchical which requires to be truncated. Truncation
of the hierarchical system is performed by the nearest-neighbor closure scheme. Also,
a novel framework is proposed for an optimal closure of the dynamical system in order
to enhance the accuracy of the model. The parameters of the model are reconstructed
by the least square approach as a constrained optimization problem by minimizing the
mismatch between the experimental data and the model outputs. The model is validated
based on the experimental data on a known Li-ion battery cathode and different approximation schemes are compared. The results clearly show that the proposed approach
significantly outperforms the conventional method. / Thesis / Master of Science (MSc)

Identiferoai:union.ndltd.org:mcmaster.ca/oai:macsphere.mcmaster.ca:11375/25783
Date January 2020
CreatorsAhmadi, Avesta
ContributorsProtas, Bartosz, Computational Engineering and Science
Source SetsMcMaster University
LanguageEnglish
Detected LanguageEnglish
TypeThesis

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