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Lifetime prediction for lithium-ion batteries undergoing fast charging protocols

Author(s)
Forsuelo, Michael.
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Massachusetts Institute of Technology. Department of Chemical Engineering.
Advisor
Richard D. Braatz.
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MIT theses are protected by copyright. They may be viewed, downloaded, or printed from this source but further reproduction or distribution in any format is prohibited without written permission. http://dspace.mit.edu/handle/1721.1/7582
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Abstract
This thesis describes the application of Porous Electrode Theory and supervised machine learning to lifetime prediction for 18650 lithium iron phosphate (LiFePO₄ LFP)/graphite cells subject to mixed galvanostatic and potentiostatic fast charging policies. Porous Electrode Theory is used to predict battery lifetime by parameteric reductions of effective solid-phase Fickian diffusivities, electrolytic Stefan-Maxwell diffusivity, and Butler-Volmer exchange currents. Parameter estimation and uncertainty quantification are formulated as least squares optimization over galvanostatic discharge curves with Bayesian estimation of uncertainties. A battery lifetime approach from the literature is extended with identifiability analysis to enhance fidelity of the inverse problem, the attribution of degradation modes, and the accuracy of parametric power-law lifetime predictions. Multiphase Porous Electrode Theory (MPET) is also explored in this thesis. In MPET, each particle of the porous electrode ensemble is described by generalized Allen-Cahn-Hilliard dynamics. Single-particle dynamics are governed by firstprinciples free energy landscapes as opposed to inductive fits to open-circuit battery voltages. Multiscale parameter estimation and central limit theorem analysis are implemented, enhancing the suitability of MPET for capacity fade predictions. Supervised machine learning algorithms utilizing feature-based correlations for battery lifetime are described. Electrochemical features that go beyond the discharge-only model provide improved lifetime predictions, generalized voltage analysis indiscrimant of (dis)charge protocol or data, and a clear connection between battery physics and machine learning, and suggest an optimal charging protocol.
Description
Thesis: S.M., Massachusetts Institute of Technology, Department of Chemical Engineering, 2019
 
Cataloged from PDF version of thesis.
 
Includes bibliographical references (pages 83-87).
 
Date issued
2019
URI
https://hdl.handle.net/1721.1/121777
Department
Massachusetts Institute of Technology. Department of Chemical Engineering
Publisher
Massachusetts Institute of Technology
Keywords
Chemical Engineering.

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