Lifetime prediction for lithium-ion batteries undergoing fast charging protocols
Name
1103320000-MIT.pdf
Size
15.54 MB
Format
Adobe PDF
Checksum (MD5)
95ef72e4327f8b4d5ce046ee8c1c4782
Author(s)
Forsuelo, Michael.
Advisor(s)
Richard D. Braatz.
Date Issued
2019
Publisher
Massachusetts Institute of Technology
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).
Subjects
Chemical Engineering.
MIT Department
Massachusetts Institute of Technology. Department of Chemical Engineering
Terms of Use
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