Dissertation Defense – Chu Xu


Author: Chu Xu

Date/Time: 08/02/2021  12:00pm-2:00pm

Examining Committee:
Dr. Hosam K. Fathy, Chair/Advisor
Dr. Chunsheng Wang, Dean’s representative
Dr. Balakumar Balachandran
Dr. Miao Yu
Dr. Michael G. Pecht
Dr. Paul Stephen Albertus

This dissertation examines the challenge of (i) estimating the internal states of lithium-sulfur (Li-S) batteries based on an experimentally-parameterized physics-based model, and (ii) optimizing the discharge trajectory to maximize the energy release of a Li-S battery over a fixed time horizon.
This research is motivated both by the potential of Li-S batteries to provide higher energy densities compared to traditional lithium-ion batteries and the potential of model-based estimation/control to improve the performance of a Li-S battery. Existing literature examines the problem of optimizing the underlying materials in Li-S batteries and develops models to furnish a fundamental understanding of the underlying reactions. The dissertation builds on the insights from the existing literature, and focuses on the control-oriented study/analysis of Li-S batteries.
This dissertation first explores the problem of parameterizing multiple zero-dimensional physics-based Li-S models, representing different sequences of reduction reactions, from experimental data. One of these models is found to offer the best tradeoff between fidelity and complexity. This model is used for online state estimation taking into consideration the multiplicity of active species in Li-S batteries. Accurate state estimation is found to be challenging in the low plateau region of the Li-S battery discharge curve due to the shallow slope of open circuit voltage with respect to state of charge (SOC) in this region. Fisher information analysis helps address this challenge by demonstrating the fundamental insight that battery SOC estimation accuracy can benefit from the dependence of battery resistance on SOC. Finally, this dissertation examines the problem of optimizing the discharge trajectory of a Li-S battery to maximize its energy release over a fixed time horizon. The overall outcomes of this dissertation include insights/algorithms that can be implemented into battery management systems to improve the performance of Li-S batteries.