Date of Award

August 2026

Document Type

Dissertation

Degree Name

Doctor of Philosophy (PhD)

Department

Energy Engineering

First Advisor

Olusegun S. Tomomewo

Abstract

The global transition to a net-zero energy economy is increasing demand for lithium-ion batteries (LIBs) and positioning battery recycling as an essential component of future supply chains. However, recycled battery materials exhibit variability due to mixed feedstocks, heterogeneous black-mass compositions, degradation histories, and recycling processes. The resulting uncertainty in material properties can affect battery performance, yet physics-based methods for quantifying these effects remain limited.This dissertation presents a novel computational framework for uncertainty quantification (UQ) and global sensitivity analysis (GSA) of lithium-ion battery performance using recycled cathode materials. The framework combines the Doyle-Fuller-Newman (DFN) electrochemical model, implemented in PyBaMM, with a Random Forest surrogate trained on 32 high-fidelity DFN simulations spanning nine uncertain recycled-material parameters. The surrogate achieved a root-mean-squared error (RMSE) of 0.0025 Ah (approximately 0.05% of nominal cell capacity), enabling efficient evaluation of 5,000 Monte Carlo realizations. A two-stage sensitivity-analysis workflow consisting of Morris screening and Sobol variance decomposition was then applied to identify the parameters most responsible for performance variability.

The results show that uncertainty in recycled-material properties propagates to measurable differences in battery performance over a 100-cycle horizon. The median capacity retention declined to approximately 85% after 100 cycles, while the 5th–95th percentile interval ranged from 84.29% to 87.16%, corresponding to a total spread of approximately 2.9 percentage points. The resulting end-of-life capacity distribution exhibited a lower-performance tail that is not captured by deterministic mean-parameter simulations. Morris and Sobol analyses consistently identified solid-phase diffusivity (Ds), particle radius (Rp), and electrode porosity (ε) as the dominant contributors to performance variability, providing a quantitative basis for prioritizing quality-control efforts within recycled-material supply chains.

This work advances the field from qualitative recognition of recycled-material variability toward quantitative, physics-based prediction. The framework establishes a direct link between uncertainty in recycled materials and battery performance outcomes, providing a foundation for performance-oriented material qualification, improved quality-control strategies, and broader adoption of recycled materials within a circular battery economy.

Download

Share

COinS