Li-ion Battery Materials Theory and Computation to Guide and Interpret Experiments

Abstract

Li-ion batteries are enabling electrification; cell energy densities, lifetimes and cost render grid energy storage solutions and personal and commercial electric modes of transportation economically and practically feasible. However, exponential market growth demands cheaper, longer lasting, more energy dense, and safer Li-ion cells. The work presented in this thesis rests at the intersection of theory, computation, and experiment; properties of Li-ion battery positive electrode materials were computed from first-principles and compared to experimental results, phenomenological equations were fit to measurement, and software was developed to analyze experimental data.

The first part of this thesis shows that within the GGA+U formalism, the calculated structural, electronic, and electrochemical properties of relevant materials for state-of-the-art positive electrodes, depend on the choice of U to a greater extent than previously recognized. In some cases, an incorrect electronic structure is predicted. These findings suggest that U should be chosen with care, and in some cases the GGA+U formalism may not be appropriate.

The second part of this thesis demonstrates how individual substituents influence electrochemical and thermal properties of Ni-rich positive electrode materials. Furthermore, a reinvented approach for Li chemical diffusion measurements, bridging theory and measurement, is developed and used to show how omitting Co altogether from Ni-rich positive electrode materials worsens rate capability. These results highlight intrinsic challenges in Li-ion battery material optimization and offer practical considerations for designing high energy-density positive electrode materials.

The final part of this thesis presents analysis software developed for experimental data. Two software suites were developed; the first enables automated yet interactive analyses of Li chemical diffusion measurements, providing users with export and fitting flexibility, and the second provides a user-interface for exploring data collected from different cyclers and automatically fitting differential capacity curves to reference data. These tools have saved many days of otherwise manual analysis.