Schwartz, Daniel TGIRI, BABITA2025-08-012025-08-012025-08-012025GIRI_washington_0250O_28519.pdfhttps://hdl.handle.net/1773/53451Thesis (Master's)--University of Washington, 2025This study introduces a data-driven framework for analyzing battery module failure patterns in hybrid-electricbuses, with the goal of optimizing maintenance strategies and enhancing operational reliability. Buses are categorized as Healthy or Swapped based on historical maintenance frequency. Failure times are statistically modeled using Weibull and Gaussian distributions to identify dominant trends. Key reliability metrics—including mean time to failure (MTTF), standard deviation, R² values, KS-statistic & p-value, and mean absolute error (MAE) are employed to assess distribution fits at both fleet and module levels. To investigate the root causes of premature failures, voltage data from more than 50,000 submodules are analyzed in time-binned intervals and correlated with documented failure events. Preliminary results reveal potential voltage-related degradation mechanisms, providing actionable insights for predictive maintenance. Ongoing research expands these analyses to higher-order modules, refining predictive models and generalizing findings across the fleet.application/pdfen-USnoneChemical engineeringChemical engineeringThesis