Degree Name
MS (Master of Science)
Program
Mathematical Sciences
Date of Award
12-2025
Committee Chair or Co-Chairs
Mostafa Zahed
Committee Members
Maryam Skafyan, Jeff R. Knisley
Abstract
This thesis provides an effective statistical model to predict the real-time state of lithium-ion batteries for reliable Battery Management Systems (BMS). It highlights battery data (voltage, current, temperature) as smooth functional curves. The principal method demonstrates diminishing trends to health outcomes like State of Health (SoH) and Remaining Useful Life (RUL) by employing Functional Principal Component Analysis (FPCA) and Bayesian Functional Linear Models (FLMs). The primary objective is to figure out how uncertain forecasts are. Simulations demonstrate that the highest accuracy (lowest MSE) is achieved through low noise levels along with large sample sizes. The final system provides a highly accurate and noise-resilient prognosis technique by integrating a multi-tier threshold monitoring system (e.g., 80\%, 50\% SoH) to translate forecasts into actionable warnings and predictive replacement schedules.
Document Type
Thesis - unrestricted
Recommended Citation
Owusu, Joshua, "Stochastic Functional Data-Driven Models for Real-Time Battery Health Forecasting Under Dynamic Operating Conditions" (2025). Electronic Theses and Dissertations. Paper 4610. https://dc.etsu.edu/etd/4610
Copyright
Copyright by the authors.
