Architectural Engineering and Construction, Durham School of
Durham School of Architectural Engineering and Construction: Dissertations, Theses, and Student Research
First Advisor
Moe Alahmad
Committee Members
Fadi AlSaleem, Milad Roohi
Date of this Version
12-2025
Document Type
Thesis
Citation
A thesis presented to the faculty of the Graduate College at the University of Nebraska in partial fulfillment of requirements for the degree of Master of Science
Major: Architectural Engineering
Under the supervision of Professor Moe Alahmad
Lincoln, Nebraska, November 2025
Abstract
The utilization of lithium-ion batteries has been rapidly expanding across diverse sectors, including electric transportation, stationary energy storage systems, and the built environment. Ensuring a high level of reliability in these applications is essential, as the performance and safety of such systems depend strongly on the accurate assessment of the battery’s State of Health (SOH). Conventional SOH estimation techniques—often based on complex electrochemical models or extensive laboratory testing—tend to require a large number of measurements, advanced instrumentation, and high computational cost. These factors make them impractical for large-scale deployment or real-time monitoring. This study introduces a simplified machine-learning-based approach for fast and reliable estimation of battery SOH using a limited set of easily measurable parameters. The proposed method leverages both simulated data generated from physics-based battery models and experimental data obtained from real lithium-ion cells. The model is trained and validated using standard machine learning algorithms optimized for regression accuracy and generalization across different operating conditions. Results demonstrate that the proposed machine learning model achieves high predictive accuracy with significantly reduced data and computational requirements compared to conventional methods. The approach eliminates the necessity for complex electrochemical simulations or continuous parameter extraction, thereby reducing implementation costs and time. The developed framework has the potential to serve as a low-cost, high-speed alternative to commercial SOH estimation tools, enabling widespread application in battery management systems, grid-level monitoring, and predictive maintenance of energy storage assets.
Advisor: Moe Alahmad
Comments
Copyright 2025, Mohammad Bakhtiari. Used by permission