Search 50,000+ articles, dissertations, theses and working papers across all
SHAREOK communities — full text, open access, and freely citable.
Browse the repository
42 Communities·178 Collections
Recent Submissions
Item type:Item, Access status: Open Access , Journal of the Faculty Senate, April 13, 2026(The University of Oklahoma Faculty Senate, 2026-04-13)Item type:Item, Access status: Open Access , Journal of the Faculty Senate, November 10, 2025(The University of Oklahoma Faculty Senate, 2025-11-10)Item type:Item, Access status: Embargo , DIAGNOSIS AND PROGNOSIS OF USED LITHIUM-ION BATTERIES(University of Oklahoma – Graduate College, 2026) Kajiura, Yuichi; Zhang, Dong; Ding, Hanping; Xu, Bin; McGovern, AmyThe growing deployment of electric vehicles is expected to produce a large stream of retired lithium-ion batteries. Reusing these batteries can reduce material demand and support sustainable electrification, but practical reuse requires more than a pass-or-fail capacity test. Retired batteries must be diagnosed rapidly enough for sorting, and their future degradation must be estimated well enough to assign them to appropriate second-life applications and operate them safely. This dissertation develops diagnosis and prognosis methods for this purpose, with emphasis on rapid nondestructive measurements and physics-informed learning from limited data. The first part of the dissertation develops rapid state-of-health (SoH) diagnosis for used lithium-ion batteries. Mechanical expansion of pouch cells is measured using digital image correlation (DIC), and the results show that surface expansion contains strong SoH information despite cell-to-cell variation and spatial heterogeneity. Representative surface points are then used to approximate full-field expansion, enabling a simpler thickness-based diagnosis method. Fusing mechanical thickness features with electrochemical impedance spectroscopy (EIS) further improves SoH estimation, demonstrating that mechanical and electrochemical measurements provide complementary diagnostic information. The diagnosis framework is then extended from direct thickness measurement to ultrasound testing (UT), where multi-point acoustic measurements provide internal mechanical information for large-format or constrained cells. Combining UT-derived features with EIS preserves the same central diagnostic principle: representative mechanical information and electrochemical information can be fused to improve rapid SoH estimation. The second part of the dissertation develops physics-informed prognosis methods for estimating hidden states and parameters. A physics-informed neural network (PINN) framework with an integration-based loss is first applied to an equivalent-circuit battery model. Instead of penalizing each differential equation separately, predicted states are integrated through the governing model and compared with measured outputs, reducing the number of competing loss terms. The method identifies model parameters during training and provides a neural-network state estimator after training. The framework is then extended to a single-particle model with electrolyte dynamics (SPMe) with degradation mechanism due to solid electrolyte interphase (SEI) formation. In this electrochemical setting, separate long short-term memory (LSTM) networks estimate lithium-ion concentration in anode, electrolyte and cathode from current and voltage histories, while the SPMe dynamics constrains those estimates and identifies degradation-related parameters such as SEI thickness. To demonstrate that the physics-informed prognosis concept is not limited to batteries, the dissertation also applies the method to motor-bearing health monitoring. In this application, physics-informed machine learning is used to infer hidden lubrication-related states, including lubricant film thickness, surface roughness, and a lubrication regime parameter, from vibration measurements. The learned physically meaningful trajectory supports remaining-useful-life estimation using interpretable degradation thresholds. Finally, the dissertation describes how these technical contributions were translated into a broader module-level second-life battery reuse program. Funding proposals, intellectual-property activity, and related collaborative projects connected rapid diagnosis, physics-informed model construction, heterogeneous pack operation, and automated disassembly. Overall, the dissertation argues that sustainable battery reuse requires both rapid diagnosis of present condition and physics-informed prognosis of future behavior, and it provides methods that move these capabilities toward practical deployment.Item type:Item, Access status: Open Access , DEVELOPMENT OF A CORRELATION-BASED INVERSION METHOD FOR AEROSOL REMOTE SENSING(University of Oklahoma – Graduate College, 2026) Huang, Taozhong; Xu, Feng; Gao, Lan; Redemann, Jens; Wang, Chenghao; Weng, BinbinAccurate characterization of aerosol microphysical properties from diverse remote sensing platforms is essential for quantifying aerosol-radiation and aerosol-cloud interactions. However, aerosol retrieval remains a highly ill-posed inversion problem, particularly when operating under limited information content. To address this challenge, this dissertation introduces two distinct applications that improve state-of-the-art retrieval techniques by incorporating well-characterized a priori constraints. The first application develops a surface model parameter conversion algorithm that bridges a priori information from semi-empirical, Ross–Li-based surface model parameters to physical, Rahman–Pinty–Verstraete (RPV)-based surface parameters. Validated through Directional Hemispherical Reflectance (DHR) and Bidirectional Reflectance Factor (BRF) comparisons, the algorithm demonstrates a highly successful conversion capability. Further retrieval tests indicate that the conversion algorithm introduces minimal impact to the aerosol inversion, providing a robust mechanism for surface a priori initialization. The second application presents the Correlation based Inversion Method for Aerosol Properties (CIMAP) framework, an aerosol inversion strategy that utilizes Principal Component Analysis (PCA) to implement intrinsic correlation constraints among aerosol properties. By retaining a targeted number of Principal Components (PCs), CIMAP substantially reduces the state vector dimensionality, enhancing both numerical stability and computational efficiency. The framework is evaluated under two configurations: CIMAP-A: Utilizes ground-based AERONET observations across three distinct regions (southeast coast of the US, northern China, and southern Africa). Using only 7–8 PCs out of 30, CIMAP-A achieves a processing speed acceleration of over 80% while maintaining high accuracy relative to official AERONET inversion products, yielding Mean Absolute Difference (MAD) of 0.005, 0.019, 0.039, 0.003, 0.013 μm, 0.02 μm and for Aerosol Optical Depth (AOD), Single Scattering Albedo (SSA), Real and Imaginary parts of Refractive Index (RIR and RII), effective radius of fine and coarse modes of particle size distribution (PSD), respectively. CIMAP-P: Applies the framework to multi-angle, multi-spectral, space-borne polarimetric observations using POLDER data, integrating the surface conversion algorithm. Validation against collocated AERONET references shows strong agreement, with AOD MAD ranging from 0.015 to 0.042 and SSA MAD from 0.048 to 0.070 across reference wavelengths and 0.032 μm and 0.402 μm for the effective radius of fine- and coarse-mode PSD, exhibiting performance competitive with multiple Generalized Retrieval of Aerosol and Surface Properties (GRASP) algorithm configurations. Limitations of the CIMAP configurations are discussed, alongside potential expansions toward globally covering observations and possible improvements to constraint construction. Ultimately, the flexible and computationally efficient architecture of CIMAP, with its surface initialization capability, positions it as a highly promising inversion framework for maximizing the rich information content offered by next-generation polarimetric missions.Item type:Item, Access status: Open Access , DEVELOPMENT AND EVALUATION OF NOVEL AIR HANDLING UNIT CONTROL STRATEGIES FOR IMPROVED BUILDING DEMAND FLEXIBILITY AND THERMAL COMFORT(University of Oklahoma – Graduate College, 2026) Tiamiyu, Nurayn; SONG, LI; TANG, CHOON YIK; CAI, JIE; SHABGARD, HAMIDREZA; ZHANG, DONGBuildings account for a significant share of electrical power demand, up to 80% in some regions in the United States. With many flexible loads, buildings have the potential to reduce power demand during peak times and provide demand flexibility. Building enables demand flexibility through measures such as shifting energy use to another time and shedding of load. Applying advanced control strategies to heating, ventilation, and air conditioning (HVAC) loads allows buildings to reduce the electricity consumed during peak hours. While various demand control (DC) strategies have been employed in commercial buildings, they often fail to accurately shift and shed loads while maintaining occupant thermal comfort. Additionally, existing DC strategies face challenges in quantifying load reduction, achieving precise DC and ensuring even distribution of the reduced cooling load across thermal zones. These limitations highlight a critical research gap in effectively integrating demand flexibility with occupant comfort in building energy management systems. This dissertation develops and evaluates advanced control strategies for building demand control and thermal comfort using both simulation and experimental test beds. An innovative Energy Feedback (EF) control strategy and a novel Duct Static Pressure (DSP) cascade control strategy were first proposed, and their individual control performances were assessed using an experimental test bed. Subsequently, the integrated dynamic performance of the proposed EF and DSP cascade control strategies was evaluated against a baseline control strategy using a calibrated Modelica-based virtual test bed. Performance metrics such as settling time, relative error (RE), and the range of average zone temperature (RZT) are used as measures of quick response, control accuracy, and even cooling load sharing, respectively. The most effective strategy for minimizing the negative impact on occupant comfort during demand control is identified. The proposed control strategy demonstrated the fastest response and achieved precise load shedding, with a settling time of 0.03 hours, an RE of 0.01%, and an RZT of 0.00448°C. In contrast, the baseline strategy had a long settling time of 5.05 hours, an RE of 12.52%, and an RZT of 0.05°C. An optimization-based control strategy was incorporated into the EF Controller to provide effective setpoint control for achieving load shifting through precooling and load shedding during peak demand periods while minimizing electricity cost. The optimization-based control strategy enabled the modulation of the cooling load setpoint based on weather conditions and the building's daily load profile. This involved developing a reduced-order thermal model, estimating its parameters for control purposes, and developing and implementing the Optimization-Based EF Controller on the calibrated virtual test bed. The performance of the proposed Optimization-Based EF–DSP Cascade Control strategy was evaluated and compared with the baseline demand control strategy and a non-optimized EF–DSP Control strategy that utilized constant cooling load setpoints. The Optimization-Based EF Controller yielded a total electricity cost saving of 12.8% compared with the non-optimized controller, including an 8.7% reduction during the on-peak period and a 15.4% reduction during the off-peak period. The proposed Optimization-Based EF Control provides an effective approach for achieving demand-flexible HVAC operation by simultaneously reducing cooling energy consumption, minimizing electricity cost, maintaining occupant thermal comfort, and mitigating rebound effects. Finally, the performance of the Optimization-Based EF Controller was experimentally validated, and two practical implementation approaches for applying the optimization-derived EF setpoints on an HVAC system, namely the Schedule-Based EF Control and the Adaptive EF Control, were evaluated. Among the key contributions of this dissertation are the following: (i) the development and experimental evaluation of novel EF and DSP cascade control strategies to achieve accurate demand control and evenly distribute the reduced cooling load among thermal zones; (ii) the development and calibration of a Modelica-based virtual test bed that comprises of an AHU system and building envelope, and its application to the integrated performance assessment of the proposed control strategies; and (iii) the development and implementation of an optimization-based control framework that generates and communicates optimized control signals to the EF controller.