UMD Theses and Dissertations
Permanent URI for this collectionhttp://hdl.handle.net/1903/3
New submissions to the thesis/dissertation collections are added automatically as they are received from the Graduate School. Currently, the Graduate School deposits all theses and dissertations from a given semester after the official graduation date. This means that there may be up to a 4 month delay in the appearance of a given thesis/dissertation in DRUM.
More information is available at Theses and Dissertations at University of Maryland Libraries.
Browse
2 results
Search Results
Item BATTERY STATE OF CHARGE ESTIMATION BASED ON DATA-DRIVEN MODELS WITH MOVING WINDOW FILTERS AND PHYSICS-BASED MODELS WITH EFFICIENT SOLID-PHASE DIFFUSION PDES SOLVED BY THE OPTIMIZED PROJECTION METHOD(2018) He, Wei; Pecht, Michael; Mechanical Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)State of charge (SOC) estimation is one of the most important functions of battery management systems (BMSs), which is defined as the percentage of the remaining charge inside the battery to its maximum capacity. SOC indicates when the battery needs to be recharged. It is necessary for many battery management applications, for example, charge/discharge control, remaining useful time/ driving range predictions, and battery power capability estimations. Inaccurate SOC estimations can lead to user dissatisfaction, mission failures, and premature battery failures. This thesis focuses on the development of advanced battery models and algorithms for SOC estimations. Two SOC estimation approaches are investigated, including electrochemical models and data-driven models. Electrochemical models have intrinsic advantages for SOC estimation since it can relate battery internal physical parameters, e.g. lithium concentrations, to SOC. However, the computational complexity of the electrochemical model is the major obstacle for its application in a real-time BMS. To address this problem, an efficient solution for the solid phase diffusion equations in the electrochemical model is developed based on projection with optimized basis functions. The developed method generates 20 times fewer equations compared with finite difference-based methods, without losing accuracy. The results also show that the developed method is three times more efficient compared with the conventional projection-based method. Then, a novel moving window filter (MWF) algorithm is developed to infer SOC based on the electrochemical model. MWF converges to true values nearly 15 times faster compared with unscented Kalman filter in experimental test cases. This work also develops a data-driven SOC estimation approach. Traditional data-driven approaches, e.g. neural network, have generalization problems. For example, the model over-fits to training data and generate erroneous results in the testing data. This thesis investigates algorithms to improve the generalization capability of the data-driven model. An algorithm is developed to select optimal neural network structure and training data inputs. Then, a hybrid approach is developed by combining the neural network and MWF to provide stable SOC estimations. The results show that the SOC estimation error can be reduced from 8% to less 4% compared with the original neural network approach.Item REACTION FACTORIZATION FOR DISTRIBUTED DEPOSITION SYSTEMS: APPLICATION TO COPPER FILM GROWTH(2015) Arana-Chavez, David; Adomaitis, Raymond A; Chemical Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)A model reduction methodology based on a Gauss-Jordan reaction factoriza- tion for thin-film deposition reaction systems is developed in this thesis. The fac- torization generates a transformation matrix that is used to create a new coordinate system that guides the separation of the deposition process time scales by decoupling the net-forward reaction rates to the greatest extent possible. The new coordinate space enables recasting the original model as a singular perturbation problem and consequently as a semi-explicit system of differential-algebraic equations (DAE) for the dominant dynamics in the pseudo-equilibrium limit. Additionally, the factor- ization reveals conserved quantities in the new reaction coordinate system as well as potential structural problems with the deposition reaction network. The reaction factorization methodology is formulated to be suitable for appli- cation to dynamic, spatially distributed reaction systems. The factorization provides a rigorous pathway to decouple the time evolution and the spatial distributions of deposition systems when the dynamics of reactor-scale gas-phase transport are fastrelative to the deposition process. Moreover, the factorization approach provides a solution to the problem of formulating Danckwerts-type boundary conditions where gas-phase equilibrium reactions are important. The reaction factorization is used to study the chemical vapor deposition of copper on a tubular hot-wall reactor using copper iodide as the Cu precursor. A film-growth mechanism is proposed from experimental observations that the copper films deposited on quartz substrates suggest a Volmer-Weber growth mode. A model based on this mechanism is used to track spatial distribution of the average Cu island size in the reactor. The rate expressions used in the Cu deposition model are determined using absolute rate theory. To carry out these calculations in an organized manner, a library of object-oriented classes are created in the Python programming language.