Date of Completion
Battery Management Systems, Condition-based Maintenance, State of Charge, State of Health, Remaining Useful Life, Fault Diagnosis and Isolation, Prognosis, Model Predictive Control, Machine Learning, Pattern Recognition, Neural Networks, Optimization, Markov Decision Processes, Hidden Markov Model, Support Vector Machine, Least Squares, Parameter Estimation, System Identification
Prof. Krishna R. Pattipati
Prof. Yaakov Bar-Shalom
Prof. Shengli Zhou
Asst. Research Prof. Balakumar Balasingam
Field of Study
Doctor of Philosophy
This thesis aims to solve several important problems in engineering. Four fundamental areas of research have been examined: (i) Battery Management Systems; (ii) Battery Fuel Gauging; (iii) Condition-based Maintenance; and (iv) Prognosis in Coupled Systems. The prognostication algorithms developed have been validated on data collected from either real-world or hardware-in-the-loop experiments or both. The approaches proposed are modular and have the potential to be applicable to a wide variety of complex systems, ranging from portable applications to automotive and aerospace systems.
Pattipati, Bharath, "Advanced Adaptive Prognostication Algorithms with Application to BMS, Automotive, and Aerospace Systems" (2014). Doctoral Dissertations. 652.