"A new modeling technique and a method for automating the modeling process are introduced for analyzing complex switched-capacitor (SC) converters. The model uses conventional circuit analysis methods to derive state-space models of each switching state. Steady-state performance is derived and expressed as an equivalent resistance. Whereas previous techniques have provided either the detailed performance of a simple SC converter or the limiting performance of a complex SC converter, this new model is flexible enough to provide detailed performance for any practical converter. Nonuniform component choices, asymmetric duty cycles, and other deviations from an ideal converter can be readily included. Dynamics can also be analyzed. Iterative methods of design based on this model would require the formulation of many equations, which is time consuming if done manually. Therefore, an algorithm is introduced to automatically generate the equations required for this state-space based modeling. The state equations are generated algorithmically given a standard node incidence matrix generated from a user-defined netlist. The algorithm enables a designer to quickly iterate SC converter design solutions based on its predicted performance. The model and algorithm have been validated through simulation techniques and experimental data collected from laboratory testing"--Abstract, page iii.
Kimball, Jonathan W.
Cox, Norman R.
Electrical and Computer Engineering
M.S. in Electrical Engineering
National Science Foundation (U.S.)
University of Missouri Research Board
Missouri University of Science and Technology
Journal article titles appearing in thesis/dissertation
- Practical performance analysis of complex switched-capacitor converters
xi, 84 pages
© 2010 Jordan Michael Henry, All rights reserved.
Thesis - Open Access
Microelectronics -- Power supply
Switched capacitor circuits
Print OCLC #
Electronic OCLC #
Link to Catalog Record
Henry, Jordan M., "Modeling the practical performance of switched-capacitor converters and a method for automating state-space model generation" (2010). Masters Theses. 4862.