Please use this identifier to cite or link to this item:
http://hdl.handle.net/11375/20297
Title: | An Intelligent Cell-Level Battery/Ultracapacitor Hybrid |
Authors: | Zeiaee, Mohammad |
Advisor: | Habibi, Saeid |
Department: | Mechanical Engineering |
Publication Date: | 2016 |
Abstract: | In the strive to use electric energy as the sole source of traction force in the automotive industry, the electric storage unit poses serious challenges. Although Li-Ion batteries have shown promising features, there is still room to improve the shortcomings of the electric storage unit. The chemical process of energy extraction and storage in the batteries results in inherently limited power density and lifetime. The notion of pairing a Li-Ion battery cell with an ultracapacitor will invigorate power capability of the hybrid cell. Furthermore, enabling local processing capability in each hybrid cell makes it possible to run sophisticated control and power management algorithms that result in increased battery longevity. Also, hybridization at cell level provides greater access to each cell’s momentary states which results in more accurate analysis, improved safety, and a broad control possibility. In this research, a Li-Ion battery cell is combined with an ultracapacitor. An appropriate configuration is selected for this particular application after reviewing several hybridization schemes and converter structures. A comprehensive power management unit is designed and implemented that is able to execute several strategies to mitigate battery stresses while improving power capabilities. A current regulator is also designed and implemented to realize the proposed strategies by providing the power converter with proper duty cycle. Rather than simulation under a wide range of test loads, a prototype hybrid cell is built and tested in an experimental setup. |
URI: | http://hdl.handle.net/11375/20297 |
Appears in Collections: | Open Access Dissertations and Theses |
Files in This Item:
File | Description | Size | Format | |
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ZEIAEE_MOHAMMAD_2016AUG_MASC.pdf | 5.49 MB | Adobe PDF | View/Open |
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