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Advanced efficiency solutions for hybrid electric vehicles (HEVs)


Advanced efficiency solutions for hybrid electric vehicles (HEVs)

Li, Xin (2008) Advanced efficiency solutions for hybrid electric vehicles (HEVs). Masters thesis, Concordia University.

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As an alternative to conventional vehicles (CVs), hybrid electric vehicles (HEVs) are touted to be a practically attractive measure to create an energy-wise and sustainable society. By employing electric energy as one of the traction energy sources, HEVs are able to reduce costly fuel consumption as well as greenhouse gas (GHG) emissions. There are some commercially available HEVs in the market, employing various drive train configurations; however, their drive trains and control strategies are not optimally designed. In this thesis, parametric and power component stage based efficiency analysis methods are introduced to assess the overall drive train efficiencies for different HEV configurations. Hence, it is possible to find the key parameters that significantly affect the overall drive train efficiency. A mid-sized sport utility vehicle (SUV) is modeled in different hybrid configurations within the Advanced Vehicle Simulator (ADVISOR) software. Simulations are carried out based on the modeled SUV over varied load demands. The thesis also defines regenerative braking efficiency and the term "hybridization factor" for series and parallel HEVs. In addition, a method to analyze and calculate regenerative braking efficiency is also introduced. Finally, the thesis focuses on optimizing system control strategies for series and parallel HEVs, to enhance their regenerative braking efficiency. The optimized fuzzy logic and electric assist control strategies are simulated and tested in ADVISOR, thus providing the data for eventually designing a novel control strategy, to improve the overall drive train efficiency.

Divisions:Concordia University > Gina Cody School of Engineering and Computer Science > Electrical and Computer Engineering
Item Type:Thesis (Masters)
Authors:Li, Xin
Pagination:xiii, 88 leaves : ill. ; 29 cm.
Institution:Concordia University
Degree Name:M.A. Sc.
Program:Electrical and Computer Engineering
Thesis Supervisor(s):Williamson, Sheldon S
Identification Number:LE 3 C66E44M 2008 L5
ID Code:975944
Deposited By: Concordia University Library
Deposited On:22 Jan 2013 16:17
Last Modified:13 Jul 2020 20:09
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