Desai, Chirag (2010) Design and Optimization of Hybrid Electric Vehicle Drivetrain and Control Strategy Parameters Using Evolutionary Algorithms. Masters thesis, Concordia University.

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Abstract
Advanced propulsion technologies such as hybrid electric vehicles (HEVs) have demonstrated improved fuel economy with lower emissions compared to conventional vehicles. Superior HEV performance in terms of higher fuel economy and lower emissions, with satisfaction of driving performance, necessitates a careful balance of drivetrain component design as well as control strategy parameter monitoring and tuning. In this thesis, an evolutionary global optimizationbased derivativefree, multiobjective genetic algorithm (MOGA) is proposed, to optimize the component sizing of a NOVA® parallel hybrid electric transit bus drivetrain. In addition, the proposed technique has been extended to the design of an optimal supervisory control strategy for effective onboard energy management. The proposed technique helps find practical trade offsolutions for the objectives. Simulation test results depict the tremendous potential of the proposed optimization technique in terms of improved fuel economy and lower emissions (nitrousoxide, NOx, carbon monoxide, CO, and hydrocarbons, HC). The tests were conducted under varying drive cycles and control strategies
Divisions:  Concordia University > Faculty of Engineering and Computer Science > Electrical and Computer Engineering 

Item Type:  Thesis (Masters) 
Authors:  Desai, Chirag 
Institution:  Concordia University 
Degree Name:  M.A. Sc. 
Program:  Electrical and Computer Engineering 
Date:  13 December 2010 
Thesis Supervisor(s):  Williamson, Sheldon 
Keywords:  Hybrid electric vehicle, control strategy, evolutionary algorithm, multiobjective optimization. 
ID Code:  7496 
Deposited By:  CHIRAG DESAI 
Deposited On:  08 Jun 2011 18:36 
Last Modified:  21 Nov 2012 20:27 
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