Masters Theses

Shweta Neglur

Abstract

"Electric-drive vehicles have been identified as one of the promising technologies of the future. Electric-drive vehicles including fuel cell, hybrid electric, and plug-in hybrid electric vehicles have the potential to improve the fuel economy and reduce gas emissions when compared to conventional vehicles. One of the important challenges in the advancement of the electric-drive vehicles is to develop a control strategy which meets the power requirements of the vehicles. The control strategy is an algorithm designed to command the battery and the internal combustion engine of the vehicle for specific power demands. In this thesis, load follower and thermostat control algorithms have been analyzed and compared. A control strategy based on the combined urban and highway driving cycles has been proposed in order to obtain better fuel economy. In addition to this, proper choice of the energy storage system with respect to cost and capacity is another design challenge for electric-drive vehicles. In this thesis, an investigation has been done to identify the impact of different battery capacities and state of charge operating windows on the fuel economy of the vehicle. It is proven that the vehicle fuel economy is highly dependent on the battery state of charge whereas, battery sizing largely depends on the average daily driving distance and the driving conditions"--Abstract, page iii.

Advisor(s)

Ferdowsi, Mehdi

Committee Member(s)

Chowdhury, Badrul H.
Kimball, Jonathan W.

Department(s)

Electrical and Computer Engineering

Degree Name

M.S. in Electrical Engineering

Publisher

Missouri University of Science and Technology

Publication Date

Summer 2010

Pagination

ix, 64 pages

Note about bibliography

Includes bibliographical references (pages 76-79).

Rights

© 2010 Shweta Neglur, All rights reserved.

Document Type

Thesis - Open Access

File Type

text

Language

English

Subject Headings

Hybrid electric vehicles -- DesignHybrid electric vehicles -- Energy consumptionHybrid electric vehicles

Thesis Number

T 9679

Print OCLC #

691254981

Electronic OCLC #

752193221

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