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Simultaneous plant/controller optimi...

Tong, Kuo-Feng.

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Simultaneous plant/controller optimization of traction control for electric vehicle.

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Simultaneous plant/controller optimization of traction control for electric vehicle./
Author:	Tong, Kuo-Feng.
Description:	215 p.
Notes:	Source: Masters Abstracts International, Volume: 46-04, page: 2247.
Contained By:	Masters Abstracts International46-04.
Subject:	Engineering, Automotive. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=MR35336
ISBN:	9780494353363

Simultaneous plant/controller optimization of traction control for electric vehicle.
Tong, Kuo-Feng.

Simultaneous plant/controller optimization of traction control for electric vehicle. - 215 p.

Source: Masters Abstracts International, Volume: 46-04, page: 2247.

Thesis (M.A.Sc.)--University of Waterloo (Canada), 2007.

Development of electric vehicles is motivated by global concerns over the need for environmental protection. In addition to its zero-emission characteristics, an electric propulsion system enables high performance torque control that may be used to maximize vehicle performance obtained from energy-efficient, low rolling resistance tires typically associated with degraded road-holding ability.

ISBN: 9780494353363Subjects--Topical Terms:

1018477
Engineering, Automotive.

Simultaneous plant/controller optimization of traction control for electric vehicle.
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100 1 $a Tong, Kuo-Feng. $3 1272485
245 1 0 $a Simultaneous plant/controller optimization of traction control for electric vehicle.
300 $a 215 p.
500 $a Source: Masters Abstracts International, Volume: 46-04, page: 2247.
502 $a Thesis (M.A.Sc.)--University of Waterloo (Canada), 2007.
520 $a Development of electric vehicles is motivated by global concerns over the need for environmental protection. In addition to its zero-emission characteristics, an electric propulsion system enables high performance torque control that may be used to maximize vehicle performance obtained from energy-efficient, low rolling resistance tires typically associated with degraded road-holding ability.
520 $a A simultaneous plant/controller optimization is performed on an electric vehicle traction control system with respect to conflicting energy use and performance objectives. Due to system nonlinearities, an iterative simulation-based optimization approach is proposed using a system model and a genetic algorithm (GA) to guide search space exploration.
520 $a The system model consists of: a drive cycle with a constant driver torque request and a step change in coefficient of friction, a single-wheel longitudinal vehicle model, a tire model described using the Magic Formula and a constant rolling resistance, and an adhesion gradient fuzzy logic traction controller.
520 $a Optimization is defined in terms of the all at once variable selection of: either a performance oriented or low rolling resistance tire, the shape of a fuzzy logic controller membership function, and a set of fuzzy logic controller rule base conclusions. A mixed encoding, multi-chromosomal GA is implemented to represent the variables, respectively, as a binary string, a real-valued number, and a novel rule base encoding based on the definition of a partially ordered set (poset) by delta inclusion.
520 $a Simultaneous optimization results indicate that, under straight-line acceleration and unless energy concerns are completely neglected, low rolling resistance tires should be incorporated in a traction control system design since the energy saving benefits outweigh the associated degradation in road-holding ability. The results also indicate that the proposed novel encoding enables the efficient representation of a fix-sized fuzzy logic rule base within a GA.
590 $a School code: 1141.
650 4 $a Engineering, Automotive. $3 1018477
650 4 $a Engineering, Electronics and Electrical. $3 626636
690 $a 0540
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710 2 $a University of Waterloo (Canada). $3 1017669
773 0 $t Masters Abstracts International $g 46-04.
790 $a 1141
791 $a M.A.Sc.
792 $a 2007
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