Show simple item record

dc.contributor.authorTong, Kuo-Feng 14:39:01 (GMT) 14:39:01 (GMT)
dc.description.abstractDevelopment 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. 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. 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. 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. 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.en
dc.publisherUniversity of Waterlooen
dc.subjectelectric vehicle (EV)en
dc.subjectmixed-encoding genetic algorithm (GA)en
dc.subjectfuzzy logic controlen
dc.subjecttraction controlen
dc.subjectrolling resistanceen
dc.subjectsimultaneous optimizationen
dc.subjectall at once selectionen
dc.subjectsimulation optimizationen
dc.subjectposet by delta inclusionen
dc.titleSimultaneous Plant/Controller Optimization of Traction Control for Electric Vehicleen
dc.typeMaster Thesisen
dc.subject.programElectrical and Computer Engineeringen and Computer Engineeringen
uws-etd.degreeMaster of Applied Scienceen

Files in this item


This item appears in the following Collection(s)

Show simple item record


University of Waterloo Library
200 University Avenue West
Waterloo, Ontario, Canada N2L 3G1
519 888 4883

All items in UWSpace are protected by copyright, with all rights reserved.

DSpace software

Service outages