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http://hdl.handle.net/10012/3194
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| Title: | Simultaneous Plant/Controller Optimization of Traction Control for Electric Vehicle |
| Authors: | Tong, Kuo-Feng |
| Keywords: | electric vehicle (EV)
mixed-encoding genetic algorithm (GA)
fuzzy logic control
traction control
rolling resistance
simultaneous optimization
all at once selection
simulation optimization
poset by delta inclusion |
| Approved Date: | 30-Aug-2007 |
| Date Submitted: | 2007 |
| Abstract: | 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.
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. |
| Program: | Electrical and Computer Engineering |
| Department: | Electrical and Computer Engineering |
| Degree: | Master of Applied Science |
| URI: | http://hdl.handle.net/10012/3194 |
| Appears in Collections: | Faculty of Engineering Theses and Dissertations
Electronic Theses and Dissertations (UW)
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