Simultaneous Plant/Controller Optimization of Traction Control for Electric Vehicle
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Date
2007-08-30T14:39:01Z
Authors
Tong, Kuo-Feng
Journal Title
Journal ISSN
Volume Title
Publisher
University of Waterloo
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.
Description
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