Simultaneous Plant/Controller Optimization of Traction Control for Electric Vehicle

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;Electrical and Computer Engineering

Tong, Kuo-Feng

University of Waterloo

关键词: 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;    Electrical and Computer Engineering;   

Others  :  https://uwspace.uwaterloo.ca/bitstream/10012/3194/1/ktong2007.pdf

瑞士|英语

来源: UWSPACE Waterloo Institutional Repository

【 摘 要 】

Development of electric vehicles is motivated by global concerns over the needfor environmental protection. In addition to its zero-emission characteristics, anelectric propulsion system enables high performance torque control that may beused to maximize vehicle performance obtained from energy-efficient, low rollingresistance tires typically associated with degraded road-holding ability.A simultaneous plant/controller optimization is performed on an electric vehicletraction control system with respect to conflicting energy use and performanceobjectives. Due to system nonlinearities, an iterative simulation-based optimizationapproach is proposed using a system model and a genetic algorithm (GA) to guidesearch space exploration.The system model consists of: a drive cycle with a constant driver torque requestand 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: eithera performance oriented or low rolling resistance tire, the shape of a fuzzy logiccontroller membership function, and a set of fuzzy logic controller rule base conclusions.A mixed encoding, multi-chromosomal GA is implemented to represent thevariables, respectively, as a binary string, a real-valued number, and a novel rulebase encoding based on the definition of a partially ordered set (poset) by deltainclusion.Simultaneous optimization results indicate that, under straight-line accelerationand unless energy concerns are completely neglected, low rolling resistance tiresshould be incorporated in a traction control system design since the energy savingbenefits outweigh the associated degradation in road-holding ability. The resultsalso indicate that the proposed novel encoding enables the efficient representationof a fix-sized fuzzy logic rule base within a GA.