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research-article

Optimal design of an auto-tensioner in an automotive belt drive system via a dynamic adaptive PSO-GA

[+] Author and Article Information
Hao Zhu

State Key Laboratory of Mechanical Transmissions, School of Automobile Engineering, Chongqing University, Chongqing 400044, ChinaDepartment of Mechanical Engineering, University of Maryland, Baltimore County, 1000 Hilltop Circle, Baltimore, MD 21250
haozu@cqu.edu.cn

Yumei Hu

State Key Laboratory of Mechanical Transmissions, School of Automobile Engineering, Chongqing University, Chongqing 400044, China
cdrhym@163.com

W. D. Zhu

Department of Mechanical Engineering, University of Maryland, Baltimore County, 1000 Hilltop Circle, Baltimore, MD 21250
wzhu@umbc.edu

Yangjun Pi

State Key Laboratory of Mechanical Transmissions, School of Automobile Engineering, Chongqing University, Chongqing 400044, China
cqpp@cqu.edu.cn

1Corresponding author.

ASME doi:10.1115/1.4036997 History: Received August 30, 2016; Revised May 13, 2017

Abstract

Noise, vibration, and harshness performances are always concerns in design of an automotive belt drive system. The design problem of the automotive belt drive system requires minimum transverse vibration of each belt span and minimum rotational vibrations of each pulley and the tensioner arm at the same time, with constraints on tension fluctuations in each belt span. The auto-tensioner is a key component to maintain belt tensions, avoid belt slip, and absorb vibrations in the automotive belt drive system. In this work, a dynamic adaptive particle swarm optimization and genetic algorithm (DAPSO-GA) is proposed to find an optimum design of an auto-tensioner to solve this design problem and achieve design targets. A dynamic adaptive inertia factor is introduced in the basic PSO to balance the convergence rate and global optimum search ability by adaptively adjusting the search velocity during the search process. GA-related operators including a selection operator with time-varying selection probability, crossover operator, and n-point random mutation operator are incorporated in the PSO to further exploit optimal solutions generated by the PSO. These operators are used to diversify the swarm and prevent premature convergence. The objective function is established using a weighted-sum method and the penalty function method is used to deal with constraints. Optimization on an example automotive belt drive system shows that the system vibration is greatly improved after optimization compared with that of its original design.

Copyright (c) 2017 by ASME
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