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Multi-objective Optimal Design of Vehicle Suspension Parameters Based on Reliable Gray Particle Swarm Optimization |
JIA Ai-qin1, CUI Jian-feng1, CHEN Jian-jun2, GAO Wei3 |
1. School of Electromechanical Engineering, Zhengzhou Institute of Aeronautical Industry Management, Zhengzhou Henan 450015, China;
2. School of Electromechanical Engineering, Xidian University, Xi'an Shaanxi 710071, China;
3. School of Civil and Environmental Engineering, University of New South Wales, Sydney NSW 2052, Australia |
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Abstract This study presents a multi-objective optimal design of automobile suspension systems to improve vehicle ride comfort and reduce tire-induced dynamic excitations on road surface simultaneously. In the optimal model, spring stiffness and the damper coefficient are considered design variables, whereas the maximum deflection of the suspension system is regarded as a constraint. Meanwhile, the root-mean-square values of the vertical acceleration of the vehicle body and the dynamic loadings of the front and rear tires are treated as objective functions. Multi-objective optimization is implemented using the gray particle swarm algorithm. Globally optimal solutions are obtained by introducing the variance of relevant sequence numbers into gray relevant theory. A half-car model is used to illustrate the proposed optimal model and solution method. Results show that the minimum acceleration of the vehicle body and the minimum dynamic loads exerted by tires on road surfaces can be achieved through the proposed multi-objective optimal design.
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Received: 19 May 2015
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Fund:Supported by the Science and Technology Research Projects of Henan Provincial Education Department (No.14B460030, No.14a590001); the General Science and Technology Projects of Zhengzhou (No.141PPTGG348); the National Natural Science Foundation of China (No.70971120); the Aeronautical Science Foundation of China(No.2012ZD55009); and the Foundation for the University Key Teacher by Henan Province (No.2014GGJS-104) |
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