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
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.
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)
JIA Ai-qin,CUI Jian-feng,CHEN Jian-jun, et al. Multi-objective Optimal Design of Vehicle Suspension Parameters Based on Reliable Gray Particle Swarm Optimization[J]. Journal of Highway and Transportation Research and Development, 2015, 9(3): 102-110.
[1] SPENTZAS, KONSTANTINOS. Design of a Non-linear Hybrid Car Suspension System Using Neural Networks[J]. Mathematics and Computers in Simulation, 2002, 60(3):369-378.
[2] DUAN Min, ZHANG Li-jun, SHI Jing, et al. Optimization of Suspension Parameters of a Light Bus Based on Neural Network Technology[J]. Automotive Engineering,2003,25(2):190-192.(in Chinese)
[3] GUO Kong-hui, KONG Fan-sen, ZONG Chang-fu. The Application of Genetic Algorithm to Evaluation of Motor Vehicle Maneuver Ability and Structure Optimum[J]. Chinese Journal of Mechanical Engineering, 2000, (10):34-36.(in Chinese)
[4] LU Peng-min, HE Li-mei, YOU Jin-min. Optimization of Vehicle Suspension Parameters Based on Comfort and Tyre Dynamic Load[J]. China Journal of Highway and Transport, 2007, 20(1):112-118.(in Chinese)
[5] LIU Ren-yun, ZHANG Yi-ming, LIU Qiao-ling. Structural Robust Reliability Design for Multi-objective Optimization[J]. Chinese Journal of Applied Mechanics, 2007, 24(1):267-271.(in Chinese)
[6] YUE Heng, ZHANG Ha-jun, CAI Tian-you. Strategic Study of RBF Neural Network Parameter Optimization[J]. Control Engineering of China, 2006, 13(6):525-529. (in Chinese)
[7] ZHANG Yong-lin, ZHONG Yi-fang. Time Domain Model of Road Unduation Excitation to Vehicles[J]. Journal of Agricultural Machinery, 2004, 35(2):9-13. (in Chinese)
[8] ZHANG Yong-lin, ZHANG Jia-fan. Numerical Simulation of Stochastic Road Process Using White Noise Filtration[J]. Mechanical System and Signal Processing, 2006, 20(2):363-372. (in Chinese)
[9] YOSHIMURA. A Semi-active Suspension of Passenger Cars Using Fussy Reasoning and the Field Testing[J]. International Journal of Vehicle Design, 1998, 19(2):150-166.
[10] ZHANG Yong-lin. Time Domain Model of Road Irregularities Simulation Using the Harmony Superposition Method[J]. Transactions of the Chinese Society of Agricultural Engineering, 2003, 19(6):32-35. (in Chinese)
[11] ZHENG Jun, ZHONG Zhi-hua. Neural Network Optimization for Nonlinear Vehicle Ride Comfort Model[J]. Automotive Engineering, 2001, 23(3):172-176. (in Chinese)
[12] CHEN Shi-an, LIU Hong-guang, LU Sen-lin. Fuzzy Control System for Automobile Active Suspension with Four Free Dimensions[J]. Automotive Engineering, 2001, 23(6):375-376. (in Chinese)
[13] WANG Tao. Multi-objective and Multi-criteria Decision Optimization of Automobile Suspension Parameters[J]. Journal of Agricultural Machinery, 2009, 40(4):27-32. (in Chinese)
[14] KENNEDY J, EBERHART R C. Particle Swarm Optimization[C]//Proceedings of IEEE International Conference on Neural Networks. Australia:IEEE, 1995:1942-1948.
[15] ATRAY V S, ROSCHKE P N. Neuro-fuzzy Control of Railcar Vibration Using Semiactive Dampers[J]. Computer-Aided Civil and Infrastrcture Engineering, 2004, 19(1):81-92.
[16] YI Feng, ZHU Rao, JIN Yan, et al. Analysis of Wavelet Denoising for Settlement with Predicting Soft Soil Foundation Grey Model[J]. Journal of Highway and Transportation Research and Development, 2014, 31(2):21-26. (in Chinese)
[17] ZHANG Gan-qing, GONG Xian-sheng, WANG Huan-huan, et al. Multi-objective Optimization Design on Gear Train of Planetary Reducer in Shield Tunneling Machine Based on Reliably Grey Particle Swarm Optimization[J]. Journal of Mechanical Engineering, 2010, 46(23):136-147. (in Chinese)
[18] YANG Guang, WU Xiao-ping, CHANG Han-bao. Research on Synthesized Fault Diagnosis Technique Based on Fuzzy Gray Relational Neural Network[J]. Journal of Wuhan University of Technology, 2008, 32(5):861-867. (in Chinese)
2015, Vol. 9 
