Parameters optimisation of a vehicle suspension system using a particle swarm optimisation algorithm

The purpose of this paper is to determine the lumped suspension parameters that minimise a multi-objective function in a vehicle model under different standard PSD road profiles. This optimisation tries to meet the rms vertical acceleration weighted limits for human sensitivity curves from ISO 2631 [ISO-2631: guide for evaluation of human exposure to whole-body vibration. Europe; 1997] at the driver's seat, the road holding capability and the suspension working space. The vehicle is modelled in the frequency domain using eight degrees of freedom under a random road profile. The particle swarm optimisation and sequential quadratic programming algorithms are used to obtain the suspension optimal parameters in different road profile and vehicle velocity conditions. A sensitivity analysis is performed using the obtained results and, in Class G road profile, the seat damping has the major influence on the minimisation of the multi-objective function. The influence of vehicle parameters in vibration attenuation is analysed and it is concluded that the front suspension stiffness should be less stiff than the rear ones when the driver's seat relative position is located forward the centre of gravity of the car body. Graphs and tables for the behaviour of suspension parameters related to road classes, used algorithms and velocities are presented to illustrate the results. In Class A road profile it was possible to find optimal parameters within the boundaries of the design variables that resulted in acceptable values for the comfort, road holding and suspension working space.

[1]  Marc J. Richard,et al.  Safety and comfort analysis of a 3-D vehicle model with optimal non-linear active seat suspension , 2006 .

[2]  Riccardo Poli,et al.  Particle swarm optimization , 1995, Swarm Intelligence.

[3]  Frans van den Bergh,et al.  An analysis of particle swarm optimizers , 2002 .

[4]  Xianfu Cheng,et al.  Multiobjective Robust Design of the Double Wishbone Suspension System Based on Particle Swarm Optimization , 2014, TheScientificWorldJournal.

[5]  Jasbir S. Arora,et al.  Optimization of structural and mechanical systems , 2007 .

[6]  Guohai Liu,et al.  Improved particle swarm optimization algorithm and its global convergence analysis , 2010, 2010 Chinese Control and Decision Conference.

[7]  Yuping He,et al.  Multidisciplinary Optimization of Multibody Systems with Application to the Design of Rail Vehicles , 2005 .

[8]  Zhang Zhe,et al.  Nonstationary random vibration analysis of coupled vehicle‐bridge systems , 2010 .

[9]  Anil Shirahatt,et al.  Optimal Design of Passenger Car Suspension for Ride and Road , 2008 .

[10]  Maurice Clerc,et al.  The particle swarm - explosion, stability, and convergence in a multidimensional complex space , 2002, IEEE Trans. Evol. Comput..

[11]  Alireza Alfi,et al.  Parameter Identification Based on a Modified PSO Applied to Suspension System , 2010, J. Softw. Eng. Appl..

[12]  Yuping He,et al.  DESIGN OPTIMIZATION OF VEHICLE SUSPENSIONS WITH A QUARTER-VEHICLE MODEL , 2008 .

[13]  Zhan Shu,et al.  Passive vehicle suspensions employing inerters with multiple performance requirements , 2014 .

[14]  Amit Konar,et al.  Particle Swarm Optimization and Differential Evolution Algorithms: Technical Analysis, Applications and Hybridization Perspectives , 2008, Advances of Computational Intelligence in Industrial Systems.

[15]  Giorgio Rizzoni,et al.  A Method for the Characterization of Off-Road Terrain Severity , 2006 .

[16]  Han Tong Loh,et al.  Advances of Computational Intelligence in Industrial Systems , 2008, Studies in Computational Intelligence.

[17]  Paul H. Calamai,et al.  Application of genetic algorithms to the design optimization of an active vehicle suspension system , 1998 .

[18]  L. Aulisa,et al.  Biomechanics of the spine. , 2000, Rays.

[19]  Jo Yung Wong,et al.  Theory of ground vehicles , 1978 .

[20]  G. R. Allen,et al.  Ride quality and international standard ISO 2631 (Guide for the evaluation of human exposure to whole-body vibration) , 1975 .

[21]  L. Zuo,et al.  Low order continuous-time filters for approximation of the ISO 2631-1 human vibration sensitivity weightings , 2003 .

[22]  Frank L. Lewis,et al.  Active suspension control of ground vehicle based on a full-vehicle model , 2000, Proceedings of the 2000 American Control Conference. ACC (IEEE Cat. No.00CH36334).

[23]  T D Gillespie,et al.  Fundamentals of Vehicle Dynamics , 1992 .

[24]  Peter Andren,et al.  Power spectral density approximations of longitudinal road profiles , 2006 .

[25]  D. Newland.  An introduction to random vibrations and spectral analysis , 1975 .

[26]  M J Griffin,et al.  Prevalence and pattern of occupational exposure to whole body vibration in Great Britain: findings from a national survey , 2000, Occupational and environmental medicine.

[27]  J. D. Robson,et al.  The description of road surface roughness , 1973 .

[28]  Yunqing Zhang,et al.  Non-stationary Random Vibration Analysis of Vehicle with Fractional Damping , 2008, 2008 International Conference on Intelligent Computation Technology and Automation (ICICTA).