417 14 th International Research/Expert Conference ”Trends in the Development of Machinery and Associated Technology” TMT 2010, Mediterranean Cruise, 11-18 September 2010 OPTIMAL DESIGN OF QUARTER CAR VEHICLE SUSPENSION SYSTEM Ramë Likaj, Ahmet Shala, Mirlind Bruqi & Mehmet Qelaj University of Prishtina, Mechanical Engineering Faculty 10000, Prishtina, Kosova ABSTRACT In this paper it will be presented the comparison of two optimisation algorithms: Sequential Quadratic Program (SQP) and Genetic Algorithms (GAs) for the optimal design of quarter car vehicle suspensions. During the optimisation the three design criteria which have been used are; vertical vehicle body acceleration, dynamic tire load and suspension working space. For implementing a optimisation will be chosen five design parameters: sprung and un-sprung mass, spring stiffness, damping coefficient and tire stiffness. Through the simulation in Matlab it will be shown that GA is more powerful tool to find the global optimal point, without any restrictive requirements on the gradient and Hessian Matrix, while SQP has local convergence properties. In order to overcome the permanent conflict between vehicle comfort and vehicle handling under different riding conditions and speed, the main focus of this research will be on minimising the vertical vehicle body acceleration subjected to a suspension working space and the dynamic tire load. At the end of this paper, it will be shown the comparison between the simulation carried out with nominal and optimal values of design parameters. Keywords: optimisation algorithms, design parameters, SQP, GA. 1. INTRODUCTION Vehicle suspension design includes a number of compromises that have to do with suspension system which should be smooth to provide good levelling and ride comfort. On the other hand must be strong, to fix changes the behaviour of the vehicle and to ensure road holding for varying external conditions. Traditional design practices of vehicle suspensions have been based on trial and error approaches. Now the focus of vehicle suspension design has switched from pure numerical analysis to extensive design synthesis using optimization approaches. There are numerous methods available and even the choice of an efficient optimization algorithm is a non-trivial problem [2]. Genetic algorithms (GAs) have been used in various applications such as function optimization, system identification and control systems. GAs are general-purpose stochastic optimization methods for solving search problems to seek a global optimum. However, GAs are characterized by a large number of function evaluations [1]. The pattern search algorithm (PSA) is typically based on function comparison techniques. Most of these procedures are heuristic in nature and derivative evaluations are not needed. They can be used to solve problems where the objective function is not differentiable and continuous [3]. On the other hand, traditional methods, such as sequential quadratic programming (SQP), are well known to exploit all local information in an efficient way, provided that certain conditions are met and the function to be minimized is 'well-conditioned' in the neighborhood of a unique optimum. These methods require adequate local information to be known (such as the gradient and Hessian matrix) [2, 3]. If the basic requirements are not satisfied, the reliability of the SQP method is greatly jeopardized [2]. By means of the ride quality analysis in the frequency domain, the vertical vehicle body acceleration (VBA), suspension working space (SWS) and dynamic tire load (DTL) can be obtained [1]. In this design optimization, the main objective is to minimize the VBA acceleration. In the