Optimal seat and suspension design for a quarter car with driver model using genetic algorithms

Abstract

This paper presents an optimization of a four-degrees-of-freedom quarter car seat and suspension system using genetic algorithms to determine a set of parameters to achieve the best performance of the driver. Since the health of the driver is as important as the stability of the car, the desired objective is proposed as the minimization of a multiobjective function formed by the combination of not only suspension deflection and tire deflection but also the head acceleration and crest factor (CF), which is not practiced as usual by the designers. The optimization results are compared through step and frequency responses of the seat and suspension system for the optimum and currently used suspension systems. Comparatively better results are obtained from the optimized system in terms of resonance peaks, CF, and vibration dose value. The concept and the ideas set forth in this work are directly applicable to both the car suspension and seat design in industry.