Integration of Analytical and Numerical Models for Optimizing Planar Double Wishbone Suspension Geometry

Abstract This study aims to develop a design methodology for a double wishbone automotive suspension using the particle swarm optimization (PSO) algorithm. To achieve this, a mathematical relationship was established to connect the geometry and dynamic characteristics of the suspension with a quarter-car model. This relationship was utilized to solve the mathematical model and determine the dynamic behavior of the system under various operating conditions. Based on the results, an analysis of the performance improvements in the generated solutions was conducted, considering parameters such as stability, comfort, and dynamic response. These criteria were integrated into the PSO algorithm to identify the optimal suspension configuration. The process facilitated the exploration of different parameter combinations, aiming to enhance performance. The results were compared with an initial model, revealing that the proposed method effectively improved indicators, such as reducing the oscillation amplitude of the sprung mass, increasing the normal force between the tire and the ground, and optimizing the camber gain. This study highlights the potential of using metaheuristic methods in conjunction with analytical models for the development of complex systems in automotive engineering.