The use of optimization methods in engineering is growing, allowing the best possible way to fulfill the requirements of the project. For vehicle suspensions, there are various conditions, which involve comfort, safety, stability, maneuverability, among others. A safety and stability evaluation is carried out by several tests, including Double Lane Change. In this maneuver, the vehicle must change lanes quickly twice, allowing it to be assessed for stability in sudden movements. For ride comfort, it is common for the design to be based on the vehicle’s natural vibration frequencies. In this context, this work aims to present a methodology for optimizing the suspension parameters of a vehicle, based on the natural frequencies of vibration and the simulation of a Double Lane Change maneuver. For that, it is employed vertical and lateral dynamics mathematical models, with hypotheses that allow the adequate adaptation to the represented phenomena. Finally, Particle Swarm Optimization (PSO) is used, which is a stochastic algorithm, based on nature. It has low computational cost, with reasonable results, allowing the parameters to be estimated and comprising the two objectives simultaneously.

优化方法在工程中的使用正在增长，这是满足项目要求的最佳方法。对于车辆悬架，存在各种条件，其中包括舒适性，安全性，稳定性，可操纵性等。通过多项测试（包括双车道变更）进行了安全性和稳定性评估。在这种操作中，车辆必须快速两次改变车道，以便评估其突然运动的稳定性。为了提高乘坐舒适性，通常根据车辆的固有振动频率进行设计。在这种情况下，这项工作旨在提出一种基于振动的固有频率和双车道变更操纵的仿真来优化车辆悬架参数的方法。为了那个原因，它采用了纵向和横向动力学数学模型，其假设允许对所表示的现象进行适当的调整。最后，基于自然，使用了粒子群优化算法（PSO），它是一种随机算法。它具有较低的计算成本和合理的结果，可以估算参数并同时包含两个目标。