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首页> 外文期刊>Journal of digital information management >Applying the Multi-objective Optimization Techniques in the Design of Suspension Systems
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Applying the Multi-objective Optimization Techniques in the Design of Suspension Systems

机译：多目标优化技术在悬架系统设计中的应用

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The questionable quality of the roads represents the main factor of discomfort, being directly responsible for the accidents, affecting car components, but also the security of passengerscausing death and serious injuries. According to statistics released by the World Health Organization, road accidents, in underdeveloped countries, tends to increase by 80 % in 2020 compared to 2000. In terms of road infrastructure,the low-and middle-income countries are characterized by a higher accident rate, reason for which the cars designers must approach the suspension problem slightly different and the parameters obtained by optimization algorithms should be differentfrom the same model of car depending on where they will be driven / sold. This paper presents the optimization of a quarter-car model with two degree-of-freedom using evolutionary algorithms to determine the optimal parameters for a vehicle suspension, in order to improve ride comfort. The optimization problem consists in minimizing the sprung mass acceleration and sprung mass displacement subject to several constraints that arise from kinematic considerations. The vehicle model is considered to travel at a constant speed on a random road profile generated according to the ISO 8608 standard. The design variables to be optimized are the suspension stiffness and damping coefficients. We analyzed the algorithms In multiple scenarios so we can compare their performance in terms of fast convergence and solution diversity. The results showed that the optimization algorithms find solutions in small number of iterations, with slightly better performance obtained by Fast Pareto Genetic Algorithm.
机译：道路质量可疑是造成不舒适的主要因素，直接造成事故，影响汽车部件，还造成死亡和重伤，对乘客的安全造成直接影响。根据世界卫生组织发布的统计数据，与2000年相比，不发达国家的道路交通事故到2020年将增加80％。就道路基础设施而言，低收入和中等收入国家的事故率更高，汽车设计者必须解决悬架问题的原因略有不同，并且根据优化算法所获得的参数应该根据其驾驶/销售地点而与同一型号的汽车有所不同。本文提出了一种具有两个自由度的四分之一汽车模型的优化方法，该算法使用进化算法来确定车辆悬架的最佳参数，以提高乘坐舒适性。最优化问题在于使弹簧承受的加速度和弹簧承受的位移最小化，这要受到运动学上的考虑。车辆模型被视为在按照ISO 8608标准生成的随机道路轮廓上以恒定速度行驶。要优化的设计变量是悬架刚度和阻尼系数。我们在多种情况下分析了算法，因此我们可以在快速收敛和解决方案多样性方面比较它们的性能。结果表明，该优化算法能以较小的迭代次数找到解，而快速帕累托遗传算法的性能要好一些。

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