多目标优化模式选择策略下的混合智能切换液压互联悬挂系统的开发与实际应用

Arash Darvish Damavandi, B. Mashadi, M. Masih-Tehrani
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引用次数: 0

摘要

降低垂直加速度对汽车制造商来说至关重要。这一参数可评估舒适性指数。此外,操控指数也是必须研究的另一个特征。悬架系统对这两项指数有着至关重要的影响。我们提出了一种混合智能切换液压互联悬架(HISHIS)。在悬架系统中,平行配置与对角配置相结合。讨论了在对角配置和平行配置之间选择布局的模式选择策略。平行配置的唯一模式被称为反斜度配置。对角配置有三种模式,包括防侧倾配置、防转向过度配置和防振动配置。根据模式选择策略,可从这三种对角线配置模式中选择一种。侧倾和俯仰的产生源于惯性力和道路输入。Hopfield 神经网络可以识别产生侧倾和俯仰的起源。因此,阀门的性能会随着 Hopfield 神经网络的识别而改变。不同操作的结果表明,在各种任务中,每个目标参数都得到了独立改善。数据记录器收集了车辆在实际条件下的加速度。然而,选择策略模块的余量是通过遗传算法进行多目标优化的,以便在实际条件下获得更好的响应。滚动角、偏航率、允许暴露时间和俯仰角平均分别提高了 63%、5%、40% 和 99%。此外,优化选择策略还能将允许曝光时间提高 9%。显然,通过将两种布局相结合,可以灵活地改善驾驶舒适性和操控情况。此外,还有一种有条件的策略可以选择不同的布局和模式,以达到更好的响应效果。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Development of a hybrid intelligent switching hydraulically interconnected suspension system under a multi-objective optimized mode selection strategy with real-world condition
Reduction in vertical acceleration is crucial for car manufacturers. This parameter evaluates the comfort index. Moreover, the handling index is another feature that must be under investigation. The suspension system has an essential impact on these two indices. A hybrid intelligent switching hydraulically interconnected suspension (HISHIS) is proposed. The parallel configuration is combined with the diagonal configuration in the suspension system. A mode selection strategy is discussed to select the layout between diagonal and parallel configurations. The only mode of parallel configuration is called Anti-pitch configuration. The diagonal configuration has three modes, including Anti-roll, Anti-oversteering, and Anti-vibration configuration. According to the mode selection strategy, one of these three diagonal configuration modes might be selected. The origin of roll and pitch generation is inertia force and road inputs. Hopfield neural network can recognize the origin of roll and pitch generation. Therefore, the performance of valves will change according to Hopfield neural network recognition. The results of different maneuvers show the improvement at each targeted parameter in various tasks independently. The data logger gathered the acceleration of the vehicle in real-world conditions. However, the margins of the selection strategy block are multi-objective optimized with a genetic algorithm to reach better responses in real-world conditions. The roll angle, yaw rate, allowable exposure time, and pitch angle are improved by 63%, 5%, 40%, and 99% on average. Also, optimizing the selection strategy improves the allowable exposure time by 9%. Obviously, by combining two layouts, it is possible to have a flexible situation to improve ride comfort and handling situations. In addition, there is a conditional strategy to select different layouts and modes to reach a better response.
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