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基于近似模型的车辆悬架参数优化研究
Optimization and Research of Vehicle Suspension Parameters Based on Approximate Model
【作者】 刘宇;
【导师】 刘平;
【作者基本信息】 西南交通大学 , 车辆工程, 2013, 硕士
【摘要】 在兼顾操纵稳定性的基础上,为了提高某款轿车的平顺性,对车辆的悬架系统进行了多目标优化,以期使整车在保证操稳性的条件下平顺性得到改善;同时通过本课题的一系列研究方法与手段,为悬架的优化设计提供一种新的思路,为完善本领域的研究提供一种有益参考。本文的具体研究方法和内容包括:首先,根据现有的国际和国内标准以及前人的研究成果,对操稳性和平顺性的试验方法以及评价指标作了总结,综合权衡这些试验方法和评价指标,提出一套合适的评价指标。其次,根据相关整车数据,在ADAMS/CAR多体动力学仿真软件中建立了精确的整车刚柔耦合虚拟样机模型。为了评价振动对人体舒适和健康的影响,特别建立了驾驶员及座椅模型。然后根据本文的评价方法,对整车进行了关于操稳性的双移线仿真试验和关于平顺性的随机输入行驶仿真试验以及脉冲输入行驶仿真试验,重点对整车的平顺性进行了全面评价。以悬架系统的特性参数(悬架弹簧刚度和减震器阻尼)为试验因子,以操稳性和平顺性评价指标为输出响应,分别利用最优拉丁超立方设计、随机拉丁超立方设计、正交数组、中心复合设计和BOX-Behnken设计对系统输入输出进行了试验设计,对比了这五种试验设计方法,验证了最优拉丁超立方设计的优越性。在试验设计的基础上,分别利用响应面法、径向基神经网络法和克里格法对系统输入输出进行了近似建模。通过建模误差分析,得出克里格法的建模精度最高,因此以克里格法作为本研究的近似建模方法。基于克里格法建立的近似模型,为了提高整车平顺性,同时兼顾操纵稳定性,利用多目标粒子群优化算法,以不舒适性参数和悬架前、后动挠度为优化目标,以悬架弹簧刚度和减震器阻尼为设计变量,对悬架系统进行了多目标优化。最后,通过优化前和优化后的仿真对比,结果表明:在保证车辆操稳性的同时,表征平顺性指标之一的不舒适性参数在各车速下平均下降了9%,得到了较大改善,而悬架的动挠度也优化到了一个合理的范围内,证明了优化的有效性。
【Abstract】 On the basis of considering the handling stability, in order to improve the ride comfort of the car, the vehicle’s suspension system was optimized by the multi-objective optimization method, then make the vehicle’s ride comfort improved under the condition of guaranteeing the handling stability. At the same time, through a series of research methods and means, providing a new train of thought of suspension optimum design, providing a beneficial reference of the research in this field. The concrete research methods and content of this article includes:Firstly, according to the current international and domestic standards and research achievements of predecessors, a summary about the testing methods and evaluation indicators of handling stability and ride comfort were made. Weighing the test methods and evaluation indicators, a set of suitable evaluation indicators were put forward.Secondly, according to the relevant data of the vehicle, by using multi-body dynamics software ADAMS/CAR, a accurate vehicle rigid-flexible coupled model was established. Specially, in order to evaluate the vibration effects on human comfort and health, driver and seat model were established.Then, according to the evaluation method in this paper, for handling stability, a double lane change test was made, for ride comfort, random input simulation and pulse input test were carried out. In particular, the comprehensive evaluation about ride comfort was carried out.Using the characteristic parameters of suspension system (suspension spring stiffness and shock absorber damping) as the experimental factor, the handling stability and ride comfort evaluation indicators as the output response. The design of experiment of system’s input and output were carried out by using Opt LHD, LHD, Orthogonal Arrays, CCD, Box-Behnken respectively. The five kinds of experimental design methods were compared, the Opt LHD’s design superiority was proved.On the basis of experimental design, the input and output’s approximate model of the system were built by using RSM, RBF neural netword and Kriging separately. Through the modeling error analysis, proving that the Kring model’s modeling accuracy was the best one. So in this article, using Kring method as the approximate modeling method.Based on the Kring model, in order to improve the vehicle’s ride comfort, handing stability was considered simultaneously, using multi-objective particle swarm optimization algorithm, using r.m.s. value of weighted body acceleration, the suspension dynamic deflection and dynamic tyre load as optimization objectives, suspension spring stiffness and shock absorber damping as the design variables, the suspension system was optimized.Finally, through comparison of optimized and un-optimized, the results show that: while ensured the handling stability, one of the ride comfort indicators, r.m.s. value of weighted body acceleration dropped9%on average at different speeds, were significantly improved. At the same time, the suspension dynamic deflection was optimized to a reasonable range. These verified the effectiveness of the suspension optimization.
【Key words】 handling stability; ride comfort; design of experiment; approximate model; suspension optimization; particle swarm optimization algorithm;