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基于TPA和遗传算法的动力总成悬置系统优化设计 被引量:4

Optimization design of a powertrain mount-system based on the TPA and genetic algorithm
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摘要 针对某前置前驱(FF)的乘用车的怠速抖动问题,提出了基于传递路径分析(TPA)和遗传算法的动力总成悬置系统优化设计方法。利用TPA技术,将悬置系统固有特性和车内目标点振动响应相联系,并综合利用悬置系统6自由度模型,考虑其解耦率和固有频率分布,建立了悬置系统优化数学模型,进而利用NSGA-II遗传算法对其进行多目标优化,获取最优的刚度参数,通过悬置试制,进行整车匹配测试。实验结果表明,优化后的悬置系统隔振性能大幅提升,车内目标的振动响应量表现较优,有效解决了乘用车的怠速抖动问题,验证了所提出的优化方法的有效性。 Aiming at the idle jitter problem of a passenger car with front engine front wheeldrive(FF)installation,an optimal design method of its powertrain mount system based on the transfer path analysis(TPA)and genetic algorithm was proposed.By the TPA technology,the inherent characteristics of the mount-system were correlated to the associated vibration response at target points of the vehicle.Comprehensively utilizing a six degrees of freedom model of the mount-system and considering the decoupling rate and the inherent frequency distribution,a mount-system optimization mathematical model was established.Then the NSGA-II genetic algorithm was used in the multi-objective optimization and the optimal stiffness parameters were obtained.The optimal results were used to remake the mount-system and to do the test.The experimental results show that the vibration isolation performance of the optimized mount-system is greatly improved,the vibration response is better than that of the original target car,the idling jitter problem of the passenger car is effectively solved,and the effectiveness of the proposed optimization method is verified.
作者 潘公宇 付博文 王功强 陈清爽 朱瑞 李东 PAN Gongyu;FU Bowen;WANG Gongqiang;CHEN Qingshuang;ZHU Rui;LI Dong(School of Automotive and Transport Engineering,Jiangsu University,Zhenjiang 212013,China;Jiangxi Province Key Laboratory of Vehicle Noise and Vibration(Jiangling Motors Corporation,Ltd.),Nanchang 330013,China)
出处 《振动与冲击》 EI CSCD 北大核心 2021年第14期279-286,共8页 Journal of Vibration and Shock
基金 国家自然科学基金(51375212) 江西省汽车噪声与振动重点实验室2018年度开放基金(JXNVHKB-KFKT-201802)。
关键词 动刚度法 能量解耦 遗传算法 刚度优化 dynamic stiffness method energy decoupling genetic algorithm stiffness optimization
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