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作 者:常宇健[1,2] 程林 陈恩利 张雨雨[4] 李韶华 CHANG Yujian;CHENG Lin;CHEN Enli;ZHANG Yuyu;LI Shaohua(State Key Laboratory of Mechanical Behavior and System Safety of Traffic Engineering Structures, Shijiazhuang Tiedao University, Shijiazhuang 050043, China;School of Electrical and Electronic Engineering, Shijiazhuang Tiedao University, Shijiazhuang 050043, China;School of Mechanical Engineering, Shijiazhuang Tiedao University, Shijiazhuang 050043, China;School of Mechanical Science & Engineering, Huazhong University of Science and Technology, Wuhan 430074, China)
机构地区:[1]石家庄铁道大学省部共建交通工程结构力学行为与系统安全国家重点实验室,石家庄050043 [2]石家庄铁道大学电气与电子工程学院,石家庄050043 [3]石家庄铁道大学机械工程学院,石家庄050043 [4]华中科技大学机械科学与工程学院,武汉430074
出 处:《中国科技论文》2022年第2期212-220,共9页China Sciencepaper
基 金:国家自然科学基金资助项目(11972238);石家庄铁道大学研究生创新资助项目(YC2020069)。
摘 要:为了优化被动分数阶电动汽车悬架的参数,引入车身振动加速度、悬架动挠度、轮胎动载荷和电机垂直加速度这4个平顺性指标,建立含分数阶被动控制悬架系统的三自由度1/4电动汽车非线性模型。利用蒙特卡洛(Monte Carlo)算法对悬架参数的不确定性进行仿真及灵敏度分析,并选取灵敏度高的参数,采用改进的粒子群优化(particle swarm optimization,PSO)算法对悬架参数进行优化与设计。与传统PSO算法的对比结果表明,改进的PSO算法不仅降低了车身振动加速度,还降低了轮胎动载荷。In order to optimize the parameters of passive fractional-order electric vehicle suspension,four ride comfortability indexes including vertical acceleration of vehicle body,dynamic deflection of suspension,dynamic load of tire and vertical acceleration of motor were introduced in this system.Nonlinear 1/4 model of electric vehicle with fractional passive control suspension system having three degrees of freedom was built,and the uncertainty of the suspension model parameters was simulated and analyzed by using the Monte Carlo algorithm.The parameters with high sensitivity were selected,and the improved particle swarm optimization(PSO)algorithm was used to optimize and design the suspension parameters.Compared with the traditional PSO algorithm,the results show that the improved PSO algorithm can not only reduce the vibration acceleration of vehicle body,but also decrease the dynamic load of tire.
关 键 词:被动控制悬架 分数阶微积分 平顺性 蒙特卡洛算法 粒子群优化算法
分 类 号:O322[理学—一般力学与力学基础]
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