伴随着资源枯竭和环境污染等问题日益突出,新能源汽车逐渐活跃于大众视野。与传统燃油汽车的驱动装置不同,新能源汽车采用电机集成驱动,电机噪声远小于内燃机噪声,但由于缺失了发动机噪声的掩蔽效应,传动系统产生的振动噪声显得更为突出。为了追求更高的行驶速度,电机高转速输入导致对齿轮传动系统的设计也更加严格,多级齿轮传动系统的振动和噪声成为关注的重点。本文以某款新能源汽车的多级齿轮传动系统为研究对象,以改善多级齿轮传动系统的振动和噪声为主要目的进行研究。（1）借助Romax软件建立多级齿轮传动系统的三维模型进行多工况仿真分析,研究传动系统的动力学特性和振动噪声特性。针对齿轮啮合特性确定齿向结合齿廓的齿轮三维修形方式,计算得到各修形参数的取值范围,基于最优拉丁超立方方法进行抽样,并进行修形参数化建模获取对应的最大振动加速度值,为后续流程提供数据支撑。（2）利用XGBoost机器学习算法探究修形参数和振动加速度之间的映射关系,创建修形参数—振动噪声预测模型,训练优化模型使得模型准确度达到98%。并以最小振动加速度为优化目标,引用标准粒子群算法求解预测模型得到修形参数最优解。在等速和加速工况下,创建修形参数化三维模型进行仿真分析,与修形前相比较,齿轮副传动误差和载荷分布都有极大的改善,最大振动加速度分别降低63%和76%,且预测值和仿真值误差不超过2%,验证了最优解的有效性和降噪设计的可行性。（3）综合等速和加速工况提出一种多工况修形降噪设计,以工况时间占比和振动贡献量为影响因素,加权综合等速和加速工况下的最优修形参数得到多工况综合修形参数组合。建立多目标模糊优选评价模型,以两级齿轮副的传动误差、最大单位长度载荷和最大振动加速度为目标,基于等速和加速工况综合评价整体修形效果,仿真分析验证了多工况综合修形优化方法的可行性。

减速器传动系统作为新能源汽车的核心部件,承担着传递动力的重要任务。实际运行过程中,因传动系统本身结构、制造和装配误差、齿轮啮合冲击等原因,减速器传动系统成为汽车室内噪声的主要来源。以新能源汽车二级减速器的传动系统作为研

In view of traction gear vibration and noise affecting the performance of the transmission system and the comfort of passengers when the electric multiple units (EMU) is running at high speed, taking the traction gear transmission system of an EMU as the research object by using Romax software to construct the parametric modification model of the gear transmission system based on gear modification theory. Combined with multibody dynamics, the vibration response characteristics of the transmission system are simulated and analyzed. A radiated noise prediction model is established using the acoustic boundary element method, based on the generalized regression neural network (GRNN). To further explore the influence of gear modification methods and parameters on vibration and noise characteristics and minimize gear transmission's radiation noise. A particle swarm optimization (PSO) algorithm is designed to solve the optimal modification parameters. The simulation results reveal that after the optimization and modification, the gear transmission error is significantly reduced, the contact status is considerably improved, and the root mean square value of the acoustic power level is reduced by 13.10 dB, which is a reduction of 14%. It shows that the design can effectively reduce the radiation noise of EMU gear trans-mission system.

Under high-speed operating conditions, the noise caused by the vibration of the traction gear transmission system of the Electric Multiple Units (EMU) will distinctly reduce the comfort of passengers. Therefore, analyzing the dynamic characteristics of traction gears and reducing noise from the root cause through comprehensive modification of gear pairs have become a hot research topic. Taking the G301 traction gear transmission system of the CRH380A high-speed EMU as the research object and then using Romax software to establish a parametric modifi- cation model of the gear transmission system, through dynamics, modal and Noise Vibration Harshness (NVH) simulation analysis, the law of howling noise of gear pair changes with modification parameters is studied. In the small sample training environment, the noise prediction model is constructed based on the priority weighted Back Propagation (BP) neural network of small noise samples. Taking the minimum noise of high-speed EMU traction gear transmission as the optimization goal, the simulated annealing (SA) algorithm is introduced to solve the model, and the optimal combination of modification parameters and noise data is obtained. The results show that the prediction accuracy of the prediction model is as high as 98.9%, and it can realize noise prediction under any combination of modification parameters. The optimal modification parameter combination obtained by solving the model through the SA algorithm is imported into the traction gear transmission system model. The vibration acceleration level obtained by the simulation is 89.647 dB, and the amplitude of the vibration acceleration level is reduced by 25%. It is verified that this modification optimization design can effectively reduce the gear transmission. (C) 2021 Institute of Noise Control Engineering.