School of Mechanical and Electrical Engineering, Zhengzhou University of Industrial Technology, Zhengzhou, China.
PLoS One. 2024 Jun 10;19(6):e0302814. doi: 10.1371/journal.pone.0302814. eCollection 2024.
In this study, we introduce an optimization method for high-speed gear trimming in electric vehicles, focusing on variations in input torque and speed. This approach is designed to aid in vibration suppression in electric vehicle gears. We initially use Tooth Contact Analysis (TCA) and Loaded Tooth Contact Analysis (LTCA) to investigate meshing point localization, considering changes in gear tooth surface and deformations due to load. Based on impact mechanics theory, we then derive a formula for the maximum impact force. A 12-degree-of-freedom bending-torsion-axis coupled dynamic model for the helical gear drive in the gearbox's input stage is developed using the centralized mass method, allowing for an extensive examination of high-speed gear vibration characteristics. Through a genetic algorithm, we optimize the tooth profile and tooth flank parabolic modification coefficients, resulting in optimal vibration-suppressing tooth surfaces. Experimental results under various input torques and speeds demonstrate that the overall vibration amplitude is stable and lower than that of conventional gear shaping methods. Specifically, the root mean square of vibration acceleration along the meshing line under different conditions is 58.02 m/s2 and 20.33 m/s2, respectively. The vibration acceleration in the direction of the meshing line is 20.33 m/s2 and 20.02 m/s2 under varying torques and speeds, with 20.33 m/s2 being the lowest. Furthermore, the average magnitude of the meshing impact force is significantly reduced to 5015.2. This high-speed gear reshaping method not only enhances gear dynamics and reliability by considering changes in input torque and speed but also effectively reduces vibration in electric vehicle gear systems. The study provides valuable insights and methodologies for the design and optimization of electric vehicle gears, focusing on comprehensive improvement in dynamic performance.
在这项研究中,我们引入了一种针对电动汽车高速齿轮修形的优化方法,重点关注输入扭矩和速度的变化。这种方法旨在帮助抑制电动汽车齿轮的振动。我们首先使用齿面接触分析(TCA)和加载齿面接触分析(LTCA)来研究啮合点的定位,同时考虑了齿轮齿面的变化和由于负载引起的变形。然后,根据冲击力学理论,我们推导出最大冲击力的公式。使用集中质量法为齿轮箱输入级的斜齿轮传动建立了 12 自由度弯曲-扭转-轴耦合动力学模型,从而可以广泛研究高速齿轮的振动特性。通过遗传算法,我们优化了齿廓和齿廓抛物线修形系数,得到了最优的减振齿面。在不同输入扭矩和速度下的实验结果表明,整体振动幅度稳定且低于传统齿轮成形方法。具体而言,在不同条件下沿啮合线的振动加速度均方根分别为 58.02 m/s2 和 20.33 m/s2。在不同的扭矩和速度下,沿啮合线的振动加速度分别为 20.33 m/s2 和 20.02 m/s2,其中 20.33 m/s2 为最低值。此外,啮合冲击力的平均幅值显著降低至 5015.2。这种高速齿轮修形方法不仅考虑输入扭矩和速度的变化来增强齿轮的动力学和可靠性,而且有效地降低了电动汽车齿轮系统的振动。该研究为电动汽车齿轮的设计和优化提供了有价值的见解和方法,重点是全面提高动态性能。