Author:
Zhao Linlin,Zhou Zhongwang,Wu Tao
Abstract
The lightweight development of electric vehicle motors is a prominent future trend, with the challenge of transmission vibration and noise acting as a key bottleneck that limits the enhancement of power and speed in electric vehicle drive systems. The noise generated by electric vehicle transmissions is primarily associated with the transmission system and gear structure. In line with this, the present study proposes an analysis of transmission error and response mechanisms through gear modifications. The research delves into the analysis of gear deformation and error generation characteristics. It further investigates methods for parametric equation modeling, tooth profile modification, deformation imprint analysis, and vibration response modeling to examine excitation response analysis and noise reduction techniques pertaining to transmission errors. The findings demonstrate that, under 40 % torque, the shaped gear exhibited a maximum reduction in transmission error of 34.2 %, resulting in an overall error improvement of over 5.7 %. Moreover, the maximum error difference after tooth profile and tooth direction shaping exceeded 2 %. The gear-shaping-based electric vehicle transmission showcased favorable economic and technical performance, while its excitation response mechanism provided valuable guidance for mass production. Overall, these results highlight the significance of analyzing transmission errors through gear modifications in achieving lightweight electric vehicle motors. By addressing transmission vibration and noise issues, this research contributes to overcoming limitations and promoting advancements in power and speed within electric vehicle drive systems.
Reference25 articles.
1. L. Jing, W. Tang, T. Wang, T. Ben, and R. Qu, “Performance analysis of magnetically geared permanent magnet brushless motor for hybrid electric vehicles,” IEEE Transactions on Transportation Electrification, Vol. 8, No. 2, pp. 2874–2883, Jun. 2022, https://doi.org/10.1109/tte.2022.3151681
2. X. Tang, J. Chen, H. Pu, T. Liu, and A. Khajepour, “Double deep reinforcement learning-based energy management for a parallel hybrid electric vehicle with engine start-stop strategy,” IEEE Transactions on Transportation Electrification, Vol. 8, No. 1, pp. 1376–1388, Mar. 2022, https://doi.org/10.1109/tte.2021.3101470
3. N. G. Lee et al., “A study on the improvement of transmission error and tooth load distribution using micro-geometry of compound planetary gear reducer for tractor final driving shaft,” Journal of Drive and Control, Vol. 17, No. 1, pp. 1–12, Mar. 2020, https://doi.org/10.7839/ksfc.2020.17.1.001
4. Z. He, Q. Shi, Y. Wei, J. Zheng, B. Gao, and L. He, “A torque demand model predictive control approach for driving energy optimization of battery electric vehicle,” IEEE Transactions on Vehicular Technology, Vol. 70, No. 4, pp. 3232–3242, Apr. 2021, https://doi.org/10.1109/tvt.2021.3066405
5. C. Panchal, S. Stegen, and J. Lu, “Review of static and dynamic wireless electric vehicle charging system,” Engineering Science and Technology, an International Journal, Vol. 21, No. 5, pp. 922–937, Oct. 2018, https://doi.org/10.1016/j.jestch.2018.06.015