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Optimization of the Quality of the Automatic Transmission Shift and the Power Transmission Characteristics

Author

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  • Qinguo Zhang

    (Ningbo Research Institute, Zhejiang University, Ningbo 315100, China
    State Key Laboratory of Fluid Power and Mechatronic Systems, Zhejiang University, Hangzhou 310027, China)

  • Xiaojian Liu

    (Ningbo Research Institute, Zhejiang University, Ningbo 315100, China
    State Key Laboratory of Fluid Power and Mechatronic Systems, Zhejiang University, Hangzhou 310027, China)

Abstract

We have established a simulation platform for the machine–electro-hydraulic coupling system of the transmission system and the control system to study the root causes of the problems of large shifting impact and slow change of the machine tool transmission system. The dynamic analysis of the gear shift work of the gearbox was carried out, and the main factors affecting its shift instability were studied. With the impact and sliding power as the optimization goals, the shift quality is optimized based on the multi-objective genetic algorithm. Through the shift experiment, it was found that the power interruption phenomenon during the shift process was eliminated after optimization, and the quality of the shift was improved. Simulated planetary row wheel gear meshing force was found in the same gear, and the second planetary row gear meshing force was the largest among the planetary rows. The stress of the node near the top of the tooth is greater than the stress of the node near the node circle and the root of the tooth, and the two sides of the tooth top are relatively larger than the intermediate stress. The dynamic simulation model of the planetary gearbox system with rigid–soft hybrid can obtain the stress distribution of the solar wheel at the maximum impact moment and the stationary stage, as well as the dynamic stress of the key nodes of the solar wheel, which lays the foundation for the fatigue strength and life prediction of the gear system.

Suggested Citation

  • Qinguo Zhang & Xiaojian Liu, 2022. "Optimization of the Quality of the Automatic Transmission Shift and the Power Transmission Characteristics," Energies, MDPI, vol. 15(13), pages 1-15, June.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:13:p:4672-:d:848013
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    1. Neshat, Mehdi & Nezhad, Meysam Majidi & Abbasnejad, Ehsan & Mirjalili, Seyedali & Groppi, Daniele & Heydari, Azim & Tjernberg, Lina Bertling & Astiaso Garcia, Davide & Alexander, Bradley & Shi, Qinfen, 2021. "Wind turbine power output prediction using a new hybrid neuro-evolutionary method," Energy, Elsevier, vol. 229(C).
    2. Xue, Jie & Yip, Tsz Leung & Wu, Bing & Wu, Chaozhong & van Gelder, P.H.A.J.M., 2021. "A novel fuzzy Bayesian network-based MADM model for offshore wind turbine selection in busy waterways: An application to a case in China," Renewable Energy, Elsevier, vol. 172(C), pages 897-917.
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    1. Xihui Chen & Xinhui Shi & Chang Liu & Wei Lou, 2022. "Research on a Denoising Method of Vibration Signals Based on IMRSVD and Effective Component Selection," Energies, MDPI, vol. 15(23), pages 1-21, November.

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