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Research on Transmission Efficiency Prediction of Heavy-Duty Tractors HMCVT Based on VMD and PSO–BP

Author

Listed:
  • Kai Lu

    (State Key Laboratory of Intelligent Agricultural Power Equipment, Luoyang 471003, China
    Luoyang Tractor Research Institute Co., Ltd., Luoyang 471003, China)

  • Jing Liang

    (School of Electrical and Information Engineering, Zhengzhou University, Zhengzhou 450001, China)

  • Mengnan Liu

    (State Key Laboratory of Intelligent Agricultural Power Equipment, Luoyang 471003, China
    Luoyang Tractor Research Institute Co., Ltd., Luoyang 471003, China)

  • Zhixiong Lu

    (College of Engineering, Nanjing Agricultural University, Nanjing 210031, China)

  • Jinzhong Shi

    (Luoyang Tractor Research Institute Co., Ltd., Luoyang 471003, China)

  • Pengfei Xing

    (Luoyang Tractor Research Institute Co., Ltd., Luoyang 471003, China)

  • Lin Wang

    (Luoyang Tractor Research Institute Co., Ltd., Luoyang 471003, China)

Abstract

Transmission efficiency is a key characteristic of Hydro-mechanical Continuously Variable Transmission (HMCVT), which is related to the performance of heavy-duty tractors. Predicting the HMCVT transmission efficiency is beneficial for the real-time adjustment of transmission ratio during heavy-duty tractor operations, so as to obtain better performance. Aiming at the problems of accurate method, low accuracy, and high noise in the prediction of HMCVT transmission efficiency, this paper proposes a method based on Variational Mode Decomposition (VMD), Particle Swarm Optimization (PSO), and Back Propagation (BP) neural networks to improve the quality of transmission efficiency prediction. Firstly, a simple theoretical model was established to obtain the influencing factors of transmission efficiency. Then, based on these factors, the transmission efficiency was tested on the bench under multiple conditions and the influence degree of each factor on transmission efficiency was divided using Partial Least Squares (PLS) method. Finally, the VMD method was used to denoise the test data, and a BP model, which was improved using the PSO method, was established to predict the processed data. The results showed that transmission efficiency of HMCVT is most affected by output speed, followed by power, and least by input speed. The VMD method can accurately extract effective signals and noise signals from the original data, and reconstruct signals, reducing the noise proportion. Using three conditions, the prediction regression accuracy of the PSO–BP model is 7.02%, 7.88%, and 9.26% higher than that of the BP model, respectively. In the three prediction experiments, the maximum differences in the MAE, the MAPE, and the RMSE of the PSO–BP model are 0.002, 0.463%, and 0.004, respectively, which are 0.006, 0.796%, and 0.003 lower than those of the BP model.

Suggested Citation

  • Kai Lu & Jing Liang & Mengnan Liu & Zhixiong Lu & Jinzhong Shi & Pengfei Xing & Lin Wang, 2024. "Research on Transmission Efficiency Prediction of Heavy-Duty Tractors HMCVT Based on VMD and PSO–BP," Agriculture, MDPI, vol. 14(4), pages 1-16, March.
  • Handle: RePEc:gam:jagris:v:14:y:2024:i:4:p:539-:d:1366121
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