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

Kai Lu, Jing Liang, Mengnan Liu (), Zhixiong Lu, Jinzhong Shi, Pengfei Xing and Lin Wang
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Kai Lu: State Key Laboratory of Intelligent Agricultural Power Equipment, 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
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

Agriculture, 2024, vol. 14, issue 4, 1-16

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.

Keywords: HMCVT; transmission efficiency prediction; VMD; PSO–BP model; tractor (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2024
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