Configurational Comparison of a Binary Logic Transmission Unit Applicable to Agricultural Tractor Hydro-Mechanical Continuously Variable Transmissions and Its Wet Clutch Optimization Design Based on an Improved General Regression Neural Network

Wenjie Li, Zhun Cheng () and Mengchen Yang
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Wenjie Li: College of Automobile and Traffic Engineering, Nanjing Forestry University, Nanjing 210037, China
Zhun Cheng: College of Automobile and Traffic Engineering, Nanjing Forestry University, Nanjing 210037, China
Mengchen Yang: College of Automobile and Traffic Engineering, Nanjing Forestry University, Nanjing 210037, China

Agriculture, 2025, vol. 15, issue 8, 1-34

Abstract: Binary logic transmission (BLT), a stepped transmission system, has been utilized in military vehicles and heavy-duty commercial vehicles due to its high transmission efficiency, strong load-bearing capacity, and compact structure. Its adaptability to agricultural tractor operations is notable. This study modularizes BLT into a binary logic transmission unit (BLT-U) for application in agricultural tractor Hydro-Mechanical Continuously Variable Transmission (HMCVT), optimizing its wet clutch to enhance HMCVT shifting performance. This provides a basis for BLT-U’s application in other transmission systems and subsequent optimization. A wet clutch test bench was employed to validate the modeling approach. The optimal BLT-U configuration was selected using both light/heavy load conditions and subjective–objective evaluation criteria. The WOA improved the spread value in the GRNN algorithm, establishing a GRNN to predict the optimal range for wet clutch design values in BLT-U; the model validation showed an average correlation coefficient of 0.92 for speed curves and an average relative error of 5.58% for dynamic loads. Under light-load conditions, the optimal configuration improved average and maximum scores by 13.38% and 11.53%, respectively, while under heavy-load conditions, the corresponding improvements were 9.38% and 5.86%. Under light-load conditions, the optimized GRNN reduced total relative error by 39.6%, while under heavy-load conditions, it achieved a 61% reduction. This study confirms the rationality of the modeling method, identifies Configuration 1 as optimal, and determines the optimal range for clutch design values under light-load and heavy-load conditions, respectively.

Keywords: agricultural tractor; HMCVT; binary logic transmission unit; improved neural network algorithm; wet clutch optimization design (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: 2025
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