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Research on Wet Clutch Switching Quality in the Shifting Stage of an Agricultural Tractor Transmission System

Author

Listed:
  • Yuting Chen

    (Department of Vehicle Engineering, Nanjing Forestry University, Nanjing 210037, China)

  • Zhun Cheng

    (Department of Vehicle Engineering, Nanjing Forestry University, Nanjing 210037, China)

  • Yu Qian

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

Abstract

In order to improve the working quality of wet clutch switching in an agricultural tractor, in this paper, we took a power shift system composed of multiple wet clutches as the research object for full-factorial performance measurement, multi-factor analysis of the degree of influence, establishment of a single evaluation index model, formation of a comprehensive evaluation index, and formulation of adjustable factor control strategies. We studied the simulation test platform of an agricultural tractor power transmission system based on the SimulationX software and obtained 225 sets of sample data under a full-use condition. Partial least squares and range analysis were applied to comprehensively analyze the influence of multiple factors on the working quality of wet clutches. In this paper, we proposed a modeling method for a single evaluation index of the wet clutch (combined with polynomial regression and tentative method, the goal is determined in the form of a model with the maximum coefficient of determination) and two control strategy optimization methods for the wet clutch adjustable factors, i.e., Method 1 (integrated optimization) and Method 2 (step-by-step optimization), both methods were based on an improved genetic algorithm. The results showed that oil pressure, flow rate, and load had significant effects on the dynamic load characteristics (the degrees were 0.38, −0.44, and −0.63, respectively, with a negative sign representing an inverse correlation); rate of flow and load had significant effects on speed drop characteristics (the degrees were −0.56 and 0.73, respectively). A multivariate first-order linear model accurately described the dynamic load characteristics ( R 2 = 0.9371). The accuracy of the dynamic load characteristic model was improved by 5.5037% after adding the second-order term and interaction term of oil pressure. The polynomial model containing the first-order oil pressure, first-order flow rate, second-order flow rate, and interaction terms could explain the speed drop characteristics, with an R 2 of 0.9927. If agricultural tractors operate under medium and large loads, the oil pressure and flow rate in their definitional domains should be small and large values, respectively; if operating under small loads, both oil pressure and flow rate should be high. When the wet clutch dynamic load and speed drop characteristics were improved, the sliding friction energy loss also decreased synchronously (the reduction could reach 70.19%).

Suggested Citation

  • Yuting Chen & Zhun Cheng & Yu Qian, 2022. "Research on Wet Clutch Switching Quality in the Shifting Stage of an Agricultural Tractor Transmission System," Agriculture, MDPI, vol. 12(8), pages 1-16, August.
    • Handle: RePEc:gam:jagris:v:12:y:2022:i:8:p:1174-:d:882350
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    References listed on IDEAS

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    1. Zhun Cheng & Yuting Chen & Wenjie Li & Pengfei Zhou & Junhao Liu & Li Li & Wenjuan Chang & Yu Qian, 2022. "Optimization Design Based on I-GA and Simulation Test Verification of 5-Stage Hydraulic Mechanical Continuously Variable Transmission Used for Tractor," Agriculture, MDPI, vol. 12(6), pages 1-13, June.
    2. Xiaomei Xu & Ping Lin, 2021. "Parameter identification of sound absorption model of porous materials based on modified particle swarm optimization algorithm," PLOS ONE, Public Library of Science, vol. 16(5), pages 1-16, May.
    3. Zhun Cheng & Zhixiong Lu, 2022. "Research on Dynamic Load Characteristics of Advanced Variable Speed Drive System for Agricultural Machinery during Engagement," Agriculture, MDPI, vol. 12(2), pages 1-13, January.
    4. Antonina Kalinichenko & Valerii Havrysh & Vasyl Hruban, 2018. "Heat Recovery Systems for Agricultural Vehicles: Utilization Ways and Their Efficiency," Agriculture, MDPI, vol. 8(12), pages 1-18, December.
    5. Zhun Cheng & Huadong Zhou & Zhixiong Lu, 2022. "A Novel 10-Parameter Motor Efficiency Model Based on I-SA and Its Comparative Application of Energy Utilization Efficiency in Different Driving Modes for Electric Tractor," Agriculture, MDPI, vol. 12(3), pages 1-20, March.
    6. Chengcheng Chang & Yanping Zheng & Yang Yu, 2020. "Estimation for Battery State of Charge Based on Temperature Effect and Fractional Extended Kalman Filter," Energies, MDPI, vol. 13(22), pages 1-24, November.
    7. Zhun Cheng & Zhixiong Lu, 2022. "Regression-Based Correction and I-PSO-Based Optimization of HMCVT’s Speed Regulating Characteristics for Agricultural Machinery," Agriculture, MDPI, vol. 12(5), pages 1-18, April.
    8. Hao Wang & Yanping Zheng & Yang Yu, 2021. "Lithium-Ion Battery SOC Estimation Based on Adaptive Forgetting Factor Least Squares Online Identification and Unscented Kalman Filter," Mathematics, MDPI, vol. 9(15), pages 1-12, July.
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    Cited by:

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    2. Mengqiang Zhang & Yurong Tang & Hong Zhang & Haipeng Lan & Hao Niu, 2023. "Parameter Optimization of Spiral Fertilizer Applicator Based on Artificial Neural Network," Sustainability, MDPI, vol. 15(3), pages 1-13, January.

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