IDEAS home Printed from https://ideas.repec.org/a/gam/jagris/v12y2022i8p1174-d882350.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2077-0472/12/8/1174/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2077-0472/12/8/1174/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Mustafa Ucgul & Chung-Liang Chang, 2023. "Design and Application of Agricultural Equipment in Tillage Systems," Agriculture, MDPI, vol. 13(4), pages 1-3, March.
    2. Li, Xianzhe & Liu, Mengnan & Hu, Chenming & Yan, Xianghai & Zhao, Sixia & Zhang, Mingzhu & Xu, Liyou, 2024. "Parameters collaborative optimization design and innovation verification approach for fuel cell distributed drive electric tractor," Energy, Elsevier, vol. 292(C).
    3. 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.
    4. Ya Li & Xiaohan Chen & Xiaorong Han & Ning Xu & Zhiqiang Zhai & Kai Lu & Youfeng Zhu & Guangming Wang, 2024. "Application of Computer Simulation Technology in the Development of Tractor Transmission Systems," Agriculture, MDPI, vol. 14(9), pages 1-34, September.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. 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.
    2. 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.
    3. Cheng, Zhun, 2023. "High nonlinearity of BEV's stepped automatic transmission design objectives and its optimal solution by a novel ISA-RSA," Energy, Elsevier, vol. 282(C).
    4. Yongqiang Zhang & Zhuang Hu & Min Zhang & Wenting Ba & Ying Wang, 2022. "Emergency Response Resource Allocation in Sparse Network Using Improved Particle Swarm Optimization," IJERPH, MDPI, vol. 19(16), pages 1-11, August.
    5. Mustafa Ucgul & Chung-Liang Chang, 2023. "Design and Application of Agricultural Equipment in Tillage Systems," Agriculture, MDPI, vol. 13(4), pages 1-3, March.
    6. 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.
    7. Zhun Cheng & Zhixiong Lu, 2021. "Research on Load Disturbance Based Variable Speed PID Control and a Novel Denoising Method Based Effect Evaluation of HST for Agricultural Machinery," Agriculture, MDPI, vol. 11(10), pages 1-18, October.
    8. Valerii Havrysh & Antonina Kalinichenko & Grzegorz Mentel & Tadeusz Olejarz, 2020. "Commercial Biogas Plants: Lessons for Ukraine," Energies, MDPI, vol. 13(10), pages 1-24, May.
    9. Oleksandr Cherednichenko & Valerii Havrysh & Vyacheslav Shebanin & Antonina Kalinichenko & Grzegorz Mentel & Joanna Nakonieczny, 2020. "Local Green Power Supply Plants Based on Alcohol Regenerative Gas Turbines: Economic and Environmental Aspects," Energies, MDPI, vol. 13(9), pages 1-20, May.
    10. Chin-Hung Kuan & Yungho Leu & Wen-Shin Lin & Chien-Pang Lee, 2022. "The Estimation of the Long-Term Agricultural Output with a Robust Machine Learning Prediction Model," Agriculture, MDPI, vol. 12(8), pages 1-15, July.
    11. Rong Ran & Lei Hua & Tingrou Li & Yejing Chen & Junfu Xiao, 2023. "Why Have China’s Poverty Eradication Policy Resulted in the Decline of Arable Land in Poverty-Stricken Areas?," Land, MDPI, vol. 12(10), pages 1-18, September.
    12. Antonina Kalinichenko & Valerii Havrysh & Igor Atamanyuk, 2019. "The Acceptable Alternative Vehicle Fuel Price," Energies, MDPI, vol. 12(20), pages 1-20, October.
    13. Daniel Słyś & Kamil Pochwat & Dorian Czarniecki, 2020. "An Analysis of Waste Heat Recovery from Wastewater on Livestock and Agriculture Farms," Resources, MDPI, vol. 9(1), pages 1-19, January.
    14. Kai Lu & Zhixiong Lu, 2022. "Analysis of HMCVT Shift Quality Based on the Engagement Characteristics of Wet Clutch," Agriculture, MDPI, vol. 12(12), pages 1-15, November.
    15. Wanming Pan & Junkang Li & Guotao Zhang & Le Zhou & Ming Tu, 2022. "Multi-Objective Optimization of Organic Rankine Cycle (ORC) for Tractor Waste Heat Recovery Based on Particle Swarm Optimization," Energies, MDPI, vol. 15(18), pages 1-24, September.
    16. Pan, Yingjiu & Fang, Wenpeng & Ge, Zhenzhen & Li, Cheng & Wang, Caifeng & Guo, Baochang, 2024. "A hybrid on-line approach for predicting the energy consumption of electric buses based on vehicle dynamics and system identification," Energy, Elsevier, vol. 290(C).
    17. Meng Yang & Xiaoxu Sun & Xiaoting Deng & Zhixiong Lu & Tao Wang, 2023. "Extrapolation of Tractor Traction Resistance Load Spectrum and Compilation of Loading Spectrum Based on Optimal Threshold Selection Using a Genetic Algorithm," Agriculture, MDPI, vol. 13(6), pages 1-20, May.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jagris:v:12:y:2022:i:8:p:1174-:d:882350. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.