IDEAS home Printed from https://ideas.repec.org/a/gam/jagris/v12y2022i5p580-d798683.html
   My bibliography  Save this article

Regression-Based Correction and I-PSO-Based Optimization of HMCVT’s Speed Regulating Characteristics for Agricultural Machinery

Author

Listed:
  • Zhun Cheng

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

  • Zhixiong Lu

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

Abstract

To improve the speed regulating characteristics of continuously variable transmission for agricultural machinery, in order to meet the engineering and technical requirements of precision agriculture and intelligent agriculture, the paper researches and proposes a method combining the analysis of speed regulating characteristics, regression-based correction, and the improved particle swarm optimization (I-PSO) algorithm. First, the paper analyzes the degree of deviation between the linearization degree and the theoretical value of the speed regulating characteristics of the variable-pump constant-motor system of agricultural machinery according to the measurement results of the bench test. Next, the paper corrects the speed regulating characteristics and compares the regression results based on four models. Finally, the paper proposes a design method for the expected speed regulating characteristics of agricultural machinery and it completes the optimization of speed regulating characteristics and the matching of transmission parameters with the I-PSO algorithm. Results indicate that the speed regulating characteristics of the variable-pump constant-motor system show high linearization (with a coefficient of determination of 0.9775). The theoretical and measured values of the speed regulating characteristics have a certain deviation (with a coefficient of determination of 0.8934). Therefore, correcting the speed regulating characteristics of the variable-pimp constant-motor system is highly necessary. In addition, the second reciprocal function model proposed has the highest correction precision (with a coefficient of determination of 0.9978). The I-PSO algorithm is applicable to the design and application of hydro-mechanical continuously variable transmission (HMCVT) for agricultural machinery. The new method proposed can improve the HMCVT’s speed regulating characteristics efficiently and quickly. It also ensures that the speed regulating characteristics are highly consistent with the expected design characteristics (with a mean error of 1.73%). Thus, the research offers a theoretical direction and design basis for the research and development of continuously variable transmission units in agricultural machinery.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jagris:v:12:y:2022:i:5:p:580-:d:798683
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2077-0472/12/5/580/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2077-0472/12/5/580/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Zhijie Liu & Guoqiang Zhang & Guoping Chu & Hanlin Niu & Yazhou Zhang & Fuzeng Yang, 2021. "Design Matching and Dynamic Performance Test for an HST-Based Drive System of a Hillside Crawler Tractor," Agriculture, MDPI, vol. 11(5), pages 1-21, May.
    2. Yu Qian & Zhun Cheng & Zhixiong Lu & Atila Bueno, 2021. "Bench Testing and Modeling Analysis of Optimum Shifting Point of HMCVT," Complexity, Hindawi, vol. 2021, pages 1-13, May.
    3. Zhun Cheng & Zhixiong Lu, 2018. "A Novel Efficient Feature Dimensionality Reduction Method and Its Application in Engineering," Complexity, Hindawi, vol. 2018, pages 1-14, October.
    4. 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.
    5. Volodymyr Bulgakov & Aivars Aboltins & Semjons Ivanovs & Ivan Holovach & Volodymyr Nadykto & Hristo Beloev, 2020. "A Mathematical Model of Plane-Parallel Movement of the Tractor Aggregate Modular Type," Agriculture, MDPI, vol. 10(10), pages 1-22, October.
    6. 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.
    7. Junyan Wang & Changgao Xia & Xin Fan & Junyu Cai, 2020. "Research on Transmission Characteristics of Hydromechanical Continuously Variable Transmission of Tractor," Mathematical Problems in Engineering, Hindawi, vol. 2020, pages 1-14, August.
    8. Jie Tian & Jun Tong & Shi Luo, 2018. "Differential Steering Control of Four-Wheel Independent-Drive Electric Vehicles," Energies, MDPI, vol. 11(11), pages 1-18, October.
    9. 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.
    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. 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.
    3. 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.
    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.

    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 & 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.
    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. 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.
    5. 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.
    6. Valerii Havrysh & Antonina Kalinichenko & Grzegorz Mentel & Tadeusz Olejarz, 2020. "Commercial Biogas Plants: Lessons for Ukraine," Energies, MDPI, vol. 13(10), pages 1-24, May.
    7. Zhaorui Xu & Jiabo Wang & Yanqiang Yang & Guangming Wang & Shenghui Fu, 2023. "Optimization of Shifting Quality for Hydrostatic Power-Split Transmission with Single Standard Planetary Gear Set," Agriculture, MDPI, vol. 13(9), pages 1-18, August.
    8. 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.
    9. Xiaoting Deng & Hailong Sun & Zhixiong Lu & Zhun Cheng & Yuhui An & Hao Chen, 2022. "Research on Dynamic Analysis and Experimental Study of the Distributed Drive Electric Tractor," Agriculture, MDPI, vol. 13(1), pages 1-21, December.
    10. Antonina Kalinichenko & Valerii Havrysh & Igor Atamanyuk, 2019. "The Acceptable Alternative Vehicle Fuel Price," Energies, MDPI, vol. 12(20), pages 1-20, October.
    11. Noemí DeCastro-García & Ángel Luis Muñoz Castañeda & David Escudero García & Miguel V. Carriegos, 2019. "Effect of the Sampling of a Dataset in the Hyperparameter Optimization Phase over the Efficiency of a Machine Learning Algorithm," Complexity, Hindawi, vol. 2019, pages 1-16, February.
    12. 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.
    13. Thanh Vo-Duy & Minh C. Ta & Bảo-Huy Nguyễn & João Pedro F. Trovão, 2020. "Experimental Platform for Evaluation of On-Board Real-Time Motion Controllers for Electric Vehicles," Energies, MDPI, vol. 13(23), pages 1-28, December.
    14. SongSong Sun, 2020. "A new stress field intensity model and its application in component high cycle fatigue research," PLOS ONE, Public Library of Science, vol. 15(7), pages 1-20, July.
    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. 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.
    17. 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.

    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:5:p:580-:d:798683. 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.