IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v90y2015ip1p691-700.html
   My bibliography  Save this article

How to select air pressures in the tires of MFWD (mechanical front-wheel drive) tractor to minimize fuel consumption for the case of reasonable wheel slip

Author

Listed:
  • Janulevičius, Algirdas
  • Damanauskas, Vidas

Abstract

In agriculture, tractor is the most fuel-consuming machine. The research indicates that 20–55% of available tractor power is lost in the process of interaction between tires and soil surface. Tire pressure and vertical wheel load are both easily managed parameters, which play a significant role in controlling the slip, the traction force and the fuel consumption of a tractor. The purpose of the research was to base theoretically and experimentally the tire pressures that ensure a minimum kinematic mismatch between the drive wheels for MFWD (mechanical front-wheel drive) tractor, and thereby reduce the fuel consumption at a reasonable tire slip. Close to one coefficient of kinematic mismatch between the front and the rear wheels was observed when combinations of pressures in the rear/front tires were made, respectively: 150/70, 190/110, and 230/115 kPa. When tractor (MFWD) was driving on a hard road surface without thrust load and with above mentioned tire pressure combinations, the lowest fuel consumption was reached, namely, in the range from 3.75 to 3.8 L h−1.

Suggested Citation

  • Janulevičius, Algirdas & Damanauskas, Vidas, 2015. "How to select air pressures in the tires of MFWD (mechanical front-wheel drive) tractor to minimize fuel consumption for the case of reasonable wheel slip," Energy, Elsevier, vol. 90(P1), pages 691-700.
    • Handle: RePEc:eee:energy:v:90:y:2015:i:p1:p:691-700
    DOI: 10.1016/j.energy.2015.07.099
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0360544215009949
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.energy.2015.07.099?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Van linden, Veerle & Herman, Lieve, 2014. "A fuel consumption model for off-road use of mobile machinery in agriculture," Energy, Elsevier, vol. 77(C), pages 880-889.
    2. Taghavifar, Hamid & Mardani, Aref, 2015. "Evaluating the effect of tire parameters on required drawbar pull energy model using adaptive neuro-fuzzy inference system," Energy, Elsevier, vol. 85(C), pages 586-593.
    3. Taghavifar, Hamid & Mardani, Aref & Karim-Maslak, Haleh, 2014. "Multi-criteria optimization model to investigate the energy waste of off-road vehicles utilizing soil bin facility," Energy, Elsevier, vol. 73(C), pages 762-770.
    4. Taghavifar, Hamid & Mardani, Aref, 2014. "Applying a supervised ANN (artificial neural network) approach to the prognostication of driven wheel energy efficiency indices," Energy, Elsevier, vol. 68(C), pages 651-657.
    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. Zhang, Sheng-li & Wen, Chang-kai & Ren, Wen & Luo, Zhen-hao & Xie, Bin & Zhu, Zhong-xiang & Chen, Zhong-ju, 2023. "A joint control method considering travel speed and slip for reducing energy consumption of rear wheel independent drive electric tractor in ploughing," Energy, Elsevier, vol. 263(PD).
    2. Wang, Xudong & Wang, Qi & Wang, Wei & Cui, Yongjie & Song, Yuling, 2023. "Performance investigation of piezoelectric-mechanical electromagnetic compound vibration energy harvester for electric tractor," Energy, Elsevier, vol. 281(C).
    3. Mohammad Askari & Yousef Abbaspour-Gilandeh & Ebrahim Taghinezhad & Ahmed Mohamed El Shal & Rashad Hegazy & Mahmoud Okasha, 2021. "Applying the Response Surface Methodology (RSM) Approach to Predict the Tractive Performance of an Agricultural Tractor during Semi-Deep Tillage," Agriculture, MDPI, vol. 11(11), pages 1-14, October.
    4. Moinfar, AbdolMajid & Shahgholi, Gholamhossein & Gilandeh, Yousef Abbaspour & Gundoshmian, Tarahom Mesri, 2020. "The effect of the tractor driving system on its performance and fuel consumption," Energy, Elsevier, vol. 202(C).
    5. Vilma Naujokienė & Kristina Lekavičienė & Egidijus Šarauskis & Asta Bendoraitytė, 2022. "Using a Soil Bioregeneration Approach to Reduce Soil Compaction and Financial Costs of Planting Winter Wheat and Rapeseed," Agriculture, MDPI, vol. 12(5), pages 1-13, May.
    6. Šarauskis, Egidijus & Vaitauskienė, Kristina & Romaneckas, Kęstutis & Jasinskas, Algirdas & Butkus, Vidmantas & Kriaučiūnienė, Zita, 2017. "Fuel consumption and CO2 emission analysis in different strip tillage scenarios," Energy, Elsevier, vol. 118(C), pages 957-968.
    7. Wen, Chang-kai & Zhang, Sheng-li & Xie, Bin & Song, Zheng-he & Li, Tong-hui & Jia, Fang & Han, Jian-gang, 2022. "Design and verification innovative approach of dual-motor power coupling drive systems for electric tractors," Energy, Elsevier, vol. 247(C).
    8. Rudolf Abrahám & Radoslav Majdan & Katarína Kollárová & Zdenko Tkáč & Štefan Hajdu & Ľubomír Kubík & Soňa Masarovičová, 2022. "Fatigue Analysis of Spike Segment of Special Tractor Wheels in Terms of Design Improvement for Chernozem Soil," Agriculture, MDPI, vol. 12(4), pages 1-17, March.
    9. Zhenhao Luo & Jihang Wang & Jing Wu & Shengli Zhang & Zhongju Chen & Bin Xie, 2023. "Research on a Hydraulic Cylinder Pressure Control Method for Efficient Traction Operation in Electro-Hydraulic Hitch System of Electric Tractors," Agriculture, MDPI, vol. 13(8), pages 1-18, 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. Taghavifar, Hamid & Mardani, Aref & Hosseinloo, Ashkan Haji, 2015. "Appraisal of artificial neural network-genetic algorithm based model for prediction of the power provided by the agricultural tractors," Energy, Elsevier, vol. 93(P2), pages 1704-1710.
    2. Taghavifar, Hamid & Mardani, Aref, 2015. "Evaluating the effect of tire parameters on required drawbar pull energy model using adaptive neuro-fuzzy inference system," Energy, Elsevier, vol. 85(C), pages 586-593.
    3. Taghavifar, Hamid & Mardani, Aref & Karim Maslak, Haleh, 2015. "A comparative study between artificial neural networks and support vector regression for modeling of the dissipated energy through tire-obstacle collision dynamics," Energy, Elsevier, vol. 89(C), pages 358-364.
    4. Shafaei, S.M. & Mousazadeh, H., 2023. "Motion energy perspective of tracked locomotion system of autonomous tractor-trailer robot," Energy, Elsevier, vol. 264(C).
    5. Chetan Badgujar & Sanjoy Das & Dania Martinez Figueroa & Daniel Flippo, 2023. "Application of Computational Intelligence Methods in Agricultural Soil–Machine Interaction: A Review," Agriculture, MDPI, vol. 13(2), pages 1-39, January.
    6. Rossi, Francesco & Velázquez, David, 2015. "A methodology for energy savings verification in industry with application for a CHP (combined heat and power) plant," Energy, Elsevier, vol. 89(C), pages 528-544.
    7. Hegazy, Omar & Barrero, Ricardo & Van den Bossche, Peter & El Baghdadi, Mohamed & Smekens, Jelle & Van Mierlo, Joeri & Vriens, Wouter & Bogaerts, Bruno, 2016. "Modeling, analysis and feasibility study of new drivetrain architectures for off-highway vehicles," Energy, Elsevier, vol. 109(C), pages 1056-1074.
    8. Huysveld, Sophie & Van Meensel, Jef & Van linden, Veerle & De Meester, Steven & Peiren, Nico & Muylle, Hilde & Dewulf, Jo & Lauwers, Ludwig, 2017. "Communicative farm-specific diagnosis of potential simultaneous savings in costs and natural resource demand of feed on dairy farms," Agricultural Systems, Elsevier, vol. 150(C), pages 34-45.
    9. Cen, Zhongpei & Wang, Jun, 2019. "Crude oil price prediction model with long short term memory deep learning based on prior knowledge data transfer," Energy, Elsevier, vol. 169(C), pages 160-171.
    10. Wang, H., 2015. "A generalized MCDA–DEA (multi-criterion decision analysis–data envelopment analysis) approach to construct slacks-based composite indicator," Energy, Elsevier, vol. 80(C), pages 114-122.
    11. Moinfar, AbdolMajid & Shahgholi, Gholamhossein & Gilandeh, Yousef Abbaspour & Gundoshmian, Tarahom Mesri, 2020. "The effect of the tractor driving system on its performance and fuel consumption," Energy, Elsevier, vol. 202(C).
    12. Abbas, Ahmed K. & Bashikh, Ali A. & Abbas, Hayder & Mohammed, Haider Q., 2019. "Intelligent decisions to stop or mitigate lost circulation based on machine learning," Energy, Elsevier, vol. 183(C), pages 1104-1113.
    13. Taghavifar, Hadi & Khalilarya, Shahram & Jafarmadar, Samad, 2014. "Diesel engine spray characteristics prediction with hybridized artificial neural network optimized by genetic algorithm," Energy, Elsevier, vol. 71(C), pages 656-664.
    14. Ekinci, Şerafettin & Çarman, Kazım & Kahramanlı, Humar, 2015. "Investigation and modeling of the tractive performance of radial tires using off-road vehicles," Energy, Elsevier, vol. 93(P2), pages 1953-1963.
    15. Mardani, Aref & Taghavifar, Hamid, 2016. "An overview on energy inputs and environmental emissions of grape production in West Azerbayjan of Iran," Renewable and Sustainable Energy Reviews, Elsevier, vol. 54(C), pages 918-924.
    16. Taghavifar, Hamid & Mardani, Aref & Karim-Maslak, Haleh, 2014. "Multi-criteria optimization model to investigate the energy waste of off-road vehicles utilizing soil bin facility," Energy, Elsevier, vol. 73(C), pages 762-770.
    17. Zhang, Sheng-li & Wen, Chang-kai & Ren, Wen & Luo, Zhen-hao & Xie, Bin & Zhu, Zhong-xiang & Chen, Zhong-ju, 2023. "A joint control method considering travel speed and slip for reducing energy consumption of rear wheel independent drive electric tractor in ploughing," Energy, Elsevier, vol. 263(PD).
    18. Kun-Jung Kim & Kee-Ho Yu, 2020. "Multidisciplinary Design Optimization for a Solar-Powered Exploration Rover Considering the Restricted Power Requirement," Energies, MDPI, vol. 13(24), pages 1-28, December.
    19. Li, Tianyu & Huang, Lingtao & Liu, Huiying, 2019. "Energy management and economic analysis for a fuel cell supercapacitor excavator," Energy, Elsevier, vol. 172(C), pages 840-851.
    20. Sueyoshi, Toshiyuki & Yuan, Yan & Goto, Mika, 2017. "A literature study for DEA applied to energy and environment," Energy Economics, Elsevier, vol. 62(C), pages 104-124.

    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:eee:energy:v:90:y:2015:i:p1:p:691-700. 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: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/energy .

    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.