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Applying a supervised ANN (artificial neural network) approach to the prognostication of driven wheel energy efficiency indices

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  • Taghavifar, Hamid
  • Mardani, Aref

Abstract

This paper examines the prediction of energy efficiency indices of driven wheels (i.e. traction coefficient and tractive power efficiency) as affected by wheel load, slippage and forward velocity at three different levels with three replicates to form a total of 162 data points. The pertinent experiments were carried out in the soil bin testing facility. A feed-forward ANN (artificial neural network) with standard BP (back propagation) algorithm was practiced to construct a supervised representation to predict the energy efficiency indices of driven wheels. It was deduced, in view of the statistical performance criteria (i.e. MSE (mean squared error) and R2), that a supervised ANN with 3-8-10-2 topology and Levenberg–Marquardt training algorithm represented the optimal model. Modeling implementations indicated that ANN is a powerful technique to prognosticate the stochastic energy efficiency indices as affected by soil-wheel interactions with MSE of 0.001194 and R2 of 0.987 and 0.9772 for traction coefficient and tractive power efficiency. It was found that traction coefficient and tractive power efficiency increase with increased slippage. A similar trend is valid for the influence of wheel load on the objective parameters. Wherein increase of velocity led to an increment of tractive power efficiency, velocity had no significant effect on traction coefficient.

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  • 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.
    • Handle: RePEc:eee:energy:v:68:y:2014:i:c:p:651-657
    DOI: 10.1016/j.energy.2014.01.048
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    1. Liang, Aizhen & McLaughlin, Neil B. & Ma, B.L. & Gregorich, Edward G. & Morrison, Malcolm J. & Burtt, Stephen D. & Patterson, B. Scott & Evenson, Lynne I., 2013. "Changes in mouldboard plough draught and tractor fuel consumption on continuous corn after 18 years of organic and inorganic N amendments," Energy, Elsevier, vol. 52(C), pages 89-95.
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    Cited by:

    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 & 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.
    3. 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.
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    6. 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.
    7. 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).
    8. Taghavifar, Hamid & Mardani, Aref & Hosseinloo, Ashkan Haji, 2015. "Experimental analysis of the dissipated energy through tire-obstacle collision dynamics," Energy, Elsevier, vol. 91(C), pages 573-578.
    9. 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.
    10. 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.
    11. 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.
    12. 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.
    13. 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.
    14. 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.

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