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Multi-criteria optimization model to investigate the energy waste of off-road vehicles utilizing soil bin facility

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  • Taghavifar, Hamid
  • Mardani, Aref
  • Karim-Maslak, Haleh

Abstract

The main objective of the present study was to determine the energy use efficiency of soil-wheel interaction in a soil bin testing facility. Input parameters were velocity at three levels (i.e. 0.7, 1.4 and 2 m/s), tire inflation pressure at three levels (i.e. 100, 200 and 300 kPa) and wheel load at five levels (i.e. 1, 2, 3, 4, and 5 kN) where the output was the energy waste of soil-tire interface. The potential of nonparametric technique of DEA (data envelopment analysis) and hybrid statistical-mathematical modeling approach of RSM (response surface methodology) were assessed in the present investigation. Response surface contours were constructed to determine the optimum conditions for the objective parameter. The present investigation spearheads the practice of DEA and RSM approaches in the optimization of energy waste of off-road vehicles. The findings revealed that RSM with optimized value of 5.7175 J, which corresponds to wheel load of 1 kN, velocity of 0.7 m/s, and tire inflation pressure of 150 kPa, is achievable. Additionally, input-oriented option of DEA resulted in the mean efficiency of 0.4379. Moreover, contribution of each input factor for energy saving was assessed by DEA approach.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:energy:v:73:y:2014:i:c:p:762-770
    DOI: 10.1016/j.energy.2014.06.081
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    References listed on IDEAS

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    Cited by:

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    3. Shafaei, S.M. & Mousazadeh, H., 2023. "Motion energy perspective of tracked locomotion system of autonomous tractor-trailer robot," Energy, Elsevier, vol. 264(C).
    4. 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.
    5. 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.
    6. 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.
    7. 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.

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