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Investigation and modeling of the tractive performance of radial tires using off-road vehicles

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  • Ekinci, Şerafettin
  • Çarman, Kazım
  • Kahramanlı, Humar

Abstract

In order to utilize energy in the most efficient way in off-road vehicles, soil–wheel interaction should be investigated carefully since considerable amount of energy is lost due to tractive performance. In this study, the effects of radial tire on tractive performance at three different tire lug heights, axle loads and inflation pressures were experimentally determined. To obtain sufficient performance data, a new single wheel tester was designed and manufactured. Prior to experiments, properties of stubble field were determined. The tractive efficiency was found to increase with increasing dynamic axle load while decreasing with increasing tire inflation pressure. Dynamic axle load of the tire was the major contributory factor in the traction performance as compared with other independent variables. Seven different Artificial Neural Network and two types of Support Vector Regression models have been designed to predict the tractive efficiency. To evaluate the success of system, various statistical measures such as Mean Absolute Error, Root Mean Squared Error and Coefficient Determination have been used. The results show that the Artificial Neural Network model trained using Levenberg–Marquardt algorithm has produced more accurate results.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:energy:v:93:y:2015:i:p2:p:1953-1963
    DOI: 10.1016/j.energy.2015.10.070
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    References listed on IDEAS

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    1. Çay, Yusuf & Korkmaz, Ibrahim & Çiçek, Adem & Kara, Fuat, 2013. "Prediction of engine performance and exhaust emissions for gasoline and methanol using artificial neural network," Energy, Elsevier, vol. 50(C), pages 177-186.
    2. 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.
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    1. Kaihua Liu & Marco Benetti & Marco Sozzi & Franco Gasparini & Luigi Sartori, 2022. "Soil Compaction under Different Traction Resistance Conditions—A Case Study in North Italy," Agriculture, MDPI, vol. 12(11), pages 1-23, November.

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