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

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.

[1]  Mortaza Aghbashlo,et al.  The use of artificial neural network to predict exergetic performance of spray drying process: A preliminary study , 2012 .

[2]  Abbas Rohani,et al.  Predictions of apple bruise volume using artificial neural network , 2012 .

[3]  K. P. Pandey,et al.  A review on traction prediction equations , 2010 .

[4]  Elif Derya íbeyli.  Recurrent neural networks with composite features for detection of electrocardiographic changes in partial epileptic patients , 2008 .

[5]  Azmi Yahya,et al.  Effect of inflation pressure on motion resistance ratio of a high-lug agricultural tyre , 2006 .

[6]  Jaime Arriagada,et al.  Artificial neural network simulator for SOFC performance prediction , 2002 .

[7]  Jiawei Han,et al.  Data Mining: Concepts and Techniques , 2000 .

[8]  Adem Çiçek,et al.  Prediction of engine performance and exhaust emissions for gasoline and methanol using artificial neural network , 2013 .

[9]  Ahmad Reza Naghsh-Nilchi,et al.  Classification of epileptic states using root-MUSIC and MLPNN , 2009, 2009 17th European Signal Processing Conference.

[10]  Kadir Kavaklioglu,et al.  Modeling and prediction of Turkey’s electricity consumption using Support Vector Regression , 2011 .

[11]  M. G. Bekker,et al.  Theory of Land Locomotion: The Mechanics of Vehicle Mobility , 1962 .

[12]  Hamid Taghavifar,et al.  Applying a supervised ANN (artificial neural network) approach to the prognostication of driven wheel energy efficiency indices , 2014 .

[13]  Kyeong Uk Kim,et al.  Effect of inflation pressure on tractive performance of bias-ply tires , 1997 .

[14]  Sancho Salcedo-Sanz,et al.  Significant wave height estimation using SVR algorithms and shadowing information from simulated and real measured X-band radar images of the sea surface , 2015 .

[15]  Martin Fodslette Møller,et al.  A scaled conjugate gradient algorithm for fast supervised learning , 1993, Neural Networks.

[16]  B. Hanamoto,et al.  The analytical determination of drawbar pull as a function of slip for tracked vehicles in deformable soils , 1961 .

[17]  Eddie C. Burt,et al.  Dynamic load and inflation pressure effects on contact pressures of a forestry forwarder tire , 2004 .

[18]  Elif Derya Übeyli,et al.  Multilayer perceptron neural networks to compute quasistatic parameters of asymmetric coplanar waveguides , 2004, Neurocomputing.

[19]  A. Douik,et al.  Cereal varieties classification using wavelet techniques combined to multi-layer neural networks , 2008, 2008 16th Mediterranean Conference on Control and Automation.

[20]  Wei-Chiang Hong,et al.  Hybrid evolutionary algorithms in a SVR-based electric load forecasting model , 2009 .

[21]  Cengiz Yildiz,et al.  Original paper: The prediction of seedy grape drying rate using a neural network method , 2011 .

[22]  Azmi Yahya,et al.  Net traction ratio prediction for high-lug agricultural tyre , 2006 .

[23]  Kasthurirangan Gopalakrishnan,et al.  Effect of training algorithms on neural networks aided pavement diagnosis , 2010 .

[24]  Alvin C. Bailey,et al.  A Machine for Testing Tractor Tires in Soil Bins , 1980 .

[25]  Itzhak Shmulevich,et al.  The effect of velocity on rigid wheel performance , 1998 .

[26]  Itzhak Shmulevich,et al.  Traction performance of a pushed/pulled drive wheel , 2003 .

[27]  Geoffrey E. Hinton,et al.  Learning representations by back-propagating errors , 1986, Nature.

[28]  R. D. Wismer,et al.  Off-road traction prediction for wheeled vehicles , 1973 .

[29]  S. Tarhan,et al.  Modeling the Torque and Power Requirements of Traction Tires of Horticultural Tractors using Dimensional Analysis , 2004 .

[30]  J. L. Glancey,et al.  Development of a Unique, Mobile, Single Wheel Traction Testing Machine , 1986 .

[31]  Mohammed Abdalla Ayoub,et al.  Application of Resilient Back-Propagation Neural Networks for Generating a Universal Pressure Drop Model in Pipelines , 2011 .

[32]  Majeed Safa,et al.  Modeling Fuel Consumption in Wheat Production Using Neural Networks , 2009 .

[33]  Nor Ashidi Mat Isa,et al.  A Study on Neural Network Training Algorithm for Multiface Detection in Static Images , 2010 .

[34]  Wei-Chiang Hong,et al.  SVR with hybrid chaotic genetic algorithms for tourism demand forecasting , 2011, Appl. Soft Comput..

[35]  K. Carman.  Effect of different tillage systems on soil properties and wheat yield in Middle Anatolia , 1997 .

[36]  A. Okello,et al.  A review of soil strength measurement techniques for prediction of terrain vehicle performance , 1991 .

[37]  Rubiyah Yusof,et al.  Analytical modeling and simulation of I–V characteristics in carbon nanotube based gas sensors using ANN and SVR methods , 2014 .

[38]  Pau Martí,et al.  An artificial neural network approach to the estimation of stem water potential from frequency domain reflectometry soil moisture measurements and meteorological data , 2013 .

[39]  Amir Ahmad Dehghani,et al.  Modeling of wheat soaking using two artificial neural networks (MLP and RBF) , 2009 .

[40]  Hiroshi Nakashima,et al.  An indoor traction measurement system for agricultural tires , 2006 .

[41]  A. C. Bailey,et al.  Load and Inflation Pressure Effects on Tires , 1982 .

[42]  P. Werbos,et al.  Beyond Regression : "New Tools for Prediction and Analysis in the Behavioral Sciences , 1974 .