• Transactions of the Korean Society of Automotive Engineers
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• v.24 no.5
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• pp.581-587
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• 2016

Braking is a basic and an important safety feature for all vehicles, and the final braking performance of a vehicle is determined by the vehicle's ABS performance and tire performance. However, the combination of excellent ABS and tires will not always ensure good braking performance. This is due to the fact that tire performance has non-linearity and uncertainty in predicting the repeated increase and decrease of wheel slip when activating the ABS, thus increasing the uncertainty of tire performance prediction. Furthermore, existing studies predicted braking performance after using an ABS that used a wheel slip control as a controller, which was different from an actual vehicle's ABS that controlled angular acceleration, therefore causing a decrease in the prediction accuracy of the braking performance. This paper reverse-designed the ABS that controlled angular acceleration based on the information on brake pressure, etc., which were obtained from vehicle tests, and established a braking performance prediction analysis model by combining a multi-body dynamics(MBD) vehicle model and a magic formula(MF) tire model. The established analysis model was verified after comparing it with the results of the braking tests of an actual vehicle. Using this analysis model, this study analyzed the braking effect by vehicle factor, and finally designed a tire that had optimized braking performance. As a result of this study, it was possible to design the MF tire model whose braking performance improved by 9.2 %.

• Journal of Drive and Control
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• v.21 no.2
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• pp.44-52
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• 2024

In this study, we predicted PTO power requirements based on torque predicted by the discrete element method and the multi-body dynamics coupling method. Six different scenarios were simulated to predict PTO power requirements in different soil conditions. The first scenario was a tillage operation on cohesionless soil, and the field was modeled using the Hertz-Mindlin contact model. In the second through sixth scenarios, tillage operations were performed on viscous soils, and the field was represented by the Hertz-Mindlin + JKR model for cohesion. To check the influence of surface energy, a parameter to reproduce cohesion, on the power requirement, a simple regression analysis was performed. The significance and appropriateness of the regression model were checked and found to be acceptable. The study findings are expected to be used in design optimization studies of agricultural machinery by predicting power requirements using the discrete element method and the multi-body dynamics coupling method and analyzing the effect of soil cohesion on the power requirement.