• Smart Media Journal
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• v.11 no.5
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• pp.75-81
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• 2022

In order to respond to carbon neutrality and climate change in agriculture, agricultural machinery, which has been developed centered on internal combustion engines, needs to be converted to an electric-based technology that does not emit greenhouse gases. In this study, simulations for electric UTV suspension design were performed to reduce vibration and shock of electric UTV for agricultural use and to improve driving stability and control performance of the vehicle. The simulation was performed by dividing the tolerance load of the vehicle body and the loaded load state. The range of motion of the suspension spring of UTV is within 30% of the range of motion under condition B under tolerance, the displacement of the UTV suspension with full load is reduced from 264mm to 121mm, and the damping speed is 260mm/s to 300mm/s that it can be seen that the range of motion is within 60%. Suspension design of electric UTV for multi-purpose agricultural work is a very important factor for maintaining agricultural work ability in towing work such as tillage as well as driving and terrain adaptation. The results of this study can be usefully used to determine the spring parameters with the appropriate damping range so that the electric UTV can be used for various agricultural tasks.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.18 no.8
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• pp.8-17
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• 2019

In this study, a model reduction technique was used to develop a precise noise and vibration prediction model for the individual components of a driveline system. The dynamic reduced-order model generated by the Craig-Bampton method was applied to perform dynamic analysis of an electric agricultural power cart. The natural frequency and acceleration response results were analyzed according to the different number of dominant sub-structural modes contained in the reduced-order models. Through the analysis results, it was confirmed that a sufficient number of dominant sub-structures to satisfy the operating conditions should be selected to construct an optimal reduced-order model.