• Korean Journal of Agricultural Science
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• v.50 no.4
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• pp.773-784
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• 2023

In this study, we developed a dynamic model and steering controller model for an autonomous tractor and evaluated their performance. The traction force was measured using a 6-component load cell, and the rotational speed of the wheels was monitored using proximity sensors installed on the axles. Torque sensors were employed to measure the axle torque. The PI (proportional integral) controller's coefficients were determined using the trial-error method. The coefficient of the P varied in the range of 0.1 - 0.5 and the I coefficient was determined in 3 increments of 0.01, 0.05, and 0.1. To validate the simulation model, we conducted RMS (root mean square) comparisons between the measured data of axle torque and the simulation results. The performance of the steering controller model was evaluated by analyzing the damping ratio calculated with the first and second overshoots. The average front and rear axle torque ranged from 3.29 - 3.44 and 6.98 - 7.41 kNm, respectively. The average rotational speed of the wheel ranged from 29.21 - 30.55 rpm at the front, and from 21.46 - 21.63 rpm at the rear. The steering controller model exhibited the most stable control performance when the coefficients of P and I were set at 0.5 and 0.01, respectively. The RMS analysis of the axle torque results indicated that the left and right wheel errors were approximately 1.52% and 2.61% (at front) and 7.45% and 7.28% (at rear), respectively.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.29 no.2 s.233
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• pp.270-276
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• 2005

This paper develops the flexible computational model of a heavy truck by interfacing the frame modeled as a flexible body to the heavy truck's computational model composed of rigid bodies. The frame is modeled by the finite element method. Three torsional modes and three bending modes of the frame are considered for the interface of the heavy truck's computational model. The actual vehicle test is conducted off road with a velocity of 20km/h. The vertical accelerations at the cab and front axle are measured in the test. For the verification of the developed computational model, the measured vertical acceleration profiles are compared with the simulation results of the heavy truck's flexible computational model. E grade irregular road profile of ISO is used as an excitation input in the simulation. The verified flexible computational model is used to compare the vibration characteristics of a front suspension system having a multi-leaf spring and that having a tapered leaf spring. The comparison results show that the front suspension having a tapered leaf spring has a higher vertical acceleration at the front axle but a lower vertical acceleration at the cab than the suspension system having a multi-leaf spring.

• 한국도로학회:학술대회논문집
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• 2006.10a
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• pp.487-492
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• 2006

• Korean Journal of Agricultural Science
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• v.51 no.3
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• pp.315-329
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• 2024

This study was conducted for simulation model development of an electric all-wheel-drive vehicle to adapt the agricultural machinery. Data measurement system was installed on a four-wheel electric driven vehicle using proximity sensor, torque-meter, global positioning system (GPS) and data acquisition (DAQ) device. Axle torque and rotational speed were measured using a torque-meter and a proximity sensor. Driving test was performed on an upland field at a speed of 7 km·h^-1. Simulation model was developed using a multi-body dynamics software, and tire properties were measured and calculated to reflect the similar road conditions. Measured and simulated data were compared to validate the developed simulation model performance, and axle rotational speed was selected as simulation input data and axle torque and power were selected as simulation output data. As a result of driving performance, an average axle rotational speed was 115 rpm for each wheel. Average axle torque and power were 4.50, 4.21, 4.04, and 3.22 Nm and 53.42, 50.56, 47.34, and 38.07 W on front left, front right, rear left, and rear right wheel, respectively. As a result of simulation driving, average axle torque and power were 4.51, 3.9, 4.16, and 3.32 Nm and 55.79, 48.11, 51.62, and 41.2 W on front left, front right, rear left, and rear right wheel, respectively. Absolute error of axle torque was calculated as 0.22, 7.36, 2.97, and 3.11% on front left, front right, rear left, rear right wheel, respectively, and absolute error of axle power was calculated as 4.44, 4.85, 9.04, and 8.22% on front left, front right, rear left, and rear right wheel, respectively. As a result of absolute error, it was shown that developed simulation model can be used for driving performance prediction of electric driven vehicle. Only straight driving was considered in this study, and various road and driving conditions would be considered in future study.

• Journal of Biosystems Engineering
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• v.43 no.1
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• pp.1-13
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• 2018

Purpose: The development of compact tractors that can be used in dry fields, greenhouses, and orchards for pest control, weeding, transportation, and harvesting is necessary. The development and performance evaluation of power transmission units are very important when it comes to tractor development. This study evaluates the performance of a driving power transmission unit of a 50 kW multi-purpose narrow tractor. Methods: The performance of the transmission and forward-reverse clutch, which are the main components of the driving power transmission unit of multi-purpose narrow tractors, was evaluated herein. The transmission performance was evaluated in terms of power transmission efficiency, noise, and axle load, while the forward-reverse clutch performance was evaluated in terms of durability. The transmission's power transmission efficiency accounts for the measurement of transmission losses, which occur in the transmission's gear, bearing, and oil seal. The motor's power was input in the transmission's input shaft. The rotational speed and torque were measured in the final output shaft. The noise was measured at each speed level after installing a microphone on the left, right, and upper sides. The axle load test was performed through a continuous equilibrium load test, in which a constant load was continuously applied. The forward-reverse clutch performance was calculated using the engine torque to axle torque ratio with the assembled engine and transmission. Results: The loss of power in the transmission efficiency test of the driving power unit was 6.0-9.7 kW based on all gear steps. This loss of horsepower was equal to 11-18% of the input power (52 kW). The transmission efficiency of the driving power unit was 81.5-89.0%. The noise of the driving power unit was 50-57 dB at 800 rpm, 70-77 dB at 1600 rpm, and 76-83 dB at 2400 rpm. The axle load test verified that the input torque and axle revolutions were constant. The results of the forward-reverse clutch performance test revealed that hydraulic pressure and torque changes were stably maintained when moving forward or backward, and its operation met the hydraulic design standards. Conclusions: When comprehensively examined, these research results were similar to the main driving power transmission systems from USA and Japan in terms of performance. Based on these results, tractor prototypes are expected to be created and supplied to farmhouses after going through sufficient in-situ adaptability tests.

• Proceedings of the KSR Conference
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• 2007.05a
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• pp.99-108
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• 2007

The bi-modal tram guided by the magnetic guidance system has two car-bodies and three axles. Each axle of the vehicle has an independent suspension to lower the floor of the car and improve ride quality. The turning radius of the vehicle may increase as a consequence of the long wheel base. Therefore, the vehicle is equipped with the All-Wheel-Steering(AWS) system for safe driving on a curved road. Front and rear axles should be steered in opposite directions, which means a negative mode, to minimize the turning radius. On the other hand, they also should be steered in the same direction, which means a positive mode, for the stopping mode. Moreover, only the front axle is steered for stability of the vehicle upon high-speed driving. In summary, steering angles and directions of the each axle should be changed according to the driving environment and steering mode. This paper proposes an appropriate AWS control algorithm for stable driving of the bi-modal tram. Furthermore, a multi-body model of the vehicle is simulated to verify the suitability of the algorithm. This model can also analyze the different dynamic characteristics between 2WS and AWS.

• Journal of the Korean Society for Railway
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• v.10 no.4
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• pp.444-450
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• 2007

The bi-modal tram guided by the magnetic guidance system has two car-bodies and three axles. Each axle of the vehicle has an independent suspension to lower the floor of the car and improve ride quality. The turning radius of the vehicle may increase as a consequence of the long wheel base. Therefore, the vehicle is equipped with the All-Wheel-Steering(AWS) system for safe driving on a curved road. Front and rear axles should be steered in opposite directions, which means a negative mode, to minimize the turning radius. On the other hand, they also should be steered in the same direction, which means a positive mode, for the stopping mode. Moreover, only the front axle is steered for stability of the vehicle upon high-speed driving. In summary, steering angles and directions of the each axle should be changed according to the driving environment and steering mode. This paper proposes an appropriate AWS control algorithm for stable driving of the bi-modal tram. Furthermore, a multi-body model of the vehicle is simulated to verify the suitability of the algorithm. This model can also analyze the different dynamic characteristics between 2WS and AWS.

• Proceedings of the Computational Structural Engineering Institute Conference
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• 2002.10a
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• pp.45-51
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• 2002

An acoustic finite element model of a bridge is developed to evaluate the noise generated by the traffic-induced vibration of the bridge. The dynamic response of a multi-girder bridge, modeled by a 3-dimensional frame element model, is analyzed with a 3-axle 8 DOFs truck model and a 5-axle 13 DOFs semi-trailer. The flat plate element is used to analyze the acoustic pressure due to the fluid-structure interactions between the vibrating surface and contiguous acoustic fluid medium. The radiation fields of noise with a specified distribution of vibrating velocity and pressure on the structural surface are also computed using the Kirchhoff-Helmholtz integral. Although the noise produced by the bridge vibration is not serious in itself, which is below the audible frequency range, it should be considered as an interaction problem between vehicle noise and bridge vibration noise in order to evaluate the traffic noise around the bridge.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.39 no.10
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• pp.1039-1044
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• 2015

During the development of vehicles, durability tests are time consuming and costly. Recently, automobile companies have attempted to develop their own durability evaluation procedures by modifying and complementing . In this paper, we propose an integrated computer-aided engineering (CAE) method to evaluate the durability of a torsion beam axle (TBA). We compare this method with the standardized durability evaluation method used by an actual automobile company in order to determine the feasibility of this method. We compare the results with the test result data to enable us to estimate the reliability of the analysis results. In this study, we analyze the processes and results of the quasi-static fatigue analysis, and found improved methods and problems. Furthermore, we perform a thorough test using the requirements of the actual company. Based on the results, the structural analysis process in the quasi-static fatigue analysis method was superseded by the multi-body dynamics analysis process. Generally, this method is referred to as the resonance-fatigue analysis method.

• Proceedings of the Computational Structural Engineering Institute Conference
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• 2006.04a
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• pp.772-777
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• 2006

An acoustic finite element model of a bridge is developed to evaluate the noise generated by the traffic-induced vibration of the bridge. The dynamic response of a multi-girder bridge, modeled by a 3-dimensional frame element model, is analyzed with a 3-axle(8DOF) truck model and a 5-axle(l3DOF) semi-trailer. The flat plate element is used to analyze the acoustic pressure due to the fluid-structure interactions between the vibrating surface and contiguous acoustic fluid medium. The radiation fields of noise with a specified distribution of vibrating velocity and pressure on the structural surface are also computed using the Kirchhoff-Helmholtz integral. In an attempt to illustrate the influence of the structural vibration noise of a bridge to total noise level around the bridge, the random function is used to generate the vehicle noise source including the engine noise and the rolling noise interacting between the road and tire. Among the diverse parameters affecting the dynamic response of bridge, the vehicle velocity, the vehicle weight, the spatial distribution of the road surface roughness, the stiffness degradation of the bridge and the variation of the air temperature changing the air density are found to be the main factors that increase the level of vibration noise. Consequently, The amplification rate of noise increases with the traveling speed and the vehicle weight.