• Journal of the Korean Society for Precision Engineering
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• v.19 no.9
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• pp.51-58
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• 2002

Vehicle dynamics control systems are. complex and non-linear, so they have difficulties in developing a controller for the anti-lock braking systems and the auto-traction systems. Currently the fuzzy-logic technique to estimate the absolute vehicle speed is good results in normal conditions. But the estimation error in severe braking is discontented. In this paper, we estimate the absolute vehicle speed by using the wheel speed data from standard 50-tooth anti-lock braking system wheel speed sensors. Radial symmetric basis function of the neural network model is proposed to implement and estimate the absolute vehicle speed, and principal component analysis on input data is used. Ten algorithms are verified experimentally to estimate the absolute vehicle speed and one of those is perfectly shown to estimate the vehicle speed with a 4% error during a braking maneuver.

• Journal of the Korean Society for Precision Engineering
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• v.20 no.3
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• pp.58-65
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• 2003

Vehicle acceleration data from an accelerometer and wheel speed data from standard, 50-tooth antilock braking system wheel speed sensors are used to estimate the absolute longitudinal speed of a vehicle. We develop the four velocity estimation algorithms. And we compare experimental results with the Butterworth filtered speed from the fifth wheel and find that it is possible to estimate absolute longitudinal vehicle speed during a hard braking maneuver lasting three seconds.

• Transactions of the Korean Society of Automotive Engineers
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• v.10 no.1
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• pp.179-186
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• 2002

The absolute longitudinal speed of a vehicle is estimated by using vehicle acceleration data from an accelerometer and wheel speed data from standard 50-tooth antiknock braking system wheel speed sensors. An intuitive solution to this problem is, "When wheel slip is low, calculate absolute velocities from the wheel speeds; when wheel slip is high, calculate absolute velocity by integrating the accelerometer." Fuzzy logic is introduced to implement the above idea and a new algorithm of "modified velocities with step integration" is proposed. This algorithm is verified experimentally to estimate speed of a vehicle, and is also shown to estimate absolute longitudinal vehicle speed with a 6% worst-case error during a hard braking maneuver lasting three seconds.

• Journal of Navigation and Port Research
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• v.28 no.3
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• pp.227-232
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• 2004

Vehicle dynamics control systems are complex and non-linear, so they have difficulties in developing a controller for the anti-lock braking systems and the auto-traction systems. Currently the fuzzy-logic technique to estimate the absolute vehicle speed supplies good results in normal conditions. But the estimation error in severe braking is discontented In this paper, we estimate the absolute vehicle speed of UCT(Unmanned Container Transporter) by using the wheel speed data from standard anti-lock braking system wheel speed sensors. Radial symmetric basis function of the neural network model is proposed to implement and estimate the absolute vehicle speed, and principal component analysis on input data is used 10 algorithms are verified experimentally to estimate the absolute vehicle speed and one of them is perfectly shown to estimate the vehicle speed within 4％ error during a braking maneuver.

• Transactions of the Korean Society of Automotive Engineers
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• v.10 no.6
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• pp.234-240
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• 2002

This paper treats the problem of estimating the longitudinal velocity of a braking vehicle using measurements from an accelerometer and wheel speed data from standard anti-lock braking wheel speed sensors. We develop and experimentally test three velocity estimation algorithms of increasing complexity. The algorithm that works the best gives peak errors of less than 3 percent even when the accelerometer signal is significantly biased.

• International Journal of Automotive Technology
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• v.6 no.4
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• pp.359-365
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• 2005

The absolute longitudinal speed of a vehicle is estimated by using data from an accelerometer of the vehicle and wheel speed sensors of a standard 50-tooth antilock braking system. An intuitive solution to this problem is, 'When wheel slip is low, calculate the vehicle velocity from the wheel speeds; when wheel slip is high, calculate the vehicle speed by integrating signal of the accelerometer.' The speed estimator weighted with fuzzy logic is introduced to implement the above concept, which is formulated as an estimation method. And the method is improved through experiments by how to calculate speed from acceleration signal and slip ratios. It is verified experimentally to usefulness of estimation speed of a vehicle. And the experimental result shows that the estimated vehicle longitudinal speed has only a $6\%$ worst-case error during a hard braking maneuver lasting a few seconds.

• 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.

• Proceedings of the KSR Conference
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• 2011.10a
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• pp.1838-1844
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• 2011

In this paper, the propulsion control of a high-speed maglev train is studied. Electromagnetic suspension is used to levitate the vehicle, and linear synchronous motors (LSM) are used for propulsion. In general, a low-speed maglev train uses a linear induction motor (LIM) for propulsion that is operated under 300[km/h] due to the power-collecting and end-effect problem of LIM. In case of the high-speed maglev train over 500[km/h], a linear synchronous motor (LSM) is more suitable than LIM because of a high-efficiency and high-output properties. An optical barcode positioning system is used to obtain the absolute position of the vehicle due to its wide working distance and ease of installation. However, because the vehicle is working completely contactless, the position measured on the vehicle has to be transmitted to the ground for propulsion control via wireless communication. For this purpose, Bluetooth is used and communication hardware is designed. A propulsion controller using a digital signal processor (DSP) in the ground receives the delayed position information, calculates the required currents, and controls the stator currents through inverters. The performance of the implemented propulsion control is analyzed with a small maglev train which was manufactured for experiments, and the applicability of the high-speed maglev train will be explored.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.58 no.3
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• pp.598-604
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• 2009

This paper proposes a vehicle detector with an anisotropic magnetoresistive (AMR) sensor and addresses experimental results to show the detector's performance. The detector consists of an AMR sensor and mechanical and electronic apparatuses. The AMR sensor, composed of four magnetoresistors, senses disturbance of the earth's magnetic field caused by a vehicle moving over the sensor and then produces an output indicative of the moving vehicle. This paper verifies performance of the detector on the basis of experimental results obtained from the field tests carried under the two traffic conditions on local highways in Korea. First, I show the vehicle counting performance on a low speed congested highway by comparing the vehicle counts measured by the detector with the exact counts. Second, both vehicle counts and average speeds calculated from the measured point-occupancy on another continuously free running highway are compared with the reference values obtained from a loop detector which has two independent loop coils, where I have used several performance indices including mean absolute percentage error (MAPE) to show the performance consistency between the two types of detectors.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.60 no.3
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• pp.600-605
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• 2011

This paper shows field test and evaluation results for a wireless vehicle detector with anisotropic magneto-resistive (AMR) sensors. The detector consists of two AMR sensors and mechanical and electronic apparatuses. The AMR sensor senses disturbance of the earth magnetic field caused by a vehicle moving over the sensor and then produces an output indicative of the moving vehicle. In this paper, vehicle speeds are calculated by using two AMR sensors fixed on a board, with constant distance. To test and evaluate the accuracy of the detector in real traffic situations, the detector was installed on a local highway and vehicle speeds and volumes were measured both in a free running and a highly congested traffic. The measurements from the detector are compared with the reference measurements obtained from a traffic camera with the Mean Absolute Percentage Errors (MAPE), which has proved the usefulness of the detector in the field.