• Transactions of the Korean Society of Automotive Engineers
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• v.4 no.1
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• pp.63-74
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• 1996

Automatic transmission for heavy duty vehicles is a part of the power pack which includes steering and braking systems. This transmission in different from the one for passenger car. Therefore, in order to understand the trend of the important design parameters, maneuverability, acceleration performance and maximum speed, we need to analyze the total performance characteristics of the power transmission systems. In this study, modeling of the automatic transmission in heavy duty vehicle is carried out and the performance analysis method is presented. Results can be used for performance estimation data in the analysis for several combination method which determines the optimal parameters on the basis of penalty functions and weightings. And the estimation method of the important performance parameters such as engine inertia or power loss of engine by experiments is presented.

• The Transactions of the Korean Institute of Electrical Engineers P
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• v.56 no.2
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• pp.74-82
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• 2007

Interior permanent magnet synchronous motor(IPMSM) has become a popular choice in electric vehicle applications, due to their excellent power to weight ratio. This paper proposes efficiency optimization control of IPMSM drive using adaptive fuzzy learning control fuzzy neural network (AFLC-FNN) controller. In order to maximize the efficiency in such applications, this paper proposes the optimal control method of the armature current. The controllable electrical loss which consists of the copper loss and the iron loss can be minimized by the optimal control of the armature current. The minimization of loss is possible to realize efficiency optimization control for the proposed IPMSM. The optimal current can be decided according to the operating speed and the load conditions. This paper considers the design and implementation of novel technique of high performance speed control for IPMSM using AFLC-FNN controller. Also, this paper proposes speed control of IPMSM using AFLC-FNN and estimation of speed using ANN controller. The back propagation neural network technique is used to provide a real time adaptive estimation of the motor speed. The proposed control algorithm is applied to IPMSM drive system controlled AFLC-FNN controller, the operating characteristics controlled by efficiency optimization control are examined in detail.

• The Transactions of the Korean Institute of Electrical Engineers B
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• v.55 no.2
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• pp.89-97
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• 2006

Interior permanent magnet synchronous motor(IPMSM) has become a popular choice in electric vehicle applications, due to their excellent power to weight ratio. The paper is proposed maximum torque control of IPMSM drive using learning mechanism-fuzzy neural network(LM-FNN) controller and artificial neural network(ANN). The control method is applicable over the entire speed range and considered the limits of the inverter's current and voltage rated value. For each control mode, a condition that determines the optimal d-axis current $i_{d}$ for maximum torque operation is derived. This paper considers the design and implementation of novel technique of high performance speed control for IPMSM using LM-FNN controller and ANN controller. The hybrid combination of neural network and fuzzy control will produce a powerful representation flexibility and numerical processing capability. Also, this paper is proposed speed control of IPMSM using LM-FNN and estimation of speed using ANN controller. The back propagation neural network technique is used to provide a real time adaptive estimation of the motor speed. The proposed control algorithm is applied to IPMSM drive system controlled LM-FNN and ANN controller, the operating characteristics controlled by maximum torque control are examined in detail. Also, this paper is proposed the analysis results to verify the effectiveness of the LM-FNN and ANN controller.

• Journal of Korean Society for Atmospheric Environment
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• v.9 no.1
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• pp.69-77
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• 1993

Exhaust emissions are calculated as a product of the emission factor and the vehicle kilometer traveled(VKT). The emission factor is a function of several parameters such as vehicle model year, vehicle mileage, traffic conditions, etc. The representative driving cycles classified as ten different types of an average vehicle speed were selected by analyzing passenger car driving patterns in Seoul. 51 vehicles were sampled and analyzed by types of vehicles, fuels used, model years and vehicle mileages also, exhaust emissions of them were measured by chassis dynamometer. Regression equations between average vehicle speeds and exhaust emissions are made for the estimation of emission factors at different vehicle speeds. Annual emission rates of air pollutants from motor vehicles in Korea were 1116$\times10^3 ton, 149\times10^3 ton, 413\times10^3 ton and 67\times10^3$ ton for CO, HC, NOx and particulats, respetively in 1990. It was found that 56% of CO and 49% of HC were originated from passenger cars and taxis, in addition, 87% of NOx and 100% of particulates were from buses and trucks using diesel fuels.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.26 no.2D
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• pp.251-258
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• 2006

This study is to develop an estimation method of freeway design capacity through the analysis of time headway distribution in continuum flow. Traffic flow-speed diagram and time headway distribution plotted from individual vehicle data shows: a) a road capacity is not deterministic but stochastic, b) time headway distribution for each vehicle speed group follows pearson type V distribution. The freeway design capacity estimation model is developed by determining a minimum time headway for capacity with stochastic method. The estimated capacity values for each design speed are lower when design speed ${\leq}80km/h$, and higher when design speed ${\geq}106km/h$ in comparison with HCM(2000)'s values. In addition, The distinguish difference is that this model leads flexible application in planning level by defining the capacity as stochastic distribution. In detail, this model could prevent a disutility to add a lane for only one excess demand in a road planning level.

• Structural Monitoring and Maintenance
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• v.9 no.4
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• pp.305-321
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• 2022

Bridge structural health monitoring with the aim of continuously assessing structural safety and reliability represents a topic of major importance for worldwide infrastructure managers. In the last two decades, due to their potential economic and operational advantages, drive-by approaches experienced growing consideration from researcher and engineers. This work addresses two technical topics regarding indirect frequency estimation methods: bridge and vehicle dynamics overlapping, and bridge expansion joints impact. The experimental campaign was conducted on a mixed multi-span bridge located in Lombardy using a Ford Galaxy instrumented with a mesh of wireless accelerometers. The onboard time series were acquired for a number of 10 passages over the bridge,performed at a travelling speed of 30 km/h, with no limitations imposed to traffic. Exploiting an ad-hoc sensors positioning, pitch vehicle motion was compensated, allowing to estimate the first two bridge bending frequencies from PSD functions; moreover, the herein adopted approach proved to be insensitive to joints disturbance. Conclusively, a sensitivity study has been conducted to trace the relationship between estimation accuracy and number of trips considered in the analysis. Promising results were found, pointing out a clear positive correlation especially for the first bending frequency.

• Journal of Korean Society of Transportation
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• v.28 no.4
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• pp.107-118
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• 2010

Wireless communication technologies including vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) enable the development of more sophisticated and effective traffic information systems. This study presents a method to estimate vehicular travel times in a vehicular ad hoc network (VANET) environment. A novel feature of the proposed method is estimating individual vehicle travel times through advanced on-board units in each vehicle, referred to as self-estimated travel time in this study. The method uses travel information including vehicle position and speed at each given time step transmitted through the V2V and V2I communications. Vehicle trajectory data obtained from the VISSIM simulator is used for evaluating the accuracy of estimated travel times. Relevant technical issues for successful field implementation are also discussed.

• Journal of the Korea Institute of Military Science and Technology
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• v.8 no.3 s.22
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• pp.5-10
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• 2005

This paper presents the new in-flight alignment method using the flight distance of vehicle in order to improve the performance of the heading error estimation. In the proposed method, the Kalman filter having the difference between GPS and SDINS position as measurements is used for levelling of SDINS and heading error is estimated utilizing the flight distance information. It is shown in the simulation results that the in-flight method proposed in this paper has the high accuracy in heading error estimation and the heading error can be very quickly estimated at the high speed vehicle, compared with the existing method using the Kalman filter.

• Proceedings of the KSME Conference
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• 2007.05a
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• pp.1103-1108
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• 2007

This paper describes a robust model-based roll state estimator for application to the detection of impending vehicle rollover. The roll state estimator is based on a 2-D bicycle model and a roll model to estimate the maneuver-induced vehicle roll motion. The measurement signals are lateral acceleration, yaw rate, steering angle, and vehicle speed. Vehicle mass is adapted to obtain robust performance of the estimator. Computer simulation is conducted to evaluate the proposed roll state estimator by using a validated vehicle simulator. It is shown that the roll state estimator shows robust performance without exact vehicle mass information.

• International Journal of Highway Engineering
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• v.14 no.2
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• pp.29-36
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• 2012

In this study, frequently passing vehicles with two, three, four, and five axles were chosen through traffic volume analysis in Kyung-In Expressway in order to analyze how the road roughness and vehicle speed affect on the dynamic loads for roads in various vehicle classes. Dynamic loads according to chosen vehicles are estimated by TruckSim program. Dynamic load amplification factor is ratio between dynamic and static loads, and it is also determined for each vehicle classes. From the result of dynamic loads estimated by the dynamic load amplification factor, it is shown that for three-axles vehicle, when IRI is 3.5 and vehicle speed is 100km/hr, asphalt pavements receive additional 36% of static loads in maximum. The analysis of the amplification factor according to each vehicle classes also indicates that the amplification factor increases as the distance between the axles becomes smaller and each axle receives more loads.