• The Journal of the Korea institute of electronic communication sciences
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• v.13 no.3
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• pp.555-560
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• 2018

In this paper, we focus on the study of educational contents using real vehicle based electric vehicle diagnosis system. To understand the electric vehicle, we describe the driving modes and operating principles of the core components and electric vehicles that make up the electric vehicles. In addition, we implemented a diagnostic system for analyzing the characteristics of the electric vehicle test bed and analyzed the characteristics of the vehicle based on the driving mode.

• Journal of Korean Society of Transportation
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• v.26 no.6
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• pp.39-47
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• 2008

With dramatic development of IT technology and start of DMB service, installing the DMB equipment in a vehicle for watching TV programs and literal or pictorial traffic information are increasing. Watching the DMB during driving the vehicle could cause visual and cognitive distraction to drivers as much as eating food, operating radio and using mobile phone. However, there is not much empirical research for this topic and no research examined the effect of watching the DMB on driving behaviors in Korea. So, the present study examined the effect of watching the DMB on the driving behaviors with car simulator experiment. Within subject design was used in the study. That is, all subjects drove the vehicle both in the watching DMB condition and the non-watching DMB condition. The results indicated that subjects in the watching DMB condition took longer time to arrive at the destination and operated accelerator and brake pedal rapidly than subjects in the non-watching DMB condition. That is, their overall driving stability was lower than non-watching subjects'. Additionally, we examined the difference among the DMB control conditions (i.e., keypad condition, touch-pad condition and remote controller condition) in the driving behaviors. Finally, we discussed the limitations and the implications of the present study.

• The Transactions of the Korean Institute of Power Electronics
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• v.25 no.2
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• pp.79-86
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• 2020

This study examines the power generation characteristics of a vehicle rooftop photovoltaic module with urban driving conditions. Actual test data with an illuminometer and a thermometer were used to analyze the power generation characteristics of the vehicle rooftop photovoltaic module. In addition, the power generation characteristics were analyzed in terms of urban driving conditions, irradiance, ambient temperature, and photovoltaic module temperature. This study also analyzes the power generation characteristics of the vehicle rooftop photovoltaic module with urban driving conditions through a wavelet transform filtering method. The power generation characteristics of the vehicle rooftop photovoltaic module with urban driving conditions depend on the change in irradiance rather than that in photovoltaic module temperature.

• Journal of ILASS-Korea
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• v.19 no.1
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• pp.40-46
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• 2014

Recently rise in oil prices feet the burden on not only diesel vehicle driver but also LPG vehicle driver, and get interested in various way to reduce fuel costs. In this study discuss on exhaust emissions characteristics on driving cycle mode and ignition advance condition change of CNG/LPLI Bi-Fuel vehicle. Experimental test was performed by changing the conditions of fuel (LPG/CNG), spark advance (Base, $10^{\circ}CA$, $15^{\circ}CA$), and driving mode (FTP-75, HWFET, and NEDC). In case of CO emission, in the order of CNG Base, CNG S/A10, S/A15 condition are average reduced -21%, -35%, -29% respectively compared to LPG fuel. The active emission reduction from the initial engine start, spark retard is likely to be beneficial in catalyst warm-up and improve combustion stability rather than spark advance.

• Transactions of the Korean Society of Automotive Engineers
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• v.21 no.6
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• pp.123-134
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• 2013

Strengthening vehicle emission regulation is one of important policies to improve air quality in urban area. Due to the limitation of specified driving cycles for certification test to reflect real driving conditions, additional off-cycle emission regulations have been adopted in US and being developed in Europe. The driving cycles of US or Europe have been used in emission certification for Korean light-duty vehicles, but it has not been known how well the driving cycles reflect various real driving patterns in Korea. In that point of view, it is required to estimate vehicle emission based on real road driving conditions to raise the effectiveness of vehicle emission regulation in Korea. In this study, real driving emission measurements have been conducted for three Korean light-duty vehicles with PEMS. The driving routes consisted of urban, rural and motorway in Seoul and Incheon. The data have been analyzed with various averaging methods including moving averaging windows method and compared to emission limits set with emission certification modes applied to tested vehicles. The results have shown that the real driving pollutant emissions of a gasoline and a LPG vehicles have been ranged quite lower than those of emission limits on CVS-75 driving cycle. But real driving NOx of a light duty diesel vehicle has been considerably higher than emission limit of NEDC driving cycle. The higher than expected NOx emission of a diesel vehicle might be caused by different strategy to control EGR in real driving condition from NEDC driving.

• Transactions of the Korean Society of Automotive Engineers
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• v.20 no.6
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• pp.61-66
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• 2012

Conventional method to transform vehicle driving condition to engine operation mode is to use vehicle road load under neutral gear and mechanical efficiency of drivetrain. But this method requires additional measurement of efficiency of drivetrain on a test rig. And this measurement is normally done at fixed speed and thus estimated accuracy of engine operation mode is not considered to be high enough. This study suggests new method to calculate engine operation mode for prescribed driving mode such as NEDC using vehicle coastdown test under gear engaged condition without measurement of mechanical efficiency of drivetrain. Coastdown test was done under neutral and gear engaged condition for comparison and also trial to extract mechanical loss of drivetrain was carried out. Calculated engine torque by conventional and newly suggested method was compared with actually measured torque of a vehicle on a chassis dynamometer during NEDC. Newly suggested method showed slightly higher accuracy of accumulated brake work during NEDC.

• Journal of Korean Port Research
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• v.14 no.3
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• pp.303-312
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• 2000

In this paper, we describe an image processing algorithm which is able to recognize the road lane. This algorithm performs to recognize the interrelation between AGV and the other vehicle. We experimented on AGV driving test with color CCD camera which is setup on the top of vehicle and acquires the digital signal. This paper is composed of two parts. One is image preprocessing part to measure the condition of the condition of the lane and vehicle. This finds the information of lines using RGB ratio cutting algorithm, the edge detection and Hough transform. The other obtains the situation of other vehicles using the image processing and viewport. At first, 2 dimension image information derived from vision sensor is interpreted to the 3 dimension information by the angle and position of the CCD camera. Through these processes, if vehicle knows the driving conditions which are lane angle, distance error and real position of other vehicles, we should calculate the reference steering angle.

• Transactions of the Korean Society of Automotive Engineers
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• v.7 no.7
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• pp.136-148
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• 1999

Detailed mathematical models of hybrid drivertrain components are presented and numerical simulations are carried out to analyze the shift characteristics and to improve the driving comfortability when the hybrid drivetrain is applied at the vehicle . Theoretical results are compared with experimental ones from the dynamometer as same condition in order to prove the appropriateness of modeling . Adding the vehicle body modeling, included in the suspension and the engine mount, it is possible to predict the dynamic behavior and shift characteristics more actually when shifts are occurred by automated manual transmission(AMT). these additional results are also compared with the same simulation ones of internal combustion engined vehicle equipped conventional manual transmission. Hence, it can be expected that the hybrid vehicle with AMT has a good shift quality.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2016.05a
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• pp.577-578
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• 2016

This paper presents a method of vehicle safety system using a pulse sensor which will be able to occurs drowsy driving accident when people driving. The proposed vehicle safety system alarms according to the driver drowsy condition, therefore the driver prevent the direct and $2^{nd}$ accident beforehand cognitive unexpected and dangerous accident using vehicle safety system.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.12
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• pp.5842-5861
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• 2019

A current autonomous vehicle determines its driving strategy by considering only external factors (Pedestrians, road conditions, etc.) without considering the interior condition of the vehicle. To solve the problem, this paper proposes "An Optimal Driving Support Strategy(ODSS) based on an Genetic Algorithm for Autonomous Vehicles" which determines the optimal strategy of an autonomous vehicle by analyzing not only the external factors, but also the internal factors of the vehicle(consumable conditions, RPM levels etc.). The proposed ODSS consists of 4 modules. The first module is a Data Communication Module (DCM) which converts CAN, FlexRay, and HSCAN messages of vehicles into WAVE messages and sends the converted messages to the Cloud and receives the analyzed result from the Cloud using V2X. The second module is a Data Management Module (DMM) that classifies the converted WAVE messages and stores the classified messages in a road state table, a sensor message table, and a vehicle state table. The third module is a Data Analysis Module (DAM) which learns a genetic algorithm using sensor data from vehicles stored in the cloud and determines the optimal driving strategy of an autonomous vehicle. The fourth module is a Data Visualization Module (DVM) which displays the optimal driving strategy and the current driving conditions on a vehicle monitor. This paper compared the DCM with existing vehicle gateways and the DAM with the MLP and RF neural network models to validate the ODSS. In the experiment, the DCM improved a loss rate approximately by 5%, compared with existing vehicle gateways. In addition, because the DAM improved computation time by 40% and 20% separately, compared with the MLP and RF, it determined RPM, speed, steering angle and lane changes faster than them.