• Journal of the Korean Society of Manufacturing Process Engineers
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• v.19 no.5
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• pp.8-13
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• 2020

In this paper, the electric vehicle (EV) and internal combustion engine vehicle (ICEV) are compared for different driving cases. The EV exhibits a lower powertrain efficiency when driven on the aggressive driving cycle than when driven on the moderate cycle. In particular, EV powertrain efficiency is low when the battery state of charge (SOC) is low, but ICEV efficiency increases when the driving cycle changes from the moderate cycle to the aggressive cycle. Based on these results, attempts can be made to increase EV powertrain efficiency. EV charging before the battery power drops to a low charging state can reduce energy consumption by 2.7% for an urban area. Furthermore, ECO driving has a more significant effect on EVs than on ICEVs.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.12 no.1
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• pp.19-25
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• 2011

A vehicle fuel economy is very important issue in view of fuel cost and environmental regulation. The technology development for the fuel economy improvement improved the engine, power train and many components of vehicle. So, the fuel economy is much improved, but up to now the measurement of it tests the given mode(LA-4, FTP-75, etc) within computer simulation program and engine dynamo. In this paper, to deduct the method of its improvement of real road, the test vehicle ran 213Km Youngdong real highway using 3 different algorithms in computer simulation. For this, I extracted the distance and altitude data from received GPS data and calculated the grade angle, road load and accomplished the velocity profiles according to algorithms in all 213Km distance. The vehicle runs in computer with AVL Cruise simulation program using velocity profile. I calculate the fuel economy using AVL Cruise simulation result and propose the Eco-driving method of them.

• Journal of the Korea Convergence Society
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• v.10 no.11
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• pp.327-333
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• 2019

The Fuel-cut driving is started when the acceleration pedal released with transmission gear engaged. Fuel economy of the vehicle improves by active fuel-cut driving. A deep-learning technique is proposed to predict fuel-cut driving with vehicle speed, acceleration and road gradient data in the study. It's 3~10 of hidden layers and 10~20 of variables and is applied to the 9600 data obtained in the test driving of a vehicle in the road of 12km. Its accuracy is about 84.5% with 10 variables, 7 hidden layers and Relu as activation function. Its error is regarded from the fact that the change rate of input data is higher than the rate of fuel consumption data. Therefore the accuracy can be better by the normalizing process of input data. It's unnecessary to get the signal of vehicle injector or OBD, and a deep-learning technique applied to the data to be got easily, like GPS. It can contribute to eco-drive for the computing time small.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.43 no.4
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• pp.485-492
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• 2023

Among the methods to improve traffic congestion by providing real-time traffic information and solving problems like traffic congestion and traffic crashes, private enterprise is implementing policies to lower insurance premiums like compensation for drivers' driving safety scores. Despite the emergence of various incentive policies, a study on the level of incentive payment for safe/eco-friendly driving is insufficient. The research analyzed the satisfactory factors that affect the scale of incentives through questionnaires and the applicable scale of incentives that enable safe/eco-friendly driving using a binary logistic regression model. As a result of analyzing the incentive scale of the appropriate payment amount for each driving score increase, 0.4% of the toll fee was derived when the driving score increased by 20 points, and 0.5% of the toll fee was derived when the driving score increased by 30 points. This study on calculating the appropriate incentive payment scale for driver information sharing and driving score increase will help optimize incentives and prepare system implementation plans.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.35 no.6
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• pp.1377-1385
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• 2015

Construction industry that was the core driving force of Korean economic growth is now being classified as an industry with the largest environmental load emission. Although various efforts were made accordingly to realize eco-friendliness, its effects is insufficient currently. Based on the understanding that eco effect of construction industry would be significant if Eco VE is applicable in the initial project stage, NIA model for understanding the eco VE decision of project owner was developed with simulations in this paper. Based on the proposal report of a private project to develop the Gyeongbuk Northern Region Environmental Energy Town, 20 questions on eco VE decision were used through simulations. The result showed 50% eco-friendly decision. The result of this study can be used to provide decision-making data for eco design in the initial stage of construction by utilizing NIA upon conducting survey research to deduce eco VE decision.

• Transactions of the Korean Society of Automotive Engineers
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• v.18 no.4
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• pp.115-120
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• 2010

The reduction rate of fuel consumption by showing the fuel injection data for driver was measured in this study. The fuel injection data are composed of injection period, real time fuel economy and average fuel economy. The fuel consumption was measured by processing the voltage signal of injector and driven distance by GPS sensor. The fuel consumption was reduced by driving more carefully, i.e driving more steady without sudden acceleration and deceleration watching these fuel injection data. The reduction rate was up to 37% and the rate increased as the driver is customed to this driving pattern.

• Hwankyungkyoyuk
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• v.19 no.3
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• pp.114-122
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• 2006

Since 1985, the Ministry of Environment have supported environment conservation model schools (ECMS) to promote more structured and effective environmental education in schools. For driving ECMS to playa central role for sustainable schools in the community in Korea, international trends in foreign environmental education model schools need to be analysed in addition to overall evaluations of the present status. Eco-school related initiatives as whole-school approaches in other countries include Eco-Schools, Green School, Enviro School, and Sustainable School, etc. and most of them are usually involved in international network such as ENSI(Environment and School Initiatives) and FEE(Foundation for Environmental Education). This study was based on the literature analysis, which included available documents and reports of other countries from country reports, web-pages or official documents. Also, face-to-face or email interviews with personnels responsible for running such initiatives in a couple of countries were conducted. Implications for operating and supporting ECMS were provided based on the analysis and investigation on eco-school related initiatives of other countries, which included the main objective of initiatives, overall program direction, supporting methods, criteria for selection or accreditation, evaluation and monitoring.

• Transactions of the Korean Society of Automotive Engineers
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• v.23 no.2
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• pp.161-168
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• 2015

Recently, due to various environmental problems such as global warming, increases of international oil prices, exhaustion of resource, a paradigm of world automobile market is rapidly changing from conventional vehicles using internal combustion engine to eco-friendly vehicles using electric power such as EV, HEV, PHEV and FCEV. Generally, in order to measure fuel consumption and pollutant emissions of cars, chassis dynamometer tests are performed on various driving cycles before actual driving test. There are many driving cycles for performance evaluation of conventional vehicles. However, there is a lack of researches on driving cycle for EV. In this study, the urban driving cycle for performance evaluation of electric vehicles was developed. This study is composed of two parts. In the part 1, the urban driving cycle 'GUDC-EV(Gwacheon-city Urban Driving Cycle for Electric Vehicles)' was developed by using driving data, which were obtained through actual driving experiment, and statistic analysis with chronological table. In this paper part 2, in order to verify the developed driving cycle GUDC-EV, virtual EV platforms were configured and simulations were performed with actual driving data using In addition, simulation results were compared with existing driving cycles such as FTP-72, NEDC and Japan 10-15.

• Transactions of the Korean Society of Automotive Engineers
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• v.22 no.7
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• pp.117-126
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• 2014

Recently, due to various environmental problems such as global warming, increasing of international oil prices and exhaustion of resource, a paradigm of world automobile market is rapidly changing from vehicles using internal combustion engine to eco-friendly vehicles using electric power such as EV (Electric Vehicle), HEV (Hybrid Electric Vehicle), PHEV (Plug-in Hybrid electric Vehicle) and FCEV (Fuel Cell Electric Vehicle). There are many driving cycles for performance evaluation of conventional vehicles. However there is a lack of researches on driving cycle for EV. This study is composed of part 1 and part 2. In this paper part 1, in order to develop urban driving cycle for performance evaluation of electric vehicles, Gwacheon-city patrol route of police patrol car was selected. Actual driving test was performed using EV. The driving data such as velocity, time, GPS information etc. were recorded. GUDC-EV (Gwacheon-city Urban Driving Cycle for Electric Vehicles) including road gradient was developed through the results of analyzing recorded data. Reliability of the driving cycle development method was substantiated through comparison of electricity performance. In the second part of this study, the developed driving cycle was compared to simulation result of the existing urban driving cycle. Verification of the developed driving cycle for EV performance evaluation was described.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2017.10a
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• pp.258-260
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• 2017

Smart mobility is rapidly emerging as a next-generation eco-friendly transportation system, and the market is booming every year. However, due to the characteristics of the devices that use electricity as the power source, the motor and the battery are different from the performance and actual performance indicated by the manufacturer depending on the user's weight and driving environment. Therefore, The frequency of the overload is increased and the failure and damage of the device are increasing. In this paper, we propose an application that provides personalized recommended driving guidance and overloaded driving situation notification at the actual driving separately from the recommended driving provided by the manufacturer after measuring the driving environment of the user, so as to prevent malfunction and damage of the smart mobility device, To ensure safety.