• Journal of the Korean Society of Safety
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• v.28 no.2
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• pp.6-13
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• 2013

This study focuses on finding ECO driving patterns which consider driving safety of the ATO system train and reliability and which optimize efficiency of the driving energy consumption. Research results derived by performing simulation of those 5 models show that the emergency braking which affects safety of passenger and the machinery is minimized, and safe driving speed is maintained by the prohibition of drastic acceleration/deceleration, coasting and constant-speed driving. Therefore if this result is applied to the urban railway train by amending or making ATO program to save energy usage that improve environmental quality, its effects as ECO driving pattern is huge.

• Journal of the Korean Society of Safety
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• v.28 no.3
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• pp.23-28
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• 2013

This study focuses on finding ways to derive train's optimal ECO driving pattern, which can improve the ride quality and reduce driving energy consumption with keeping the time interval between the stations. As research method, we compared difference of currently operating train's ATO and MCS driving patterns, and concentrated upon the things need to consider in simulation in order to improve the existing pattern of ATO driving pattern's issues with securing the train operation safety. Determining driving pattern minimizing energy consumption by controlling powering within speed limit and controlling switching to coasting at appropriate point considering the track conditions for each section, and determining braking control starting time considering ride comfort and precise stopping is considered to be most important.

• Journal of Information Processing Systems
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• v.13 no.2
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• pp.285-304
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• 2017

Big data information and pattern analysis have applications in many industrial sectors. To reduce energy consumption effectively, the eco-driving method that reduces the fuel consumption of vehicles has recently come under scrutiny. Using big data on commercial vehicles obtained from digital tachographs (DTGs), it is possible not only to aid traffic safety but also improve eco-driving. In this study, we estimate fuel consumption efficiency by processing and analyzing DTG big data for commercial vehicles using parallel processing with the MapReduce mechanism. Compared to the conventional measurement of fuel consumption using the On-Board Diagnostics II (OBD-II) device, in this paper, we use actual DTG data and OBD-II fuel consumption data to identify meaningful relationships to calculate fuel efficiency rates. Based on the driving pattern extracted from DTG data, estimating fuel consumption is possible by analyzing driving patterns obtained only from DTG big data.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2013.05a
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• pp.957-960
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• 2013

Eco driving comprises the use of feedback information that informs the driver of vehicle performance. This paper evaluated the performance of feedback device that reported instantaneous fuel economy to drivers while driving. We took the measurement by getting data through OBD port from commercial vehicle covered 67 km on road. The changes observed in fuel efficiency were established 10.6 % improvement in fuel economy.

• 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.

• Journal of Digital Convergence
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• v.20 no.5
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• pp.375-381
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• 2022

The DEA-BCC model. And the Malmquist index have been used, from static and dynamic perspectives, to measure the eco-efficiency of 30 cities and provinces in China from 2011 to 2020. The results shows that: the average static eco-efficiency of 30 cities and provinces in China is 0.643. Differences exists all over China. While Shanghai and Beijing are ecologically efficient, other 28 cities and provinces are faced with different extents of inefficiency. There are also differences among regions, which generally show the spatial distribution pattern with high efficiency in the eastern regions while low in the western regions. The Malmquist index of eco-efficiency in total 30 cities and provinces shows a healthy growth trend, and the technological progress. Acts as its main driving force. Therefore, eastern regions should enhance the. radiation capacity, strengthen the synergy among regions, give full play to. The advantages of each regions. It is sensible to improve the eco-efficiency by means of optimizing the industrial structure, enhancing the technological level and improving eco-efficiency of China and realizing green development.

• Transactions of the Korean Society of Automotive Engineers
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• v.19 no.2
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• pp.133-141
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• 2011

The effect of payload on fuel consumption and emission of light duty freight truck during acceleration driving has been analyzed. Running tests were carried out with various payload conditions on chassis dynamometer. A typical driving pattern for urban cities was used. Real time emission measurement systems for gaseous and soot emission were utilized to investigate the real time dynamic of fuel use and exhaust emissions. It was observed that fuel use and pollutant emissions were increased as payload was increased. Under the same payload condition, the increased amount of acceleration driving is much higher than that of steady state driving. The results demonstrated the advantages of eco-driving, which is an environmentally friendly driving manner, could be emphasized in heavier payload condition. Inertial tractive power was introduced for considering the parameters affecting emission during acceleration driving, which are speed, acceleration and payload. Fuel use and emission in various driving conditions were expressed as functions of inertial tractive power. The estimated result by these functions well predicted measured result within 10 % deviation.

• Proceedings of the KSR Conference
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• 2011.10a
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• pp.2357-2364
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• 2011

Diesel locomotive operates the generator with the power from the diesel engine, and it consists of the typical serial-hybrid system which operates the train wheels by converting its generated electric energy into the torque of DC (or AC) motor. However, the technology of locomotives is only focused on trains' controlling power generation mechanism. Hence, it is a current issue that the efficiency of its engine and its generator is relatively lower than that of auto vehicles'. Particularly, since there are no proper equipment to measure the amount of fuel which is essentially necessary for the efficient use of fuel, it is not easy to confirm the instant amount of fuel use as well as the exact average fuel consumption per an hour. Due to those difficulties, it is urgent to develop the device that measures the fuel consumption. Plus, this use of the developed measuring device allows the various and useful analysis relating to the fuel consumption, and this could lead to establishing the efficient driving pattern regarding to fuel saving. This device consists of two flux (fuel level) measuring censors, MCU for calculating the measured values, the information recorder for saving measured values, and the display device for indicating the fuel amount consumed during driving.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.65 no.11
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• pp.1793-1799
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• 2016

As one of the main trends in global industries is eco-friendly energy, the interest on Electric Vehicle(EV) has been increased. However, if large amount of EVs start to charging, it could cause rapid increase in demand power of the power system. To guarantee stable operation of the power system, those unpredictable power consume should be mitigated. In this paper, therefore, we propose a practical smart EVs charging scheme to prevent the rapid increase of the demand power and also provide load flattening function. For that we considered Time-of-Use(ToU) price and actual data such as driving pattern and parameters of distribution system. Simulation results show that the proposed method provides proper load flattening function while preventing the rapid increase of the demand power of the power system.