• KEPCO Journal on Electric Power and Energy
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• v.6 no.3
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• pp.315-319
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• 2020

The modern transportation and mobility sector is expected to encounter high penetration of Electric Vehicles (EVs) because EVs contribute to reducing the harmful emissions from fossil fuel-powered vehicles. With the prospective growth of EVs, sufficient and convenient facilities for fast charging are crucial toward satisfying the EVs' quick charging demand during their trip. Therefore, the Fast Electric Vehicle Charging Stations (FECS) will be a similar role to gas stations. In this paper, we study a charging scheduling problem for the FECS with solar photovoltaic (PV) and an Energy Storage System (ESS). We formulate an optimization problem that minimizes the operational costs of FECS. There are two cost and one revenue terms that are buying cost from main grid power, ESS degradation cost, and revenue from the charging fee of the EVs. Simulation results show that the proposed scheduling algorithm reduces the daily operational cost by effectively using solar PV and ESS.

• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.24.2-24
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• 2001

In this paper, the authors present a row dynamic route planning by Q-learning. The proposed algorithm is executed in a cellular automation based traffic simulator, which is also newly created. In Vehicle Information and Communication System(VICS), which is an active field of Intelligent Transport System(ITS), information of traffic congestion is sent to each vehicle at real time. However, a centralized navigation system is not realistic to guide millions of vehicles in a megalopolis. Autonomous distributed systems should be more flexible and scalable, and also have a chance to focus on each vehicles demand. In such systems, each vehicle can search an own optimal route. We employ Q-learning of the reinforcement learning method to search an optimal or sub-optimal route, in which route drivers can avoid traffic congestions. We find some applications of the reinforcement learning in the "static" environment, but there are ...

• Journal of the Korean Operations Research and Management Science Society
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• v.30 no.4
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• pp.45-60
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• 2005

We consider a vehicle routing problem with time window and dock capacity constraints (VRPTD). In most traditional models of vehicle routing problems with time window (VRPTW), each customer must be assigned to only one vehicle route. However demand of a customer may exceed the capacity of one vehicle, hence at least two vehicles may need to visit the customer We assume that each customer has Its own dock capacity. Hence, the customer can be served by only a limited number of vehicles simultaneously. Given a depot, customers, their demands, their time windows and dock capacities, VRPTD is to get a set of feasible routes which pass the depot and some customers such that all demands of each customer are satisfied Since VRPTD is NP-hard, a meta-heuristic algorithm is developed. The algorithm consists of two Procedures : the route construction procedure and the route scheduling procedure. We tested the algorithm on a number of instances and computational results are reported.

• Journal of ILASS-Korea
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• v.20 no.1
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• pp.14-19
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• 2015

Recently, R&D demand for environmental friendly vehicle has rapidly increased due to its global environmental issues such as global warming, energy and economic crisis. Under this situation, the most realistic alternative way for environmental friendly vehicle is a clean diesel vehicle. The common-rail fuel injection system, as key technology of clean diesel vehicle, consists of a high pressure pump, common-rail, high pressure fuel line and electronic control injector. In common-rail high-pressure fuel injection system, high pressure wave of injection system and geometry of injector elements have a major effects on high-pressure fuel spray. Therefore, in this study, the numerical model was developed for analysis about the common-rail fuel pressure pulsation by using AMESim code. We could secure stability of common-rail high-pressure fuel injection system through optimal design of fuel line.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.37 no.3
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• pp.397-403
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• 2013

The demand for eco-friendly and higher fuel economy vehicles has helped develop eco-friendly and fuel-efficient vehicles such as hybrid vehicles. In a hybrid vehicle, the change in the battery charge after driving should be added to the fuel consumption as the equivalent fuel usage based on its own characteristics. Thus, the fuel efficiency of a hybrid vehicle cannot be improved simply by increasing the battery capacity. In this study, I attempt to improve the total fuel economy of a hybrid vehicle, including the equivalent fuel consumption, by modeling a fuel cell hybrid vehicle using Matlab Simulink, analyzing the usage zone of the fuel cell with the existing control strategy, and optimizing the power distribution of the battery and fuel cell in the main usage zone of the fuel cell.

• Journal of Energy Engineering
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• v.19 no.2
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• pp.92-102
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• 2010

Plug-in hybrid electric vehicle(PHEV) is a hybrid electric vehicle (HEV) with more added battery capacity that can be recharged from the electric power grid. Plug-in battery electric vehicle(PBEV) is a pure electric vehicle that uses only electric motor using electricity from battery that recharged from the power grid. PHEV and PBEV requires recharging of batteries in the vehicles from electric power grid. Recently, PHEVs and PBEV are being developed around the world. It is important to understand how these electric vehicles affect power demands and carbon dioxide emissions. From vehicle customer viewpoint, running energy cost will be imporatnt factor to consider. This paper analyzes the potential impacts of PHEVs and PBEVs on electric power demand, and associated CO2 emissions in 2020 with an projection that the vehicles will be penetrated with 10% market share. Energy costs for the vehicles are also calculated and compared with the conventional combustion vehicle.

• Journal of Korean Institute of Industrial Engineers
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• v.32 no.3
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• pp.226-235
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• 2006

Telematics is expected to be one of the fastest growing businesses in information technology area. It may create a new emerging market in industry related to automotive, telecommunications, and information services. Especially vehicle navigation service is considered as a killer application among telematics service applications. The current vehicle navigation service typically recommends a single path that is based on the traveling time or distance from the origin to the destination. The system provides two options for users to choose either via highway or via any road. Since the traffics and road conditions of big cities are very complicated and dynamic, the demand of multi-path guidance system is increasing in telematics market. The multi-path guidance system should allow drivers to choose a path based on their individual preferences such as traveling time, distance, or route familiarity. Using the Lawler's algorithm, it is possible to find multiple paths; however, due to the lengthy computational time, it is not suitable for the real-time services. This study suggests a computationally feasible and efficient heuristic multiple paths finding algorithm that is reliable for the real-time vehicle navigation services.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.12 no.3
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• pp.114-128
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• 2013

Greenhouse gas emissions have been an important issue in different countries because of their effects on global warming. The government has to organize greenhouse gas reduction measures suitable to regional characteristics by establishing annual implementation plans and comprehensive policies based on the UNFCCC. The transportation sector is one of the major contributors of air pollution; hence increasing need to estimate current and future traffic emissions precisely. Under these circumstances, a number of emission models have been developed recently. However, current methods of estimation cannot carry out effective analyses because it does not reflect vehicle movement characteristics. This study aims to present a new method for calculating road traffic emissions in Goyang city. A travel demand model is utilized to carry out GHG emission estimates according the traffic data (fleet composition, vehicle kilometers travelled, traffic intensity, road type, emission factors and speed). This study evaluates two approaches to estimate the road traffic emissions in Goyang City: Pollution-Emis and the Handbook of Emission Factors for Road Transport (HBEFA v.3.1) which is representative of the "average speed" and the "traffic situation" model types. The evaluation of results shows that the proposed emission estimation method may be a good practice if vigilant implementation of model inputs is observed.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.18 no.6
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• pp.431-437
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• 2017

In this study, based on the RPS algorithm, the application results to an electrically controlled suspension system using previewed road information are presented. Reducing the excessive vibration induced by a disturbance transmitted to the system and secure its stability is a major issue. In particular, in the automotive industry, the demand is constantly being raised. A typical external disturbance causing vibration and instability of a vehicle is an irregular roadway surface that contacts a running vehicle tire. Therefore, obtaining such profile information is an important process. The RPS algorithm using a multi sensor system was constructed and implemented in a real car. Through experimental work using the RPS system included non-contact type optical sensors, it could robustly reconstruct the road input profiles from the intermixed data onto the vehicle's dynamic motion while traveling at an uneven roadway surface. A controller with a preview control was designed in the framework of a semi-active suspension system based on the 7 degrees of freedom full vehicle model. The control performance of the system was evaluated through simulations and the results were compared with the passive vehicle condition. These results highlight the feasibility of the presented control frame.

• Journal of Korean Society of Transportation
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• v.22 no.7 s.78
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• pp.107-117
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• 2004

The growing demand for customer-response, made-to-order manufacturing and satisfactory delivery are stimulating the importance of commercial fleet management problem. Moreover, the rapid transformation to the customer-oriented multi-frequency, relatively small fleet, such as home delivery and Perishable goods, requiring prompt delivery and advanced real-time operation of vehicle fleets. In this paper we consider the vehicle routing problem(VRP) to minimize delivery completion time which is equal to the time that last customer wait for the vehicle in fleet operation. The mathematical formulation is different from those for the classical VRP which is minimizing cost/distance/time by running vehicles in manager's point of view. The key aspect of this model is not considering the return time from the last customer to depot in every vehicle path. Thereby, the vehicle dispatcher can afford to dynamically respond to customer demand and vehicle availability. The customer's position concerned with minimizing waiting time that may be applied for the delivery of product required freshness or delivery time. Extensive experiments are carried out to compare the performance of minimizing delivery completion time by using the ILOG Solver which has the advantage of solving quickly an interim solution very near an optimal solution. The experimental results show that the suggested model can easily find near optimal solution in a reasonable computational time under the various combination of customers and vehicles.