Young Jae Jang

The Optimal System Design of the Online Electric Vehicle Utilizing Wireless Power Transmission Technology

The Online Electric Vehicle (OLEV) is an innovative electric transportation system developed by the Korea Advanced Institute of Science and Technology (KAIST), Daejon, Korea, which remotely picks up electricity from power transmitters buried underground. Unlike a conventional electric vehicle that requires significant recharging downtime, the battery in the OLEV can be charged while the vehicle is in motion. Selected as one of “the 50 Best Innovations of 2010” by TIME Magazine, the OLEV is considered as a potential solution for the next-generation electric public transportation system in South Korea. The prototype of the OLEV has been developed, and the commercialization process is now in progress. One of the main tasks to achieve the successful commercialization of the system is to determine economically how to allocate the power transmitters on the given routes and how to evaluate the right battery capacity for the vehicle. The allocation of the power transmitters and the size of the battery capacity directly affect the initial infrastructure cost. In this paper, we first introduce the system design issues of the mass transportation system operating with OLEV. We then present a mathematical model and an optimization method to allocate economically the power transmitters and to determine the battery capacity of the OLEV-based mass transportation system. The particle swarm optimization (PSO) algorithm is used as the solution method for the optimization problem. Numerical problems with sensitivity analysis are presented to show the validity of the mathematical model and solution procedure.

Economic Analysis of the Dynamic Charging Electric Vehicle

A wireless charging or inductive charging electric vehicle (EV) is a type of EVs with a battery that is charged from a charging infrastructure, using a wireless power transfer technology. Wireless charging EVs are classified as stationary or dynamic charging EVs. Stationary charging EVs charge wirelessly when they are parked, and dynamic charging EVs can charge while they are in motion. The online electric vehicle developed at the Korea Advanced Institute of Science and Technology is an example of a commercially available dynamic charging transportation system. Numerous studies have reported that one of the benefits of dynamic charging is that it allows smaller and lighter batteries to be used, due to frequent charging using the charging infrastructure embedded under roads. In this paper, we quantitatively analyze the benefits of dynamic charging with an economic model of battery size and charging infrastructure allocation, using a mathematical optimization model. Particularly, we analyze by how much battery size can be reduced and what the cost saving of reducing the battery size is with the model. We also show that the dynamic charging can be beneficial to battery life.

Optimal design of the wireless charging electric vehicle

The On-Line Electric Vehicle (OLEV) is an electric vehicle system is that utilizes the innovative wireless charging solution developed at Korea Advanced Institute of Science and Technology (KAIST) in South Korea. The OLEV system consists of vehicles and road-embedded power transmitters. The battery in the vehicle is charged remotely from the transmitters buried under the road and the charge can be done even while the vehicle is moving. The prototype of the OLEV has been successfully developed and the process of developing a commercial version is in progress. The OLEV has been considered as one of the leading green mass transportation solutions in Seoul. The key issue in the commercialization of the OLEV is to determine the battery size and the allocation of the power transmitters on the route. This paper describes a method of allocating the power transmitters and evaluating the battery size using a mathematical optimization technique. Although the presented method is motivated from the actual design issue of the OLEV, the concept and approach can be applied to any electric vehicle system utilizing a wireless charging technology.

System Architecture and Mathematical Models of Electric Transit Bus System Utilizing Wireless Power Transfer Technology

We introduce a new type of electric transit bus (ETB) system that uses the innovative wireless power transfer technology developed by the Korea Advanced Institute of Technology (KAIST), which is called on-line electric vehicle (OLEV). In the ETB system, the wireless-charging infrastructure installed under the road charges the fleet of electric buses that are operative over that road. The technology is innovative in that the battery in the bus is charged while it is moving over the charging infrastructure. Unlike conventional electric vehicles, the OLEV-based ETB system is a road-vehicle integrated system. Since charging occurs while the vehicle is operational, the performance of the operation depends on the system integration of the vehicle and the road in which the charging infrastructure is embedded. In this paper, we qualitatively analyze the benefits of the OLEV-based ETB system from the energy logistics perspective. We then present two analytical economic design optimization models. The first model is for an ETB system operating in a “closed environment” with no traffic and no heavy vehicle interactions. The OLEV-based shuttle bus currently operating on the KAIST campus constitutes such a case. The second model is the “open environment model” and considers an ETB system operating in normal traffic conditions. We also present the result of numerical case studies for the optimization models. The goal of this paper is to present an innovative ETB system and a logical design framework for commercializing and deploying that system.

Introduction to Automated Material Handling Systems in LCD Panel Production Lines

This paper introduces widely used current automated material handling systems in thin-film-transistor liquid-crystal-display (TFT-LCD) panel manufacturing systems. The automated material handling system (AMHS) in this paper refers to a hardware system that transports discrete parts from one processing machine to another. The TFT-LCD panel industry has been one of the fastest growing industries in the last decade. In particular, the process equipment for the TFT-LCD has undergone significant improvement, making technology innovations possible. However, the AMHS equipment has not had much improvement and in fact most of the current TFT-LCD factories use the same AMHS concept they used 10 years ago. Now, the role of the AMHS in TFT-LCD production lines becomes more important as production efficiency becomes a primary determinant of competitiveness. Therefore, TFT-LCD manufacturers are trying to increase their productivity by adopting an efficient material handling method and technology

System optimization of the On-Line Electric Vehicle operating in a closed environment

Introduce a new type of electric vehicle called On-line Electric Vehicle (OLEV).The battery in the OLEV is charged wirelessly while the vehicle is in motion.The charge is done from the power transmitter installed under the road.Optimal allocation of the power transmitters is presented.The MIP model is solved using CPLEX and provides insights into system design. We introduce a new type of electric-powered transportation system called the On-Line Electric Vehicle ( OLEV TM ) developed by Korea Advanced Institute of Science and Technology (KAIST). The battery in the OLEV is charged remotely from power transmitters installed under the road using the innovative wireless charging technology. One of the successful commercial applications of the OLEV is the KAIST shuttle bus system operating on the KAIST campus. In this paper, we address the OLEVs system design issues. The key design and economic parameters of the OLEV are the battery size and the allocation of the power transmitters that wirelessly supply the electric energy to the vehicle. We first construct a general mathematical model for optimally allocating the power transmitters and determining the size of the battery for a transportation system with wireless charging electric vehicles. Then we apply the model to a specific model that is currently operating. We are particularly interested in the OLEV system operating in a closed environment in which vehicles operate under regulated velocity and less traffic. The OLEV shuttle bus currently operating at KAIST is a good example of the system under a closed environment. We are particularly concerned about the closed environment system since it is the potential application area where the OLEV-based transportation is effectively commercialized. The optimization problem is constructed in the form of a Mixed Integer Programming (MIP) model. The sensitivity analysis is presented using the vehicle operational data collected from the OLEV shuttle buses. The sensitivity analysis provides meaningful insight into the OLEV-based transportation system design. We also explain how the general model can be extended to different transportation systems other than the closed environment.

Potential of IMU Sensors in Performance Analysis of Professional Alpine Skiers

In this paper, we present an analysis to identify a sensor location for an inertial measurement unit (IMU) on the body of a skier and propose the best location to capture turn motions for training. We also validate the manner in which the data from the IMU sensor on the proposed location can characterize ski turns and performance with a series of statistical analyses, including a comparison with data collected from foot pressure sensors. The goal of the study is to logically identify the ideal location on the skier’s body to attach the IMU sensor and the best use of the data collected for the skier. The statistical analyses and the hierarchical clustering method indicate that the pelvis is the best location for attachment of an IMU, and numerical validation shows that the data collected from this location can effectively estimate the performance and characteristics of the skier. Moreover, placement of the sensor at this location does not distract the skier’s motion, and the sensor can be easily attached and detached. The findings of this study can be used for the development of a wearable device for the routine training of professional skiers.

The optimal economic design of the wireless powered intelligent transportation system using genetic algorithm considering nonlinear cost function

Present a new type of electric vehicle called On-Line Electric Vehicle (OLEV).Battery in OLEV charged wirelessly from charging infrastructure under the road.Propose a mathematical model to optimally allocate the charging infrastructure.Propose a mathematical model to evaluate the economic battery size of OLEV.We propose Genetic Algorithms (GA) as the solution methodology. We present a new type of electric vehicle called the On-Line Electric Vehicle (OLEV?), developed by the Korea Advanced Institute of Science and Technology (KAIST). The OLEV uses an innovative wireless charging technology that enables the battery in the vehicle to be charged through a charging infrastructure installed under the road. The vehicle can be charged while stationary or moving. The OLEV is considered a revolutionary transport solution, as it overcomes the problems facing conventional battery-powered electric vehicles, such as long charging times and the need to stop frequently to charge. Several commercial versions of the OLEV have been successfully deployed, including the trolleys serving in Seoul Grand Park and the KAIST campus shuttles. In this paper, we propose a mathematical model to optimally allocate the charging infrastructure on the route, and to determine the vehicles battery size. The model is specifically concerned with the OLEV system applied to mass transport buses such as those used in Seoul Grand Park and on the KAIST campus. This paper deals with the optimization problem considering nonlinear cost function where the cost of the power transmitter is nonlinear. We propose Genetic Algorithms (GAs) as the solution methodology for this problem.

System architecture and mathematical model of public transportation system utilizing wireless charging electric vehicles

In this paper the recent progress of the wireless charging electric transportation system developed at Korea Advanced Institute of Technology (KAIST) called On-Line Electric Vehicle (OLEV) is introduced. The system architecture and mathematical model describing the cost issue of the OLEV based public transportation system are presented. The OLEV is the vehicle and road integrated transportation system. The vehicle operates with an electric motor and battery. However, unlike conventional electric vehicles, the battery in the OLEV is charged remotely from the power transmitters installed in the road. The innovative wireless power transfer mechanism equipped in the OLEV system enables the vehicle to be charged while it is moving. Therefore, the long re-charging vehicle down time, which is the major problem of the conventional electric vehicle, is eliminated. The first commercial version of OLEV was deployed at the Seoul Grand Park. Moreover, activities to apply the OLEV technology to the public mass transportation system are being progressed. In this paper, we conceptually illustrate the design architecture of the wireless power transportation system. Also we discuss the economical design issue for applying the OLEV to the mass transportation system. In particular, with the mathematical model, we explain the cost trade off between the battery size and the power transmission allocation in the road for the OLEV based transportation system.

System Optimization for Dynamic Wireless Charging Electric Vehicles Operating in a Multiple-Route Environment

Dynamic wireless charging (DWC) technology, a novel way of supplying vehicles with electric energy, allows the vehicle battery to be recharged remotely while it is moving over power tracks, which are charging infrastructures installed beneath the road. DWC systems mitigate the range limitation of electric vehicles by using power tracks as additional sources of electric energy. This paper proposes a model and algorithm for optimally designing DWC electric vehicle (EV) systems, particularly those operating in multiple-route environments. Multi-route system comprises several single routes that share common road segments, and the vehicles operating on a specific route are equipped with identical batteries. We build a general model to optimally allocate power tracks and determine the vehicle battery size for each route. Then, we apply a particle swarm optimization algorithm to solve the given multi-route DWC-EV system optimization problem. A numerical example is solved to illustrate the characteristics of the multi-route model, and we show that the proposed modeling approach and algorithm are effective, compared with a mixed integer programming-based exact solution approach. We also conduct a sensitivity analysis to examine the solution behavior of the problem.