Min-Jae Kang

An Efficient Scheduling Scheme on Charging Stations for Smart Transportation

This paper proposes a reservation-based scheduling scheme for the charging station to decide the service order of multiple requests, aiming at improving the satisfiability of electric vehicles. The proposed scheme makes it possible for a customer to reduce the charge cost and waiting time, while a station can extend the number of clients it can serve. A linear rank function is defined based on estimated arrival time, waiting time bound, and the amount of needed power, reducing the scheduling complexity. Receiving the requests from the clients, the power station decides the charge order by the rank function and then replies to the requesters with the waiting time and cost it can guarantee. Each requester can decide whether to charge at that station or try another station. This scheduler can evolve to integrate a new pricing policy and services, enriching the electric vehicle transport system.

2008 Special Issue: Power quality control of an autonomous wind-diesel power system based on hybrid intelligent controller

Wind power generation is gaining popularity as the power industry in the world is moving toward more liberalized trade of energy along with public concerns of more environmentally friendly mode of electricity generation. The weakness of wind power generation is its dependence on nature-the power output varies in quite a wide range due to the change of wind speed, which is difficult to model and predict. The excess fluctuation of power output and voltages can influence negatively the quality of electricity in the distribution system connected to the wind power generation plant. In this paper, the authors propose an intelligent adaptive system to control the output of a wind power generation plant to maintain the quality of electricity in the distribution system. The target wind generator is a cost-effective induction generator, while the plant is equipped with a small capacity energy storage based on conventional batteries, heater load for co-generation and braking, and a voltage smoothing device such as a static Var compensator (SVC). Fuzzy logic controller provides a flexible controller covering a wide range of energy/voltage compensation. A neural network inverse model is designed to provide compensating control amount for a system. The system can be optimized to cope with the fluctuating market-based electricity price conditions to lower the cost of electricity consumption or to maximize the power sales opportunities from the wind generation plant.

Image Reconstruction of Subspace Object Using Electrical Resistance Tomography

Electrical resistance tomograpy (ERT) maps resistivity values of the soil subsurface and characterizes buried objects. The characterization includes location, size, and resistivity of buried objects. In this paper, truncated least squares (TLS) is presented for the solution of the ERT image reconstruction. Results of numerical experiments in ERT solved by the TLS approach is presented and compared to that obtained by the Gauss-Newton method.

Design of a power scheduler based on the heuristic for preemptive appliances

This paper presents a design and evaluates the performance of a power consumption scheduler in smart grid homes, aiming at reducing the peak load in individual homes or buildings with reasonable computation time. Following the task model consisting of actuation time, operation length, deadline, and a consumption profile, the scheduler first investigates all the allocations for nonpreemptive tasks. Next, for each partial allocation, slots having the smallest power consumption are selected and assigned to the preemptive task, reducing the search space complexity for a preemptive task from O(MM/2) to O(1). The performance measurement result, obtained from the implementation of the proposed scheme and comparison with the optimal schedule, shows that the accuracy loss remains below 3.9 % for the number of tasks less than 9 and also below 7.6 % for the space size distribution. Moreover, the proposed scheme can find the optimal schedule more than 80 % for the given parameter sets. After all, our scheme can decide the power consumption schedule promptly with quite a small loss of accuracy.

Battery Consumption Modeling for Electric Vehicles Based on Artificial Neural Networks

This paper presents how to develop a battery consumption model taking advantage of state-of-charge streams acquired from real-life electric vehicles. From the record consisting of timestamp, longitude, latitude, and battery remaining, learning patterns are generated to build a neural network for each of 4 major roads, essentially taken by long-distance trips in Jeju city. Our 3-layer neural network model is made up of an input node, 10 hidden nodes, and an output node. The input variable takes the approximated distance while the output variable represents the battery consumption from the start point of a road. Neural networks, being able to efficiently tracing non-linear data streams, accurately keep track of battery consumption irrespective of road shapes and elevation changes. The assessment result shows that the average errors for each road range from 0.22 to 0.33 km, indicating that this model can estimate battery demand for a given route for navigation applications.

Estimating soil parameters using the kernel function

In this paper, a fast algorithm for estimating soil parameters has been presented according to which, after obtaining the kernel function, one can compute the soil parameters of the multilayer earth structure by analyzing the kernel function. The estimated soil parameters using the proposed method are in good agreement with the given earth structure.

Electricity Quality Control of an Independent Power System Based on Hybrid Intelligent Controller

Wind power generation is gaining popularity as the power industry in the world is moving toward more liberalized trade of energy along with public concerns of more environmentally friendly mode of electricity generation. The weakness of wind power generation is its dependence on nature--the power output varies in quite a wide range due to the change of wind speed, which is difficult to model and predict. The excess fluctuation of power output and voltages can influence negatively the quality of electricity in the distribution system connected to the wind power generation plant. In this paper, the authors propose an intelligent adaptive system to control the output of a wind power generation plant to maintain the quality of electricity in the distribution system. The target wind generator is a cost-effective induction generator, while the plant is equipped with a small capacity energy storage based on conventional batteries, heater load for co-generation and braking, and a voltage smoothing device such as a static Var compensator (SVC). Fuzzy logic controller provides a flexible controller covering a wide range of energy/voltage compensation. A neural network inverse model is designed to provide compensating control amount for a system. The system can be optimized to cope with the fluctuating market-based electricity price conditions to lower the cost of electricity consumption or to maximize the power sales opportunities from the wind generation plant.

A Message Scheduling Scheme in Hybrid Telematics Networks

This paper proposes a message scheduling scheme for periodic sensor streams in hybrid telematics network system consist of infrastructure and ad-hoc network. To meet the fairness requirement of traffic information system, the proposed scheme classifies each message into 3 groups, and picks the message according to the previous transmission ratio and future behavior estimation, compensating the degraded stream. The performance of proposed scheme is evaluated via simulation using a discrete event scheduler based on the real movement data obtained from a telematics service system currently in operation, and the result demonstrates that the fairness of the message collection is improved by up to 3.8 % for the given parameters in vehicular network without sacrificing much timeliness.

Design of an Efficient Matching-Based Relocation Scheme for Electric Vehicle Sharing Systems

This paper designs an efficient object relocation scheme for electric vehicle sharing systems and measures its performance, aiming at accelerating the deployment of electric vehicles having better energy efficiency but unaffordable cost for personal ownership. For the given relocation vector, namely, the desired number of vehicles in each station after the completion of relocation, the relocation planner decides the destination of each vehicle in such a way to minimize the total moving distance. As a matching problem between two parties, one for EVs in overflow stations and the other for underflow stations, the proposed scheme adapts the well-known stable marriage problem solver. The performance measurement result obtained by a prototype implementation reveals that the relocation distance can be kept below 100 km when the number of EVs is 80 for the given service scenarios.

Design of intersection switches for the vehicular network

This paper proposes an efficient message switch scheme on the vehicular telematics network, especially for the intersection area where routing decision may be complex due to severe trafic concentration. Each switch node opens an external interface to exchange messages with vehicles proceeding to the intersection from the pre-assigned branch as well as switches the received messages via the internal interfaces, accessing two shared channels according to slot-based MAC. Within each synchronized slot, channel probing and switching can efficiently deal with channel errors. The simulation result shows that the proposed scheme improves the delivery ratio by up to 13 % for the channel error rate range as well as up to 8.1 % for the given network load distribution.