• Journal of international Conference on Electrical Machines and Systems
• /
• v.2 no.2
• /
• pp.171-178
• /
• 2013

This paper discusses to conduct experimental verification of two types of micro electric vehicles (EV) in order to realize improvement in electric mileage and shorten a charging time of the battery. First, electric double layer capacitor (EDLC) systems to use as a secondary battery are proposed. The internal resistance of EDLC is small compared with a rechargeable battery, and it is suitable for momentary charge-discharge of EV. Next, control circuits of the capacitors to increase the regenerative electric power are utilized. Then, a novel method to charge a main battery of the EV is introduced. Finally, experimental results demonstrate the validity of the proposed method.

• The Transactions of The Korean Institute of Electrical Engineers
• /
• v.60 no.3
• /
• pp.566-571
• /
• 2011

As Plug-in Hybrid Vehicle and Electric Vehicle (PHEV/EV) take a greater share in the personal automobile market, their high penetration levels may bring potential challenges to electric utility especially at the distribution level. Thus, there is a need for the flexible charging management strategy to compromise the benefits of both PHEV/EV owners and power grid side. There are many different management methods that depend on the objective function and the constraints caused by the system. In this paper, the schema and dispatching schedule of centralized PHEV/EV charging spot network are analyzed. Also, we proposed and compared three power allocation strategies for centralized charging spot. The first strategy aims to maximize state of vehicles at plug-out time, the rest methods are equalized allocation and prioritized allocation based on vehicles SoC. The simulation results show that each run of the optimized algorithms can produce the satisfactory solutions to response properly the requirement from PHEV/EV customers.

• Journal of Electrical Engineering and Technology
• /
• v.12 no.1
• /
• pp.451-458
• /
• 2017

An Electric Vehicle (EV) is operated with the electric energy of a battery in place of conventional fossil fuels. Thus, a suitable charging infrastructure must be provided to expand the use of electric vehicles. Because the battery of an EV must be charged to operate the EV, expanding the number of EVs will have a significant influence on the power supply and demand. Therefore, to maintain the balance of power supply and demand, it is important to be able to predict the numbers of charging EVs and monitor the events that occur in the distribution system. In this paper, we predict the hourly charging rate of electric vehicles using transformation matrix, which can describe all behaviors such as resting, charging, and driving of the EVs. Simulation with transformation matrix in a specific region provides statistical results using the Monte-Carlo Method.

• Transactions of the Korean Society of Automotive Engineers
• /
• v.22 no.3
• /
• pp.250-256
• /
• 2014

EV(Electric Vehicle) has many benefits such as prevention of global warming and so on. But due to driving source changing from combustion engine to battery and e-motor, new R&D difficulties have arisen which changing of desired vehicle performance and multidisciplinary design constraints by means of strong coupled multi-physics domain problems. Additionally, dynamics performances of EV becomes more important due to increasing customer's demands and expectations for EV in compare with internal combustion engine vehicle. In this paper suggests model based development platform of EV through integrated simulation environment for improving analyse & design accuracy in order to solve multi-physics problem. This simulation environment is integrated by three following specialized simulation tools IPG CarMaker, AVL Cruise, DYMOLA that adapted to each purpose. Furthermore, control algorithm of TV(Torque Vectoring) system is developed using independent driven e-motor at rear wheels for improving handling performance of EV. TV control algorithm and its improved vehicle performances are evaluated by numerical simulation from standard test methods.

• Journal of Electrical Engineering and Technology
• /
• v.8 no.1
• /
• pp.1-10
• /
• 2013

The power industry is currently facing many challenges, due to the new environment created by the introduction of smart grid technologies. In particular, the large-scale deployment of electric vehicles (EVs) may have a significant impact on demand for electricity and, thereby, influence generation and transmission system planning. However, it is difficult to deal with uncertainties in EV charging loads using deterministic planning methods. This paper presents a two-stage stochastic decomposition method with Latin-hyper rectangle sampling (LHRS) to solve the integrated generation and transmission planning problem incorporating EV deployment. The probabilistic distribution of EV charging loads is estimated by Latin-hyper rectangle sampling (LHRS) to enhance the computational performance of the proposed method. Numerical results are presented to show the effectiveness of the proposed method.

• Transactions of the Korean Society of Automotive Engineers
• /
• v.20 no.2
• /
• pp.38-46
• /
• 2012

This paper discusses the energy consumption for a mid-size electric vehicle(EV) under various conditions. In order to analyze which driving style is more efficient in terms of the system of the EV, we develop the electric vehicle model and apply several types of speed profiles such as different steady speeds, acceleration/deceleration, and a real world driving cycle including the elevation profile obtained from a GPS device. The results show that the energy consumption of the EV is affected by the operating efficiency of components when driving at low speed, while it depends on required power at wheels when driving at high speed. Also this paper investigates the effect of the elevation of a road and the rate of electrical braking on the energy consumption as well as the fuel economy of a conventional vehicle model under the same conditions.

• Journal of Internet Computing and Services
• /
• v.21 no.5
• /
• pp.119-127
• /
• 2020

As the electric vehicle (EV) market in South Korea grows, it is required to expand charging facilities to respond to rapidly increasing EV charging demand. In order to conduct a comprehensive facility planning, it is necessary to forecast future demand for electricity and systematically analyze the impact on the load capacity of facilities based on this. In this paper, we design and develop a Long Short-Term Memory (LSTM) neural network model that predicts the daily peak electric load at each charging station using the EV charging data of KEPCO. First, we obtain refined data through data preprocessing and outlier removal. Next, our model is trained by extracting daily features per charging station and constructing a training set. Finally, our model is verified through performance analysis using a test set for each charging station type, and the limitations of our model are discussed.

• International Journal of Advanced Culture Technology
• /
• v.11 no.2
• /
• pp.330-344
• /
• 2023

This research report cuts across management sciences (market strategy entry mode development) and innovative technology (Electric Vehicle (EV)) alongside measures to submerge global warming. The development of a successful entry mode for the electric Vehicle into the African continent is the main objective of the study. The study focuses on an analysis of how electric car manufacturers can enter the African market in other to achieve global sustainability and social responsibility. The methodology is based on identifying the factors that affect the choice of an entry mode into international markets by multinational companies desiring to leverage their revenue through a foreign market. It also offered a quantitative approach that can support the economic and sustainability entry mode model for EVs and a qualitative approach of Porter's five forces analysis as an entry mode coaching tool for EVs. These proxies are used in quite a wide range of multivariate statistical methods (trend analysis, ratio, and probability, comparative t-test technique, auto-regression, and ordinary least square technique). The result acknowledges joint venture and setting of the plant (physical presents) as the optimal entry mode in African EV market. It requires the EV manufacturers a tire-free emission innovation technology in order to optimize the global sustainability initiative.

• Proceedings of the KIEE Conference
• /
• 1995.07a
• /
• pp.288-290
• /
• 1995

The new power train system for electric vehicle is introduced in this paper. This system includes two light weighted and high-efficient motor, two space vector PWM inverter, one system controller using CAN controller and DSP320C50. These are developed by KERI cooperated with 5 major industry companies, 5 universities including 2 foreign universities. The reliability and performance are proven by the test rig. The reference data will be collected for further researches.

• Journal of the Korean Institute of Gas
• /
• v.23 no.1
• /
• pp.54-61
• /
• 2019

As the market for portable electronic devices expands, the demand for Lithium Ion Battery (LIB) is also increasing. LIB has higher efficiency than other secondary batteries, but there is a risk of explosion / fire due to thermal runaway reaction. Especially, Electric Vehicles (EV) equipped with a large capacity LIB cell also has a danger due to a large amount of toxic gas generated by a fire. Therefore, it is necessary to analyze the risk of toxic gas generated by EV fire to minimize accident damage. In this study, the flow of toxic gas generated by EV fire was numerically analyzed using Computational Fluid Dynamic. Scenarios were established based on literature data and EV data to confirm the effect distance according to time and exposure standard. The purpose of this study is to analyze the risk of toxic gas caused by EV fire and to help minimize the loss of life and property caused by accidents.