• Transactions of the Korean Society of Mechanical Engineers B
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• v.40 no.12
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• pp.769-776
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• 2016

In this study, a numerical simulation of a vanadium redox flow battery was investigated for reactions involving an electrochemical species using comprehensive conservation laws and a kinetic model. For a 3-D geometry of the cell, the distributions of electric potential, vanadium concentration, overpotential, and ohmic loss were calculated. The cell temperature and initial vanadium ion concentration were set as variables. The voltage and electrochemical loss were calculated for each variable. The effects of each variable's impact on the electrochemical performance of a vanadium redox flow battery was numerically analyzed using the calculated overpotential in the electrode and the ohmic loss in the electrolyte phase. The cell temperature increased from $20^{\circ}C$ to $80^{\circ}C$ when the voltage efficiency decreased from 89.34% to 87.29%. The voltage efficiency increased from 88.65% to 89.25% when the vanadium concentration was changed from $1500mol/m^3$ to $3000mol/m^3$.

• KIPS Transactions on Computer and Communication Systems
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• v.9 no.4
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• pp.77-82
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• 2020

Wireless sensor networks (WSNs) suffer from not only a short lifetime due to limited energy but also an energy imbalance between nodes close to the sink and others. In order to fundamentally solve the short lifetime, recent studies utilize the environmental energy such as solar power. Additionally, WSNs using mobile sinks are being studied to address the energy imbalance problem. This paper proposes an improved CTP (Collection Tree Protocol) scheme which uses these two approaches simultaneously. Basically, it is based on a CTP scheme which is a very popular data collection strategy designed for the typical battery-based WSNs with a fixed sink. Therefore, we tailored it for solar-powered WSNs with a mobile sink. Performance verification confirms that our scheme reduces the number of blackout nodes significantly compared to the typical CTP, thus increases the amount of data collected by the sink.

• Journal of the Institute of Convergence Signal Processing
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• v.14 no.2
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• pp.99-104
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• 2013

Replacement of sensor nodes for monitoring a wide range area such as mountains and forests needs a lot of time and cost. Using new and renewable energy around them can maximize the lifetime of wireless sensor networks, in which solar energy is infinite energy source that is available in 365 days. To design these sensor networks, solar energy model is essential and to estimate and analyze the overall photovoltaic energy. Using this, we can figure out important data such as the size and performance of solar panel needed. However, existing researches for solar energy harvesting consider parts of many factors to influence the quantity of solar energy gathered. In this paper, we suggest advanced solar energy harvesting model considering angular loss (solar cell panel), overheat loss (solar cell), rechargeable battery heat and cooling for each monthly properties. From our experimental results according to outdoor temperature, panel angle and the surface temperature of solar panel, we show these impact factors are correctly configured.

• The Journal of the Korea institute of electronic communication sciences
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• v.10 no.11
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• pp.1265-1270
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• 2015

For the sensor node or collection node operating with a battery in a wireless sensor network, MAC protocols with improved energy efficiency are important performance factors. In this paper, in order to improve the restrictive capability in accordance with the fixed activity period of the duty cycle technology in the MAC protocol for wireless sensor networks, we propose a periodic adaptive wakeup technique based on receive prediction. The proposed technique is through a performance evaluation using the CC2500 RF transceiver and C8051F330 microcontroller based wireless node, to analyze the minimum active period. As a result, it was confirmed that it is possible to improve energy efficiency by adaptively changing the sleep period in accordance with the change of period.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.48 no.6
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• pp.71-81
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• 2011

Unlike a typical internal combustion engine vehicle, the powertrain system of the pure electric vehicle, consisting of battery, inverter and motor, has direct effects on the vehicle performance and dynamics. Then, the specific modeling of such complex electro-mechanical components enables the insight into the longitudinal dynamic outputs of the vehicle and analysis of entire powertrain systems. This paper presents the dynamic model of electric vehicle powertrain systems based on theoretical approaches to predict and analyze the final output performance of electric vehicles. Additionally, the correlations between electric input signals and the final output of the mechanical system are mathematically derived. The proposed model for powertrain dynamics of electric vehicle systems are validated with a reference electric vehicle model using generic simulation platform based on Matlab/Simulink software. Consequently, the dynamic analysis results are compared with electric vehicle simulation model in some parameters such as vehicle speed/acceleration, and propulsion forces.

• Proceedings of the KIPE Conference
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• 2011.11a
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• pp.165-166
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• 2011

양방향 AC/DC 컨버터를 이용하여 EV배터리를 충전하거나 계통으로 전력을 보낼 때 PI제어기를 사용하면 임피던스를 예측할 수 없으므로 초기 구동시 오버슈트가 발생하여 회로에 스트레스가 발생한다. 본 논문에서는 Sliding Mode Controller (SMC)를 이용하여 계통으로 전력을 전달하는 알고리즘으로 소프트 스타트를 하는 알고리즘을 제안하였다. 시뮬레이션을 통하여 회로를 구성하고 제안한 제어 방법의 성능을 확인하였다.

• Journal of Energy Engineering
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• v.22 no.4
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• pp.338-346
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• 2013

The conventional lead acid battery is optimized for cranking performance of engine. Recently electric devices and fuel economy technologies of battery have influenced more deep cycle of dynamic behavior of battery. I also causes to reduce battery life-time. This study proposed that aging battery model is focused for increasing of battery durability. The stress factors of battery aging consist of discharge rate, charging time, full charging time and temperature. This paper considers the electrochemical kinetics, the ionic species conservation, and electrode porosity. For prediction of battery life cycle we consider battery model containing strong impacts, corrosion of positive grid and shedding. Finally, we validated that modeling results were compared with the accelerated thermal measurement data.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.20 no.8
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• pp.1569-1580
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• 2016

For the 2.4 GHz active RFID to be successful in the market, one of the requirements is the increased battery life. However, currently we do not have any accurate power consumption estimation method. In this study we develop a simulation model, which can be used to estimate power consumption of tag accurately. Six different simulation models are proposed depending on collision algorithm and query command method. To improve estimation accuracy, we classify tag operating modes as the wake-up receive, UHF receive, sleep timer, tag response, and sleep modes. Power consumption and operating time are identified according to the tag operating mode. Query command for simplifying collection and ack command procedure and newly developed collision control algorithm are used in the simulation. Other performance measures such as throughput, recognition time for multi-tags, tag recognition rate including power consumption are compared with those from the current standard ISO/IEC 18000-7.

• KIPS Transactions on Computer and Communication Systems
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• v.12 no.3
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• pp.93-102
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• 2023

Data processing through the cloud causes many problems, such as latency and increased communication costs in the communication process. Therefore, many researchers study edge computing in the IoT, and autonomous driving is a representative application. In indoor self-driving, unlike outdoor, GPS and traffic information cannot be used, so the surrounding environment must be recognized using sensors. An efficient autonomous driving system is required because it is a mobile environment with resource constraints. This paper proposes a machine-learning method using neural networks for autonomous driving in an indoor environment. The neural network model predicts the most appropriate driving command for the current location based on the distance data measured by the LiDAR sensor. We designed six learning models to evaluate according to the number of input data of the proposed neural networks. In addition, we made an autonomous vehicle based on Raspberry Pi for driving and learning and an indoor driving track produced for collecting data and evaluation. Finally, we compared six neural network models in terms of accuracy, response time, and battery consumption, and the effect of the number of input data on performance was confirmed.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.15 no.6
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• pp.3357-3362
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• 2014

The purpose of this research was to develop a resonance electric power generator to harvest vibration energy while the vehicle is driving on a road surface. The electric power generator in the paper was designed using the resonance phenomenon to effectively respond to vibrations from the road surface, which is a comparatively small energy source. Vibration displacement analysis using MATLAB and transient analysis using Ansys MAXWELL, which is a commercial electromagnetic analysis program, was performed to predict the input velocity for the generator and verify the electric power generation. If this electric power generator is applicable to hybrid or electric vehicles, it can be valuable around an automotive electric system and help maintain the performance of the vehicle battery.