• Transactions of the Korean Society of Mechanical Engineers B
• /
• v.40 no.12
• /
• pp.801-807
• /
• 2016

This paper aims to compare and study the cooling performance of a battery system in accordance with the inlet and outlet geometry of the air passage in an EV. The arrangement and the heat source of the battery module were fixed, and the inlet/outlet area and its geometry were varied with the analysis of the cooling performance. The results of this study provide suggestions for the air flow stream line inside of a battery, the velocity field, and the temperature distributions. It was confirmed that the volume flow rate of air should be over $400m^3/h$, in order to satisfy conditions under $50^{\circ}C$, which is the limit condition for stable operation. It was also revealed that the diffuser outlet geometry can improve the cooling performance of battery system.

• Proceedings of the KIPE Conference
• /
• 2015.07a
• /
• pp.239-240
• /
• 2015

본 논문에서는 가정용 단상 계통연계 인버터의 전압손실과 직류링크측 맥동 전압을 고려한 배터리 공칭전압 설계법을 제안한다. 일반적으로 가정용 단상 계통연계 인버터는 3kWh 정도의 낮은 에너지용량을 갖는 배터리팩의 수명 관리상 배터리의 공칭전압은 48V정도로 낮게 설계된다. 단상계통연계형 인버터의 직류링크전압에는 필연적으로 기본주파수의 2배에 해당하는 맥동성분이 포함된다. 이 맥동성분은 계통연계시 전원측 전류에 저차 고조파를 생성하여 전력품질의 저하를 초래하게 된다. 따라서 직류링크측 맥동성분 간섭을 제거하기위한 피드포워드 제어방식을 채택한 단상계통연계형 인버터의 제어시스템을 수립하고, 인버터의 전압손실과 직류링크측 맥동전압을 고려한 배터리 공칭전압 설계법을 제안하며, 시뮬레이션을 통해 타당성을 검증한다.

• Proceedings of the KIPE Conference
• /
• 2018.11a
• /
• pp.129-130
• /
• 2018

리튬 이온 배터리가 전기 자동차 및 다양한 어플리케이션에 적용됨에 따라 배터리 관리 시스템(BMS)의 중요도가 높아지고 있다. 리튬 이온 배터리의 SOC(State of Charge) 및 단자전압 추정은 BMS에서 필수적이며 다양한 알고리즘을 통해 연구되고 있다. 본 논문에서는 비지도 학습 알고리즘인 뉴럴 네트워크의 학습을 위해 특성 파라미터(Characterstic Parmeter)를 선정하였으며, 특성 파라미터의 학습을 통해 리튬 이온배터리의 단자 전압 및 SOC를 추정하였다.

• Journal of the Institute of Electronics Engineers of Korea SC
• /
• v.48 no.6
• /
• pp.34-42
• /
• 2011

With the progress of USN technology, fields to which wireless sensor node is applicable are increased under a condition that it holds a lot of problems to solve for betterment. One of the problems which acts as an obstacle to USN industry diffusion is the wireless sensor node battery exchange to their individual life cycle. Exchanging the battery of so many sensor nodes one by one requires a great deal of times and costs. Such problem is against the convenience supply -aim by applying USN technology. In this paper, using RF wireless power transmission system that power transmission / harvesting module from a distance of 5 m and the power of 10 dBm with a current of 1 mA or more for Sensor Nodes in lithium-polymer battery charging system tested and verified.

• Journal of Korea Multimedia Society
• /
• v.11 no.5
• /
• pp.641-650
• /
• 2008

As portable devices are used widely, power management algorithm is essential to extend battery use time on small-sized battery power. Although many methods have been proposed, they assumed the voltage transition overhead was negligible or was considered partially. However, the voltage transition overhead might not guarantee to schedule real-time tasks in portable multimedia systems. This paper proposes the adaptive power-aware algorithm to minimize the power consumption by considering the voltage transition overhead. It selects only a few discrete frequencies from the whole frequencies of a system and adjusts the interval between two consecutive frequencies based on the system utilization to reduce the number of frequency change. This algorithm saves the power consumption about 10 to 25 percent compared to a CC RT-DVS method and a frequency-smoothing method.

• Proceedings of the Korean Information Science Society Conference
• /
• 2007.06b
• /
• pp.238-242
• /
• 2007

최근들어 휴대용 미디어 플레이어(Portable Media Player)와, 노트북 컴퓨터, PDA(Personal Data Assistant)의 사용이 늘어나면서 얼마나 오랜 시간동안 휴대용 장치를 사용하는가에 대한 문제가 큰 이슈로 떠오르고 있다. 그에 대해 많은 연구가 이루어져 있으며, 현재도 활발히 연구가 진행되고 있다. 본 논문에서는 프로세스 상태, 배터리 상태 그리고 응용프로그램 종류로 이루어진 상황을 고려한 동적 전력관리 시스템에 대해서 제안하고 그에 따른 세부적인 모듈에 관한 설명과 실험 결과를 보여준다. 실험결과 제안한 시스템을 적용하지 않았을 경우와 비교하여 약 14%의 전력 손실 감소 효과를 볼 수 있었다.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.24 no.9
• /
• pp.1202-1208
• /
• 2020

In line with the current era of the 4th Industrial Revolution, it is necessary to prepare for the future by integrating AI elements in the ship sector. In addition, it is necessary to respond to this in the field of power management for the appearance of autonomous ships. In this study, we propose a battery-linked electric propulsion system (BLEPS) algorithm using machine learning's DNN. For the experiment, we learned the pattern of ship power consumption for each operation mode based on the ship data through LabView and derived the battery status through Python to check the flexibility of the generator and battery interlocking. As a result of the experiment, the low load operation of the generator was reduced through charging and discharging of the battery, and economic efficiency and reliability were confirmed by reducing the fuel consumption of 1% of LNG.

• The Journal of the Convergence on Culture Technology
• /
• v.9 no.5
• /
• pp.569-582
• /
• 2023

Lead-acid Battery is the oldest rechargeable battery system and has maintained its position in the rechargeable battery field. The battery causes thermal runaway for various reasons, which can lead to major accidents. Therefore, preventing thermal runaway is a key part of the battery management system. Recently, research is underway to categorize thermal runaway battery cells into machine learning. In this paper, we present a thermal runaway hazard cell detection and verification algorithm using DBSCAN and statistical method. An experiment was conducted to classify thermal runaway hazard cells using only the resistance values as measured by the Battery Management System (BMS). The results demonstrated the efficacy of the proposed algorithms in accurately classifying thermal runaway cells. Furthermore, the proposed algorithm was able to classify thermal runaway cells between thermal runaway hazard cells and cells containing noise. Additionally, the thermal runaway hazard cells were early detected through the optimization of DBSCAN parameters using a grid search approach.

• Journal of the Microelectronics and Packaging Society
• /
• v.24 no.3
• /
• pp.63-69
• /
• 2017

This paper presents a highly efficient power management system for UAV-drones. For free from the battery cell-balancing issue, the proposed system allows the drone to utilize a single-cell Li-Po battery. To realize low-voltage input of 3.7V, the switch-mode step-up DC-DC converter is optimally designed with high power efficiency. The prototype DC-DC converter was implemented with an output voltage of 5V, which will be provided to digital parts of the drone. The power efficiency was measured to be max. 91.3% with low surface temperature. The measured line and load regulations were 0.02V/V and 0.15V/A, respectively. Thanks to the proposed power management system, the available time-to-fly of the drone is expected to be significantly extended in virtue of the enhanced power efficiency.

• The Journal of the Korea Contents Association
• /
• v.12 no.5
• /
• pp.22-31
• /
• 2012

A Smartphone is an intelligent device combined mobile phone and pc's support functions, and can perform multiple functions to satisfy the demands of users. It has excellent processing power and communication modules(DMB, Wi-Fi, Bluetooth, NFC etc) to carry out the demands of users. But continuous using of battery power on processor and equipped modules causes acceleration of battery consumption. This means that effective power management in devices like smartphone is important. Therefore, the management of power consumption on system execution and communication module is a serious issue in this field of study. In this paper, we would like to propose a communication module selection algorithm based on energy consumption parameter of each communication module and data transfer time. Our scheme automatically select appropriate communication system to reduce high energy consumption on bluetooth sleep mode so that this scheme is more efficient and effective thus improving user convenience in longer usage time. Experimental results showed the 20% energy saving.