• 반도체디스플레이기술학회지
• /
• 제7권2호
• /
• pp.13-17
• /
• 2008

In this paper, in order to get the characteristics of the lithium secondary cell, such as charge and discharge characteristic, temperature characteristic, self-discharge characteristic and the capacity recovery rate etc, we build a thermal model that estimate the impedance of battery by experiment & simulation. In this one-dimensional model, Seven governing equations are made to solve seven variables c, $c_s,\;\Phi_1,\;\Phi_2,\;i_2$, j and T. The thermal model parameters used in this model have been adjusted according to the experimental data measured in the laboratory. The result(Voc, Impedance) of this research can be used in BMS(Battery Management System), so an efficient method of using battery is developed.

• Transactions on Electrical and Electronic Materials
• /
• 제18권3호
• /
• pp.136-140
• /
• 2017

The state of charge (SOC) is an important parameter in a battery-management system (BMS), and is very significant for accurately estimating the SOC of a battery. Li-ion batteries boast of excellent performance, and can only remain at their best working state by means of accurate SOC estimation that gives full play to their performances and raises their economic benefits. This paper summarizes some measures taken in SOC estimation, including the discharge experiment method, the ampere-hour integral method, the open circuit voltage method, the Kalman filter method, the neural network method, and electrochemical impedance spectroscopy (EIS. The principles of the various SOC estimation methods are introduced, and their advantages and disadvantages, as well as the working conditions adopted during these methods, are discussed and analyzed.

• 한국정보통신학회논문지
• /
• 제12권10호
• /
• pp.1835-1839
• /
• 2008

파워 매니지먼트는 휴대용 전자 기기에서 매우 중요한 역할을 한다. 휴대용 전자 기기는 배터리의 수명을 증가시키기 위해 LDO와 같은 파워 효율적인 파워 매니지먼트를 요구한다. 그래서 배터리 전원을 사용하는 휴대폰, 카메라 레코더, laptop, 자동차 전장용, 산업용 기기 등의 응용에서는 배터리의 전압변동이 크기 때문에, 배터리 전원을 그대로 사용하지 않고 내부회로의 전원을 제공해 주는 LDO를 이용한다. 레귤레이터는 배터리 전원전압 보다 낮은DC전압을 내부회로에 제공하며, 큰 변동을 보이는 배터리 전압에 관계없이 일정한 DC전압을 제공할 수 있다. 본 연구에서는 0.18um CMOS 공정기술로 제작된 온칩 LDO의 파워 세이브 모드 전류 특성과 IR-Drop 특성을 파악해 보았다.

• 한국전자통신학회논문지
• /
• 제18권3호
• /
• pp.405-412
• /
• 2023

드론 및 ICT 융합 기술의 발전에 따라 기존 잠수사가 담당하고 있던 수중 현황 탐색, 수중 구조물 검사 등의 작업을 수중 드론으로 대체하고 낚시를 위한 수중 탐사 등의 레저용 수중 드론, 다리 교각 등 산업용 등의 수중 드론의 활용성이 증대되고 있다. 기존 모터 컨트롤러는 항공 드론에 적합하며 수중 드론 전용 BLDC 모터 컨트롤러의 개발을 통해 수중 드론의 완성도와 모터 컨트롤에 대한 신뢰도를 높일 수 있다. 수중 드론 전용 배터리 관리 시스템(BMS)의 개발을 통해 충전 상태 확인, 방전 상태 확인, 셀 밸런싱 조정, 고전압 보호 기능 구현으로 배터리 안정성을 확보하였다.

• Journal of Power Electronics
• /
• 제17권5호
• /
• pp.1288-1297
• /
• 2017

The estimation of the remaining useful life (RUL) of lithium-ion (Li-ion) batteries is important for intelligent battery management system (BMS). Data mining technology is becoming increasingly mature, and the RUL estimation of Li-ion batteries based on data-driven prognostics is more accurate with the arrival of the era of big data. However, the support vector machine (SVM), which is applied to predict the RUL of Li-ion batteries, uses the traditional single-radial basis kernel function. This type of classifier has weak generalization ability, and it easily shows the problem of data migration, which results in inaccurate prediction of the RUL of Li-ion batteries. In this study, a novel multi-kernel SVM (MSVM) based on polynomial kernel and radial basis kernel function is proposed. Moreover, the particle swarm optimization algorithm is used to search the kernel parameters, penalty factor, and weight coefficient of the MSVM model. Finally, this paper utilizes the NASA battery dataset to form the observed data sequence for regression prediction. Results show that the improved algorithm not only has better prediction accuracy and stronger generalization ability but also decreases training time and computational complexity.

• The Journal of Asian Finance, Economics and Business
• /
• 제8권10호
• /
• pp.269-283
• /
• 2021

The government policies and initiatives to guarantee sustainable energy and clean environmental conditions contributed to the introduction of green technology electricity appliances in the market. This study sought to determine the physiological and socio-economics-demographic factors driving residential electricity consumers to use green technology electricity appliances, mainly solar PV, smart meter, electric vehicle, and battery storage technology. By understanding consumer intention, the investors of solar PV, battery storage, electric vehicle, and smart meter can estimate the demand and upscale the market for the corresponding products. For that purpose, the intention to use the solar PV, smart meter, electric vehicle, and battery storage function is developed by utilizing the combination of the theory of planned behavior, technology acceptance, and reasoning action. A reliable and valid structured online questionnaire and stepwise multiple regression are used to identify the possible factors that drive consumer behavior intention. The results show that the social influence, knowledge on RE, and perceived price significantly influence residential consumers' willingness to adopt the technologies offered. The findings of this study suggest that the involvement of NGOs, public figures, and citizens' cooperation are all necessary to spread information about the government's objectives and support Malaysia's present energy and environmental policies.

• ETRI Journal
• /
• 제45권4호
• /
• pp.650-665
• /
• 2023

A novel smart metering technique capable of anomaly detection was proposed for real-time home power management system. Smart meter data generated in real-time were obtained from 900 households of single apartments. To detect outliers and missing values in smart meter data, a deep learning model, the autoencoder, consisting of a graph convolutional network and bidirectional long short-term memory network, was applied to the smart metering technique. Power management based on the smart metering technique was executed by multi-objective optimization in the presence of a battery storage system and an electric vehicle. The results of the power management employing the proposed smart metering technique indicate a reduction in electricity cost and amount of power supplied by the grid compared to the results of power management without anomaly detection.

• Journal of Information Technology Applications and Management
• /
• 제29권6호
• /
• pp.13-21
• /
• 2022

In this study, we presented a method to inspect the mechanical defects of 4680 type lithium-ion batteries through image processing method. The raw X-ray images are filtered with CLAHE, then Radon inverse transformations are calculated to reconstruct 3D computed tomography of the battery. Using Haar-cascade, the ROI is targeted automatically, and the template matchings are applied twice. The variations of contrast between template and background show the appropriate values for detecting tabs. It was shown that the proposed algorithm can detect all the tab inside the battery and the distances between tabs. Finally, we successfully found the geometrical defects of battery.

• 한국전자통신학회논문지
• /
• 제15권1호
• /
• pp.71-78
• /
• 2020

신재생에너지를 활용한 발전원의 경우, 날씨 등의 영향을 많이 받아 전력 생산량이 원활하지 않을 수 있다. 태양광 및 풍력 발전의 효율성을 높이기 위해 에너지 저장 장치(ESS·Energy Storage System)를 활용한다. ESS는 배터리 보호 시스템과 운영관리, 제어체제가 미흡하거나, 설치상의 부주의 등의 원인으로 인해 화재가 속출하고 있으며, 매우 큰 인명 피해와 경제적 손실로 이어지고 있어 ESS의 안정성 및 배터리 보호 시스템 운영관리 기술이 필수적으로 요구되고 있다. 본 논문에서는 ESS 최적화 및 안정적인 운영을 위한 배터리 잔량 산출 알고리즘과 고장 예측 알고리즘을 제시한다. 제시한 알고리즘은 배터리의 충전 및 방전 수행 시 실시간으로 전류량을 누적하여 정확한 배터리 잔량을 산출하며, 배터리 셀 간의 전압불균형 현상을 이용하여 배터리의 고장 유무를 산출한다. 제시된 알고리즘들은 ESS를 최적의 상태로 운영하는데 필요한 정확한 배터리 잔량과 고장 예측이 가능하다. 따라서 ESS의 배터리의 정확한 상태 정보를 측정하고 신뢰성 있게 모니터링 하여 대형 사고를 미연에 방지할 수 있다.

• 항공우주시스템공학회지
• /
• 제8권4호
• /
• pp.7-17
• /
• 2014

Objective of this paper is to introduce a new technology known as prognostics and health management (PHM) which enables a real-time life prediction for safety critical systems under extreme loading conditions. In the PHM, Bayesian framework is employed to account for uncertainties and probabilities arising in the overall process including condition monitoring, fault severity estimation and failure predictions. Three applications - aircraft fuselage crack, gearbox spall and battery capacity degradation are taken to illustrate the approach, in which the life is predicted and validated by end-of-life results. The PHM technology may allow new maintenance strategy that achieves higher degree of safety while reducing the cost in effective manner.