• 반도체디스플레이기술학회지
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• 제15권2호
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• pp.16-19
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• 2016

More efficient use of the battery pack is dependent upon how to measure the remaining capacity of the battery accurately. Among various measurement methods, the basic correction measurement method has still been a hot research topic area to reduce the errors. In this paper, the problems of the existing methods have been investigated and the research direction for measuring more accurate remaining capacity has been suggested by applying the numerical simulations in the future.

• International Journal of Advanced Culture Technology
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• 제11권3호
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• pp.310-314
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• 2023

The purpose of this study is to predict the remaining capacity of lithium-ion batteries and evaluate their performance using five artificial intelligence models, including linear regression analysis, decision tree, random forest, neural network, and ensemble model. We is in the study, measured Excel data from the CS2 lithium-ion battery was used, and the prediction accuracy of the model was measured using evaluation indicators such as mean square error, mean absolute error, coefficient of determination, and root mean square error. As a result of this study, the Root Mean Square Error(RMSE) of the linear regression model was 0.045, the decision tree model was 0.038, the random forest model was 0.034, the neural network model was 0.032, and the ensemble model was 0.030. The ensemble model had the best prediction performance, with the neural network model taking second place. The decision tree model and random forest model also performed quite well, and the linear regression model showed poor prediction performance compared to other models. Therefore, through this study, ensemble models and neural network models are most suitable for predicting the remaining capacity of lithium-ion batteries, and decision tree and random forest models also showed good performance. Linear regression models showed relatively poor predictive performance. Therefore, it was concluded that it is appropriate to prioritize ensemble models and neural network models in order to improve the efficiency of battery management and energy systems.

• 반도체디스플레이기술학회지
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• 제10권4호
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• pp.25-29
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• 2011

Emerging demands for rechargeable battery for various applications needs more effective battery management system such as the prediction of the usable time about a battery. Many prediction methods have been suggested but none of them come into bounds of reliability. In this paper, we proposed a new prediction algorithm for the remaining capacity of a rechargeable battery by using the transformed curve based on its impedance. Hardware for monitoring a battery was designed and made. Through a series of experiment, we showed the effectiveness of the proposed prediction algorithm of a battery's remaining capacity.

• Journal of Platform Technology
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• 제11권2호
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• pp.54-65
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• 2023

전기자동차에 사용되는 배터리는 전기자동차의 특성상 정격용량이 매우 커다란 배터리이다. 전기자동차를 장기간 운행하거나 교통사고로 전기자동차가 폐차되게 되면 전기자동차용 배터리는 폐배터리가 된다. 폐차되는 차량이더라도 전기자동차용 폐배터리에 남아 있는 용량은 다른 용도로 사용하기에 충분하다. 자동차용 폐배터리는 매우 고가이기때문에 재활용 및 재사용이 필요하지만 재활용 및 재사용을 위한 폐배터리 성능등급 측정기준이 부족한 문제가 있었다. 폐배터리의 잔존용량을 측정하는 방법으로 가장 안정적이고 신뢰할 수 있는 방법은 완전 충·방전을 이용하여 배터리의 잔존용량을 측정하는 것이다. 하지만 이러한 완전 충·방전에 방식에 의한 검사 방법은 배터리의 용량에 따라 다르지만 검사하는데 하루 이상이 걸리는 단점을 가지고 있으며 많은 사람들이 이러한 문제를 해결하기 위하여 많은 노력을 하고 있다. 본 논문에서는 전기자동차 배터리에 대한 검사 시간을 줄일 수 있는 방법으로 셀간 전압 편차를 활용한 전기자동차 배터리 잔존용량 분석 기법을 연구 분석하였다. 이를 위하여 완전 충·방전 기반의 용량 측정시스템을 구성하고 코나 폐배터리를 이용하여 실험데이터를 수집하였고 배터리 팩을 구성하고 있는 배터리 셀간 전압 편차와 잔존용량과의 상관관계를 분석하여 배터리 검사에 활용할 수 있는지를 검증하였다.

• 한국콘텐츠학회논문지
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• 제10권4호
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• pp.55-65
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• 2010

낮은 이동성을 갖는 무선 애드혹 네트워크에서, 에너지 고갈에 따른 링크 단절 없이 네트워크 생존시간을 오랫동안 유지할 수 있는 방안을 제시한다. 일반적으로 에너지 잔량이 많이 남아 있는 노드는 트래픽 부하가 적은 노드이다. 따라서 노드의 에너지 잔량을 기준으로 경로를 결정하는 수정 AODV 라우팅 프로토콜을 제안한다. 한편, 기존 AODV에서는 경로 설정을 위해 엄청난 개수의 제어 패킷을 네트워크에 방송함으로써 전체 노드의 에너지 소비가 급격히 늘어난다. 이러한 제어 패킷을 효과적으로 줄이기 위해 발신지 노드는 대체 경로 정보를 자신의 경로 테이블에 저장하도록 하였다. 링크 단절이 발생했을 때, 경로 재설정을 시도하기 전에 발신지 노드는 경로 테이블에 저장된 대체 경로 중에서 해당 경로의 에너지 잔량 총합이 가장 큰 경로를 선택한다. 이렇게 함으로써 불필요한 AODV 제어 패킷 발생 가능성을 줄일 수 있다. 본 논문에서 제안한 방식을 기존 AODV, MMBCR 방식 등과 비교했을 때 네트워크 생존 시간이 최대 40% 증대됨을 알 수 있다.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2001년도 하계학술대회 논문집 D
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• pp.2187-2189
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• 2001

The proposed algorithm has produced the rules of relationship between the load voltage, current, discharging electric power and ampere-hours, electric power capacity of battery on the basis of the data. Which were acquired through the battery discharging experiment that is defined by the battery's ambient temperature and various load conditions. Especially, by calculating the parameter of second order polynomial equation relation between the remaining capacity and the electric power, the algorithm is proposed adapting for the discharging pattern. And as the depth of discharging is increasing, the calculation-method of electric power is applied to decrease the accumulated error in the calculation method of capacity accumulation. Also, the proposed algorithm has compensated the temperature considering the capacity change of battery to the temperature.

• 한국산업융합학회 논문집
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• 제7권4호
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• pp.411-417
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• 2004

Recently, researchers and engineers from, the development of reliability engineering and probability fracture mechanics, have begun to take seriously the reliability analysis and the integrity for a corroded pipeline. Pressurized pipelines containing active corrosion defects increase gradually both in extent, and depth with increased periods of exposure. This causes a reduction of the remaining strength and the carrying capacity of a pipeline; and creates uncertainty about the future capacity. The steps that are necessary in order to assess the integrity of corroded pipelines will be discussed in this paper utilizing results from an actual model.

• 한국강구조학회 논문집
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• 제13권5호
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• pp.467-476
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• 2001

고속도로 건설은 미국을 포함한 여러 국가에서 거의 완료되었으며 정부나 고속도로 관계기관은 유지관리쪽에 초점을 맞추고 있다. 교량을 효과적으로 유지관리 하기 위해 교량의 실제 내하력과 잔존수명을 예측하는 것은 매우 시급하다. 이러한 목적으로서 시간에 대한 시스템 신뢰성 해석이 필요하다. 이 논문에서는 Lifetime 분포(함수)를 이용해서 교량의 잔존수명을 예측하기 위한 전형적인 교량의 모델링기법을 개발하였다. Lifetime의 함수변수를 생성하기 위해서 몬테칼로법을 이용하였다. 결과는 지금 존재하는 교량에 대해 최적의 유지관리 계획에 이용될 수 있다.

• 전력전자학회논문지
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• 제26권3호
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• pp.167-175
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• 2021

Reusing electric vehicle batteries after they have been retired from mobile applications is considered a feasible solution to reduce the demand for new material and electric vehicle costs. However, the evaluation of the value and the performance of second-life batteries remain a problem that should be solved for the successful application of such batteries. The present work aims to estimate the remaining useful life of Li-ion batteries through the neuro-fuzzy system with the equivalent circuit parameters obtained by Electrochemical Impedance Spectroscopy (EIS). To obtain the impedance spectra of the Li-ion battery over the life, a 18650 cylindrical cell has been aged by 1035 charge/discharge cycles. Moreover, the capacity and the parameters of the equivalent circuit of a Li-ion battery have been recorded. Then, the data are used to establish a neuro-fuzzy system to estimate the remaining useful life of the battery. The experimental results show that the developed algorithm can estimate the remaining capacity of the battery with an RMSE error of 0.841%.

• 한국전기전자재료학회논문지
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• 제16권11호
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• pp.1047-1054
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• 2003

Smart battery pack (SBP) for notebook PCs was developed using a cylindrical-type lithium ion battery. Batteries were connected with three serial and two parallel, the nominal capacity and the maximum load of SBP was 4,000mAh and 4.0A, respectively. The SBP was composed of a protection IC, by which safety of lithium ion batteries is maintained against overcharge, overdischarge and overcurrent, and a smart IC, which calculates the remaining capacity and the remaining run time. In matching test on notebook PC using Battery Mark 4.0, real and smart data of END voltage coincided nearly and LB and LLB signal worked norma]]y. And there were errors of less than 1% between the real and the smart data on the residual capacity in the charge and discharge test.