• Proceedings of the KIPE Conference
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• 2019.11a
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• pp.76-78
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• 2019

에너지저장시스템(Energy storage system; ESS)의 장시간 운용에 따라 배터리 물리적, 화학적으로 배터리 내부 활물질의 변형이 일어나며, 배터리의 전기화학적 특성이 달라진다. 이의 특성을 반영한 배터리 팩의 운용이 필요하며, 안정적이고 장기간 사용하기 위해 배터리 팩 내부 셀 간 불균형을 반영이 필요하다. 하지만, 배터리 팩의 노화로 셀 간 불균형이 발생 시, 같은 조건에서 개별 셀의 노화도를 판단하는 것은 어렵다. 이러한 이유로 배터리 팩뿐만 아니라 개별 셀의 노화를 판단하기 위해 건전성 지표(Health indicator)를 사용한다. 건전성 지표를 사용하여 Hotelling t² 통계량을 적용하여 배터리의 이상 신호 탐지를 수행하였다.

• Proceedings of the KIPE Conference
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• 2020.08a
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• pp.130-132
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• 2020

배터리 시스템은 어플리케이션의 대영화에 따른 데이터 저장공간 문제 및 연속적인 배터리 신뢰성 문제 해결을 위한 건전성 예측 및 관리기술 접목에 관한 문제에 직면해 있으며, 이러한 문제 해결을 위해서는 배터리 시스템 신호를 통해 추출 가능한 건전성 지표 수립이 중요하다. 본 논문은 건전성 지표를 물리적, 간접적 지표로써 정의하고, 사이클 노화 데이터를 통해 건전성 지표로써의 성능을 검증하였다.

• Proceedings of the KIPE Conference
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• 2019.11a
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• pp.202-203
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• 2019

본 논문은 배터리 팩 기반 건전성 지표 수립을 위하여 24S1P 고용량 배터리 팩을 총 200cycle동안 열화 실험 하였으며, 배터리 성능과 직결된 내부저항과 방전 용량 및 미소 용량을 건전성 지표로 선정하였다. 선정된 파라미터를 전기적 특성 분석을 통해 열화에 따른 경향성을 분석하고 열화와의 상관성을 실험을 통해 확인하였으며, 이를 통해 건전성 지표로의 활용성을 제시한다.

• The Journal of the Convergence on Culture Technology
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• v.9 no.3
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• pp.727-730
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• 2023

The operation of electric automatic windows is used in harsh environments, and the energy density decreases as charging and discharging are repeated, and as soundness deteriorates due to damage to the internal separator, the vehicle's mileage decreases and the charging speed slows down, so about 5 to 10 Batteries that have been used for about a year are classified as waste batteries, and for this reason, as the risk of battery fire and explosion increases, it is essential to diagnose batteries and estimate SOH. Estimation of current battery SOH is a very important content, and it evaluates the state of the battery by measuring the time, temperature, and voltage required while repeatedly charging and discharging the battery. There are disadvantages. In this paper, measurement of discharge capacity (C-rate) using a waste battery of a Tesla car in order to predict SOH estimation of a lithium-ion battery. A Support Vector Machine (SVM), one of the machine models, was applied using the data measured from the waste battery.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.42 no.4
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• pp.939-949
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• 2017

Prognostics and health management(PHM) is actively utilized by industry as an essential technology focusing on accurately monitoring the health state of a system and predicting the remaining useful life(RUL). An effective PHM is expected to reduce maintenance costs as well as improve safety of system by preventing failure in advance. With these advantages, PHM can be applied to the battery system which is a core element to provide electricity for devices with mobility, since battery faults could lead to operational downtime, performance degradation, and even catastrophic loss of human life by unexpected explosion due to non-linear characteristics of battery. In this paper we mainly review a recent progress on various models for predicting RUL of battery with high accuracy satisfying the given confidence interval level. Moreover, performance evaluation metrics for battery prognostics are presented in detail to show the strength of these metrics compared to the traditional ones used in the existing forecasting applications.

• Proceedings of the KIPE Conference
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• 2019.07a
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• pp.53-55
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• 2019

리튬 이온 배터리가 소형 모바일 기기, 전기 자동차, 에너지 저장장치 등에 상용화됨에 따라서 이의 충전 상태(SOC) 추정 및 셀, 모듈의 건전성(SOH)의 예측이 배터리 사용 기기의 관리 지표로 사용되고 있다. 리튬 이온 배터리는 여러 차례의 방전으로 노화되어 기기의 요구 부하를 공급가능한지 지표로 평가되어야 한다. 정확한 SOH 추정을 위해 리튬 이온 배터리의 방전 용량 실험이 주기적으로 진행되어야 하며, 이를 통해 오프라인 기반의 SOH 추정이 가능해진다. 본 논문에서는 베이지안 회귀분석 방법을 이용하여 오프라인 SOH 추정을 진행하기 위해 방전 용량을 추정하였으며, 고출력 배터리인 18650 25R셀을 이용하여 방전 용량 추정 결과 방전 전류 1 C-rate에서 1%, 2 C-rate에서 2%의 추정 오차율을 나타냈다.

• The Journal of the Korea institute of electronic communication sciences
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• v.17 no.3
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• pp.423-432
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• 2022

This paper addresses the safety concern that the SOH of batteries in electric vehicles decreases sharply when drivers change or their driving patterns change. Such a change can overload the battery, reduce the battery life, and induce safety issues. This paper aims to present the SOH as the changes on a dashboard of an electric vehicle in real-time in response to user pattern changes. As part of the training process I used battery data among the datasets provided by NASA, and built models incorporating linear regression and ARIMA, and predicted new battery data that contained user changes based on previously trained models. Therefore, as a result of the prediction, the linear regression is better at predicting some changes in SOH based on the user's pattern change if we have more battery datasets with a wide range of independent values. The ARIMA model can be used if we only have battery datasets with SOH data.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.20 no.5
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• pp.13-19
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• 2019

After the Fukushima nuclear accident, a new power supply using a lithium polymer battery has been proposed the first time in the world as the safety of the emergency battery facility has been required. It is required to have the safety of the rack system in which the battery device is installed in order to apply the proposed technology to the field. Therefore, the purpose of this study is to evaluate the seismic performance of string and rack frame for lithium-polymer battery devices developed for the first time in the world to satisfy 72 hours capacity. (1) The natural frequency of the unit rack system was 9 Hz, and the natural frequency before and after the earthquake load did not change. This means that the connection between members is secured against the design earthquake load. (2) he vibration reduction effect by string design was about 20%. (3) As a result of the seismic performance test under OBE and SSE conditions, the rack frame system was confirmed to be safe. Therefore, the proposed rack system can be applied to the nuclear power plant because the rack system has been verified structural safety to the required seismic forces.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.19 no.12
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• pp.21-27
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• 2020

Lithium-ion batteries are the heart of energy-storing devices and electric vehicles. Owing to their superior qualities, such as high capacity and energy efficiency, they have become quite popular, resulting in an increased demand for failure/damage prevention and useable life maximization. To prevent failure in Lithium-ion batteries, improve their reliability, and ensure productivity, prognosticative measures such as condition monitoring through sensors, condition assessment for failure detection, and remaining useful life prediction through data-driven prognostics and health management approaches have become important topics for research. In this study, the residual useful life of Lithium-ion batteries was predicted using two efficient artificial recurrent neural networks-ong short-term memory (LSTM) and gated recurrent unit (GRU). The proposed approaches were compared for prognostics accuracy and cost-efficiency. It was determined that LSTM showed slightly higher accuracy, whereas GRUs have a computational advantage.

• Transactions of the Korean Society of Mechanical Engineers B
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• v.37 no.4
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• pp.313-322
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• 2013

Batteries, which are being used as energy sources in various applications, tend to degrade, and their capacity declines with repeated charging and discharging cycles. A battery is considered to fail when it reaches 80% of its initial capacity. To predict this, prognosis techniques are attracting attention in recent years in the battery community. In this study, a method is proposed for estimating the battery health and predicting its remaining useful life (RUL) based on the slope of the charge voltage curve. During this process, a Bayesian framework is employed to manage various uncertainties, and a Particle Filter (PF) algorithm is applied to estimate the degradation of the model parameters and to predict the RUL in the form of a probability distribution. Two sets of test data-one from the NASA Ames Research Center and another from our own experiment-for an Li-ion battery are used for illustrating this technique. As a result of the study, it is concluded that the slope can be a good indicator of the battery health and PF is a useful tool for the reliable prediction of RUL.