• Proceedings of the KIPE Conference
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• 2019.11a
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• pp.70-72
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• 2019

For the safe and reliable operation of Lithium-ion batteries in Electric Vehicles (EVs) or Energy Storage Systems (ESSs), it is essential to have accurate information of the battery such as State of Charge (SOC). Many kinds of different techniques to estimate the SOC of the batteries have been developed so far such as the Kalman Filter. However, when it is applied to the multiple number of batteries it is difficult to maintain the accuracy of the estimation over all cells due to the difference in parameter value of each cell. Moreover the difference in the parameter of each cell may become larger as the operation time accumulates due to aging. In this paper a novel Deep Neural Network (DNN) based SOC estimation method for multi cell application is proposed. In the proposed method DNN is implemented to learn non-linear relationship of the voltage and current of the lithium-ion battery at different SOCs and different temperatures. In the training the voltage and current data of the Lithium battery at charge and discharge cycles obtained at different temperatures are used. After the comprehensive training with the data obtained with a cell resulting estimation algorithm is applied to the other cells. The experimental results show that the Mean Absolute Error (MAE) of the estimation is 0.56% at 25℃, and 3.16% at 60℃ with the proposed SOC estimation algorithm.

• Journal of Power Electronics
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• v.13 no.4
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• pp.516-527
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• 2013

This paper presents a new overall system for state-of-available-power (SoAP) prediction for a lithium-ion battery pack. The essential part of this method is based on an adaptive network architecture which utilizes both fuzzy model (FIS) and artificial neural network (ANN) into the framework of adaptive neuro-fuzzy inference system (ANFIS). While battery aging proceeds, the system is capable of delivering accurate power prediction not only for room temperature, but also at lower temperatures at which power prediction is most challenging. Due to design property of ANN, the network parameters are adapted on-line to the current battery states (state-of-charge (SoC), state-of-health (SoH), temperature). SoC is required as an input parameter to SoAP module and high accuracy is crucial for a reliable on-line adaptation. Therefore, a reasonable way to determine the battery state variables is proposed applying a combination of several partly different algorithms. Among other SoC boundary estimation methods, robust extended Kalman filter (REKF) for recalibration of amp hour counters was implemented. ANFIS then achieves the SoAP estimation by means of time forward voltage prognosis (TFVP) before a power pulse occurs. The trade-off between computational cost of batch-learning and accuracy during on-line adaptation was optimized resulting in a real-time system with TFVP absolute error less than 1%. The verification was performed on a software-in-the-loop test bench setup using a 53 Ah lithium-ion cell.

• Proceedings of the KIEE Conference
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• 2002.04a
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• pp.129-132
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• 2002

대체 에너지(태양광, 풍력등)의 전력저장 시스템으로 널리 사용되는 겔형(Gel Type) 밀폐형 연(鉛)축전지의 노화 특성 평가 및 충전회로에 관하여 연구하였다. 납 합금을 이용한 기판 주조 기술과 전해액(Gel) 배합기술이 축전기 성능과 수명을 좌우하며 또한 배터리 충전 시스템에 따른 온도 상승이 치명적인 영향을 미치게 된다. 따라서 본 연구에서는 적외선 열화상 장치를 이용한 내부 열원 추적에 의해 노화정도를 측정하며 축전지 수명시험을 통해 입수한 데이터와 비교 평가하고자 한다. 현재 실험실 충방전 수명시험을 마치고 (주)코윈텍이 부안 해창 공원에 설치한 30kW급 풍력발전 시스템에 적용하여 Field Test를 시험중이다.

• The Transactions of the Korean Institute of Power Electronics
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• v.27 no.1
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• pp.48-55
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• 2022

This paper proposes a battery remaining useful life (RUL) prediction method using a deep learning-based EMD-CNN-LSTM hybrid method. The proposed method pre-processes capacity data by applying empirical mode decomposition (EMD) and predicts the remaining useful life using CNN-LSTM. CNN-LSTM is a hybrid method that combines convolution neural network (CNN), which analyzes spatial features, and long short term memory (LSTM), which is a deep learning technique that processes time series data analysis. The performance of the proposed remaining useful life prediction method is verified using the battery aging experiment data provided by the NASA Ames Prognostics Center of Excellence and shows higher accuracy than does the conventional method.

• Proceedings of the KIPE Conference
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• 2019.07a
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• pp.451-452
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• 2019

본 논문은 과충전 및 과방전의 원인이 되는 배터리팩 내부 셀의 불균형으로 인한 셀 간 전압 편차를 분석하기 위해 24s 1p로 구성된 배터리 팩을 사용하여 전기적 열화 실험을 진행하였다. 만충 만방 실험 결과에서 SOC 구간에 따른 셀 간 전압 편차를 분석하였으며, 사이클 증가로 인한 열화와 상관관계가 높은 구간을 분석하여 열화 판단의 파라미터로 제안한다.

• Journal of Electrochemical Science and Technology
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• v.15 no.1
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• pp.51-66
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• 2024

Modern society is making numerous efforts to reduce reliance on carbon-based energy systems. A notable solution in this transition is the adoption of lithium-ion batteries (LIBs) as potent energy sources, owing to their high energy and power densities. Driven by growing environmental challenges, the application scope of LIBs has expanded from their initial prevalence in portable electronic devices to include electric vehicles (EVs) and energy storage systems (ESSs). Accordingly, LIBs must exhibit long-lasting cyclability and high energy storage capacities to facilitate prolonged device usage, thereby offering a potential alternative to conventional sources like fossil fuels. Enhancing the durability of LIBs hinges on a comprehensive understanding of the reasons behind their performance decline. Therefore, comprehending the degradation mechanism, which includes detrimental chemical and mechanical phenomena in the components of LIBs, is an essential step in resolving cycle life issues. The LIB systems presently being commercialized and developed predominantly employ graphite anode and layered oxide cathode materials. A significant portion of the degradation process in LIB systems takes place during the electrochemical reactions involving these electrodes. In this review, we explore and organize the aging mechanisms of LIBs, especially those with graphite anodes and layered oxide cathodes.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.21 no.12
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• pp.339-345
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• 2020

In the field of defense, one-shot devices such as missiles are stored for a long period of time after they are manufactured, so it is essential to predict their storage life. A study was conducted to find the shelf life of a Li-ion battery used in one-shot devices. To do this, a Li-ion battery that has been used in weapon systems for more than 5 years was secured. A non-functional test was performed on the battery to check for external changes or failures. After the non-functional test, a discharge test was performed to measure the performance after storing it. Through the test, the performance was checked, including the initial charging voltage, discharge time, and battery temperature, and the trend of the change was identified. An F-test, One-way ANOVA, and regression analysis were performed to verify the aging, and the shelf life of the battery was estimated by an approximation formula that was derived through a regression analysis. As a result of the ANOVA, the p-value was less than the reference value of 0.05, and the performance of the battery decreased by more than 15% after a certain period of time. This change is assumed to result from the change in physical properties of the lithium polymer cell.

• Korean Chemical Engineering Research
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• v.60 no.3
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• pp.363-370
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• 2022

In this paper, an estimation algorithm for state of charge (SOC) was applied using an equivalent circuit model (ECM) and an Extended Kalman Filter (EKF) to improve the estimation accuracy of the battery system states. In particular, an observer was designed to estimate SOC along with the aged capacity. In the case of the fresh battery, when SOC was estimated by Kalman Filter (KF), the mean absolute percentage error (MAPE) was 0.27% which was smaller than MAPE of 1.43% when the SOC was calculated by the model without the observer. In the driving mode of the vehicle, the general KF or EKF algorithm cannot be used to estimate both SOC and capacity. Considering that the battery aging does not occur in a short period of time, a strategy of periodically estimating the battery capacity during charging was proposed. In the charging mode, since the current is fixed at some intervals, a strategy for estimating the capacity along with the SOC in this situation was suggested. When the current was fixed, MAPE of SOC estimation was 0.54%, and the MAPE of capacity estimation was 2.24%. Since the current is fixed when charging, it is feasible to estimate the battery capacity and SOC simultaneously using the general EKF. This method can be used to periodically perform battery capacity correction when charging the battery. When driving, the SOC can be estimated using EKF with the corrected capacity.

• Journal of the Korean Society of Safety
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• v.32 no.6
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• pp.22-28
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• 2017

According to the National Fire Data System (NFDS), more than 5,000 vehicle fires have occurred every year for the last 10 years. Vehicle fires are primarily caused by mechanical (breaking system and engine), electrical (wiring and battery), and chemical (oil and fuel gas leakage) problems. The electrical factor has increased with the installation of driver convenience equipment. For example, today, the black box is widely used to provide video data recording of motor vehicle accidents. The black box consists of a front camera, rear camera, and wires. The black box wires are directly connected to the junction box or fuse box from the start battery that operates to provide normal on power supplying for engine stop. It is extremely dangerous when the wires short circuit due to insulation aging, mechanical and electrical stress, etc. In this study, the black box wiring fire risk have been analyzed and investigated when the steady state and abnormal operations, and under the following conditions: wiring arrangements with a high temperature condition, insulation aging, poor contact, and short circuits. The results showed that black box wiring short circuits had a higher fire risk than the other fire hazard elements. To prevent fire hazards caused by black box wiring, the black boxes must be installed by qualified service personnel. Do not modify the wiring, remove the fuse and secure the wiring using cable ties or insulation tape.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.18 no.10
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• pp.15-23
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• 2017

This paper reports a SOC(State-of-Charge) estimation method using the extended Kalman filter(EKF) algorithm, which can allow real-time implementation and reduce the error of the model and be robust against noise, to accurately estimate and evaluate the charging/discharging state of the EV(Electric Vehicle) battery. The battery was modeled as the first order Thevenin model for the EKF algorithm and the parameters were derived through experiments. This paper proposes the changed method, which can have the SOC to 0% ~ 100% regardless of the aging of the battery by replacing the rated capacity specified in the battery with the maximum chargeable capacity. In addition, This paper proposes the EKF algorithm to estimate the non-linearity interval of the battery and simulation result based on Ah-counting shows that the proposed algorithm reduces the estimation error to less than 5% in all intervals of the SOC.