• 전기화학회지
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• 제1권1호
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• pp.14-17
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• 1998

MPCF는 Li ion 2차전지용 부극 활물질로 연구되고 있다. 흑연화 MPCF는 높은 방전 용량과 우수한 충방전 효율을 가진다. $0\~1$ V전위영역에서 25 mA/g의 정전류로 충방전할 때의 MPCF/Li전지의 초기 방전 용량은 300 mAh/g이며, 충방전 효율은 $90\%$ 이상을 나타낸다. $LiCoO_2$을 정극 활물질로, 혼합 탄소재료를 부극 활물질로 사용하여 원통형 Li ion 2차전지를 제작하였다. Li ion 2차전지의 수명 특성을 향상하기 위하여, 흑연화 MPCF에 이종 탄소 재료를 $10 wt\%$ 혼합하였다. 혼합 탄소재료를 사용한 Li ion 2차전지의 수명 성능은 흑연화 MPCF만을 사용한 전지보다 우수하였다.

• 전력전자학회논문지
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• 제20권4호
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• pp.351-355
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• 2015

This paper explained the application of a lithium-ion battery energy storage system to electric propulsion ships. The power distribution in electric propulsion ships has low power quality because of the variation in the power consumption of the propulsion motor. For proper operation of the ship, the power quality needs to be improved, and the battery energy storage system is used to solve power-quality problems. The simulation models of electric propulsion ship and battery energy storage systems are constructed on MATLAB/Simulink to verify the improvement in power quality. The proposed system is applied in various scenarios of the propulsion motor state. The power quality achieved by using the battery energy storage system in both voltage and frequency satisfies the standards set by IEC-60092/101.

• Journal of Electrical Engineering and Technology
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• 제13권5호
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• pp.1927-1934
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• 2018

In this paper, we studied the state of charge (SOC) estimation algorithm of a high-capacity lithium secondary battery for electric vehicles (EVs) considering temperature characteristics. Nonlinear characteristics of high-capacity lithium secondary batteries are represented by differential equations in the mathematical form and expressed by the state space equation through battery modeling to extract the characteristic parameters of the lithium secondary battery. Charging and discharging equipment were used to perform characteristic tests for the extraction of parameters of lithium secondary batteries at various temperatures. An extended Kalman filter (EKF) algorithm, a state observer, was used to estimate the state of the battery. The battery capacity and internal resistance of the high-capacity lithium secondary battery were investigated through battery modeling. The proposed modeling was applied to the battery pack for EVs to estimate the state of the battery. We confirmed the feasibility of the proposed study by comparing the estimated SOC values and the SOC values from the experiment. The proposed method using the EKF is expected to be highly applicable in estimating the state of the high-capacity rechargeable lithium battery pack for electric vehicles.

• 한국산학기술학회논문지
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• 제16권5호
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• pp.3391-3398
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• 2015

현재 대부분의 도서지역에서는 태양광발전을 효율적으로 운용하기 위하여 대용량 연축전지가 많이 사용되고 있지만, 풍력발전의 도입, 축전지 교체로 인하여 리튬이온전지의 도입이 증가하고 있다. 따라서 본 논문에서는 기존에 많이 보급되어 사용되고 있는 연축전지와 리튬이온전지의 장점을 최대한 활용하기 위하여, 연축전지와 리튬이온전지용 하이브리드 BMS 알고리즘을 제시하였다. 즉, 각 전지의 충전상태(state of charge, SOC)를 평가하는 알고리즘과 각 전지의 도입비용과 운용비용에 따른 최적 구성비를 산출하는 하이브리드 운용 알고리즘을 제안하였다. 상기의 알고리즘을 이용하여 다양한 시뮬레이션을 수행한 결과, 기존의 충전상태 평가 방법보다 오차율이 개선되어 정확한 충전상태에 대한 결과가 산출되었고, 각 전지의 도입비용과 운용비용이 최소화되는 조건에서 최적구성비를 구하여, 본 논문에서 제안한 하이브리드 BMS 알고리즘의 유용성을 확인하였다.

• 한국항공우주학회지
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• 제35권4호
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• pp.363-368
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• 2007

높은 중량에너지밀도로 배터리 무게를 줄일 수 있는 장점을 갖는 리튬이온 배터리는 중량이 중요한 관심사인 많은 항공우주 응용분야에서 빠른 속도로 Ni-Cd, Ni-H2 등의 기존 배터리를 대체하고 있다. 또한 리튬이온 배터리는 낮은 열 손실 특성과 높은 에너지 효율 그리고 저렴한 셀 단가를 갖는다. 80개의 소니 US18650 리튬이온 셀을 사용한 KSLV-I 탑재배터리 모듈은 셀을 8개씩 직렬로 구성한 후 각 열을 병렬로 10개 연결하여 요구되는 전압과 용량을 공급한다. 본 논문에서는 우주발사체용 리튬이온 배터리의 설계 및 그 특성에 대해 소개하며, 예상되는 우주환경에서 배터리가 신뢰성 있게 동작하는지를 검증하는 환경시험 프로그램 절차를 보였다. 배터리 성능은 전자부하기를 이용한 시뮬레이션 시험을 통해 확인하였고 발사체 2단에 장착하여 각 전장품들과의 연계시험을 통해 검증하였다.

• 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.

• 한국전기전자재료학회논문지
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• 제31권5호
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• pp.335-340
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• 2018

Transition metal oxide materials have attracted widespread attention as Li-ion battery electrode materials owing to their high theoretical capacity and good Li storage capability, in addition to various nanostructured materials. Here, we fabricated a CoO Li-ion battery in which Co nanoparticles (NPs) are deposited into a current collector through electrophoretic deposition (EPD) without binding and conductive agents, enabling us to focus on the intrinsic electrochemical properties of CoO during the conversion reaction. Through optimized Co NP synthesis and electrophoretic deposition (EPD), CoO Li-ion battery with 630 mAh/g was fabricated with high cycle stability, which can potentially be used as a test platform for a fundamental understanding of conversion reaction.

• 한국전기전자재료학회:학술대회논문집
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• 한국전기전자재료학회 2003년도 춘계학술대회 논문집 센서 박막재료 반도체 세라믹
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• pp.88-90
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• 2003

The purpose of this study is to research and develop tin oxide-flash composite for lithium Ion polymer battery. Tin oxide is one of the promising material as a electrode active material for lithium Ion polymer battery (LIPB). Tin-based oxides have theoretical volumetric and gravimetric capacities that are four and two times that of carbon, respectively. We investigated cyclic voltammetry and charge/discharge cycling of SnO-flyash/SPE/Li cells. The first discharge capacity of SnO-flyash composite anode was 720 mAh/g. The discharge capacity of SnO-flyash composite anode 412 and 314 mAh/g at cycle 2 and 10 at room temperature, respectively. The SnO-flyash composite anode with PVDF-PMMA-PC-EC-$LiClO_4$ electrolyte showed good capacity with cycling.

• 한국전기전자재료학회논문지
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• 제32권3호
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• pp.252-255
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• 2019

Black phosphorus (BP) is a potential candidate for an anode in lithium ion batteries due to its high theoretical capacity and the large interlayer spacing in the monolayered phosphorene form, allowing for lithium intercalation/deintercalation. In this study, large-scale exfoliation of bulk BP was accomplished using a solution of NaOH and N-methyl-2-pyrrolidone (NMP), yielding phosphorene, which can be assembled into nanoflakes using electrophoretic deposition (EPD). Through the systematic addition of NaOH and subsequent sonication, BP nanoflakes were obtained in high yields by EPD, allowing for the integration of these nanoflakes into an anode in the film state. Anodes with a charge/discharge capacity of 172 mAh/g at a rate of 200 mA/g were obtained, which are promising for battery applications through various post-film treatments.

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