• Journal of Electrical Engineering and Technology
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• v.13 no.5
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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.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.16 no.5
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• pp.3391-3398
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• 2015

Recently, the large scaled lead-acid battery is widely introduced to efficient operation of the photovoltaic system in many islands. but the demand of lithium-ion battery is getting increased by the operation of wind power and replacement of the lead-acid battery. And also, under the renewable portfolio standard(RPS) and energy efficiency resource standard(EERS) policy of Korea government, the introduction of energy storage system(ESS) has been actively increased. Therefore, this paper presents the operation algorithm of hybrid battery management system(BMS) using the lead-acid and lithium-ion batteries, in order to maximize advantage of each battery. In other words, this paper proposed the algorithm of state of charge(SOC) and hybrid operation algorithm to calculate the optimal composition rate considering the fixed cost and operation cost of each battery. From the simulation results, it is confirmed that the proposed algorithms are an effective tool to evaluate SOC and to optimally operate hybrid ESS.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.35 no.4
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• pp.363-368
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• 2007

Lithium-ion batteries providing high gravimetric energy density are rapidly replacing Ni-Cd and Ni-H2 in aerospace applications. The main advantage is the weight reduction of the battery system. Weight is a major concern in aerospace applications. Also, lithium-ion offer low thermal dissipation, high energy efficiency, and low cell cost. The Onboard battery module for KSLV-I(Korea Space Launch Vehicle) contains 80 Sony US18650 cells configured as 10 strings in parallel, with each string containing 8 series connected cells. This allows to meet voltage and capacity requirements specified for the mission. In this paper design description and specifications of lithium-ion battery developed are presented. Qualification test flow is also shown to make sure the performance in the predicted space environment. Electrical performance was simulated by dedicated program, and verified with electronic load. Lastly, the capacity was proven on real equipment load assembly.

• International Journal of Advanced Culture Technology
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• v.11 no.3
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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.

• Journal of the Korean Institute of Electrical and Electronic Material Engineers
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• v.31 no.5
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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.

• Proceedings of the Korean Institute of Electrical and Electronic Material Engineers Conference
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• 2003.05c
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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.

• Journal of the Korean Institute of Electrical and Electronic Material Engineers
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• v.32 no.3
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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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• v.17 no.5
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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.

• Journal of Electrochemical Science and Technology
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• v.6 no.1
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• pp.1-6
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• 2015

With increasing interest in flexible electronic devices and wearable appliances, flexible lithium ion batteries are the most attractive candidates for flexible energy sources. During the last decade, many different kinds of flexible batteries have been reported. Although research of flexible lithium ion batteries is in its earlier stages, we have found that developing components that satisfy performance conditions under external deformation stress is a critical key to the success of flexible energy sources. Among the major components of the lithium ion battery, electrodes, which are connected to the current collectors, are gaining the most attention owing to their rigid and brittle character. In this mini review, we discuss candidate materials for current collectors and the previous strategies implemented for flexible electrode fabrication.

• Proceedings of the KIPE Conference
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• 2008.06a
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• pp.535-537
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• 2008

This paper proposes a modularized two-stage charge equalization converter for a series-connected lithium-ion battery string. In this paper, the series-connected battery sting is modularized into M modules, and each module has K cells in series. With this modularization, low voltage stress on the electronic devices can be achieved. A two-stage dc-dc converter with cell selection switches is employed. The first stage dc-dc converter steps down the high bus voltage to about 10 V. The second stage dc-dc converter integrated with selection switches equalizes the cell voltages. A prototype for 88 lithium-ion battery cells is optimally designed and implemented. Experimental results verify that the proposed equalization method has good cell balancing performance showing low voltage stress, small size, and low cost.