• 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.

• Applied Science and Convergence Technology
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• v.23 no.1
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• pp.14-26
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• 2014

Copper is an important component from coin metal to electronic wire, integrated circuit, and to lithium battery. Copper oxides, mainly including $Cu_2O$ and CuO, are important semiconductors for the wide applications in solar cell, catalysis, lithium-ion battery, and sensor. Due to their low cost, low toxicity, and easy synthesis, copper oxides have received much research interest in recent year. Herein, we review the crystallization of copper oxides by designing various chemical reaction routes, for example, the synthesis of $Cu_2O$ by reduction route, the oxidation of copper to $Cu_2O$ or CuO, the chemical transformation of $Cu_2O$ to CuO, the chemical precipitation of CuO. In the designed reaction system, ligands, pH, inorganic ions, temperature were used to control both chemical reactions and the crystallization processes, which finally determined the phases, morphologies and sizes of copper oxides. Furthermore, copper oxides with different structures as electrode materials for lithium-ion batteries were also reviewed. This review presents a simple route to study the reaction-crystallization-performance relationship of Cu-based materials, which can be extended to other inorganic oxides.

• Journal of the Institute of Electronics and Information Engineers
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• v.54 no.4
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• pp.99-107
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• 2017

This paper proposes a battery model coefficient correction method for improving the accuracy of existing lithium battery equivalent models. BMS(battery management system) has been researched and developed to minimize shortening of battery life by keeping SOC(state of charge) and state of charge of lithium battery used in various industrial fields such as EV. However, the cell balancing operation based on the battery cell voltage can not follow the SOC change due to the internal resistance and the capacitor. Various battery equivalent models have been studied for estimation of battery SOC according to the internal resistance of the battery and capacitors. However, it is difficult to apply the same to all the batteries, and it tis difficult to estimate the battery state in the transient state. The existing battery electrical equivalent model study simulates charging and discharging dynamic characteristics of one kind of battery with error rate of 5~10% and it is not suitable to apply to actual battery having different electric characteristics. Therefore, this paper proposes a battery model coefficient correction algorithm that is suitable for real battery operating environments with different models and capacities, and can simulate dynamic characteristics with an error rate of less than 5%. To verify proposed battery model coefficient calibration method, a lithium battery of 3.7V rated voltage, 280 mAh, 1600 mAh capacity used, and a two stage RC tank model was used as an electrical equivalent model of a lithium battery. The battery charge/discharge test and model verification were performed using four C-rate of 0.25C, 0.5C, 0.75C, and 1C. The proposed battery model coefficient correction algorithm was applied to two battery models, The error rate of the discharge characteristics and the transient state characteristics is 2.13% at the maximum.

• Proceedings of the KIPE Conference
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• 2009.11a
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• pp.249-251
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• 2009

In this paper, we present a study on the 50kW class power station development using lithium-polymer battery. The 50kW class power station purposes to compensate for peak load using 326[V] lithium-polymer battery bank. The output voltage is 380[V] in three-phase, peak load compensation is controlled based on load power. Finally, a 50kW power station system is built to verify the experiment.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.63 no.8
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• pp.1085-1091
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• 2014

A model based SOC estimation scheme using parameter identification is described and applied to a Lithium-ion battery module that can be installed in electric vehicles. Simulation studies are performed to verify the effect of sensor faults on the SOC estimation results for terminal voltage sensor and load current sensor. The sensor faults should be detected and isolated as soon as possible because the SOC estimation error due to any sensor fault seriously affects the overall performance of the BMS. A new fault detection and isolation(FDI) scheme by which the fault of terminal voltage sensor and load current sensor can be detected and isolated is proposed to improve the reliability of the BMS. The proposed FDI scheme utilizes the parameter estimation of an input-output model and two fuzzy predictors for residual generation; one for terminal voltage and the other for load current. Recently developed dual polarization(DP) model is taken to develope and evaluate the performance of the proposed FDI scheme. Simulation results show the practical feasibility of the proposed FDI scheme.

• Carbon letters
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• v.9 no.3
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• pp.210-217
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• 2008

Petroleum pitch and coke with wet mixture method or with dry mixture method were investigated to develop the composite anodic carbon material of high power lithium ion battery. Cokes coated with pitch were obtained by the heat treatment of mixture of cokes and pitch with different weight ratios at $800{\sim}1200^{\circ}C$. The charge and discharge characteristic of the consequent composite anodic carbon material assembled in batteries was tested. Cokes with wet mixture method have a smooth surface and their capacity changed little with changing temperature and content as compared to the cokes with dry mixture method. Although the reversible capacities showed different values by the anode manufacturing method, the composite anode with the mixture of 20 wt% of petroleum pitch and 80 wt% of coke showed the higher power capability and initial efficiency than the pitch based anode. However, the reversible capacity of the composite anode showed the reduced value as compared with the pitch based anode.

• Smart Media Journal
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• v.12 no.11
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• pp.103-112
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• 2023

Recently, the need to prevent battery fires and accidents has emerged, as the use of lithium-ion batteries has increased. In order to prevent accidents, it is necessary to predict the state of health (SOH) and check the replacement timing of the battery with a lot of degradation. This paper proposes a model for predicting the degradation state of a battery by using four battery degradation indicators: maximum voltage arrival time, current change time, maximum temperature arrival time, and incremental capacity (IC) that can be obtained in the battery charging process, and LSTM using an attention mechanism. The performance of the proposed model was measured using the NASA battery data set, and the predictive performance was improved compared to that of the general LSTM model, especially in the SOH 90-70% section, which is close to the battery replacement cycle.

• Korean Chemical Engineering Research
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• v.59 no.3
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• pp.393-398
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• 2021

In order to optimize the performance of a lithium-ion battery, a performance prediction modeling technique that considers various degradation factors is required. In this work, mathematical modeling was carried-out to predict the change in discharging behavior and cycle life, taking into account the cycle aging of lithium-ion batteries. In order to validate the modeling, a cycling test was performed at the charge/discharge rate of 0.25C, and discharging behavior was measured through RPT (Reference Performance Test) performed at 30 cycle intervals. The accuracy of cycle life prediction was improved by considering the break-in mechanism, one of the phenomena occurring in the BOL (beginning of life), in the model for predicting the cycle life of lithium-ion batteries. The predicted change in cycle life based on the model was in good agreement with the experimental results.

• Journal of IKEEE
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• v.24 no.4
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• pp.1098-1103
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• 2020

This paper deals with a method for estimating the battery state-of-health(SOH) through electrical experiments and electrochemical modeling of lithium-ion secondary battery. In order to confirm the actual battery SOH through the battery electrical aging experiment, the current integration method was used. The SOH is estimated using the internal resistance value derived from the electrical experiment. Also, in electrochemical modeling, the SOH is estimated through the change of the SEI layer with the increase of the number of cycles. The new SOH is derived by applying weighting factor to the three methods of estimating SOH, including the actual battery SOH.

• Bulletin of the Korean Chemical Society
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• v.32 no.10
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• pp.3682-3686
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• 2011

The electrochemical properties of lithium-sulfur batteries with binary electrolytes based on DME and DOL, TEGDME and DOL mixed solvent containing $LiClO_4$, LiTFSI, and LiTF salts were investigated. The ionic conductivity of 1M LiTFSI and $LiClO_4$ electrolytes based on TEGDME and DOL increased as the volume ratio of DOL solvent increased, because DOL effectively reduces the viscosity of the above electrolytes medium under the same salts concentration. The first discharge capacity of lithium-sulfur batteries in the DME and DOL-based electrolyte followed this order: LiTFSI (1,000 mAh/g) > LiTF (850 mAh/g) > $LiClO_4$ (750 mAh/g). In case of the electrolyte based on TEGDME and DOL, the first discharge capacity of batteries followed this order: $LiClO_4$ (1,030 mAh/g) > LiTF (770 mAh/g) > LiTFSI (750 mAh/g). The cyclic efficiency of lithium-sulfur batteries at 1M $LiClO_4$ electrolytes is higher than that of batteries at other lithium salts-based electrolytes. Lithium-sulfur battery showed discharge capacity of 550 mAh/g until 20 cycles at all electrolytes based on DME and DOL solvent. By contrast, the discharge capacity of batteries was about 450 mAh/g at 1M LiTFSI and LiTF electrolytes based on TEGDME and DOL solvent after 20 cycles.