• Journal of the Semiconductor & Display Technology
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• v.21 no.3
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• pp.114-118
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• 2022

The stabilized all-solid-state battery structure indicate a fundamental alternative to the development of next-generation energy storage devices. Existing liquid electrolyte structures severely limit battery stability, creating safety concerns due to the growth of Li dendrites during rapid charge/discharge cycles. In this study, a low-dimensional graphene quantum dot layer structure was applied to demonstrate stable operating characteristics based on Li+ ion conductivity and excellent electrochemical performance. Transmission electron microscopy analysis was performed to elucidate the microstructure at the interface. The low-dimensional structure of GQD-based solid electrolytes has provided an important strategy for stable scalable solid-state lithium battery applications at room temperature. This study indicates that the low-dimensional carbon structure of Li-GQDs can be an effective approach for the stabilization of solid-state Li matrix architectures.

• Journal of the Korean Electrochemical Society
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• v.5 no.4
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• pp.173-177
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• 2002

Electrochemical characteristics of a graphite/lithium and a $LiCoO_2/lithium$ half cell and a $graphite/LiCoO_2$ full cell were analyzed using a GCSOC (gradual control test of the state of charge) technique. The IIE (initial intercalation coulombic efficiency), which represents lithium intercalation property of the electrode material, and the $lIC_s$ (initial irreversible capacity by the surface), which represents irreversible reaction between the electrode surface and the electrolyte were obtained from the GCSOC analysis. Linear-fittable capacity ranges of IIE of graphite and $LiCoO_2$ electrodes were 370 and 150 mAh/g, respectively, based on material weight. The value of lIE for graphite and $LiCoO_2$ electrodes were $93-94\%$ and $94-95\%$, respectively. The value of IICs for graphite and $LiCoO_2$ electrodes were 15-17 mAh/g and 0.3-1.7 mAh/g, respectively. The value of IIE for $graphite/LiCoO_2$ full cell, used GX25 and DJG311 as a graphite, was $89-90\%$ that lower than that for the half cells. Parameters of IIE and IICs can also be used to represent not only half cell but also full cell.

• Journal of the Korean Institute of Electrical and Electronic Material Engineers
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• v.31 no.1
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• pp.61-67
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• 2018

High-power lithium batteries are suitable for equipment with high power output needs, such as for ESS's initial start-up. However, their management cost is increased by the installation of air-conditioning to minimize the risk of explosion due to internal temperature rise and also by a restriction on the number of charge/discharge cycles. High-capacity flow batteries, on the other hand, have many advantages. They can be used for over 20 years due to their low management costs, resulting from no risk of explosion and a high number of charge/discharge cycles. In this paper, we propose an ESS based on hybrid batteries that uses a lithium iron phosphate battery (LiFePO) at the initial startup and a vanadium redox flow battery (VRFB) from the end of the transient period, with a bi-directional PCS to operate two batteries with different DC voltage levels and using an efficient energy management control algorithm.

• 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 Power Electronics
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• v.12 no.5
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• pp.778-786
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• 2012

In this paper, a novel scheme for the condition monitoring of lithium polymer batteries is proposed, based on the sigma-point Kalman filter (SPKF) theory. For this, a runtime-based battery model is derived, from which the state-of-charge (SOC) and the capacity of the battery are accurately predicted. By considering the variation of the serial ohmic resistance ($R_o$) in this model, the estimation performance is improved. Furthermore, with the SPKF, the effects of the sensing noise and disturbance can be compensated and the estimation error due to linearization of the nonlinear battery model is decreased. The effectiveness of the proposed method is verified by Matlab/Simulink simulation and experimental results. The results have shown that in the range of a SOC that is higher than 40%, the estimation error is about 1.2% in the simulation and 1.5% in the experiment. In addition, the convergence time in the SPKF algorithm can be as fast as 300 s.

• Journal of Electrical Engineering and Technology
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• v.13 no.2
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• pp.733-741
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• 2018

Capacity estimation is indispensable to ensure the safety and reliability of lithium-ion batteries in electric vehicles (EVs). Therefore it's quite necessary to develop an effective on-board capacity estimation technique. Based on experiment, it's found constant current charge time (CCCT) and the capacity have a strong linear correlation when the capacity is more than 80% of its rated value, during which the battery is considered healthy. Thus this paper employs CCCT as the health indicator for on-board capacity estimation by means of relevance vector machine (RVM). As the ambient temperature (AT) dramatically influences the capacity fading, it is added to RVM input to improve the estimation accuracy. The estimations are compared with that via back-propagation neural network (BPNN). The experiments demonstrate that CCCT with AT is highly qualified for on-board capacity estimation of lithium-ion batteries via RVM as the results are more precise and reliable than that calculated by BPNN.

• Journal of Power Electronics
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• v.13 no.3
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• pp.429-436
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• 2013

Online simulations are utilized to reduce time and cost in the development and performance optimization of plug-in hybrid electric vehicle (PHEV) and electric vehicles (EV) systems. One of the most important factors in an online simulation is the accuracy of the model. In particular, a model of a battery should accurately reflect the properties of an actual battery. However, precise dynamic modeling of high-capacity battery systems, which significantly affects the performance of a PHEV, is difficult because of its nonlinear electrochemical characteristics. In this study, a dynamic model of a high-capacity battery cell for a PHEV is developed through the extraction of the equivalent impedance parameters using electrochemical impedance spectroscopy (EIS). Based on the extracted parameters, a battery cell model is implemented using MATLAB/Simulink, and charging/discharging profiles are executed for comparative verification. Based on the obtained results, the model is optimized for a high-capacity battery cell for a PHEV. The simulation results show good agreement with the experimental results, thereby validating the developed model and verifying its accuracy.

• 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.

• The Transactions of the Korean Institute of Power Electronics
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• v.20 no.2
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• pp.175-181
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• 2015

In this study, a lithium polymer battery (LiPB) is simply expressed by a primary RC equivalent model. The PI state observer is designed in Matlab/Simulink. The non-linear relationship with the OCV-SOC is represented to be linearized with 0.1 pu intervals by using battery parameters obtained by constant-current pulse discharge. A state equation is configured based on battery parameters. The state equation, which applied Peukert's law, can estimate SOC more accurately. SOC estimation capability was analyzed by utilizing reduced Federal Test Procedure (FTP-72) current profile and using a bi-directional DC-DC converter at temperature ($25^{\circ}C$). The PI state observer, which is designed in this study, indicated a SOC estimation error rate of ${\pm}2%$ in any of the initial SOC states. The PI state observer confirms a strong SOC estimation performance despite disturbances, such as modeling errors and noise.

• Journal of Electrochemical Science and Technology
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• v.13 no.1
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• pp.32-53
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• 2022

Although lithium-ion batteries are currently the most reliable power supply system for various mobile applications, further improvement in energy density is still required as the need for batteries in large energy-consuming devices is rapidly growing. However, in the anode, the most widely commercialized graphite-based anode materials almost face theoretical limitations. In addition, sodium-ion batteries have been actively studied to replace expensive charge carriers with cheaper ones. Accordingly, conversion-based materials have been extensively studied as high-capacity anode materials in both lithiumion batteries and sodium-ion batteries because their theoretical capacity is twice or thrice higher than that of insertion-based materials. This review will provide a comprehensive understanding of conversion-based materials, including basic charge storage behaviors, critical drawbacks that should be overcome, and practical material design for high-performance.