• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.30 no.1
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• pp.21-27
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• 2016

The study concerning about the grid connection of the large-capacity battery energy storage system(BESS) is increasing. However, the protection study which is necessary to maintain the reliability of distribution system has been hardly performed. Therefore, this paper analyzes the effect of reclosing among protection issues in distribution system with BESS and proposes the new relcosing method. To verify the proposed method, the BESS, distribution system, and proposed method are modeled by using EMTP/ATPDraw and the various simulations according to the fault clearing time are performed. The simulation results show that the reclosing in distribution system with BESS is successfully performed by proposed method and the operation of BESS is not affected from reclosing.

• The Transactions of the Korean Institute of Power Electronics
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• v.27 no.3
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• pp.265-274
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• 2022

This paper proposes a machine learning-based screening algorithm to build the retired battery pack of the energy storage system. The proposed algorithm creates the dataset of various performance parameters of the retired battery, and this dataset is preprocessed through a principal component analysis to reduce the overfitting problem. The retried batteries with a large deviation are excluded in the dataset through a density-based spatial clustering of applications with noise, and the K-means clustering method is formulated to select the group of the retired batteries to satisfy the deviation requirement conditions. The performance of the proposed algorithm is verified based on NASA and Oxford datasets.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.24 no.4
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• pp.141-146
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• 2024

Energy Storage System(ESS) have been developed to store and efficiently utilize energy, transitioning from the traditional method of producing and consuming energy immediately via fossil fuels and generators. With the advancement of this technology, Battery Management System(BMS) that manage Li-ion batteries at the cell level play a crucial role in enhancing battery performance, lifespan, and safety. Among the BMS functions, cell balancing, which aligns the imbalanced voltages of cells, is essential for optimizing capacity in devices like ESS. It ensures all cells maintain the same voltage and capacity, improving performance and output stability. Therefore, this paper examines the operational characteristics of the cell balancing method within BMS when charging an imbalanced Li-ion battery.

• Journal of the Korean Society for Geothermal and Hydrothermal Energy
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• v.20 no.2
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• pp.1-10
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• 2024

The introduction of the sector coupling concept has expanded the scope of ESS utilization, resulting in the importance of thermal management of ESS. To ensure the safe use of the lithium-ion batteries that are used in ESS, it is important to use the batteries at the optimal temperature. To examine the utilization of liquid cooling in ESS, numerical study was conducted on the thermal characteristics of 21700 battery modules (16S2P array) during liquid cooling using Novec-649 as insulating fluid. The NTGK model, an MSMD model in ANSYS fluent, was used to investigate thermal characteristics on the battery modules with liquid immersion cooling. The results show that the final temperature of the battery module discharged at 5 C-rate is 68.9℃ using natural convection and 48.3℃ using liquid cooling. However, the temperature difference among cells in the battery module was up to 0.5℃ when using natural convection cooling and 5.8℃ when using liquid cooling, respectively, indicating that the temperature difference among cells was significantly increased when liquid cooling was used. As the mass flow rate increased from 0.01 kg/s to 0.05 kg/s, the average temperature of the battery module decreased from 48.3℃ to 38.4℃, confirming that increasing the mass flow rate of the insulating fluid improves the performance of liquid immersion cooling. Although partial liquid immersion cooling has a high cooling performance compared to natural convection cooling, the temperature difference between modules was up to 8.9℃, indicating that the thermal stress of the battery cells increased.

• Proceedings of the KIPE Conference
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• 2018.11a
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• pp.131-132
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• 2018

The world market for BESS (Battery Energy Storage System) is growing rapidly, and battery technology is also developing. It is important to understand the battery characteristics and develop a control strategy to develop the optimal BMS (Battery Management System). In this paper, we compare and analyze the parameter characteristics of NCM LIB (Lithium Ion Battery) according to the temperature change.

• New & Renewable Energy
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• v.16 no.1
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• pp.1-14
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• 2020

Liquid air energy storage (LAES) using gas liquefaction has attracted considerable attention because of its mature technology, high energy density, few geographical constraints, and long life span. On the other hand, LAES has not yet been commercialized and is being developed recently. Therefore, few studies have performed an economic analysis of LAES. In this study, the levelized cost of electricity was calculated and compared with that of other energy storage systems. As a result, the levelized cost of electricity of LAES was $371/MWh. This is approximately $292/MWh, $159/MWh, $118/MWh, and $3/MWh less than that of the LiCd battery, VRFB battery, Lead-acid battery, and NaS battery. In addition, the cost was approximately $62/MWh and $195/MWh more than that of Fe-Cr flow battery and PHS. Sensitivity analysis of the levelized cost of electricity according to the main economic factors was performed, and economic uncertainty analysis was performed through a Monte-Carlo simulation. The cumulative probability curve showed the levelized cost of electricity of LAES, reflecting price fluctuations in the air compressor cost, electricity cost, and standing reserve hourly fee.

• 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.

• Transactions on Electrical and Electronic Materials
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• v.17 no.6
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• pp.311-316
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• 2016

Batteries are critical components of electric vehicles and energy storage systems. The connection of a battery to the power grid for charge and discharge greatly affects energy storage. Therefore, an accurate and easy-to-observe battery model should be established to achieve systematic design, simulation, and SOC (state of charge) estimations. In this review, several equivalent circuit models of representative significance are explained, and their respective advantages and disadvantages are compared to determine and outline their reasonable applications to Li-ion batteries. Numerous commonly used model parameter identification principles are summarized as well, and basic model verification methods are briefly introduced for the convenient use of such models.

• Proceedings of the KIPE Conference
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• 2013.07a
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• pp.388-389
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• 2013

This paper presents the method for the fluctuation smoothing control by using relaxation time variable control of battery. When the output power of wind farm is changed suddenly, it is necessary to control the output power of wind farm. The smoothing relaxation time is changed within limits of battery output power. Using the hybrid energy storage system (HESS) combined with battery energy storage system and electric double layer capacitor, it is possible to control the output power of wind farm. The capacity of battery is determined by considering the case of the disconnecting wind farm from the grid. To verify the proposed method, simulations are carried out by using PSCAD/EMTDC with actual data of wind farm in the Jeju Island.

• Proceedings of the KIPE Conference
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• 2016.11a
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• pp.15-16
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• 2016

This paper proposes an isolated DCDC converter that consists of unregulated LLC resonant converter and non-isolated synchronous buck converter for battery charger of energy storage systems application. The unregulated converter operates as transformer with fixed duty ratio and switching frequency. The synchronous buck converter is installed in the output of the LLC resonant converter. And the converter charges and discharges the battery by controlling a current of battery. The proposed converter can get the high efficiency by separating function. This paper explains design of an unregulated converter and synchronous converter.