• Journal of the Korean Society of Industry Convergence
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• v.21 no.6
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• pp.329-335
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• 2018

In an electric vehicle and Energy Storage System(ESS), a large number of batteries are connected in series or parallel to obtain high voltage and current. The battery management system(BMS) is needed because battery has a characteristic that explode in overcharging and overcurrent situations due to the nature of the battery material and the battery life is dramatically reduced when the battery is overdischarged below the specified voltage. In this paper, we proposed a system that can manage a large amount of batteries through the communication of master-slave type with multiple microcontroller. We confirmed the stable operation of the proposed system through the balancing-charging and storage mode experiments.

• Proceedings of the KIPE Conference
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• 2018.07a
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• pp.156-158
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• 2018

The inconsistencies between paralleled battery cells are becoming more considerable issue in high capacity battery applications like electric vehicles. Due to differences in state-of-charge (SOC) and internal resistance within individual cells in parallel, charging or discharging current is not appropriately balanced to each cell in terms of SOC, which may shorten the lifetime or sometimes cause safety issues. In this paper, an intelligent cell-balancing algorithm is proposed to overcome the inconsistency issue especially for paralleled battery cells. In this scheme, SOC information collected in the sub-BMS module is sent to the main-BMS module, where the number of parallel cells to be connected to DC bus is continuously updated based on the suggested SOC comparison rule. To verify the method, operation of the algorithm on 4 paralleled battery cells are simulated on Matlab/Simulink. The simulation result shows that the SOCs of paralleled cells are evenly redistributed. It is expected that the proposed algorithm provides high reliable and prolong the life cycle and working capacity of the battery pack.

• Journal of the Korean Electrochemical Society
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• v.16 no.3
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• pp.111-122
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• 2013

Sodium sulfur (NAS) battery is a high energy storage system (ESS). These days, as the use of renewable green energy like wind energy, solar energy and ocean energy is rapidly increasing, the demand of ESS is increasing and NAS battery is considered to be one of the most promising ESS. Since NAS battery has a high energy density(3 times of lead acid battery), long cycle life and no self-charge and discharge, it is a good candidate for ESS. A NAS battery consists of sulfur as the positive electrode, sodium as the negative electrode and ${\beta}$"-alumina as the electrolyte and a separator simultaneously. Since sulfur is an insulator, carbon felt should be used as conductor with sulfur and so the composition and property of the cathode could largely influence the cell performance and life cycle. Therefore, in this paper, the composition of NAS battery, the property of carbon felt and sodium polysulfides ($Na_2S_x$, intermediates of discharge), and the effects of these factors on cycle performance of cells are described in detail.

• Proceedings of the KIPE Conference
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• 2019.11a
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• pp.216-217
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• 2019

The battery powered electric vehicle (EV) is one of most promising technologies in 21^st century. Though the lithium batteries are playing an important role in the EVs, they are only applicable until their capacities reach 80%, the end of its useful first life. Yet, these batteries can live a second life such as Energy Storage Systems (ESS). In order to utilize the Residual Useful Life (RUL) of the batteries the State of Health (SOH) of them needs to be estimated by a nondestructive test such as Electrochemical Impedance Spectroscopy (EIS) technique. Though many kinds of different EIS instruments are commercially available, most of them can only test a battery module less than 10V and the price of the instrument is very high. In this paper a low-cost EIS instrument suitable for measuring the impedance spectrum of the high voltage battery module is proposed and its validity is verified through the experiments. In order to prove the accuracy of the developed EIS instrument its measured impedance spectrum is compared with the results obtained by a commercial instrument. The Chi Square value calculated between two impedance spectrum measured by both developed and commercial instruments are less than 2%, which prove the strong correlation between two results.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.29 no.4
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• pp.85-94
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• 2015

Recently, research on the smart grid that combines ICT(Information & Communication technology) to the power system has been actively progressed. If the occupancy of the EV(Electric vehicle) is increased. the V2G(Vehicle to grid) system is available which constitutes the micro-grid through battery of EV. V2G system performs load leveling and efficient energy consumption by battery operation considering load condition. But, if the battery is used only depending on the electricity rates, it doses not consider the life of the battery. The ACC(Achievable cycle) and the total transferable energy of battery varies corresponding to the selected DOD(Depth of discharge). In this paper, the optimal DOD selection method of V2G system considering battery wear cost and average driving distance of EV. Also, the total revenue prediction of various nation is presented considering the actual electricity costs per hour.

• Journal of the Korean Society for Geothermal and Hydrothermal Energy
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• v.16 no.2
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• pp.7-12
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• 2020

The battery of an electric vehicle is a key part of the energy supply to operate the vehicles. There are many factors affecting battery life such as charging method, discharge rate, and ambient temperature those are requires systematic monitoring and management. To solve the issues like environmental problems and fuel consumption reduction the battery needs more performance improvement. In this study, it was analyzed the thermal characteristics and securing battery stability for electric vehicle battery cooling system. The simulation test was operated using GT-suite software with several conditions like cooling capacity 1, 2 and 4 kW, cooling flow rate 5, 10, 20 and 30 LPM, and battery initial temperatures 40, 35, and 30℃ at the temperature of ambient 25℃. The results shown that the case of cooling flow rate at 20 LPM was most efficient among all above conditions.

• Journal of Power Electronics
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• v.18 no.4
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• pp.1211-1222
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• 2018

This study presents a power distribution control scheme for a three-phase interleaved parallel DC/DC converter in a battery energy storage system. To extend battery life and increase the power equalization rate, a control method based on the nth order of the state of charge (SoC) is proposed for the charging and discharging processes. In the discharging process, the battery sets with high SoC deliver more power, whereas those with low SoC deliver less power. Therefore, the SoC between each battery set gradually decreases. However, in the two-stage charging process, the battery sets with high SoC absorb less power, and thus, a power correction algorithm is proposed to prevent the power of each particular battery set from exceeding its rated power. In the simulation performed with MATLAB/Simulink, results show that the proposed scheme can rapidly and effectively control the power distribution of the battery sets in the charging and discharging processes.

• Proceedings of the KIEE Conference
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• 1989.07a
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• pp.277-283
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• 1989

For the battery energy storage system (BESS), battery is one of most important parts. Various new type batteries for load shifting are under developing. The lead acid battery technology status such as structure, charge and discharge characteristics, life cycle etc. is reviewed and research trend is also introduced.

• Proceedings of the KIPE Conference
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• 2013.07a
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• pp.242-243
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• 2013

This paper presents a voltage and frequency regulation of a Micro-grid using a Battery energy storage system (BESS) and super-capacitor (SPC). The control methods for the both BESS and SPC are proposed to improve the regulation performance and battery life span. The simulation results using the PSCAD/EMTDC are provided to show the effectiveness of the proposed control.

• The Transactions of the Korean Institute of Power Electronics
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• v.27 no.1
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• pp.48-55
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• 2022

This paper proposes a battery remaining useful life (RUL) prediction method using a deep learning-based EMD-CNN-LSTM hybrid method. The proposed method pre-processes capacity data by applying empirical mode decomposition (EMD) and predicts the remaining useful life using CNN-LSTM. CNN-LSTM is a hybrid method that combines convolution neural network (CNN), which analyzes spatial features, and long short term memory (LSTM), which is a deep learning technique that processes time series data analysis. The performance of the proposed remaining useful life prediction method is verified using the battery aging experiment data provided by the NASA Ames Prognostics Center of Excellence and shows higher accuracy than does the conventional method.