• Journal of the Korean Institute of Electrical and Electronic Material Engineers
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• v.20 no.5
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• pp.410-414
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• 2007

To prevent degradation of battery efficiency generated by serious current variation in rechargeable batteries, we researched a hybrid battery combining a super capacitor and a rechargeable battery. The hybrid battery shows high efficiency in a lifetime and a voltage drop. The hybrid battery was composed of a rechargeable battery, a current regulator and a super capacitor that can be used with supporting power. Before the experiment, the hybrid battery was simulated for current regulation and an electric current in a super capacitor by using the Pspice program. After that, we compared the efficiency of the hybrid battery with the efficiency of the normal battery. In this result, we demonstrated that the hybrid battery has a higher efficiency and a longer lifespan than the normal battery.

• Journal of Power Electronics
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• v.13 no.3
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• pp.497-506
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• 2013

This paper proposes a new method to improve the available battery capacity in electric vehicles by connecting lead-acid batteries with lithium-ion battery in parallel to supply power. In addition, this method combines the discharge characteristics of batteries to improve their efficiency and lower their cost for electric vehicles. A lithium-ion battery set is used to connect with N sets of lead-acid batteries in parallel. The lead-acid battery supplies the initial power. When the lead-acid battery is discharged by the load current until its output voltage drops to the cut-off voltage, the power management unit controls the lead-acid battery and changes it to discharge continuously with a small current. This discharge can be achieved by connecting the lead-acid battery to a lithium-ion battery in parallel to supply the load power or to discharge its current to another lead-acid or lithium-ion battery. Experimental results demonstrates that the available capacity can be improved by up to 30% of the rated capacity of the lead-acid batteries.

• Journal of Intelligence and Information Systems
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• v.24 no.2
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• pp.243-264
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• 2018

Although the worldwide battery market is recently spurring the development of lithium secondary battery, lead acid batteries (rechargeable batteries) which have good-performance and can be reused are consumed in a wide range of industry fields. However, lead-acid batteries have a serious problem in that deterioration of a battery makes progress quickly in the presence of that degradation of only one cell among several cells which is packed in a battery begins. To overcome this problem, previous researches have attempted to identify the mechanism of deterioration of a battery in many ways. However, most of previous researches have used data obtained in a laboratory to analyze the mechanism of deterioration of a battery but not used data obtained in a real world. The usage of real data can increase the feasibility and the applicability of the findings of a research. Therefore, this study aims to develop a model which predicts the battery deterioration using data obtained in real world. To this end, we collected data which presents change of battery state by attaching sensors enabling to monitor the battery condition in real time to dozens of golf carts operated in the real golf field. As a result, total 16,883 samples were obtained. And then, we developed a model which predicts a precursor phenomenon representing deterioration of a battery by analyzing the data collected from the sensors using machine learning techniques. As initial independent variables, we used 1) inbound time of a cart, 2) outbound time of a cart, 3) duration(from outbound time to charge time), 4) charge amount, 5) used amount, 6) charge efficiency, 7) lowest temperature of battery cell 1 to 6, 8) lowest voltage of battery cell 1 to 6, 9) highest voltage of battery cell 1 to 6, 10) voltage of battery cell 1 to 6 at the beginning of operation, 11) voltage of battery cell 1 to 6 at the end of charge, 12) used amount of battery cell 1 to 6 during operation, 13) used amount of battery during operation(Max-Min), 14) duration of battery use, and 15) highest current during operation. Since the values of the independent variables, lowest temperature of battery cell 1 to 6, lowest voltage of battery cell 1 to 6, highest voltage of battery cell 1 to 6, voltage of battery cell 1 to 6 at the beginning of operation, voltage of battery cell 1 to 6 at the end of charge, and used amount of battery cell 1 to 6 during operation are similar to that of each battery cell, we conducted principal component analysis using verimax orthogonal rotation in order to mitigate the multiple collinearity problem. According to the results, we made new variables by averaging the values of independent variables clustered together, and used them as final independent variables instead of origin variables, thereby reducing the dimension. We used decision tree, logistic regression, Bayesian network as algorithms for building prediction models. And also, we built prediction models using the bagging of each of them, the boosting of each of them, and RandomForest. Experimental results show that the prediction model using the bagging of decision tree yields the best accuracy of 89.3923%. This study has some limitations in that the additional variables which affect the deterioration of battery such as weather (temperature, humidity) and driving habits, did not considered, therefore, we would like to consider the them in the future research. However, the battery deterioration prediction model proposed in the present study is expected to enable effective and efficient management of battery used in the real filed by dramatically and to reduce the cost caused by not detecting battery deterioration accordingly.

• Journal of Computing Science and Engineering
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• v.5 no.4
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• pp.338-345
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• 2011

Nowadays mobile devices are used for various applications such as making voice/video calls, browsing the Internet, listening to music etc. The average battery consumption of each of these activities and the length of time a user spends on each one determines the battery lifetime of a mobile device. Previous methods have provided predictions of battery lifetime using a static battery consumption rate that does not consider user characteristics. This paper proposes an approach to predict a mobile device's available battery lifetime based on usage patterns. Because every user has a different pattern of voice calls, data communication, and video call usage, we can use such usage patterns for personalized prediction of battery lifetime. Firstly, we define one or more states that affect battery consumption. Then, we record time-series log data related to battery consumption and the use time of each state. We calculate the average battery consumption rate for each state and determine the usage pattern based on the time-series data. Finally, we predict the available battery time based on the average battery consumption rate for each state and the usage pattern. We also present the experimental trials used to validate our approach in the real world.

• Journal of Electrochemical Science and Technology
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• v.13 no.3
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• pp.323-338
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• 2022

Heat generation and temperature of a battery is usually presented by an equation of current. This means that we need to adopt time domain calculation to obtain thermal characteristics of the battery. To avoid the complicated calculations using time domain, 'state of charge (SOC)' can be used as an independent variable. A SOC based calculation method is elucidated through the comparison between the calculated results and experimental results together. Experiments are carried for rapid resistive discharge of a large-capacitive lithium secondary battery to evaluate variations of cell potential, current and temperature. Calculations are performed based on open-circuit cell potential (SOC,T), internal resistance (SOC,T) and entropy (SOC) with specific heat capacity.

• Journal of IKEEE
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• v.18 no.1
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• pp.96-105
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• 2014

This paper proposes a battery lifetime enhancement method based on the nonlinear discharge charisteristics called recovery effect. In general, the stored energy in a battery is considered in the prediction of battery lifetime. However, due to the chemical reaction in a battery, more energy can be drawn from a battery when it is not continuously but intermittently discharged, which is called recovery effect. In the proposed method, several battery cells are alternately discharged, and some battery cells rest while maintaining the system power supply. This makes recovery effect of battery cells, which extends battery lifetime. In the experiment, battery lifetime increases about 7% in the alternating discharge of two battery cells, when compared with conventional parallel discharge.

• Journal of the Korean Institute of Electrical and Electronic Material Engineers
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• v.31 no.6
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• pp.422-426
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• 2018

The lithium-ion battery pack of an electric vehicle (EV) deserves to be considered for an alternative use within smart-grid infrastructure. Despite the long automotive service life, EV batteries retain over 70~80% of their initial capacity. These battery packs must be managed for their reliability and safety. Therefore, a battery management system (BMS) should use specific algorithms to measure and estimate the status of the battery. Most importantly, the BMS of a grid-connected energy storage system (ESS) must ensure that the lithium-ion battery does not catch fire or explode due to an internal short from uncontrolled dendrite growth. In other words, the BMS of a lithium-ion battery pack should be capable of detecting the battery's status based on the electrochemical reaction continuously until the end of the battery's lifespan. In this paper, we propose a new protection algorithm for a dendritic lithium battery. The proposed algorithm has applied a parameter from battery pack aging results and has control power managing.

• The Transactions of the Korean Institute of Power Electronics
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• v.25 no.3
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• pp.171-180
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• 2020

This paper presents the implementation and control methods of a battery simulator. The proposed battery simulator can emulate the dynamic characteristics of any actual battery using the second RC ladder model of the equivalent circuit. Moreover, it can emulate the variation of impedance, which is the result of the change of battery characteristics due to the aging effect. The parameters of the battery simulator can be derived from the sequence of tests of the actual battery or only from the data supplied by the battery manufacturer. Proposed methods for the battery simulator are tested by extensive experiments. Test results show that the proposed battery simulator can emulate not only the dynamic characteristics but also the aging effects of the actual battery in real time.

• Journal of the Korean Society of Industry Convergence
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• v.25 no.5
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• pp.715-724
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• 2022

In many electric vehicles, large-capacity pouch-type lithium-ion battery packs are mainly used to increase the mileage on a single charge. The lithium ion battery should be operated within the temperature range of 25℃ to 40℃ because the battery performance can be rapidly deteriorated due to an increase in internal temperature. Battery thermal management system (BTMS) can give the suitable temperature conditions to battery by water cooling method. In this research, the heat transfer characteristics (the battery temperature distributions and the water flow characteristics) were analyzed by CFD method to investigate the thermal performance of the cooling plate with 4-pass water flow structure. Moreover, the effect of the presence of fins between the battery cell was identified. The fins made smooth temperature distributions between the battery cells due to the heat spreading and lower the average battery cells temperature.

• Transactions of the Korean hydrogen and new energy society
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• v.28 no.4
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• pp.407-418
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• 2017

Battery heat management is essential for high power and high energy battery system because it affects its performance, longevity, and safety. In this paper, we investigated the temperature of the 18650 Lithium Ion Battery Module used in a Energy Storage System (ESS) and the cooling method to minimize cell-to-cell temperature variation of battery module. For uniform temperature distribution within a battery module, the flow direction of the coolant in a battery module has been changed according to the time interval, and studied the effect of the cooling method on the temperature uniformity in a battery module which includes a number of battery cells. The experimental results show that bi-directional battery cooling method can effectively reduce the cell-to-cell temperature variation compared with the one-directional battery cooling. Furthermore, it is also found that bi-directional battery cooling can reduce the maximum temperature in a battery module.