• Transactions of the Korean Society of Automotive Engineers
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• v.22 no.3
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• pp.179-185
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• 2014

Average temperature and temperature uniformity in a battery cell are the important criteria of the thermal management of the battery pack for hybrid electric vehicles and electric vehicles (HEVs and EVs) because high power with large size cell is used for the battery pack. Thus, liquid cooling system is generally applied for the HEV/EV battery pack. The liquid cooling system is made of multiple cooling plates with coolant flow paths. The cooling plates are inserted between the battery cells to reject the heat from batteries to coolant. In this study, the cooling plate with U-shaped coolant flow paths is considered to evaluate the effects of coolant flow condition on the cooling performance of the system. The counter flow and parallel flow set up is compared and the effect of flow rate is evaluated using CFD tool (FLUENT). The number of counter-flows and flow rate are changed and the effect on the cooling performance including average temperature, differential temperature, and standard deviation of temperature are investigated. The results show that the parallel flow has better cooling performance compared with counter flow and it is also found that the coolant flow rate should be chosen with the consideration of trade-off between the cooling performance and pressure drop.

• Proceedings of the KIEE Conference
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• 1999.07g
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• pp.3141-3143
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• 1999

Fuel cell systems offer high energy efficiencies for transportation application. In addition, they can use alcohols and alternative fuels as the fuel, while producing little or no noxious emissions. Fuel cell-powered energy source should be competitive in performance characteristics and in capital and maintenance costs with internal combustion engine systems. From computer simulation program, battery and fuel cell energy output and total power profile, motor power, battery energy output, fuel cell energy output. It simulates the performance of fuelcell/battery powered energy source operation over any user inputted transit route cycle, and provides performance criteria through user specifications for preliminary design consideration.

• Proceedings of the KIPE Conference
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• 2013.07a
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• pp.169-170
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• 2013

One important test for formation and grading of the lithium-ion battery is to confirm the performance of the battery while discharging battery down to zero volts. In this paper, a novel charge/discharge converter with zero-voltage discharge function is proposed. The proposed converter is able to discharge the battery until the voltage reaches to zero volts. The phase-shifted full bridge method is used to charge the battery and the current-fed push-pull method with bidirectional switches is used for the discharge. The ZVS turn-on is achieved in the charge operation and the ZVS turn-off in the discharge operation. The performance of the system is verified by the experiments using lithium-ion batteries.

• Proceedings of the KIPE Conference
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• 2019.07a
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• pp.459-460
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• 2019

The performance dynamics of battery is very sensitive to operating conditions (i.e temperature, load current, and state of charge). A model developed based on certain conditions may perform well under the similar conditions but can not accurately predict the performance for changing conditions. Thus, a generalized model is needed which can accurately emulate the battery dynamic behavior under all conditions. In addition, the components of the model should relate to the physicochemical processes that occur inside the battery. Electrochemical impedance curve shows better visible reflection of the processes inside battery as compared to voltage curve. The model trained for parameterization using neural network has better generalization than simple curve fitting. Thus, this study proposes recurrent neural network based parameterization of the Lithium ion battery model followed by impedance based identification.

• International Journal of Internet, Broadcasting and Communication
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• v.16 no.3
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• pp.228-235
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• 2024

Battery use is increasing worldwide to achieve carbon neutrality and improve energy efficiency, but batteries are a finite resource and their application is determined by capacity and specifications. Battery performance deteriorates as the number of uses increases. A certain level of battery performance degradation has become an issue in the field of reuse and recycling, and various studies are being conducted on reuse to solve power shortages. Waste batteries from electric vehicles are suitable for building ESS based on reusable batteries, and for stable use, technical skills are needed to accurately predict battery life and determine status information. Predicting battery life and determining status information are difficult due to non-linearity due to internal structure or chemical changes. In this paper, we manufactured a modular internal resistance measuring device and compared the measured values with Hioki equipment to minimize the error rate through a correction method. As a result of testing Hioki equipment and modular measuring instruments to ensure efficiency and safety based on reusable batteries, an accuracy of over 95% was confirmed.

• Journal of Hydrogen and New Energy
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• v.18 no.3
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• pp.284-291
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• 2007

The Ni-MH batteries for HEV and industry are normally placed in outdoor, consequently causing an too weak discharge power problem due to a cold weather specially in winter time. In order to improve the low temperature performances of the Ni-MH battery for HEV and industrial uses, it has been investigated the low temperature discharge characteristics of Ni-MH battery with various electrolytes at $-18^{\circ}C$. The summary of experimental results are as follows. The low temperature characteristics depended strongly on the characteristics of electrolytes. When the concentration of the electrolytes were too high or too low the low temperature performance was poor. The best electrolyte was composed of KOH 6.2M+LiOH 1.2M. An addition of RbOH or CsOH to electrolyte improved the low temperature performance. The best total concentration of electrolyte composed of KOH, NaOH and LiOH was about 7M.

• The Transactions of the Korean Institute of Power Electronics
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• v.21 no.2
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• pp.160-167
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• 2016

An energy storage system (ESS) is applied to increase the energy efficiency of large plants or buildings that consume much energy, to improve the power quality of power systems, and to stabilize renewable energy source such as photovoltaic or wind turbine. The ESS is composed of a power conditioning system (PCS) and an energy storage. The battery is used as the energy storage. The battery is needed to design and verify a hardware and control system of PCS. Usually, a battery simulator is used instead of a battery, which is costly and hard to manage. In this paper, the development of the battery simulator for performance verification of the MW-class PCS is described. The battery simulator simulates the charging and discharging characteristics of batteries to design and verify the hardware and control system of PCS.

• Journal of Energy Engineering
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• v.20 no.1
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• pp.1-7
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• 2011

The performance and life-cycle costs of electric vehicle(EV) and hybrid electric vehicle(HEV) depend inherently on battery packs. Temperature uniformity in a pack is an important factor for obtaining optimum performance for an EV or HEV battery pack, because uneven temperature distribution in a pack leads to electrically unbalanced battery cells and reduced pack performance. In this work, a three-dimensional modeling was carried out to investigate the effects of operating conditions on the thermal behavior of a lithium-ion battery pack for an EV or HEV application. Thermal conductivities of various compartments of the battery were estimated based on the equivalent network of parallel/series thermal resistances of battery components. Heat generation rate in a cell was calculated using the modeling results of the potential and current density distributions of a battery cell.

• Journal of Aerospace System Engineering
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• v.15 no.5
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• pp.43-49
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• 2021

This research shows how is designed, and its performance is evaluated on the ground for the VTOL drone before the flight test initiates. The targeted drone weight is approximately 45 kg including battery packs, and 4 motors are utilized to produce thrust and control directions. 30 min flight schedules were simulated to estimate the total power consumptions which result in 2.4 kWh. Then, two packs of 13-cells lithium-polymer battery with operating voltage ranging between 54 V and 44 V with up to 4 C-rate were fabricated to safely operate a VTOL drone. Moreover, the battery management system was installed to prevent over and under-voltage and over-current while running a battery system. To finally verify battery's performance, we conducted a ground evaluation for discharging battery tests at -10 ℃, 25 ℃ and 40 ℃, resulting in satisfying simulated power consumption conditions for flight schedules.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.16 no.6
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• pp.385-391
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• 2023

As the use of electric vehicles has increased to minimize carbon emissions, the analyzing the state and performance of lithium-ion batteries that is instrumental in electric vehicles have been important. Comprehensive analysis using not only the voltage, current and temperature of the battery pack, which can affect the condition and performance of the battery, but also the driving data and charging pattern data of the electric vehicle is required. Therefore, a thorough analysis is imperative, utilizing electric vehicle operation data, charging pattern data, as well as battery pack voltage, current, and temperature data, which collectively influence the condition and performance of the battery. Therefore, collection and preprocessing of battery data collected from electric vehicles, collection and preprocessing of data on driver driving habits in addition to simple battery data, detailed design and modification of artificial intelligence algorithm based on the analyzed influencing factors, and A battery analysis and evaluation model was designed. In this paper, we gathered operational data and battery data from real-time electric buses. These data sets were then utilized to train a Random Forest algorithm. Furthermore, a comprehensive assessment of battery status, operation, and charging patterns was conducted using the explainable Artificial Intelligence (XAI) algorithm. The study identified crucial influencing factors on battery status, including rapid acceleration, rapid deceleration, sudden stops in driving patterns, the number of drives per day in the charging and discharging pattern, daily accumulated Depth of Discharge (DOD), cell voltage differences during discharge, maximum cell temperature, and minimum cell temperature. These factors were confirmed to significantly impact the battery condition. Based on the identified influencing factors, a battery analysis and evaluation model was designed and assessed using the Random Forest algorithm. The results contribute to the understanding of battery health and lay the foundation for effective battery management in electric vehicles.