• 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.

• Journal of the Korean Solar Energy Society
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• v.40 no.4
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• pp.35-44
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• 2020

Currently, various policies regarding ecofriendly vehicles are being proposed to reduce carbon emissions. In this study, the required areas for charging electric vehicle (EV) batteries using electricity produced by photovoltaic (PV) power plants were estimated. First, approximately 2.4 million battery EVs, which represented 10% of the total number of vehicles, consume approximately 404 GWh. Second, the power required for charging batteries is approximately 0.3 GW, and the site area of the PV power plant is 4.62 ㎢, which accounts for 0.005% of the national territory. Third, from the available sites of buildings based on the region, Jeju alone consumes approximately 0.2%, while the rest of the region requires approximately 0.1%. Fourth, Seoul, which has the smallest available area of mountains and farmlands, utilizes 0.34% of the site for PV power plants, while the other parts of the region use less than 0.1%. The results of this study confirmed that the area of the PV power plant site for producing battery-charging power generated through the supply of EVs is very small. Therefore, it is desirable to analyze and implement more specific plans, such as efficient land use, forest damage minimization, and safe maintenance, to expand renewable energy, including PV power.

• Proceedings of the KIPE Conference
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• 2001.07a
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• pp.114-117
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• 2001

In the Electric Vehicle(EV) driving system, the Battery Management System(BMS) is very important and an essential equipment. Particularly, BMS monitors the State of Charge(SOC), voltage, current, and temperature of the battery modules when Electric Vehicle is in the state of motoring or charging. Major roles of BMS are like these the first, estimation of State of Charge(SOC), the second, detection of the unbalance of the voltage between battery modules, the third, control of the available limit of the voltage and temperature of batteries by monitoring the batteries status during motoring or charging. In this research, We have focused on estimating SOC of battery according to the status of Electric Vehicle and the BMS operation algorithm. The result for algorithm of SOC estimation is presented. It have been modified, compensated, and verified by means of the experiment.

• The Transactions of the Korean Institute of Power Electronics
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• v.12 no.4
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• pp.324-331
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• 2007

This paper studies new estimation method for state of charge (SOC) of the hybrid electric vehicle lithium battery using sliding mode observer. A simple R-C Lithium battery modeling technique is established and the errors caused by simple modeling was compensated by the sliding mode observer. The structure of the sliding mode observer is simple, but it shows robust control property against modeling errors and uncertainties. The performance of the system has been verified by the UUDS test. The test results of the proposed observer system shows robust tracking performance under real driving environments.

• Transactions of the Korean Society of Automotive Engineers
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• v.21 no.3
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• pp.105-112
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• 2013

In this paper, EV (Electric Vehicle) and ICE (Internal Combustion Engine) vehicle simulators are developed to compare maximum driving range of EV and ICE vehicle according to different driving patterns. And, simulations are performed for fourteen constant velocity cases (20, 30, 40, ${\ldots}$, 150 km/h) and four different driving cycles. From the simulation results of constant velocity, it is found that the decreasing rate of maximum driving range for EV is larger than the one for ICE as both the vehicle velocity and the driving power increase. It is because the battery efficiency of EV decreases as both the velocity and the driving power increase, whereas the engine and transmission efficiencies of ICE vehicle increase. From the results of four driving cycle simulation, the maximum driving range of EV is shown to decrease by 50% if the average driving power of driving cycle increases from 10 to 20kW. It is because the battery efficiency decreases as the driving power increases. In contrast, the maximum driving range of ICE vehicle also increases as the average driving power of driving cycle increases. It is because the engine and transmission efficiencies also increase as the driving power increases.

• Proceedings of the KIEE Conference
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• 2011.07a
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• pp.864-865
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• 2011

Battery Disconnecting System (BDS) is the important equipment in electric vehicle system. Therefore, most of electric vehicle companies, i.e. Hyundai Motors, Renault Motors, General Motors, want to have the reliability of 15 years - 150, 000 miles. Recently, reliability prediction through Siemens Norm SN 29500 is considered without testing. In this paper, we will introduce the standard and various input parameters. Also the case study will be shown for BDS. Prediction model is constructed by listing all the components of BDS. It calculates the $\pi$ factors for each components using the conversion equation in the standard and converts the reference failure rates to the expected operating failure rates. According to the result, the parts which will be improved are EV-Relays.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.19 no.5
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• pp.8-13
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• 2020

In this paper, the electric vehicle (EV) and internal combustion engine vehicle (ICEV) are compared for different driving cases. The EV exhibits a lower powertrain efficiency when driven on the aggressive driving cycle than when driven on the moderate cycle. In particular, EV powertrain efficiency is low when the battery state of charge (SOC) is low, but ICEV efficiency increases when the driving cycle changes from the moderate cycle to the aggressive cycle. Based on these results, attempts can be made to increase EV powertrain efficiency. EV charging before the battery power drops to a low charging state can reduce energy consumption by 2.7% for an urban area. Furthermore, ECO driving has a more significant effect on EVs than on ICEVs.

• Corrosion Science and Technology
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• v.18 no.4
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• pp.117-120
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• 2019

Considering the tendency of development of electrification vehicles, development and verification of new evaluation technology is needed because of new technology applications. Recently, as the battery package is set outdoors of an electric vehicle, such vehicles are exposed to corrosive environments. Among major components connected to the battery package, rust prevention of high-voltage cables and connectors is considered the most important issue. For example, if corrosion of high voltage cable connectors occurs, the corrosion durability assessment of using an electric vehicle will be different from general environmental corrosion phenomena. The purpose of this study is to investigate the corrosion mechanism of high voltage cable connectors of an electric vehicle under various driving environments (road surface vibration, corrosion environment, current conduction by stray current, etc.) and develop an optimal rust prevention solution. To improve our parts test method, we have proposed a realistic test method to reproduce actual electric vehicle corrosion issues based on the principle test.

• Transactions of the Korean Society of Automotive Engineers
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• v.22 no.3
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• pp.60-67
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• 2014

The battery cells in lithium-ion battery pack assembled with high-capacity and high-power pouch cells, are commonly cooled with thin aluminum cooling plates in contact with the cells. For HEV/EV lithium-ion battery systems assembled with high-capacity, high-power pouch cells, the cells are commonly cooled with thin aluminum cooling plates in contact with the cells. Thin aluminum cooling plates are cooled by cold plate with coolant flow paths. In this study, the effect of the battery cooling system design including aluminum cooling plate thickness and various position of cold plate on the cooling performance are investigated by using finite element methods (FEM). Optimal cooling plate and cold plate design are proposed for improving the uniformity in temperature distributions as well as lowering average temperature for the cells with large capacities based on the simulation results.

• The Transactions of the Korean Institute of Power Electronics
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• v.26 no.3
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• pp.167-175
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• 2021

Reusing electric vehicle batteries after they have been retired from mobile applications is considered a feasible solution to reduce the demand for new material and electric vehicle costs. However, the evaluation of the value and the performance of second-life batteries remain a problem that should be solved for the successful application of such batteries. The present work aims to estimate the remaining useful life of Li-ion batteries through the neuro-fuzzy system with the equivalent circuit parameters obtained by Electrochemical Impedance Spectroscopy (EIS). To obtain the impedance spectra of the Li-ion battery over the life, a 18650 cylindrical cell has been aged by 1035 charge/discharge cycles. Moreover, the capacity and the parameters of the equivalent circuit of a Li-ion battery have been recorded. Then, the data are used to establish a neuro-fuzzy system to estimate the remaining useful life of the battery. The experimental results show that the developed algorithm can estimate the remaining capacity of the battery with an RMSE error of 0.841%.