• Journal of Information Technology Applications and Management
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• v.30 no.1
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• pp.1-9
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• 2023

Interest in the future of the battery market is growing as Tesla announces plans to increase production of electric vehicles and to produce batteries. Tesla announced an action plan to reduce battery prices by 56% through 'Battery Day', which included expansion of factories to internalize batteries and improvement of materials and production technology. In the trend of automobile electrification, the expansion of the battery market, which accounts for 40% of the cost of electric vehicles, is inevitable, and the size of the electric vehicle battery market in 2026 is expected to increase more than five times compared to 2016. With the development of materials and process technology, the energy density of electric vehicle batteries is increasing while the price is decreasing. Soon, electric vehicles and internal combustion locomotives are expected to compete on the same line. Recently, the mileage of electric vehicles is approaching that of an internal combustion locomotive due to the installation of high-capacity batteries. In the EV battery market, Korean, Chinese and Japanese companies are fiercely competing. Based on market share in the first half of 2020, LG Chem, CATL, and Panasonic are leading the EV battery supply, and the top 10 companies included 3 Korean companies, 5 Chinese companies, and 2 Japanese companies. All-solid, lithium-sulfur, sodium-ion, and lithium air batteries are being discussed as the next-generation batteries after lithium-ion, among which all-solid-state batteries are the most active. All-solid-state batteries can dramatically improve stability and charging speed by using a solid electrolyte, and are excellent in terms of technology readiness level (TRL) among various technology alternatives. In order to increase the competitiveness of the battery industry in the future, efforts to increase the productivity and economy of electric vehicle batteries are also required along with the development of next-generation battery technology.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2013.10a
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• pp.882-883
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• 2013

With the development of the society, pure electric vehicles will be surely important of the future. Electric vehicle requires various technology like motor driving, battery management, operational efficiencies and so on. Battery management is indeed the most important to enhance battery's performance and life. This paper has deeply discussed and studied on the lithium-polymer battery management system of pure electric vehicle. First of all we have analyzed the characteristic of the lithium-polymer batteries and the factors influenced on the state of charge. Then a logical SOC measuring method has been raised, which is the combination of open circuit voltage and Ah integration. The next we will introduce the design of battery management system, the battery management system performs many functions, such as inspecting the whole process, when it's running cell equalization protecting and diagnosing the battery, estimating the state of charge. The module design style including microcontroller, data aquisition module, charging control module and serial communication module. To arrive at conclusions, the battery management system which this paper has introduced is reliable and economical.

• Transactions of the Korean Society of Automotive Engineers
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• v.18 no.3
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• pp.53-59
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• 2010

The Developing & Producing of Eco-Friendly vehicle have been more incremented, as People appreciate the importance of Earth Environment Conservation. The needs of Neighborhood Electric Vehicle(NEV) that suits Current people's short drive distance is incremented. In this Paper, we define Neighborhood Electric Vehicle through out National Highway Traffic Safety Administration of United States of America's regulation and explain motor and battery of primary constituents of that. We used MATLAB and ADVISOR 200 programs for Simulation, and propsed NEV's Model that tow people can be got in. In this Model, the battery is Lead-acid battery(72V, 85Ah) and the motor is 8kW permanent magnet synchronous motor(PM motor). We compared change of driving range of NEV through out non-changing speed Driving(10km/h, 20km/h, 30km/h, 40km/h) and Manhattan driving schedule.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2013.05a
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• pp.909-910
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• 2013

• Journal of Institute of Convergence Technology
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• v.10 no.1
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• pp.37-40
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• 2020

Automotive battery system consists of various components such as battery cells, mechanical structures, cooling system, and control system. Recently, various computational technologies are required to develop an automotive battery system. Physics-based cell modeling is used for designing a new battery cell by conducting optimization of material selection and composition in electrodes. Structural analysis plays an important role in designing a protective system of battery system from mechanical shock and vibration. Thermal modeling is used in development of thermal management system to maintain the temperature of battery cells in safe range. Finally, vehicle simulation is conducted to validate the performance of electric vehicle with the developed battery system.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.18 no.7
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• pp.29-35
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• 2019

In this paper, the component loss models of the electric vehicle(EV) and the internal combustion engine vehicle(ICEV) are developed to analyze the losses and efficiencies of these two types of vehicles. The EV powertrain efficiency decreases as the vehicle velocity increases over most of the vehicle velocity range because the battery efficiency decreases. Especially, the EV powertrain efficiency decreases significantly when the battery SOC is low. But the ICEV powertrain efficiency increases as the vehicle velocity increases. This is because the efficiencies of both the transmission and engine increases.

• Journal of Institute of Convergence Technology
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• v.11 no.1
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• pp.19-23
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• 2021

These days, sales of battery electric vehicles have been rapidly increasing due to the strict CO₂ regulations. However, since it take too long to measure the energy economy of electric vehicles, it has been required to improve the procedure of energy economy measurement. In order to improve this problem, the present study analyzed the battery charge/discharge pattern according to the changes in battery SOC (state of charge). In general, the energy economy test is started with a battery SOC charged to 100 %. However, it was identified that when the battery is fully charged, it can actually be charged over the 100 % (e.g., 100.5 %). This can induce errors in the energy economy measurement. Therefore, the present study recommend to start the test at SOC 99.9 %. The regenerative braking was partly restricted for the SOC over 90 %. This made it difficult to estimate the overall energy economy of the electric vehicle. However, it was identified that there was no change in the battery charge/discharge characteristics under the SOC 90 %. Therefore, the energy economy test can be shortened by predicting the overall energy economy through a short mileage test.

• Journal of Korea Multimedia Society
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• v.24 no.9
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• pp.1203-1210
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• 2021

In this paper, we developed the inspection system of electric vehicle pouch battery using image processing. Line scan cameras are used for acquiring the all parts of the pouch battery, and several steps of image processing for extracting significant dimensions(User Required Position) of the battery. In image processing, edge lines, node points, dimension lines, etc. were extracted using Preprocessor, Square Edge Detection, and Size Detection algorithms. This is used to measure the dimensions of the location requested by the user on the pouch battery. For verification of the inspection system, the dimensions of three pouch batteries produced in the same process were measured, and the mean and standard deviation were obtained to confirm the precision.

• Journal of Electrical Engineering and Technology
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• v.13 no.2
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• pp.742-751
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• 2018

Electric vehicles (EV) are emerging as the future transportation vehicle reflecting their potential safe environmental advantages. Vehicle to Grid (V2G) system describes the hybrid system in which the EV can communicate with the utility grid and the energy flows with insignificant effect between the utility grid and the EV. The paper presents an optimal power control and energy management strategy for Plug-In Electric Vehicle (PEV) charging stations using Wind-PV-FC-Battery renewable energy sources. The energy management optimization is structured and solved using Multi-Objective Particle Swarm Optimization (MOPSO) to determine and distribute at each time step the charging power among all accessible vehicles. The Model-Based Predictive (MPC) control strategy is used to plan PEV charging energy to increase the utilization of the wind, the FC and solar energy, decrease power taken from the power grid, and fulfil the charging power requirement of all vehicles. Desired features for EV battery chargers such as the near unity power factor with negligible harmonics for the ac source, well-regulated charging current for the battery, maximum output power, high efficiency, and high reliability are fully confirmed by the proposed solution.

• Proceedings of the KIEE Conference
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• 1998.07b
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• pp.560-562
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• 1998

A new approach to developing battery SOC indicator for electric vehicle is discussed in this paper. One of the most difficult problems associated with the development of electric vehicle is the battery indicator which reliably informs the state of charge(SOC) of the battery to the driver. And the condition to be satisfied with SOC indicator installed on the electric vehicle is that it should be used under frequently variable load. A new method to determining SOC using neural networks(NN) is proposed to satify the condition. The training data of NN are obtained by using mathematical model of lead-acid battery, and calculating discharge currents and terminal voltages while battery discharges with constant current. The 3-layered NN with back propagation algorithm is used Simulation results show that the proposed method is appropriate as SOC indicator of the battery.