• 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.

• Journal of the Korean Electrochemical Society
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• v.4 no.2
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• pp.53-57
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• 2001

When an EV is parked for a long period time, the battery capacity naturally decreases due to selfdischarge. Therefore, this effect must be considered for the accurate measurement of the state of charge of EV battery. Battery selfdischarge simulations using the design of experiments among computer simulation methods are compared with experimental data for Ni/MH batteries for electric vehicles. The motivation is to predict the selfdischarge rate of the battery for electric vehicle at all temperature conditions and standing time when electric vehicle could be operated. We developed a general equation representing the seudischarge rate of the electric vehicle battery using design of experiments, and the equation is determined by temperature and standing time of the battery. We selected Ni/MH battery, 12 V-95 Ah, for pure electric vehicle for this study. ID develop the equation using design of experiments we selected temperature range of $-20^{\circ}~30^{\circ}C$ and standing time of 1 day$\~15$ days. We conducted several selfdischarge tests of Ni/MH battery to verify the integrity of the equation. The results showed that the computation values were in good agreement with experimental data.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.17 no.4
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• pp.255-265
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• 2024

To address environmental challenges and improve energy efficiency, the adoption of electric vehicles has led to a surge in related research. However, to comprehensively understand the research trends within the field of electric vehicles, it is necessary to systematically analyze vast amounts of data. This study systematically analyzed research trends in the field of electric vehicles and identified key research topics through LDA topic modeling, based on 36,519 papers related to electric vehicles collected from the SCIE database. The data analysis revealed a total of 10 major topics, of which three were identified as hot topics showing an upward trend: Electric Vehicle Charging Infrastructure, Energy and Environmental Policy, and Optimization and Algorithms. Conversely, five topics were identified as cold topics exhibiting a downward trend: Battery Temperature and Cooling, Battery Materials and Chemistry, Motor and Mechanical Design, Control Strategies and Systems, and Battery Components and Materials. This study provides basic data for understanding the current research trends in electric vehicles and offers valuable information for researchers in selecting research topics related to electric vehicles.

• 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.

• 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.

• The Journal of the Korea institute of electronic communication sciences
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• v.16 no.2
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• pp.255-260
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• 2021

The importance of electric vehicles is gradually increasing due to the recent depletion of fossil fuels. In order to use an electric vehicle, the battery built into the vehicle must be frequently charged. Electric vehicles has very good performance in terms of noise and vibration. However, due to the limitations of the battery, the mileage is considerably shorter than that of an internal combustion engine vehicle once it is charged, and the battery charging time is relatively long compared to the refueling time. There are two types of charging methods for electric vehicle batteries: plug-in and wireless charging. In this paper, we introduced the wireless charging technology for electric vehicles and the current state of technology development and standards in major countries.

• Journal of ILASS-Korea
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• v.27 no.3
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• pp.155-160
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• 2022

Recently, major developed countries have strengthened automobile fuel efficiency regulations and carbon dioxide emission allowance standards to curb climate change caused by global warming worldwide. Accordingly, research and manufacturing on electric vehicles that do not emit pollutants during actual driving on the road are being conducted. Several automobile companies are producing and testing electric vehicles to commercialize them, but it takes a lot of manpower and time to test and evaluate mass-produced electric vehicles with driving mileage of more than 300km on a per-charge. Therefore, in order to reduce this, a simulation model was developed in this study. This study used vehicle information and MCT speed profile of small electric vehicle as basic data. It was developed by applying Simulink, which models the system in a block diagram method using MATLAB software. Based on the vehicle dynamics, the simulation model consisted of major components of electric vehicles such as motor, battery, wheel/tire, brake, and acceleration. Through the development model, the amount of change in battery SOC and the mileage during driving were calculated. For verification, battery SOC data and vehicle speed data were compared and analyzed using CAN communication during the chassis dynamometer test. In addition, the reliability of the simulation model was confirmed through an analysis of the correlation between the result data and the data acquired through CAN communication.

• The Journal of the Convergence on Culture Technology
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• v.5 no.2
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• pp.367-373
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• 2019

As the number of Battery Electric Vehicle (BEV) is increasing dramatically Vehicle-to-Grid (V2G) te chnology also has been spotlight from industry and academia recently. With help of V2G technology Battery of EV can play many important roles like as energy storage system (ESS) and electric energy resource in Smart Grid environment. This paper provides comprehensive review of Vehicle-to-Home(V2H), Vehicle-to-Building(V2B) and Vehicle-to-Grid(V2G) technologies. The economical analysis of these technologies is also discussed.

• Transactions of the Korean Society of Automotive Engineers
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• v.17 no.1
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• pp.130-136
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• 2009

A lumped parameter model of Li-ion battery in hybrid electric vehicle(HEV) is constructed and system parameters are identified by using recursive least square estimation for different C-rates, SOCs and temperatures. The system characteristics of pole and zero in frequency domain are analyzed with the parameters obtained from different conditions. The parameterized model of Li-ion battery indicates highly dependant of temperatures. The system pole and internal resistance changes 6.6 and 18 times at $-20^{\circ}C$, comparing with those at $25^{\circ}C$, respectively. These results will be utilized on constructing model-based state observer or an on-line identification and an adaptation of the model parameters in battery management systems for hybrid electric vehicle applications.

• Journal of Energy Engineering
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• v.19 no.1
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• pp.39-50
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• 2010

Hybrid Electric Vehicle(HEV) and Plug-in Hybrid Electric Vehicle(PHEV) will replace Conventional Gasolene Engine Vehicle at a rapid rate to eliminate emission gases and improve fuel economy. This review describes Fuzzy Logic Control strategy and Optimization for Parallel Hybrid Electric Vehicle. Recent progress on Electric Motor and Li-ion Battery for HEV and PHEV are given. Analysis on competitiveness of Korean HEV and PHEV technology based on the number of papers published and patents registered are also performed.