• The Transactions of The Korean Institute of Electrical Engineers
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• v.57 no.5
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• pp.839-844
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• 2008

A novel state of health estimation method for hybrid electric vehicle lithium battery using sliding mode observer has been presented. A simple R-C circuit method has been used for the lithium battery modeling for the reduced calculation time and system resources due to the simple matrix operations. The modeling errors of simple model are compensated by the sliding mode observer. The design methodology for state of health estimation using dual sliding mode observer has been presented in step by step. The structure of the proposed system is simple and easy to implement, but it shows robust control property against modeling errors and temperature variations. The convergence of proposed observer system has been proved by the Lyapunov inequality equation and the performance of system has been verified by the sequence of urban dynamometer driving schedule test. The test results show the proposed observer system has superior tracking performance with reduced calculation time under the real driving environments.

• Proceedings of the KIPE Conference
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• 2010.11a
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• pp.349-350
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• 2010

본 논문에서는 리튬이온 배터리 충전알고리즘 중에서, 기존의 정전류/정전압충전(CC/CV)과 부스트충전(Boostcharging)을 비교 분석한다. 또한 기존의 정전류/정전압 충전방식에 비해 부스트충전이 갖는 장점과 부스트충전알고리즘의 근거를 제시하고 부스트충전 알고리즘을 시뮬레이션을 통해 구현한다. PSIM 6.0 과 C Programming을 이용하여 시뮬레이션 모델을 구축하였고 구성된 시뮬레이션 결과를 통해 타당성을 검증한다.

• Membrane Journal
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• v.27 no.1
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• pp.92-103
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• 2017

In this study, electrochemical performance of the hydrophilized separator for the lithium ion battery is studied. The polyolefin based material used as the separator for the lithium ion battery is hydrophobic, and the electrolytic solution using a carbonate-based organic solvent is hydrophilic. Therefore, the polyolefin separator is hydrophilized using various hydrophilic polymers because lithium ion battery uses an aqueous electrolyte solution. In order to evaluate change of the coated separator, the performances of separator in terms of surface morphology, porosity and the wettability are investigated. Finally, the resistance and the ionic conductivity of separator coated with lithium ion are measured to evaluate the performance of lithium ion battery. Separator coated with PMVE shows good hydrophilicity and excellent ionic conductivity because the porosity of the separator is maintained. We can confirm that this property makes potential candidates for lithium ion battery.

• Resources Recycling
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• v.29 no.5
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• pp.73-80
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• 2020

In order to develop a process for the recovery of valuable metals from spent LiBs, solvent extraction experiments were performed to separate Cu(II) and/or Co(II) from synthetic hydrochloric acid solutions containing Li(I), Mn(II), and Ni(II). Commercial amines (Alamine 336 and Aliquat 336) were employed and the extraction behavior of the metals was investigated as a function of the concentration of HCl and extractants. The results indicate that HCl concentration affected remarkably the extraction efficiency of the metals. Only Cu(II) was selectively at 1 M HCl concentration, while both Co(II) and Cu(II) was extracted by the amines when HCl concentration was higher than 5 M, leaving the other metal ions in the raffinate. Therefore, it was possible to selectively extract either Cu(II) or Co(II)/Cu(II) by adjusting the HCl concentration.

• Resources Recycling
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• v.28 no.4
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• pp.51-58
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• 2019

Dismantlement of lithium primary batteries without explosion is required to recycle the lithium primary batteries which could be exploded by heating too much or crushing. In the present study, the optimum discharging condition was investigated to dismantle the batteries without explosion. When the batteries were discharged with $0.5kmol{\cdot}m^{-3}$ sulfuric acid, the reactivity of the batteries decreased after 4 days at $35^{\circ}C$ and after 1 day at $50^{\circ}C$, respectively. This result shows that higher temperature removed the high reactivity of the batteries. Because loss of metals recycled increases when the batteries are discharged only with the sulfuric acid, discharging process using acid solution and water was newly proposed. When the batteries were discharged with water during 24 hours after discharging with $0.5kmol{\cdot}m^{-3}$ sulfuric acid during 6 hours, the batteries discharged were dismantled without explosion. Because decrease in loss of metals was accomplished by new process, the recycling process of the batteries could become economic by the 2-step discharging process.

• Journal of the Korea Organic Resources Recycling Association
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• v.30 no.4
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• pp.59-66
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• 2022

Lithium-ion batteries have greatly expanded along with the mobile phone market, and as the electric vehicle business is activated in earnest, they will attract many people's attention even afterwards. Until now, many people have attracted attention to the recovery of valuable metals inside lithium-ion batteries, but graphite, which is mainly used as an anode material, is also worth recycling. Therefore, in order to recover graphite with high purity and valuable metals, graphite that can be used as an anode material of a secondary battery may be generated again through a regeneration process of purifying and separating graphite from a waste lithium-ion battery and recovering electrical characteristics of graphite. This paper describes the process of converting waste graphite into regenerated graphite and the environmental and economic effects of regenerated graphite.

• Korean Chemical Engineering Research
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• v.61 no.1
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• pp.32-38
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• 2023

In this study, the electrochemical properties of dopamine coated silicon/silicon carbide/carbon(Si/SiC/C) composite materials were investigated to improve cycle stability and rate performance of silicon-based anode active material for lithium-ion batteries. After synthesizing CTAB/SiO₂ using the Stöber method, the Si/SiC composites were prepared through the magnesium thermal reduction method with NaCl as heat absorbent. Then, carbon coated Si/SiC anode materials were synthesized through polymerization of dopamine. The physical properties of the prepared Si/SiC/C anode materials were analyzed by SEM, TEM, XRD and BET. Also the electrochemical performance were investigated by cycle stability, rate performance, cyclic voltammetry and EIS test of lithium-ion batteries in 1 M LiPF₆ (EC: DEC = 1:1 vol%) electrolyte. The prepared 1-Si/SiC showed a discharge capacity of 633 mAh/g and 1-Si/SiC/C had a discharge capacity of 877 mAh/g at 0.1 C after 100 cycles. Therefore, it was confirmed that cycle stability was improved through dopamine coating. In addition, the anode materials were obtain a high capacity of 576 mAh/g at 5 C and a capacity recovery of 99.9% at 0.1 C/0.1 C.

• Resources Recycling
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• v.31 no.4
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• pp.3-11
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• 2022

Owing to the increasing demand for electric vehicles (EVs), appropriate management of their waste batteries is required urgently for scrapped vehicles or for addressing battery aging. With respect to technological developments, data-driven diagnosis of waste EV batteries and management technologies have drawn increasing attention. Moreover, robot-based automatic dismantling technologies, which are seemingly interesting, require industrial verifications and linkages with future battery-related database systems. Among these, it is critical to develop and disseminate various advanced battery diagnosis and assessment techniques to improve the efficiency and safety/environment of the recirculation of waste batteries. Incorporation of lithium-related chemical substances in the public pollutant release and transfer register (PRTR) database as well as in-depth risk assessment of gas emissions in waste EV battery combustion and their relevant fire safety are some of the necessary steps. Further research and development thus are needed for optimizing the lifecycle management of waste batteries from various aspects related to data-based diagnosis/classification/disassembly processes as well as reuse/recycling and final disposal. The idea here is that the data should contribute to clean design and manufacturing to reduce the environmental burden and facilitate reuse/recycling in future production of EV batteries. Such optimization should also consider the future technological and market trends.

• 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.

• 한국신재생에너지학회:학술대회논문집
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• 2010.06a
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• pp.145.1-145.1
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• 2010

현재 운송수단의 주 동력원으로 사용되고 있는 내연기관은 연료로 사용되는 석유로부터 온실가스가 발생하기 때문에 지구 환경악화의 주범으로 작용하기 때문에 교토 의정서에 의해 많은 제재를 받고 있으며, 이 문제를 해결하기 위해서 다양한 종류의 하이브리드 제품으로 대체하고자 하는 연구개발이 세계 각국에서 활발히 진행되고 있다. 한국은 저탄소 녹색성장이라는 기치하에 2012년 까지 2000만대의 자전거 보급과 세계3대 자전거 생산국으로 발전한다는 비전을 제시하여 향후 자전거 산업이 비약적으로 발전할 것으로 예상되고 있다. 이와 같은 비전을 달성하기 위해서는 저가이면서 출력이 크고 수명이 긴 축전지의 개발이 필수적이며, 이와 더불어 축전지의 성능 발휘를 위해서 이것을 제어할 수 있는 battery management system(BMS)의 개발도 같이 이루어져야 한다. 리튬 배터리의 성능이 우수한 것은 익히 알려진 사실이지만, 고가이기 때문에 소비자들의 부담이 적지 아니하다. 따라서 혁신적인 기술이 개발되어 저가의 배터리를 사용하기 전까지는 납축전지도 같이 사용될 것이며, 본 과제에서는 이와 같은 납 축전지의 수명을 연장시킬 수 있는 BMS를 개발하였다.