• Journal of Electrochemical Science and Technology
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• 제15권3호
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• pp.414-420
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• 2024

Lithium-ion batteries are widely used in many applications due to their high energy density, high efficiency, and excellent cycle ability. Once an unknown Li-ion battery is reusable, it is important to measure its lifetime and state of health. The most favorable measurement method is the cycle test, which is accurate but time- and capacity-consuming. In this study, instead of a cycle test, we present an empirical model based on the C-rate test to understand the state of health of the battery in a short time. As a result, we show that the partially accelerated charge/discharge condition of the Li-ion battery is highly effective for the degradation of battery capacity, even when half of the charge/discharge conditions are the same. This observation provides a measurable method for predicting battery reuse and future capacity degradation.

• 대한전자공학회:학술대회논문집
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• 대한전자공학회 2001년도 The 6th International Symposium of East Asian Resources Recycling Technology
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• pp.252-256
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• 2001

The rapid development of communication equipment and information processing technology has led to a constant improvement in cordless communication. Lithium ion batteries used in cellular phones and laptop computers, in particular, have been in the forefront of the above revolution. These batteries use high value added raw materials and have a high and stable energy output and are increasingly coming into common use. The development of the material for the negative terminal has led to an improvement in the quality and efficiency of the batteries, whereas a reduction in the cost of the battery by researching new materials for the positive anode has become a research theme by itself. These long life batteries, it is being increasingly realized, can have value added to them by recycling. Research is increasingly being done on recycling the aluminum case and the load casing for the negative diode. This paper aims to introduce the current situation of recycling of lithium ion batteries. 1. Introduction 2. Various types of batteries and the situation of their recycling and the facts regarding recycling. 3. Example of cobalt recycling from waste Lithium ion secondary cell. 3-1) Flow Chart of Lithium ion battery recycling 3-2) Materials that make a lithium ion secondary cell. 3-3) Coarse grinding of Lithium ion secondary cell, and stabilization of current discharge 3-4) Burning 3-5) Grinding 3-6) Magnetic Separation 3-7) Dry sieving 3-8) Dry Classifying 3-9) Content Ratio of recycled cobalt parts 3-10) Summary of the Line used for the recovery of Cobalt from waste Lithium ion battery. 4. Conclusion.

• 전력전자학회:학술대회논문집
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• 전력전자학회 2013년도 전력전자학술대회 논문집
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• pp.465-466
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• 2013

In this paper, we propose a dynamic battery equivalent modeling of lithium-ion batteries that can be applied to the battery simulator. In order to apply battery model to battery simulator, the profile of battery model should be equal to that of actual battery. Therefore, the equivalent model was selected by considering the transient and steady-state characteristics of lithium-ion batteries. Also, to obtain transient-state behavior of the battery, the RC values of the battery are selected through the lithium-ion battery charge/discharge experiments. The validity of proposed battery model is verified from the experimental results.

• 한국산업융합학회 논문집
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• 제25권5호
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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.

• 멤브레인
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• 제27권1호
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• pp.92-103
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• 2017

본 연구에서는 리튬이온전지용 친수화된 세퍼레이터의 전기화학적 성능에 대한 연구를 진행하였다. 리튬이온전지용 분리막으로 사용되는 폴리올레핀 소재는 소수성이고, 카보네이트 계열의 유기용매를 사용하는 전해액은 친수성을 가진다. 따라서 리튬이온전지는 수계전해액을 사용하기 때문에 폴리올레핀계 분리막에 다양한 친수성 고분자를 도입하여 친수화 처리하였다. 코팅된 세퍼레이터의 변화를 평가하기 위해, 표면 관찰, 코팅시간에 따른 친수화도, 다공성, 젖음성에 대한 특성평가를 수행하였다. 최종적으로 리튬이온이 코팅된 세퍼레이터의 저항과., 이온전도도를 측정하여 리튬이온전지 성능평가를 진행하였다. PMVE로 코팅한 세퍼레이터의 친수화 정도가 우수하며, 세퍼레이터의 기공이 잘 유지되어 우수한 이온전도도를 나타냄으로써 이차전지 배터리에 적용을 위한 잠재성을 가짐을 확인하였다.

• International Journal of Advanced Culture Technology
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• 제12권3호
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• pp.466-470
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• 2024

This study proposes a deep learning-based LSTM model to predict the state of charge (SOC) of lithium-ion batteries. The model was trained using data collected under various temperature and load conditions, including measurement data from the CS2 lithium-ion battery provided by the University of Maryland College of Engineering. The LSTM model effectively models temporal patterns in the data by learning long-term dependencies. Performance evaluation by epoch showed that the predicted SOC improved from 14.8400 at epoch 10 to 12.4968 at epoch 60, approaching the actual SOC value of 13.5441. The mean absolute error (MAE) and root mean squared error (RMSE) also decreased from 0.9185 and 1.3009 at epoch 10 to 0.2333 and 0.5682 at epoch 60, respectively, indicating continuous improvement in predictive performance. This study demonstrates the validity of the LSTM model for predicting the SOC of lithium-ion batteries and its potential to enhance battery management systems.

• 한국정밀공학회:학술대회논문집
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• 한국정밀공학회 2012년도 춘계학술대회 논문집
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• pp.339-340
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• 2012

• 한국가스학회지
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• 제14권3호
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• pp.14-20
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• 2010

본 논문은 리튬이온전지의 불꽃방전에 의한 폭발위험성에 관한 연구로서 휴대용기기의 전원으로 사용되는 리튬이온전지(일반용, 노트북용)를 시료로 선정하고, 폭발성 시험가스인 메탄, 프로판, 에틸렌, 수소를 대상으로 IEC형 불꽃점화 시험장치를 이용하여 불꽃점화실험을 실시하여 불꽃방전에 의한 폭발위험성을 규명하였다. 또한, 사고 시 단락전류에 의한 자체점화 가능성을 확인하고자 열화상카메라를 이용하여 온도변화를 측정하였다. 실험결과 리튜이온전지는 폭발성가스가 존재하는 폭발위험장소에서 사용할 때는 안전에 각별히 주의하여 사용 설계되어야 한다.

• 한국기계가공학회지
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• 제20권12호
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• pp.54-64
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• 2021

Electric vehicles use lithium-ion battery packs as the power supply, where the batteries are connected in series or parallel. The temperature control of each battery is essential to ensure a consistent overall temperature. This study focused on reducing ohmic heating caused by batteries to realize a uniform battery temperature. The battery spacing was optimized to improve air cooling, and the tilt angle between the batteries was varied to optimize the internal structure of the batterypack. Simulations were performed to evaluate the effects of these parameters, and the results showed that the optimal scheme effectively achieved a uniform battery temperature under a constant power discharge. These findings can contribute to future research on cooling methods for battery packs.

• Journal of Power Electronics
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• 제14권5호
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• pp.1038-1046
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• 2014

Lithium-ion batteries are widely used in hybrid and pure electric vehicles. State-of-charge (SOC) estimation is a fundamental issue in vehicle power train control and battery management systems. This study proposes a novel model-based SOC estimation method that applies closed-loop state observer theory and a comprehensive battery model. The state-space model of lithium-ion battery is developed based on a three-order resistor-capacitor equivalent circuit model. The least square algorithm is used to identify model parameters. A multi-state closed-loop state observer is designed to predict the open-circuit voltage (OCV) of a battery based on the battery state-space model. Battery SOC can then be estimated based on the corresponding relationship between battery OCV and SOC. Finally, practical driving tests that use two types of typical driving cycle are performed to verify the proposed SOC estimation method. Test results prove that the proposed estimation method is reasonably accurate and exhibits accuracy in estimating SOC within 2% under different driving cycles.