• Journal of the Korean institute of surface engineering
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• v.57 no.1
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• pp.31-37
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• 2024

The surge in demand for lithium is primarily fueled by the expanding electric vehicle market, the necessity for renewable energy storage, and governmental initiatives aimed at achieving carbon neutrality. This study proposes a straightforward method for lithium extraction utilizing cellulose nanofiber (CNF) via a vacuum filtration process. This approach yields a porous CNF film, showcasing its potential utility as a lithium extractor and indicator. Given its abundance and eco-friendly characteristics, cellulose nanofiber (CNF) emerges as a material offering both economic and environmental advantages over traditional lithium extraction techniques. Hence, this research not only contributes to lithium recovery but also presents a sustainable solution to meet the growing demand for lithium in energy storage technologies.

• Journal of Electrical Engineering and Technology
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• v.13 no.2
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• pp.733-741
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• 2018

Capacity estimation is indispensable to ensure the safety and reliability of lithium-ion batteries in electric vehicles (EVs). Therefore it's quite necessary to develop an effective on-board capacity estimation technique. Based on experiment, it's found constant current charge time (CCCT) and the capacity have a strong linear correlation when the capacity is more than 80% of its rated value, during which the battery is considered healthy. Thus this paper employs CCCT as the health indicator for on-board capacity estimation by means of relevance vector machine (RVM). As the ambient temperature (AT) dramatically influences the capacity fading, it is added to RVM input to improve the estimation accuracy. The estimations are compared with that via back-propagation neural network (BPNN). The experiments demonstrate that CCCT with AT is highly qualified for on-board capacity estimation of lithium-ion batteries via RVM as the results are more precise and reliable than that calculated by BPNN.

• Journal of Nuclear Fuel Cycle and Waste Technology(JNFCWT)
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• v.18 no.1
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• pp.83-90
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• 2020

The Graphite Isotope Ratio Method (GIRM) can verify non-proliferation of nuclear weapon by estimating the total plutonium production in a graphite-moderated reactor. Using the reactor, plutonium is generated and accumulated through the ²³⁸U neutron capture reaction, and impurities in the graphite are converted to nuclides due to the nuclear reaction. Therefore, the amount of plutonium production and concentration of the impurities are correlated. However, the plutonium production cannot be predicted using only the absolute concentration of the impurities. It can only be predicted when the initial concentration of the impurities is obtained because the concentration, at a certain time, depends on it. Nevertheless, the ratios of the isotopes in an element are known regardless of the impurity of an element in the graphite moderator. Thus, the correlation between the isotope ratio and amount of plutonium produced helps predict plutonium production in a graphite-moderated reactor. Boron, Lithium, Chlorine, Titanium, and Uranium are known as indicator elements in the GIRM. To assess whether the correlation between the indicator isotope and amount of plutonium produced is independent of the initial concentration of the impurities, four different impurity compositions of graphite were used. ¹⁰B/¹¹B, ³⁶Cl/³⁵Cl, ⁴⁸Ti/⁴⁹Ti, and ²³⁵U/²³⁸U had a consistent correlation with the cumulative plutonium production, regardless of the initial impurity concentration of the graphite, because these isotopes were not generated through the nuclear reaction of other elements. On the other hand, the correlation between ⁶Li/⁷Li and plutonium production depended on the initial concentration of the impurities in graphite. Although ⁷Li can be produced through the neutron capture reaction of ⁶Li, the (n, α) reaction of ¹⁰B was the major source of ⁷Li. Therefore, the initial concentration of ¹⁰B affected the production of ⁷Li, making Li unsuitable as an indicator element for the GIRM.

• Journal of the Korean Society of Industry Convergence
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• v.25 no.6_3
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• pp.1275-1284
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• 2022

This study provides an investigating the electrolyte reaction characteristics during thermal runaway of a lithium-ion battery(LIB). Dimethyl carbonate(DMC) is known as the main substance that makes up the electrolyte. The mono-molecular decomposition characteristics of DMC were derived through numerical analysis. Cobalt oxide can release oxygen under high temperature conditions. Also, DMC is converted to CH₄, H₂, CO, and CO₂. Especially, it was found that the decomposition of the DMC begins at a temperature range of 340-350℃, which dramatically increases the internal pressure of the LIB. In the by-products gases, the molar ratio of CO and CO₂ changed according to the molecular structure of DMC and temperature conditions. The correlation of the [CO]/[CO₂] ratio according to the temperature during thermal runaway was derived, and the characteristics of the reaction temperature could be estimated using the molar ratio as an indicator. In addition, the oxidation and decomposition characteristics of DMC according to the residence time for each temperature were estimated. When DMC is exposed to low temperature for a long time, both oxidation and decomposition may occur. There is possibility of not only increasing the internal pressure of the LIB, but also promoting thermal runaway. In this study, internal environment of LIB was identified and the reaction characteristics between the active materials of the cathode and electrolyte were investigated.

• The Transactions of the Korean Institute of Power Electronics
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• v.25 no.5
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• pp.376-384
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• 2020

An energy storage system is composed of lithium-ion batteries in modern applications. Batteries are regarded as storage devices for renewable and residual energy. The failure of batteries can cause the performance reduction and explosion of battery systems. High maintenance cost is essential when dealing with the problem of battery safety. Therefore an accurate health diagnosis is required to ensure the high reliability of battery systems. A battery pack is a combination of single cells in series and parallel connections. A battery pack has to consider various factors to assess battery health. Battery health involves conventional factors and additional factors, such as cell-to-cell imbalance. For large applications, state-of-health (SOH) can be inaccurate because of the lack of factors that indicate the state of the battery pack. In this study, six characterization factors are proposed for improving the SOH estimation of battery packs. The six proposed characterization factors can be regarded as health indicators (HIs). The six HIs are applied to the principal component analysis (PCA) algorithm. To reflect information regarding capacity, voltage, and temperature, the PCA algorithm extracts new degradation factors by using the six HIs. The new degradation factors are applied to a multiple regression model. Results show the advancement and improvement of SOH estimation.

• Bulletin of the Korean Chemical Society
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• v.28 no.1
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• pp.68-72
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• 2007

A new PVC membrane potentiometric sensor that is highly selective to Hg2+ ions was prepared, using bis(2-hydroxybenzophenone) butane-2,3-dihydrazone (HBBD) as an excellent hexadendates neutral carrier. The sensor works satisfactorily in the concentration range of 1.0 × 10-6 to 1.0 × 10-1 mol L-1 (detection limit 4 × 10-7 mol L-1) with a Nernstian slope of 29.7 mV per decade. This electrode showed a fast response time (~8 s) and was used for at least 12 weeks without any divergence. The sensor exhibits good Hg2+ selectivity for a broad range of common alkali, alkaline earth, transition and heavy metal ions (lithium, sodium, potassium, magnesium, calcium, copper, nickel, cobalt, zinc, cadmium, lead and lanthanum). The electrode response is pH independent in the range of 1.5-4.0. Furthermore, the developed sensor was successfully used as an indicator electrode in the potentiometric titration of mercury ions with potassium iodide and the direct determination of mercury in some binary and ternary mixtures.

• Journal of IKEEE
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• v.26 no.1
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• pp.10-18
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• 2022

Accurate SOC estimation is an important indicator of battery operation strategies, and many studies have been conducted. The simulation method which was mainly used in previous studies, is difficult to conduct real-time SOC estimation like real BMS environment. Therefore, this paper aims to implement a real-time battery SOC estimation embedded system and analyze problems that can arise during the verification process. In environment consisting of two Raspberry Pi boards, SOC estimation with the EKF uses data measured by the Simscape battery model. Considering that the operating characteristics of the battery vary depend on the temperature, the results were analyzed at various ambient temperatures. It was confirmed that accurate SOC estimation was performed even when offset fault and packet loss occurred due to communication or sensing problems. This paper proposes a guide for embedded system strategies that enable real-time SOC estimation with errors within 5%.