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Pseudocapacitive Characteristics of Low-Carbon Silicon Oxycarbide for Lithium-Ion Capacitors
Halim, Martin,Liu, Guicheng,Ardhi, Ryanda Enggar Anugrah,Hudaya, Chairul,Wijaya, Ongky,Lee, Sang-Hyup,Kim, A-Young,Lee, Joong Kee American Chemical Society 2017 ACS APPLIED MATERIALS & INTERFACES Vol.9 No.24
<P>Lithium-ion capacitors (LICs) and lithium-ion batteries (LIBs) are important energy storage devices. As a material with good mechanical, thermal, and chemical properties, low-carbon silicon oxycarbide (LC-SiOC), a kind of silicone oil-derived SiOC, is of interest as an anode material, and we have examined the electrochemical behavior of LC-SiOC in LIB and LIC devices. We found that the lithium storage mechanism in LC-SiOC, prepared by pyrolysis of phenyl-rich silicon oil, depends on an oxygen-driven rather than a carbon-driven mechanism within our experimental scope. An investigation of the electrochemical performance of LC-SiOC in half- and full-cell LIBs revealed that LC-SiOC might not be suitable for full-cell LIBs because it has a lower capacity (238 mAh g(-1)) than that of graphite (290 mAh g(-1)) in a cutoff voltage range of 0-1 V versus Li/Li+, as well as a substantial irreversible capacity. Surprisingly, LC-SiOC acts as a pseudocapacitive material when it is tested in a half-cell configuration within a narrow cutoff voltage range of 0-1 V versus Li/Li+. Further investigation of a 'hybrid' supercapacitor, also known as an LIC, in which LC-SiOC is coupled with an activated carbon electrode, demonstrated that a power density of 156 000 W kg(-1) could be achieved while maintaining an energy density of 25 Wh kg(-1). In addition, the resulting capacitor had an excellent cycle life, holding similar to 90% of its energy density even after 75 000 cycles. Thus, LC-SiOC is a promising active material for LICs in applications such as heavy-duty electric vehicles.</P>
Halim Lee,Ali A. Abdallah,Jongmin Park,Jiwon Seo,Zaher M. Kassas 제어로봇시스템학회 2020 제어로봇시스템학회 국제학술대회 논문집 Vol.2020 No.10
A neural network (NN)-based approach for indoor localization via cellular long-term evolution (LTE) signals is proposed. The approach estimates, from the channel impulse response (CIR), the range between an LTE eNodeB and a receiver. A software-defined radio (SDR) extracts the CIR, which is fed to a long short-term memory model (LSTM) recurrent neural network (RNN) to estimate the range. Experimental results are presented comparing the proposed approach against a baseline RNN without LSTM. The results show a receiver navigating for 100 m in an indoor environment, while receiving signals from one LTE eNodeB. The ranging root-mean squared error (RMSE) and ranging maximum error along the receiver’s trajectory were reduced from 13.11 m and 55.68 m, respectively, in the baseline RNN to 9.02 m and 27.40 m, respectively, with the proposed RNN-LSTM.
Development of Confidence Bound Visualization Tool for LTE-Based UAV Surveillance in Urban Areas
Halim Lee,Taewon Kang,Jiwon Seo 제어로봇시스템학회 2019 제어로봇시스템학회 국제학술대회 논문집 Vol.2019 No.10
We have developed a tool to visualize a situation of estimating the positions of unmanned aerial vehicles (UAVs) using long-term evolution (LTE) signals in an urban area. In our visualization tool, true positions of UAVs and evolved Node Bs (eNodeBs), estimated positions of UAVs, and the calculated horizontal and vertical protection levels (HPLs and VPLs) are displayed in a 3D city map. For a realistic simulation, the 3D city map is generated using real buildings and terrain data of Gangnam, Seoul, Korea. Users can directly specify the locations of UAVs and eNodeBs to simulate these cases. Error variances are added to the true range between UAVs and eNodeBs in the developed tool. Next, the position and the HPL and VPL of each UAV are calculated and displayed on the map. Using the developed visualization tool, we observed changes in the estimated positions and confidence bounds of UAVs by adjusting the number of eNodeBs transmitting LTE signals. Simulation results show that the size of confidence bounds decreases as the number of eNodeBs increases.
Halim Lee,서지원 사단법인 항법시스템학회 2022 Journal of Positioning, Navigation, and Timing Vol.11 No.4
An indoor navigation system that utilizes long-term evolution (LTE) signals has the benefit of no additional infrastructure installation expenses and low base station database management costs. Among the LTE signal measurements, received signal strength (RSS) is particularly appealing because it can be easily obtained with mobile devices. Propagation channel models can be used to estimate the position of mobile devices with RSS. However, conventional channel models have a shortcoming in that they do not discriminate between line-of-sight (LOS) and non-line-of-sight (NLOS) conditions of the received signal. Accordingly, a previous study has suggested separated LOS and NLOS channel models. However, a method for determining LOS and NLOS conditions was not devised. In this study, a machine learning-based LOS/NLOS classification method using RSS measurements is developed. We suggest several machine-learning features and evaluate various machinelearning algorithms. As an indoor experimental result, up to 87.5% classification accuracy was achieved with an ensemble algorithm. Furthermore, the range estimation accuracy with an average error of 13.54 m was demonstrated, which is a 25.3% improvement over the conventional channel model.
Halim, Martin,Hudaya, Chairul,Kim, A-Young,Lee, Joong Kee The Royal Society of Chemistry 2016 Journal of Materials Chemistry A Vol.4 No.7
<▼1><P>Silicon oxycarbide (SiOC) is gaining increasing attention as a promising anode material for lithium ion batteries due to its higher reversible capacity compared to incumbent graphite.</P></▼1><▼2><P>Silicon oxycarbide (SiOC) is gaining increasing attention as a promising anode material for lithium ion batteries due to its higher reversible capacity compared to incumbent graphite. The kinetic processes at a SiOC anode result in rapid capacity fading even at a relatively low current density, thereby hindering its commercialization. Herein, a distinctive, phenyl-rich silicone oil is used as a precursor for producing SiOC anode materials <I>via</I> simple pyrolysis. We find that only silicone oil with phenyl-rich rings can be converted into SiOC materials. The phenyl group was crucial for carbon incorporation to allow Si–O–C bonding and the formation of a free-carbon phase. The resulting SiOC anode exhibited stable cyclability up to 250 cycles, with a discharge capacity of 800 mA h g<SUP>−1</SUP> at a current density of 200 mA g<SUP>−1</SUP>. The remarkable cycle performance of SiOC was correlated with its low dimensional expansion (7%) during lithiation, which maintains its structure over cycling. Rate capability tests showed a highly stable performance with a maximum discharge capacity of 852 mA h g<SUP>−1</SUP> at a current density of 100 mA g<SUP>−1</SUP>. When the discharge current density was increased 64-fold, the reversible capacity of the SiOC anode was 90% of its maximum capacity, 772 mA h g<SUP>−1</SUP>. The excellent electrochemical performance of SiOC could be attributed to the rapid mobility of Li<SUP>+</SUP> within the SiOC matrix, as indicated by a Li<SUP>+</SUP> diffusion coefficient of 5.1 × 10<SUP>−6</SUP> cm<SUP>2</SUP> s<SUP>−1</SUP>.</P></▼2>