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Modeling and Applications of Electrochemical Impedance Spectroscopy (EIS) for Lithium-ion Batteries
Choi, Woosung,Shin, Heon-Cheol,Kim, Ji Man,Choi, Jae-Young,Yoon, Won-Sub The Korean Electrochemical Society 2020 Journal of electrochemical science and technology Vol.11 No.1
As research on secondary batteries becomes important, interest in analytical methods to examine the condition of secondary batteries is also increasing. Among these methods, the electrochemical impedance spectroscopy (EIS) method is one of the most attractive diagnostic techniques due to its convenience, quickness, accuracy, and low cost. However, since the obtained spectra are complicated signals representing several impedance elements, it is necessary to understand the whole electrochemical environment for a meaningful analysis. Based on the understanding of the whole system, the circuit elements constituting the cell can be obtained through construction of a physically sound circuit model. Therefore, this mini-review will explain how to construct a physically sound circuit model according to the characteristics of the battery cell system and then introduce the relationship between the obtained resistances of the bulk (R<sub>b</sub>), charge transfer reaction (R<sub>ct</sub>), interface layer (R<sub>SEI</sub>), diffusion process (W) and battery characteristics, such as the state of charge (SOC), temperature, and state of health (SOH).
Choi, Woosung,Youn, Byeng D.,Oh, Hyunseok,Kim, Nam H. Elsevier 2019 Reliability engineering & system safety Vol.184 No.-
<P><B>Abstract</B></P> <P>Accurate prediction of the remaining useful life (RUL) of plant turbines is a major scientific challenge for effective operation and maintenance in the power plant industry. This paper proposes an RUL prediction methodology that incorporates a damage index into the damage growth model. A Bayesian inference technique is used to consider uncertainties while estimating the probability distribution of a damage index from on-site hardness measurements. A Bayesian approach is proposed for the damage growth model for use with aged steam turbines. The predictive distribution of the damage index is estimated using its mean and standard deviation. As a case study, real steam turbines from power plants are examined to demonstrate the effectiveness of the proposed Bayesian approach. The results from the proposed damage growth model can be used to predict the RULs of the steam turbines of power plants regardless of load types (peak-load or base-load) of the power plant.</P> <P><B>Highlights</B></P> <P> <UL> <LI> A damage growth model for a steam turbine is proposed from the damage index distribution. </LI> <LI> RUL prediction methodologies incorporate the damage index into damage growth model estimation. </LI> <LI> A Bayesian inference technique is used to estimate the probability distribution of the damage index. from on-site measurements sporadically measured and heterogeneous on-site data from actual steam turbines </LI> <LI> Damage index is estimated from on-site measurements sporadically measured and heterogeneous on-site data from actual steam turbines. </LI> <LI> A damage threshold of 0.2 is determined for a reasonable damage distribution and RUL for a steam turbine. </LI> </UL> </P>
Multi-Functional Brain Computer Interface Using Convolutional Neural Networks
Woosung Choi,Honggi Yeom,Nakyong Ko 제어로봇시스템학회 2022 제어로봇시스템학회 국제학술대회 논문집 Vol.2022 No.11
Brain-computer interface (BCI) is a promising technology that controls computers or machines using brain signals. With this technology, people with various disabilities, such as neural paralysis, and spinal cord injury can control electric devices or express their intention by thinking. However, previous BCI studies have a limitation that they can predict only one type of intention. To use the BCI system in daily life, the BCI user should be able to achieve various tasks such as moving, text typing, and arm movements. In this paper, we propose a multi-functional BCI method that can predict various intentions simultaneously. To classify multiple intentions, we proposed two prediction models using Neural Networks (NN) and Convolutional Neural Networks (CNN) models. To evaluate the proposed BCI system, the classification accuracy of the model was measured and compared using steady state visually evoked potential (SSVEP), sensory motor rhythm (SMR), and both of them (Multiple Intention). The average prediction accuracies were 22.46% in NN, 55.86% in CNN. These results indicate that the proposed multi-functional BCI can predict multiple intentions. It also means that users of the proposed BCI system can control various electric devices simultaneously.
Choi, Woosung,Yun, Inyeol,Jeung, Jinpyeo,Park, Yun Sung,Cho, Sunghwan,Kim, Dong Wook,Kang, In Seok,Chung, Yoonyoung,Jeong, Unyong Elsevier 2019 Nano energy Vol.56 No.-
<P><B>Abstract</B></P> <P>Human cutaneous tactile receptors are deformable, and can distinguish touch, strain, relative moving distance, and relative moving velocity. In addition, the tactile potential is self-activated when external stimulation is exerted and the potential is transmitted to the nerve system, resembling the wake-up function in electronic devices. In this study, we mimic such characteristics of the human tactile receptors. We designed a stretchable triboelectric nanogenerator (TENG) for the stimuli-responsive potential generator. The TENG device has a multilayer structure independently recognizing lateral strain by the sliding mode, touch by the contact mode, the relative moving distance, and the relative moving velocity. In addition, the device design allows simultaneous sensing of strain and touch without signal interference. The self-triggered potentials generated by various body motions such as touching, joint bending, and the combinations turn on a sleeping microcontroller unit (MCU) and are used as the distinct motion signals. This study demonstrates a wearable low-power remote tactile interface that controls the 3D movements of a mobile device (drone) by the body motions.</P> <P><B>Highlights</B></P> <P> <UL> <LI> The TENG sensor can distinguish pressure, strain, distance, velocity. </LI> <LI> The sensor can extract information from random dynamic motions. </LI> <LI> The integrated wearable haptic interface can control complex 3-D movement of a drone. </LI> <LI> The wake-up function is turned on or off by the sensor signal itself. </LI> </UL> </P> <P><B>Graphical abstract</B></P> <P>[DISPLAY OMISSION]</P>