• 전기학회논문지
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• 제57권5호
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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.

• Journal of Power Electronics
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• 제16권1호
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• pp.217-226
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• 2016

A new method is proposed based on a hidden Markov model (HMM) to estimate and analyze battery states of health. Battery system health states are defined according to the relationship between internal resistance and lifetime of cells. The source data (terminal voltages and currents) can be obtained from vehicular battery models. A characteristic value extraction method is proposed for HMM. A recognition framework and testing datasets are built to test the estimation rates of different states. Test results show that the estimation rates achieved based on this method are above 90% under single conditions. The method achieves the same results under hybrid conditions. We can also use the HMMs that correspond to hybrid conditions to estimate the states under a single condition. Therefore, this method can achieve the purpose of the study in estimating battery life states. Only voltage and current are used in this method, thereby establishing its simplicity compared with other methods. The batteries can also be tested online, and the method can be used for online prediction.

• 한국지진공학회:학술대회논문집
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• 한국지진공학회 2006년도 학술발표회 논문집
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• pp.527-534
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• 2006

A real-time hybrid method, in which the experimental implementation and the numerical computation of a structure are simultaneously carried out in real-time and combined on-line, has been used as a dynamic testing technique of structure to investigate its dynamic behaviors. In this paper, an experimental hybrid method, which implements the earthquake response control of a building structure with a TLD by using only a TLD as an experimental part, is proposed and is experimentally verified through a shaking table test. In the proposed methodology, the whole building structure with a TLD is divided into the upper TLD and the lower structural parts as experimental and numerical substructures, respectively. At the moment, the control force acting between their interface is measured from the experimental TLD with shear-type load-cell which is mounted on shaking table. Shaking table vibrates the upper experimental TLD with the response calculated from the numerical substructure, which is subjected to the excitations of the measured interface control force at its top story and an earthquake input at its base. The experimental results show that the conventional method, in which both a TLD and a building model are physically manufactured and are tested, can be replaced by the proposed methodology with a simple experimental installation and a good accuracy for evaluating the control performance of a TLD.

• 한국전산구조공학회논문집
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• 제19권4호
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• pp.419-427
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• 2006

본 논문에서는, 동조액체감쇠기(이하 TLD)만을 실험적 부분구조로 이용하여 TLD가 설치된 건축구조물의 지진 응답 제어효과를 평가하기 위한 실시간 하이브리드 실험법을 제안하고 진동대 실험을 통해 실험적으로 규명한다. 제안된 실험법에서, TLD가 설치된 전체구조물은 상부의 TLD와 하부의 구조물 부분으로 각각 실험적 그리고 수치해석적 부분구조로 나누어진다. 이때 부분구조 사이의 경계면에서 작용하는 하중 또는, TLD에 의한 제어력은 진동대에 설치된 전단형 로드셀에 의해 계측되며 진동대는, 계측된 경계면에서의 제어력이 상부에 작용하고 또한 동시에 기초에 지진하중이 작용하는 수치해석적 부분구조로부터 계산된 응답으로, 상부에 설치된 TLD를 가진하게 된다. 제안된 실험법에 의한 결과와 TLD와 건물모델 모두를 제작하여 실험하는 기존의 방법에 의한 실험 결과들은 서로 잘 일치하며, 이로써 본 논문에서 제안된 실험법을 이용하여 TLD의 제어성능을 손쉽게 평가 할 수 있음을 알 수 있다.

• 한국초전도ㆍ저온공학회논문지
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• 제25권4호
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• pp.28-31
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• 2023

This study presents a comprehensive procedure for the low cycle fatigue test of ultrasonically welded (UW) coated conductor (CC) lap-joints. The entire process is examined in detail, from the robust fabrication of the UW REBCO CC joints to the reliability testing under a low number of repeated cycle fatigue conditions. A continuous I_c measurement system enables real-time monitoring of I_c variations throughout the fatigue tests. The study aims to provide a step-by-step procedure that involves joint fabrication, electromechanical property (EMP) tests under uniaxial tension for stress level determination, and subsequent low-cycle fatigue tests. The joints are fabricated using a hybrid method that combines UW with adding In-Sn soldering, achieving a flux-free hybrid welding approach (UW-HW flux-free). The selected conditions for the low cycle fatigue tests include a stress ratio of R=0.1 and a frequency of 0.02 Hz. The results reveal some insights into the fatigue behavior, irreversible changes, and cumulative damage in the CC joints.

• 한국항해항만학회:학술대회논문집
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• 한국항해항만학회 2006년도 International Symposium on GPS/GNSS Vol.1
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• pp.277-282
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• 2006

It is well known that Kalman filtering is an optimal real-time data fusion method for GPS/INS integration. However, it has some limitations in terms of stability, adaptability and observability. A Kalman filter can perform optimally only when its dynamic model is correctly defined and the noise statistics for the measurement and process are completely known. It is found that estimated Kalman filter states could be influenced by several factors, including vehicle dynamic variations, filter tuning results, and environment changes, etc., which are difficult to model. Neural networks can map input-output relationships without apriori knowledge about them; hence a proper designed neural network is capable of learning and extracting these complex relationships with enough training. This paper presents a GPS/INS integrated system that combines Kalman filtering and neural network algorithms to improve navigation solutions during GPS outages. An Extended Kalman filter estimates INS measurement errors, plus position, velocity and attitude errors etc. Kalman filter states, and gives precise navigation solutions while GPS signals are available. At the same time, a multi-layer neural network is trained to map the vehicle dynamics with corresponding Kalman filter states, at the same rate of measurement update. After the output of the neural network meets a similarity threshold, it can be used to correct INS measurements when no GPS measurements are available. Selecting suitable inputs and outputs of the neural network is critical for this hybrid method. Detailed analysis unveils that some Kalman filter states are highly correlated with vehicle dynamic variations. The filter states that heavily impact system navigation solutions are selected as the neural network outputs. The principle of this hybrid method and the neural network design are presented. Field test data are processed to evaluate the performance of the proposed method.

• 한국전산구조공학회논문집
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• 제20권2호
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• pp.217-225
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• 2007

본 연구에서는 건물모델만을 물리적인 실험체로 이용하여 동적 지반강성을 갖는 지반-구조물계의 동적거동을 모사하기 위한 하이브리드 진동대 실험법을 제안하고 이를 실험적으로 검증하였다. 본 연구에서 제안되는 실험방법은 상부구조물과 진동대의 가속도를 계측하여 진동대 제어기로 피드백하고, 전체 지반-구조물계의 동적거동을 묘사하기 위해 요구되는 기초부분의 절대가속도 응답(가속도 피드백 방법) 또는 절대속도 응답(속도 피드백 방법)을 계산하여 진동대를 구동시키는 방법이다. 지반부분을 계산하기 위해서 이론적인 동적지반강성을 제안방법에 따라서 다르게 근사화하여 진동대 제어기에 반영함으로써 실험을 수행하였다. 기초 고정계 모델에 대한 실험으로부터 계측된 응답과 본 논문에서 가정한 지반-구조물 계에 대한 실험으로부터 측정된 응답을 비교하고, 진동대 제어기에 반영한 동적지반강성과 실험데이터를 이용하여 식별된 동적지반강성을 비교함으로써 본 논문에서 제안된 실험방법의 유효성을 검증하였다.

• 한국자동차공학회논문집
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• 제16권6호
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• pp.33-38
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• 2008

Most structures of vehicle are composed of many substructures connected to one another by various types of mechanical joints. In vehicle engineering, it is important to study these jointed structures under random frequency vibration for the evaluations of fatigue life and stress concentration exactly. It is rarely obtained the accurate load history of specified positions in a jointed structure because of the errors such as modeling, measurement, and etc. In the beginning of design, exact load data are actually necessary for the fatigue strength and life analysis to minimize the cost and time of designing. In this paper, the hybrid method of practical dynamic load determination is developed by the combination of the principal stresses from F. E. Analysis and test of a jointed structure. Least square pseudo inverse matrix is adopted to obtain an inverse matrix of analyzed stresses matrix. The error minimization method utilizes the inaccurate measured error and the shifting error that the whole data is stiffed over real data. The least square criterion is adopted to avoid these errors. Finally, to verify the proposed system, a heavy-duty bus is analyzed. This measurement and prediction technology can be extended to the different jointed structures.

• Nuclear Engineering and Technology
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• 제53권12호
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• pp.4072-4079
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• 2021

This article presents a study on the state-of-the-art methods for automated radioactive material detection and identification, using gamma-ray spectra and modern machine learning methods. The recent developments inspired this in deep learning algorithms, and the proposed method provided better performance than the current state-of-the-art models. Machine learning models such as: fully connected, recurrent, convolutional, and gradient boosted decision trees, are applied under a wide variety of testing conditions, and their advantage and disadvantage are discussed. Furthermore, a hybrid model is developed by combining the fully-connected and convolutional neural network, which shows the best performance among the different machine learning models. These improvements are represented by the model's test performance metric (i.e., F1 score) of 93.33% with an improvement of 2%-12% than the state-of-the-art model at various conditions. The experimental results show that fusion of classical neural networks and modern deep learning architecture is a suitable choice for interpreting gamma spectra data where real-time and remote detection is necessary.

• Smart Structures and Systems
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• 제32권2호
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• pp.101-109
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• 2023

In order to improve the measurement accuracy of the 6-dimensional accelerometer, the cross coupling compensation method of the accelerometer needs to be studied. In this paper, the non-linear error caused by cross coupling of piezoelectric six-dimensional accelerometer is compensated online. The cross coupling filter is obtained by analyzing the cross coupling principle of a piezoelectric six-dimensional accelerometer. Linear and non-linear fitting methods are designed. A two-level calibration hybrid compensation algorithm is proposed. An experimental prototype of a piezoelectric six-dimensional accelerometer is fabricated. Calibration and test experiments of accelerometer were carried out. The measured results show that the average non-linearity of the proposed algorithm is 2.2628% lower than that of the least square method, the solution time is 0.019382 seconds, and the proposed algorithm can realize the real-time measurement in six dimensions while improving the measurement accuracy. The proposed algorithm combines real-time and high precision. The research results provide theoretical and technical support for the calibration method and online compensation technology of the 6-dimensional accelerometer.