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RISS 인기검색어
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- 저자 : Pan Tianhong (검색결과 5 건)
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DEVELOPMENT OF AN ENGINE CALIBRATION MODEL USING GAUSSIAN PROCESS REGRESSION
Tianhong Pan,Yang Cai,Shan Chen 한국자동차공학회 2021 International journal of automotive technology Vol.22 No.2
To enhance the calibration efficiency, reduce the fuel consumption and improve the emission performance of the engine, a calibration method using Gaussian Process Regression (GPR) is proposed in this work. First, the design of experiment (DoE) is constructed by using the Space-filling method, and the engine bench sampling test is implemented according to results of DoE. Then, the square exponential covariance function is selected through the comparison of four covariance functions, and the corresponding hyper-parameters are optimized by using Newton gradient algorithm. Finally, the GPR model of the engine is established and its calibration performance is validated by the experimental data. The comparison shows that the performance of the developed GPR model is superior to the Polynomial model and Neural Network model, whose coefficient of determination (R2) of Fuel Consumption (FC), NOx emission (NOx) and Soot emission (Soot) are up to 0.9980, 0.9326 and 0.9247. The case study demonstrates that the virtual calibration optimization based on GPR model improves the fuel consumption performance greatly, while taking NOx and Soot emission indicators into account.
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Structural Optimization of Compact Spherical Wind-Solar Hybrid Power System
Wei Huili,Pan Tianhong,Zhu Mingxing,Tao Jun 대한전기학회 2021 Journal of Electrical Engineering & Technology Vol.16 No.5
Conventional wind-solar hybrid power systems (WS-HPSs) have certain structural drawbacks owing to their large size and the difficulty in adjusting the tilt angle of the solar panels. To address these limitations, this study proposes a compact spherical wind-solar hybrid power system (CSWS-HPS). Furthermore, to investigate the aerodynamic performance of the designed CSWS-HPS, a computational fluid dynamics model of the wind rotor was established using the Reynolds-averaged Navier–Stokes equations, renormalization group k-ε turbulence model, and sliding mesh. Subsequently, the flow field distribution of velocity and pressure under different numbers of blades, blade installation angles, and tip-speed ratios (TSRs) were analyzed by performing a three-dimensional simulation of the CSWS-HPS. The monitored values of the moment coefficients were used to calculate the power coeffi cient value of the wind turbine to obtain the optimum structural parameters, which in turn provided the optimal values for the CSWS-HPS model. The simulation results revealed that the CSWS-HPS achieved considerable power generation efficiency in comparison with that of conventional hybrid systems. In addition, the CSWS-HPS is more compact in size and does not emit CO 2.
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A Harmonic Detection Method for Distributed Generation Connected Grid System Using DWT and HHT
Min Xu,Tianhong Pan,Jiao Chen,Zhengming Li 대한전기학회 2019 Journal of Electrical Engineering & Technology Vol.14 No.4
In distributed generation (DG) connected grid system, there is serious harmonic pollution caused by the wide use of inverter units and nonlinear loads. The traditional detection methods (such as Fast Fourier Transform and wavelet analysis) heavily depend on synchronous signal detection or basis functions selection, which don’t satisfy the requirements of the harmonic detection of the micro-grid environment. Therefore, a novel method combining discrete wavelet transform (DWT) with Hilbert–Huang transform (HHT) is proposed to detect and analyze the harmonic. The method uses multi resolution analysis of DWT to process the original signal. Then, a series of intrinsic mode function (IMF) components are obtained from the processed signal by using Empirical Mode Decomposition (EMD) algorithm. Thirdly, the fundamental component and harmonic are extracted from the IMF components. Finally, the frequency and amplitude of the signal are calculated by using the HHT. Simulation results demonstrate that the proposed algorithm has high precision and good real-time characteristics. Furthermore, the proposed algorithm satisfi es the requirements of the harmonic detection of the DG connected grid system.
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Shaoxue Jing,Tianhong Pan,Quanmin Zhu 제어·로봇·시스템학회 2022 International Journal of Control, Automation, and Vol.20 No.10
In this paper, a novel stochastic gradient algorithm based on the minimum Renyi entropy is proposed to identify a nonlinear rational model contaminated by the impulse noise. Firstly, the minimum error entropy using the Epanechnikov kernel is taken to suppress the impulse noise. Secondly, the stochastic gradient of the Renyi entropy rather than the Shannon entropy is adopted to decrease the computational cost. Finally, an adaptive step size considering the energy of the errors is used to accelerate the algorithm. The proposed algorithm is validated by numerical examples and case study. Results show that the algorithm can give accurate estimates with a fast convergence rate for the nonlinear rational model with the impulse noise.
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STATE OF CHARGE ESTIMATION OF LITHIUM-ION BATTERY USING ENERGY CONSUMPTION ANALYSIS
Chen Shan,Pan Tianhong,Jin Bowen 한국자동차공학회 2023 International journal of automotive technology Vol.24 No.2
The traditional electric current integral algorithm cannot accurately estimate a lithium-ion battery’s state of charge (SOC) under complex discharge conditions. Therefore, in this study, a new estimation method based on a power integral algorithm is proposed. First, the first-order Thevenin equivalent circuit model is selected, and the energy storage and loss of the lithium-ion battery during charging and discharging operations are analyzed. Second, the inherent disadvantages of the electric current integral algorithm are analyzed, and an SOC estimation based on the power integral algorithm is presented. The error correction for the SOC estimation is derived using the extended Kalman filter (EKF). Using the established test bench, the effects of environmental temperature, state of health, and current density on the SOC estimation are analyzed. The experimental results show that the proposed method combining the power integral and EKF can accurately estimate the SOC of a lithium-ion battery.
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