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13th International Symposium on Linear Drives for Industry Applications, LDIA 2021

作者： Wang, Yulin Lan, Zhiyong Zhu, Gang College of Automation and Electronic Information Xiangtan University XiangTan China Multi-Energy Collaborative Control Technology Hunan Engineering Research Center XiangTan China JiangLu Mechinery Electronics Group Co. Ltd XiangTan China

ISBN: (纸本)9781728172101

In order to solve the chattering problem and the errors of speed and position is large in the traditional sliding mode control (SMC) for permanent magnet linear synchronous motor (PMLSM), a sensorless control method for adaptive sliding mode observer (SMO) based on phase locked loop (PLL) of PMLSM is proposed. Firstly, an adaptive law of back-electromotive force (back-EMF) is established by using Lyapunov function, and constructed an adaptive SMO. Then, calculate the position and speed by PLL instead of traditional arctangent function, which improved the accuracy of observation. Finally, build the simulation model and the results proved the superiority of the proposed method. © 2021 IEEE

关键词： Permanent magnets

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Amplified treatment of mariculture effluent and pathogen with synchronously electricity generation in a self-biased photocatalytic fuel cell based on dual photoelectrode

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International Journal of Hydrogen energy 2025年 137卷 622-635页

作者： Zeng, Honghu Lam, Sze-Mun Low, Kuan-Chee Sin, Jin-Chung Li, Haixiang Lin, Hua Huang, Liangliang Huang, Haitao Xu, Liwei Lim, Jun-Wei Jaffari, Zeeshan Haider Guangxi Key Laboratory of Theory and Technology for Environmental Pollution Control Guilin University of Technology Guilin541004 China Collaborative Innovation Center for Water Pollution Control and Water Safety in Karst Area Guilin University of Technology Guilin541004 China Faculty of Engineering and Green Technology Universiti Tunku Abdul Rahman Jalan Universiti Bandar Barat Kampar Perak 31900 Malaysia College of Environmental Science and Engineering Guilin University of Technology Guilin541004 China HICoE-Centre for Biofuel and Biochemical Research Institute of Sustainable Energy and Resources Department of Fundamental and Applied Sciences Universiti Teknologi PETRONAS Seri Iskandar Perak Darul Ridzuan 32610 Malaysia Department of Civil Environmental and Ocean Engineering Stevens Institute of Technology 1 Castle Point Terrace HobokenNJ07030 United States

A device capable of producing electricity from wastewater treatment process is highly desirable as it addresses critical challenges in sustainable energy generation and environmental conservation. In this work, a highly efficient three-dimensional (3D) porous network structured In2O3/ZnO/FTO photoanode and a flower-like Cu2O/CuO/Cu cathode were successfully fabricated and integrated into a visible light-driven photocatalytic fuel cell (PFC) system. Material characterization revealed that In2O3 was well-dispersed on the 3D porous ZnO network, proving ample active sites for photoelectrocatalysis. The PFC utilizing mariculture effluent containing chlortetracycline hydrochloride (CTCH) as a fuel source, achieved an open-circuit voltage of 559 mV and a maximum power density of 0.3084 μW cm−2 under visible light irradiation. Furthermore, the system demonstrated a CTCH removal efficiency of 91.5 %, outperforming ZnO/FTO and In2O3/FTO electrodes in terms of degradation efficacy and energy conversion. Additionally, the cell demonstrated outstanding recyclability and remarkable antimicrobial efficacy against Escherichia coli and Bacillus cereus, underscoring its superior functionality. The boosted photoelectrocatalytic activity was credited to the formation of the In2O3/ZnO/FTO heterojunction within the porous network-structure, which provided a high specific surface area and favorable optical and electronic traits and thereby, improving the overall efficiency of the dual photoelectrode system. This PFC device facilitates simultaneous electricity generation, pollutant degradation and bacterial sterilization, offering promising multifunction applications. © 2025 Hydrogen energy Publications LLC

关键词： Escherichia coli

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Development of the Method of Suppressing Thrust Fluctuation in Permanent Magnet Linear Synchronous Motor 13

Development of the Method of Suppressing Thrust Fluctuation ...

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13th International Symposium on Linear Drives for Industry Applications, LDIA 2021

作者： Chen, Lifang Lan, Zhiyong Xie, Bin Zheng, Qiuhui College of Automation and Electronic Information Xiang Tan University Xiang Tan China Multi-Energy Collaborative Control Technology Hunan Engineering Research Center Xiang Tan China CHANGSHA EPOCH NC CO. LTD China

ISBN: (纸本)9781728172101

Permanent Magnet Linear Synchronous Motor (PMLSM) is widely used in lithography, electromagnetic ejection, cordless elevator, rail transit and other fields because of the advantages of its high thrust density, high accuracy, high acceleration, low noise, etc. However, PMLSM has large thrust fluctuation which affects its operation efficiency. At present, the study of suppressing thrust fluctuation mainly includes two aspects: optimization design of motor structure and control strategy. In this paper, the method of suppressing thrust fluctuation is summarized from the optimization design of motor structure. The paper mainly summarizes the optimization of primary structure and secondary structure. © 2021 IEEE

关键词： Permanent magnets

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Adaptive Power Allocation Strategy for Hybrid energy Storage System Based on Driving Pattern Recognition

SSRN

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SSRN 2023年

作者： Pan, Rui Wu, Yongli Wang, Yilin Chen, Jie School of Automation and Electronic Information Xiangtan University Xiangtan411105 China Hunan Engineering Research Center for Multi-Energy Cooperative Control Technology Xiangtan University Xiangtan411105 China

The power allocation strategy of hybrid energy storage systems plays a decisive role in energy management for electric vehicles. However, existing online real-time power allocation strategies primarily rely on expert knowledge to make rules. Due to the real-time changes in driving patterns, it is necessary for the power allocation strategy to possess adaptive capabilities to ensure optimal energy efficiency. Therefore, an adaptive power allocation strategy is proposed in this paper, which takes into account the driving patterns of electric vehicles. Firstly, driving pattern recognition is performed using the K-means clustering method to extract features of driving profiles. Then, the fuzzy logic control strategy is adopted to make the rules of power allocation, and the rules is adaptive adjusted based on the results of driving pattern recognition. To achieve smoother power output from lithium-ion batteries and prolong their lifespan, the digital low pass filter is used to accomplish the fuzzy logic control strategy, and the filtering frequency is online optimized by the above fuzzy controller. According to the digital low pass filter, the load power demand is decomposed high-frequency component and low-frequency component. The decomposed high-frequency component of power demand is supplied by supercapacitors, while the low-frequency component is provided by lithium-ion batteries. To validate the effectiveness of the proposed method, comparative experiments are conducted between the proposed strategy and another three strategies. The experimental results demonstrate that the proposed strategy can effectively smooth the power output of lithium-ion batteries and reduce total energy losses. © 2023, The Authors. All rights reserved.

关键词： Electric loads

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Ultra-Short Term Power Prediction of Wind Farm Based on EEMDSE-GGRU 11

Ultra-Short Term Power Prediction of Wind Farm Based on EEMD...

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11th IEEE Annual International Conference on CYBER technology in Automation, control, and Intelligent Systems, CYBER 2021

作者： Yi, Lingzhi Wang, Shitong Duan, Renzhe Liu, Bo Song, Xiaoping Deng, Dong Hunan Multi-energy Cooperative Control Technology Engineering Research Center School of Automation and Electronic Information Xiangtan University Xiangtan China School of Automation and Electronic Information of Xiangtan University Hunan China Xiangdian Wind Energy Hunan China

ISBN: (纸本)9781665425278

With the advancement of energy transformation, more and more electric power departments are required to absorb new energy such as wind power in full. The output voltage will fluctuate considerably due to the non-linear and non-stationary characteristics of wind speed itself, which will cause danger to the power system. A wind power prediction method is presented based on EEMDSE-GGRU in this paper to reduce the disadvantages of wind power. The model combines Ensemble Empirical Mode Decomposition to decompose the fluctuating time series signals, then recombine them with sample entropy. Finally, the optimal parameters of the model are obtained by unsupervised learning of the GRU model using the improved whale algorithm. During prediction, each component data is predicted in turn and the final predicted value is accumulated. The proposed method is of good prediction accuracy and stability by simulation analysis. © 2021 IEEE.

关键词： Weather forecasting

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Optimization of magnetic pole structure of high-speed permanent magnet synchronous motor weakens cogging torque 1

Optimization of magnetic pole structure of high-speed perman...

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1st IEEE China International Youth Conference on Electrical engineering, CIYCEE 2020

作者： Yi, Lingzhi Yin, Xinjian Xiangtan University Hunan Multi-Energy Collaborative Control Technology Engineering Research Center School of Information Engineering Xiangtan411105 China

ISBN: (纸本)9781728196596

In order to suppress the cogging torque of permanent magnet synchronous motors, a layered permanent magnet structure composed of different permanent magnet materials is proposed. The multiphysics coupling analysis of the electromagnetic field and the temperature field of the permanent magnet motor in ANSYS software was performed to obtain the temperature difference between the stator and the rotor due to air gap wind milling during the motor operation. Based on this, the temperature can be used near the stator layer of the permanent magnet. A low-coefficient magnetic material can effectively reduce the harmonic content of the air gap magnetic flux density of the motor, thereby reducing the cogging torque. Finally, a differential algorithm is used to optimize the thickness of the permanent magnet material on the outer surface to obtain the permanent magnet size parameters that make the motor better. Simulation results show that the layered permanent magnet structure can effectively reduce the cogging torque of the permanent magnet motor and effectively improve the sine of the back-EMF waveform after optimized design. © 2020 IEEE.

关键词： Permanent magnets

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A nanoscale observation to explain the discrepancy of electron exchange capacities between biochar containing comparable surface redox-active moieties

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Biochar 2022年第1期4卷 503-517页

作者： Shasha Li Liming Shao Hua Zhang Xuemin Lu Fan Lü Pinjing He State Key Laboratory of Pollution Control and Resource Reuse Tongji UniversityShanghai 200092People’s Republic of China Shanghai Institute of Pollution Control and Ecological Security Shanghai 200092People’s Republic of China Institute of Waste Treatment and Reclamation Tongji UniversityShanghai 200092People’s Republic of China Shanghai Multi-Source Solid Waste Collaborative Treatment and Energy Engineering Technology Research Center ShanghaiPeople’s Republic of China

Biochar,possessing electron exchange capacities(EEC),is generally involved in environmental redox reactions due to the presence of redox-active moieties(RAMs).The phenomenon that chars containing comparable RAMs possess differential EEC revealed that the accessibility of RAMs is important to the redox ***,many studies have focused on the type of RAMs,whereas the distribution has been insufficiently ***,we achieved nanoscale observation of electroactive moieties on the surface of six chars using a conductive atomic force *** the two specific kinds of chars with submicron particles and opposite current distributions,the submicron particles took up only 1-4‰wt of biochar accounting for approximately 30-50%of electron-donating capacity(EDC),and electron-accepting capacity(EAC)became 87%and 1.40 times as before after removing submicron particles,***,the combined impact of RAMs and surface topography(that uneven distribution of RAMs resulted in outstanding EEC by enhancing accessibility)was ***,direct evidence of the link between char structure and EEC(that condensed aromatic structures were indispensable to EAC while both heteroatoms and amorphous aromatics contributed to EDC)was *** findings can aid in understanding the functions of biochar in biotic and abiotic redox processes.

关键词： Biochar Pyrogenic chars Redox property Electron exchange capacity C-AFM

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Adaptive Robust Optimization Based on Data-driven Uncertainties of Source and Load for Microgrid Operation

Adaptive Robust Optimization Based on Data-driven Uncertaint...

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Cyber-energy Systems and Intelligent energy (ICCSIE), International Conference on

作者： Zibin Li Mao Tan Yuling Ren Hongwei Jiang School of Automation and Electronic Information Xiangtan University Xiangtan China Hunan Engineering Research Center of Multi-energy Cooperative Control Technology Xiangtan University Xiangtan China Hunan National Center for Applied Mathematics Xiangtan University Xiangtan China

The strong uncertainty of renewable energy output and load demand makes the stable operation of microgrids a challenging and important issue. However, the scheduling methods based on deterministic models cannot accurately describe the influence of uncertainties on operation of microgrids. To address this problem, this paper proposes a two-stage adaptive robust optimal scheduling model (TSARO) that considers both source and load uncertainties. The model first adopts the dirichlet process mixture model (DPMM) to perform cluster analysis and parameter estimation on massive historical data, and constructs a data-driven uncertainty set of source and load. Then, based on the uncertainty set, the TSARO model aiming at minimizing the microgrid operation cost is developed under the worst-case scenario. Finally, this paper solves the optimization model using column constraint generation algorithm (C&CG) to obtain the day-ahead power dispatching plan. Simulation results show that the proposed model in this paper has better economic benefits compared with several classical optimization models.

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High-throughput quantitative detection of triple-negative breast cancer-associated expressed miRNAs by rolling circle amplification on fluorescence-encoded microspheres

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Chinese Chemical Letters 2023年第9期34卷 199-203页

作者： Jieyu Liu Liming Zhang Wentao Zeng Lihua Zhang Nongyue He Zhuoxuan Lu Key Laboratory of Tropical Translational Medicine of Ministry of Education NHC Key Laboratory of Control of Tropical DiseasesSchool of Tropical MedicineMicronano Technology Research InstituteSchool of Basic Medicine and Life SciencesHainan Medical UniversityHaikou 571199China Beadstar Biotechnology Hainan Province Co. Ltd.Haikou 570110China State Key Laboratory of Bioelectronics School of Biological Science and Medical EngineeringSoutheast UniversityNanjing 210096China Economical Forest Cultivation and Utilization of 2011 Collaborative Innovation Center in Hunan Province Hunan Key Laboratory of Biomedical Nanomaterials and DevicesHunan University of TechnologyZhuzhou 412007China

Compared with other types of breast cancer,triple-negative breast cancer(TNBC)has the characteristics of a high degree of malignancy and poor *** diagnosis of TNBC through biological markers and timely development of effective treatment methods can reduce its *** research experiments have confirmed that some specific mi RNA expression profiles in TNBC can used as markers for early ***,detecting the expression profiles of multiple groups of miRNAs according to traditional detection methods is complicated and consumes many *** address this issue,we developed a method for high-throughput,high-sensitivity quantitative detection of multiple sets of miRNAs(including mi R-16,mi R-21,mi R-92,mi R-199,and mi R-342)specifically expressed in TNBC by rolling circle amplification(RCA)on fluorescence-encoded *** the optimization of reaction system conditions,the developed method showed an extensive linear dynamic range and high sensitivity for all five miRNAs with the lowest limit of detection of 2 fmol/***,this high-throughput detection method also appeared reasonable *** in the presence of a specific target miRNA,the fluorescence signal on the correspondingly encoded microspheres is significantly increased,while the fluorescence signal on other non-correspondingly encoded microspheres is almost ***,this process exhibited good recovery and reproducibility in *** advantages of this method allow us to more conveniently obtain the expression profiles of multiple groups of TNBC-associated mi RNAs,which is beneficial for the early detection of TNBC.

关键词： MicroRNAs Rolling circle amplification Fluorescence-encoded microspheres High-throughput Triple-negative breast cancer

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State of Health Estimation for Lithium-Ion Batteries by Using Partial Battery Data with a Hybrid Neural Network Model

State of Health Estimation for Lithium-Ion Batteries by Usin...

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IEEE/IAS Industrial and Commercial Power System Asia (I&CPS Asia)

作者： Tongshen Liu Wei Huang Rui Pan Yilin Wang Mao Tan Jie Chen School of Automation and Electronic Information Xiangtan University Xiangtan China Hunan National Center for Applied Mathematics Xiangtan University Xiangtan China Hunan Engineering Research Center for Multi-Energy Cooperative Control Technology Xiangtan University Xiangtan China

The growing utilization of lithium-ion batteries in new energy vehicles is emerging as a promising solution to address the global energy crisis and issues related to environmental pollution. Ensuring battery safety and reliability relies heavily on the precise assessment of the SOH, which is of utmost importance. However, the historical state information of batteries used in most data-driven methods may be incomplete or missing, especially in scenarios with small data sizes. In this research, we introduce a sliding window method to reconstruct battery charge and discharge data. This approach involves extracting voltage and current data segments during the battery's charging and discharging stages. These segments are used to construct a VI matrix. We propose a CNN-LSTM deep neural network model for estimating the SOH of lithium-ion batteries. Our model uses two CNN neural network layers to extract frequency domain information from the battery segment data and an LSTM neural network layer to extract the time-domain features of the data. The superiority and generalization capability of the proposed model was validated on the lithium-ion batteries dataset from CALCE by comparing the estimation results with those of single CNN and LSTM models.

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