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Thermal constraint control of reversible solid oxide cell for optimal system efficiency

International Journal of Hydrogen Energy 2025年 134卷 198-211页

作者： Xu, Yuanwu Yin, Jie Wang, Jingjing Wu, Xiao-long Peng, Jingxuan Tang, Ruifeng Zou, Suhang Fu, Xiaowei Li, Xi School of Artificial Intelligence and Automation Wuhan University of Science and Technology Hubei Wuhan430081 China School of Artificial Intelligence and Automation Key Laboratory of Image Processing and Intelligent Control of Education Ministry Huazhong University of Science and Technology Hubei Wuhan430074 China School of Information Engineering Nanchang University Jiangxi Nanchang330031 China School of Electrical and Electronic Engineering Nanyang Technological University 639798 Singapore China-Poland Belt and Road Joint Laboratory on Measurement and Control Technology Huazhong University of Science and Technology Hubei Wuhan430074 China Shenzhen Huazhong University of Science and Technology Research Institute Shenzhen518055 China

Reversible solid oxide cell (RSOC) demonstrates considerable promise in multimodal energy conversion architectures, particularly as a technology for hydrogen-based energy storage systems. Nevertheless, under dynamic operating regimes with time-varying current inputs, realizing time-efficient and reliable transitions to steady-state operation remains difficult. This difficulty arises from the intricate coupling of thermal, electrical, and gaseous processes within the RSOC system. This study aims to address these challenges by developing a 5 kW mathematical model of the RSOC system. The proposed model enables systematic investigation of thermal safety issues arising during current switching operations while introducing a thermal constraint control strategy to enhance both system efficiency and thermal safety performance. This efficiency-driven study employs the Grid Search algorithm to identify optimal steady-state operating points for the RSOC system, ensuring strict adherence to safety constraints across varying current inputs. Furthermore, by integrating the pre-identified optimal operating points as feedforward control with Model Predictive control (MPC)-based feedback correction, this hybrid strategy effectively mitigates stack temperature gradient overshoot while synchronously ensuring optimal system efficiency throughout current transition processes. Under simulated variable current inputs, the proposed RSOC system model and control strategy demonstrate the capability to constrain stack temperature gradient overshoot within 2.3K while enhancing transient efficiency by 5 %–8 %, achieving a peak system efficiency of 85 %. It provides a novel methodology for safe and efficient working with intermittent renewable energy. © 2025 Hydrogen Energy Publications LLC

关键词： Thermal variables control

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Multi-storage, multi-energy, and multi-policy optimization for hybrid green and grey hydrogen production: A regional study in China

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Energy Conversion and Management 2025年 334卷

作者： Yin, Zishan Tang, Xiyun Li, Xi School of Artificial Intelligence and Automation Huazhong University of Science and Technology Hubei Wuhan430074 China Key Laboratory of Imaging Processing and Intelligent Control of Education Ministry Huazhong University of Science and Technology Hubei Wuhan430074 China Belt and Road Joint Laboratory on Measurement and Control Technology Huazhong University of Science and Technology Wuhan430074 China

Green hydrogen is key to global decarbonization. This study optimizes regional green-grey hydrogen production in China, using a multi-storage (hydrogen and battery) and multi-renewable energy model. The focus is on minimizing the levelized cost of hydrogen and carbon emission factor, incorporating carbon reduction policies. The results show that the tradable green certificates policy leads to a lower cost, while the cap-and-trade mechanism accelerates the achievement of grey hydrogen substitution across China's regions by 20 years to 2060. Regions rich in renewable energy achieve $5/kg lower costs and 6 years faster substitution. Hydrogen storage is the most cost-effective solution, hybrid storage is preferable in low renewable energy areas, while battery storage incurs costs that are 25% to 150% higher. The trade-off between carbon emission and cost highlights each region's inflection point, essential for designing incentive policies. © 2025 Elsevier Ltd

关键词： Battery storage

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Optimization of working conditions in solid oxide electrolysis cell based on deep neural network and slime mold algorithm

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International Journal of Hydrogen Energy 2025年 135卷 172-181页

作者： Jiawei Zhang Zhuo Wang Ming Li Guoqiang Liu Lin Zhang Yuanwu Xu Xi Li School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan 430074 China Marine Engineering College Dalian Maritime University Dalian 116026 China Department of Early Warning Technology Air Force Early Warning Academy Wuhan 430019 China School of Information Science and Engineering Wuhan University of Science and Technology Wuhan 430081 China Shenzhen Huazhong University of Science and Technology Research Institute Shenzhen 518055 China Belt and Road Joint Laboratory on Measurement and Control Technology Huazhong University of Science and Technology Wuhan 430074 China

Solid oxide electrolysis cell (SOEC) are a highly efficient hydrogen production technology, but thermal stresses induced by temperature gradient under high temperature may accelerate performance degradation and cause equipment damage. Optimizing operating parameters to balance efficiency and thermal safety is critical to addressing these challenges. Existing optimization methods primarily rely on numerical simulations and experimental validations, but their high time and economic costs limit practical applications. To overcome this challenge, this study proposes a performance optimization method that, unlike conventional models, considers thermal safety constraints and rapidly determines optimal SOEC operating conditions with varying power inputs. By integrating the deep neural network with a one-dimensional multiphysics field model, the method leverages data generated by complex multiphysics field model to train the network and systematically analyze the effects of different operating conditions on SOEC stack power and efficiency. Subsequently, the slime mold algorithm is employed to efficiently optimize operating parameters under power and thermal constraints, achieving maximum SOEC efficiency and providing theoretical support for future dynamic control strategies.

关键词： Deep neural network Slime mold algorithm Solid oxide electrolysis cell Thermal safety constraints

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Training-free Ultra Small Model for Universal Sparse Reconstruction in Compressed Sensing

arXiv

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arXiv 2025年

作者： Tang, Chaoqing Zhuang, Huanze Tian, Guiyun Zeng, Zhenli Ding, Yi Liu, Wenzhong Bai, Xiang Lin, Lin School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan China China Belt and Road Joint Lab on Measurement and Control Technology Wuhan China School of Electric and Electrical Engineering Chongqing University of Technology Chongqing China Optics Valley Laboratory Wuhan China School of Water Conservancy and Transportation Zhengzhou University Zhengzhou China School of Software Engineering Huazhong University of Science and Technology Wuhan China

Pre-trained large models attract widespread attention in recent years, but they face challenges in applications that require high interpretability or have limited resources, such as physical sensing, medical imaging, and bioinformatics. Compressed Sensing (CS) is a well-proved theory that drives many recent breakthroughs in these applications. However, as a typical under-determined linear system, CS suffers from excessively long sparse reconstruction times when using traditional iterative methods, particularly with large-scale data. Current AI methods like deep unfolding fail to substitute them because pre-trained models exhibit poor generality beyond their training conditions and dataset distributions, or lack interpretability. Instead of following the big model fervor, this paper proposes ultra-small artificial neural models called coefficients learning (CL), enabling training-free and rapid sparse reconstruction while perfectly inheriting the generality and interpretability of traditional iterative methods, bringing new feature of incorporating prior knowledges. In CL, a signal of length n only needs a minimal of n trainable parameters. A case study model called CLOMP is implemented for evaluation. Experiments are conducted on both synthetic and real one-dimensional and two-dimensional signals, demonstrating significant improvements in efficiency and accuracy. Compared to representative iterative methods, CLOMP improves efficiency by 100 to 1000 folds for large-scale data. Test results on eight diverse image datasets indicate that CLOMP improves structural similarity index by 292%, 98%, 45% for sampling rates of 0.1, 0.3, 0.5, respectively. We believe this method can truly usher CS reconstruction into the AI era, benefiting countless under-determined linear systems that rely on sparse solution. The code and core data of this paper are available at https://***/BillTtzqgbt/***. Copyright © 2025, The Authors. All rights reserved.

关键词： Linear systems

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A novel linear search-linear programming method for non-convex multi-objective sizing of standalone microgrids

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Energy Conversion and Management 2025年 337卷

作者： Zishan Yin Jiashu Jin Linghong Zeng Xiyun Tang Zhuo Wang Xi Li School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan 430074 Hubei PR China Key Laboratory of Imaging Processing and Intelligent Control of Education Ministry Huazhong University of Science and Technology Wuhan 430074 Hubei PR China Belt and Road Joint Laboratory on Measurement and Control Technology Huazhong University of Science and Technology Wuhan 430074 Hubei PR China

Both the increased utilization of photovoltaic (PV) and the consequent expansion of energy storage, leading to higher costs, are critical factors in the development of a techno-economic microgrid. To address this trade-off, optimal sizing design of a multi-energy microgrid is essential. This paper proposes a novel linear search-linear programming method for optimizing the sizing of microgrids with diverse energy storage systems, including battery energy storage systems (BESS) and hydrogen energy storage systems (HESS). The method minimizes both the non-linear and non-convex life cycle cost and the PV curtailment rate. Simulation results demonstrate the feasibility and superiority of the proposed method, achieving enhanced computational efficiency: 52 times faster for PV/HESS microgrids, 124 times for PV/BESS, and 5 times for PV/HESS/BESS. Moreover, more comprehensive and accurate optimal configurations were identified, including 4289 PV units for both the PV/BESS and PV/HESS/BESS microgrids, and 6239 PV units along with 37 fuel cell units for the PV/HESS microgrid. Based on the advanced optimization outcomes, a techno-economic analysis shows that the PV/HESS/BESS microgrid optimally combines the advantages of both HESS and BESS, substantially improving overall system performance.

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Variable Selection Method Based on Least Absolute Shrinkage and Selection Operator(LASSO) and kNN for Faults Isolation 41

Variable Selection Method Based on Least Absolute Shrinkage ...

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第41届中国控制会议

作者： Yuanle Liu Lang Liu Yanwei Wang Ying Zheng Housheng Su China-Belt and Road Joint Laboratory On Measurement and Control Technology School of Artificial Intelligence and AutomationHuazhong University of Science and Technology School of Mechanical & Eletrical Engineering Wuhan Institute of Technology

ISBN: (数字)9789887581536

ISBN: (纸本)9781665482561

In the multivariable fault isolation of industrial process,the effectiveness of fault isolation is affected by the "smearing" effect because of the correlation between *** order to isolate all the key fault variables correctly,the fault isolation problem is regarded as a variable selection ***,LASSO is applied to the sparse contribution vector based on k-nearest ***,the LASSO-KNN model based on sliding window is established,and the sparse representation of contribution vector is obtained by least angle regression(LAR) algorithm and cumulative weight contribution percent criterion,thus the main fault variables are *** an extended EWMA algorithm combined with sliding window mechanism is introduced to achieve on-line fault ***,the effectiveness of the proposed algorithm is verified by a simulation example of TE process.

关键词： Fault diagnosis Fault isolation Variable selection k-Nearest neighbour

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A Few-shot Fault Diagnosis Method under Target Domain Data Based on Improved Metric Space SVM 41

A Few-shot Fault Diagnosis Method under Target Domain Data B...

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第41届中国控制会议

作者： Xinyi Xu Qilin Qu Yanwei Wang Ying Zheng Housheng Su China-Belt and Road Joint Laboratory On Measurement and Control Technology School of Artificial Intelligence and AutomationHuazhong University of Science and Technology School of Mechanical & Electrical Engineering Wuhan Institute of Technology

ISBN: (数字)9789887581536

ISBN: (纸本)9781665482561

The intelligent bearing diagnosis with big data has been widely *** high performance diagnosis models are constantly being proposed,these models are difficult to apply in some practical application with limited collectable *** this paper,an Improved Metric Space Support Vector Machine with a two-stage learning strategy is proposed to overcome the challenge of few-shot scenarios under target domain ***,an improved triplet loss is designed in the metric network to select features by utilizing the similarity of ***,kernel method is used to model in few-shot scenarios by maximizing the inter-class *** on CWRU dataset verify the superiority and robustness of IMSSVM comparing with popular baseline ***,the effectiveness of IMSSVM is verified with visualizing the feature embeddings by t-distributed stochastic neighbor embedding.

关键词： fault diagnosis few-shot learning metric learning kernel method

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HSE: A plug-and-play module for unified fault diagnosis foundation models

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Information Fusion 2025年

作者： Qi Li Bojian Chen Qitong Chen Xuan Li Zhaoye Qin Fulei Chu Department of Mechanical Engineering Tsinghua University Beijing 100084 PR China State Key Laboratory of Industrial Control Technology Zhejiang University Hangzhou 310058 PR China School of Mechanical and Electric Engineering Soochow University Suzhou 215000 PR China Belt and Road Joint Laboratory on Measurement and Control Technology School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan 430074 PR China

Intelligent Fault Diagnosis (IFD) plays a crucial role in industrial applications, where developing foundation models analogous to ChatGPT for comprehensive fault diagnosis remains a significant challenge. Current IFD methodologies are constrained by their inability to construct unified models capable of processing heterogeneous signal types, varying sampling rates, and diverse signal lengths across different equipment. To address these limitations, we propose a novel Heterogeneous Signal Embedding (HSE) module that projects heterogeneous signals into a unified signal space, offering seamless integration with existing IFD architectures as a plug-and-play solution. The HSE framework comprises two primary components: the Temporal-Aware Patching (TAP) module for embedding heterogeneous signals into a unified space, and the Cross-Dimensional Patch Fusion (CDPF) module for fusing embedded signals with temporal information into unified representations. We validate the efficacy of HSE through two comprehensive case studies: a simulation signal dataset and three distinct bearing datasets with heterogeneous features. Our experimental results demonstrate that HSE significantly enhances traditional fault diagnosis models, improving both diagnostic accuracy and generalization capability. While conventional approaches necessitate separate models for specific signal types, sampling frequencies, and signal lengths, HSE-enabled architectures successfully learn unified representations across diverse signal. The results from bearing fault diagnosis applications confirm substantial improvements in both diagnostic precision and cross-dataset generalization. As a pioneering contribution toward IFD foundation models, the proposed HSE framework establishes a fundamental architecture for advancing unified fault diagnosis systems.

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In-depth analysis of core-shell filaments in nonvolatile NbO_(x) memristive device as an artificial synapse for multifunctional bionic applications

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Science China(Technological Sciences) 2023年第12期66卷 3596-3603页

作者： JIANG Bei KE ShanWu TAO ZiPei JIN YaoYao MENG JiaHao SONG XingJuan XIAO YongYue JIANG Li WANG JinZhao CHEN Yong LI YueBin WEN Xin YE Cong School of Microelectronics Key Laboratory of Intelligent Sensing System and SecurityMinistry of EducationHubei UniversityWuhan 430062China Belt and Road Joint Laboratory on Measurement and Control Technology Huazhong University of Science and TechnologyWuhan 430074China School of Science Hubei University of TechnologyWuhan 430068China Faculty of Chemical Technology and Engineering West Pomeranian University of Technology in SzczecinSzczecin 71-065Poland

As a key building block of the biological cortex,synapses are powerful information processing units that enable highly complex nonlinear *** realization of artificial synapses with similar capabilities has important implications for building intelligent,neuromorphic ***,we demonstrate an artificial synapse based on NbO_(x) nonvolatile memristor to mimic multifunctional bionic applications such as nociceptor and associative *** experimental characterization with COMSOL simulation,the traditional resistance switching characteristics,which are the decisive factor for the synapse properties are in-depth *** can be proposed that the I-V characteristics of Pt/NbO_(x)/TiN memristor are governed by core-shell filaments consisting of the shell region of sub-stoichiometric Nb_(2)O_(5-δ)and the core of NbO_(2).On the basis of the core-shell filament model,it can be reasonably explained that Ohmic conduction and Poole-Frenkel conduction take turns to dominate the current flowing in the memristive device,leading to the zigzag evolution of current during the operation process of NbO_(x)-based *** simulations of synaptic plasticity,including long-term potentiation/depression(LTP/LTD),paired-pulse facilitation(PPF),and spike-timing-dependent plasticity(STDP),exhibiting that the NbO_(x) can be utilized for an artificial ***,bionic functions such as hyperalgesia and allodynia of a nociceptor and a series of associative learning behaviors in Pavlovian dog experiment are mimicked,illustrating that the Pt/NbO_(x)/TiN have great potential for highly simplified artificial neural network applications.

关键词： memristor core-shell filament synaptic plasticity nociceptor associative learning

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A Method for Detecting the Reducers' Grease Failure Based on Vibration Signal Analysis

A Method for Detecting the Reducers' Grease Failure Based on...

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2023 CAA Symposium on Fault Detection, Supervision and Safety for Technical Processes, SAFEPROCESS 2023

作者： Wang, Ditong Zhang, Hong Zheng, Ying Huazhong University of Science and Technology Belt and Road Joint Laboratory on Measurement and Control Technology Wuhan China Huazhong University of Science and Technology National Key Laboratory of Science and Technology on Multispectral Information Processing School of Artificial Intelligence and Automation Wuhan China

ISBN: (纸本)9798350337754

Real time detection of the lubricating grease's effectiveness is the best method to solve wear faults, which can achieve the purpose of predictive maintenance and improve the service life of mechanical equipment. A new online detection method for gearbox lubricating grease failure is proposed to address the issues about long offline detection cycles and high labor costs associated with phased sampling analysis. Compared with online detection methods such as inductive and photoelectric, our method is difficult to detect the specific physicochemical parameters of the grease. However, it has the advantage of simple operation without touching the grease or changing the structure of the tested object. It only needs to attach the vibration sensor table to the mechanical arm reducer to measure and analyze the vibration signal. This article briefly analyzes the impact of grease failure on vibration signals, and then we introduce a novel approach that uses support vector machines along with feature extraction through wavelet packet decomposition for our detection methodology. Next transfer the commonly used spectral image recognition algorithms for temporal signal classification to achieve failed grease detection as a baseline and compare with our traditional machine learning methods. The experimental findings indicate that the recognition accuracy of both methods are above 90%, with traditional machine learning performing better on this task. © 2023 IEEE.

关键词： Support vector machines

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