Article
Automation & Control Systems
Seokgoo Kim, Hyung Jun Park, Joo-Ho Choi, Daeil Kwon
Summary: In this article, a novel prognostic method using the particle filter (PF) is proposed for detecting state changes in lithium-ion battery degradation. The method shows better performance in anomaly detection and adaptability to new degradation processes, leading to more accurate and reliable remaining useful life (RUL) predictions compared to the original PF algorithm.
IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS
(2021)
Article
Chemistry, Analytical
Karkulali Pugalenthi, Hyunseok Park, Shaista Hussain, Nagarajan Raghavan
Summary: With the increasing presence of smart electronic devices in our everyday lives, predictive maintenance solutions are becoming more important in the electronic manufacturing industry. This study proposes a method using neural networks and Bayesian inference to predict the remaining useful life of electronic devices, and introduces weight regularization and resampling strategies to improve the efficiency and robustness of the prediction model.
Article
Energy & Fuels
Alan G. Li, Weizhong Wang, Alan C. West, Matthias Preindl
Summary: This paper proposes a novel diagnostics method called 'pulse-injection-aided machine learning' (PIAML) for lithium-ion batteries during vehicle charging. The PIAML method utilizes a feedforward neural network and the battery voltage response to accurately estimate the states of health, power, and charge, without requiring charging history or battery parameters.
Article
Chemistry, Physical
Yifei Xu, Lina Gao, Qianqian Liu, Qian Liu, Zerui Chen, Wei Zhao, Xueqian Kong, Hao Bin Wu
Summary: Metal-organic frameworks have been developed as solid electrolytes for Li batteries. A study demonstrates metal-organic solid electrolytes with superionic conductivity by grafting hemilabile anionic chains in the pore channels of an Al-based metal-organic framework. The solid electrolyte shows excellent stability and enables remarkable rate performance in Li batteries.
ENERGY STORAGE MATERIALS
(2023)
Article
Energy & Fuels
Qiang Zheng, Xiaoguang Yin, Dongxiao Zhang
Summary: Online estimation of unobservable internal states is crucial for safe operation of Li-ion batteries, and it is one of the main functions of battery management systems. This study introduces the concept of physics-informed operator learning and proposes a new architecture, PIMIONet, for reformulating the state-space representation of electrochemical models. The PIMIONet approach demonstrates high applicability and efficiency for online state estimation.
JOURNAL OF ENERGY STORAGE
(2023)
Article
Thermodynamics
Felix Kolodziejczyk, Bohayra Mortazavi, Timon Rabczuk, Xiaoying Zhuang
Summary: This study combines CNN and FEM to investigate the thermal properties of composite phase change materials, evaluating the effectiveness of a battery pack's thermal management system through modeling, creating image datasets, training CNN, and comparing with original FEM models in terms of performance.
INTERNATIONAL JOURNAL OF HEAT AND MASS TRANSFER
(2021)
Article
Engineering, Electrical & Electronic
M. A. Hannan, D. N. T. How, M. S. Hossain Lipu, Pin Jern Ker, Z. Y. Dong, M. Mansur, Frede Blaabjerg
Summary: In this study, a deep fully convolutional network model was proposed to accurately estimate the state-of-charge (SOC) of lithium-ion batteries, achieving impressive performance under different temperature conditions.
IEEE TRANSACTIONS ON POWER ELECTRONICS
(2021)
Article
Green & Sustainable Science & Technology
Fathima Ali Kayakool, Binitha Gangaja, Shantikumar Nair, Dhamodaran Santhanagopalan
Summary: The recycling and regeneration of graphite from spent Li-ion batteries can be utilized for the fabrication of Li-ion based all-carbon dual-ion batteries, achieving promising electrochemical performance.
SUSTAINABLE MATERIALS AND TECHNOLOGIES
(2021)
Article
Engineering, Mechanical
Gyumin Lee, Daeil Kwon, Changyong Lee
Summary: This study proposes a convolutional neural network model to estimate the future SOH value of Li-ion batteries using recurrence plots and Gramian angular fields. Five types of convolutional neural network models are developed and the contribution of each temporal feature is obtained. The experimental results confirm the proposed approach's effectiveness in reducing qualification test time and achieving accurate SOH estimation.
MECHANICAL SYSTEMS AND SIGNAL PROCESSING
(2023)
Article
Automation & Control Systems
Changyong Lee, Sugyeong Jo, Daeil Kwon, Michael G. Pecht
Summary: This article introduces an analysis method for capacity-fading behavior of Li-ion batteries, which can detect unhealthy batteries early and significantly reduce the number of reliability tests for unhealthy batteries in practice.
IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS
(2021)
Article
Chemistry, Physical
Qiongyu Zhou, Qinghui Li, Songli Liu, Xin Yin, Bing Huang, Minqi Sheng
Summary: A high-performance flexible poly(ethylene oxide) (PEO)-based composite electrolyte has been developed to enhance the safety and stability of all-solid-state batteries.
JOURNAL OF POWER SOURCES
(2021)
Article
Computer Science, Information Systems
Karkulali Pugalenthi, Hyunseok Park, Shaista Hussain, Nagarajan Raghavan
Summary: PHM plays a key role in the Industry 4.0 revolution by providing smart predictive maintenance solutions. The main challenges include early failure detection and prediction of remaining useful life (RUL). While model-based and data-driven methods are used for RUL estimation, data-driven methods often lack the extensive failure data needed for accuracy.
Article
Chemistry, Physical
Yatao Liu, Linhan Xu, Yongquan Yu, Mengxue He, Han Zhang, Yanqun Tang, Feng Xiong, Song Gao, Aijun Li, Jianhui Wang, Shenzhen Xu, Doron Aurbach, Ruqiang Zou, Quanquan Pang
Summary: Structural reorganization of sparingly solvating electrolytes (SSEs) using aromatic anti-solvents is crucial for taming the quasi-solid-state sulfur reaction and improving reaction kinetics. The use of aromatic anti-solvents disrupts the electrolyte structure, accelerates sulfur consumption and re-formation, and promotes the formation of a robust solid electrolyte interphase (SEI) on lithium metal.
Article
Chemistry, Multidisciplinary
Jianxun Zhu, XiaoLei Li, Changwei Wu, Jian Gao, Henghui Xu, Yutao Li, Xiangxin Guo, Hong Li, Weidong Zhou
Summary: The dual layer ceramic electrolyte of Ti-doped LLZTO/Ti-LLZTO and LLZTO shows improved interface structure and higher density, leading to enhanced interaction between Li-metal and electrolyte, reduction of interface resistance, and suppression of dendrite formation.
ANGEWANDTE CHEMIE-INTERNATIONAL EDITION
(2021)
Article
Chemistry, Physical
Zeyu Li, Feng Liu, Shaoshan Chen, Fei Zhai, Yu Li, Yiyu Feng, Wei Feng
Summary: A novel Li+ conductor based on carbon quantum dots was successfully fabricated in this study, showing high ionic conductivity and excellent stability in Li-metal batteries, as well as the ability to withstand various deformations.
Article
Chemistry, Analytical
Daniele Oboe, Luca Colombo, Claudio Sbarufatti, Marco Giglio
Summary: The inverse Finite Element Method (iFEM) is gaining attention for shape sensing due to its independence from material properties and external load. Proper definition of model geometry, boundary conditions, and optimized sensor networks to acquire strain field are required for complex structures. A simplified iFEM model with reduced geometrical complexity and tuned boundary conditions is proposed to handle structures with partial sensor application, showing effectiveness in aeronautical structures with experimentally acquired strain measurements.
Article
Chemistry, Multidisciplinary
Franco Concli, Ludovico Pierri, Claudio Sbarufatti
Summary: Transmissions play a crucial role in mechanical gearboxes, with the ability to provide specific maintenance being essential for economics and reliability. While periodic maintenance can extend system longevity, it may not prevent sporadic major failures. Structural health monitoring (SHM) offers a possible solution by identifying measurable signal variations to assess component condition, especially for large gearboxes. Model-based approaches show potential advantages for damage identification in cases where experimental examples are not readily available.
APPLIED SCIENCES-BASEL
(2021)
Article
Materials Science, Multidisciplinary
A. Esmaeili, C. Sbarufatti, K. Youssef, A. Jimenez-Suarez, A. Urena, A. M. S. Hamouda
Summary: This study investigated the effect of toroidal stirring-assisted sonication on CNT doped epoxy nanocomposites, showing that M2 batch exhibited better mechanical, electrical, and piezoresistivity performance compared to M1 batch. Tensile and fracture tests were conducted, revealing a 70% increase in tensile strength and a 17% increase in fracture toughness for M2 batch. Additionally, the piezoresistive-sensitivity of M2 batch increased by 14% compared to M1 batch. Different trends in piezoresistivity were observed in the fracture test before macroscopic damage, attributed to the state of CNT dispersion.
MECHANICS OF ADVANCED MATERIALS AND STRUCTURES
(2022)
Article
Engineering, Mechanical
Francesco Cadini, Luca Lomazzi, Marc Ferrater Roca, Claudio Sbarufatti, Marco Giglio
Summary: This study combines particle filters and neural networks-based autoencoders to propose a novel algorithm for structural damage detection and localization, which is robust and equipped with a fault indicator module for decision support. The method is demonstrated with reference to a numerical MDOF system.
MECHANICAL SYSTEMS AND SIGNAL PROCESSING
(2022)
Article
Construction & Building Technology
Tianzhi Li, Claudio Sbarufatti, Francesco Cadini, Jian Chen, Shenfang Yuan
Summary: The study utilized hybrid methods combining physical knowledge and data-driven techniques for structural health monitoring, with improved prediction performances based on the physics-based process equation demonstrated in experimental study.
STRUCTURAL CONTROL & HEALTH MONITORING
(2022)
Article
Engineering, Mechanical
Luca Colombo, M. D. Todd, C. Sbarufatti, M. Giglio
Summary: This study proposes a unique and coherent framework for optimal detector and sensing network design for SHM. Multi-objective optimization is used to optimize sensor placement, maximizing classification performance and minimizing total cost simultaneously. Numerical verification and result validation demonstrate the advantages of the optimization scheme in terms of cost savings and improvement in detection performance.
MECHANICAL SYSTEMS AND SIGNAL PROCESSING
(2022)
Article
Engineering, Aerospace
Xuan Zhou, Daniele Oboe, Dario Poloni, Claudio Sbarufatti, Leiting Dong, Marco Giglio
Summary: In the field of adhesive bonding in aeronautic structures, debonding under fatigue loading is a common failure mode. Researchers have developed a joint distribution adaptation method for regression to address multi-output problems, demonstrating significant improvements in damage quantification accuracy. The proposed approach could potentially be integrated into fleet-level digital twins for heterogeneous systems with nominally identical components.
Article
Chemistry, Analytical
Dario Poloni, Daniele Oboe, Claudio Sbarufatti, Marco Giglio
Summary: The inverse Finite Element Method (iFEM) is widely used in the field of Structural Health Monitoring (SHM). It can reconstruct the displacement field of a beam or shell structure independently of external loading conditions and material properties based on sparse strain measurements. However, the iFEM requires triaxial strain measurements, which are expensive and impractical in real-world applications. To address this issue, pre-extrapolation techniques have been developed to reduce the number of required sensors. However, for structures with regions of different thicknesses, separate extrapolation is needed due to thickness-induced discontinuities in the strain field. This paper proposes a novel method that extrapolates the measured strain field in a thickness-normalized space, effectively reducing the costs of iFEM-based SHM systems.
Article
Chemistry, Physical
Mohammad Rezasefat, Alessio Beligni, Claudio Sbarufatti, Sandro Campos Amico, Andrea Manes
Summary: This study investigates the impact of pre-existing damage on the low-velocity impact response of CFRP through experiments and numerical simulations. A material model based on continuum damage mechanics is developed in Abaqus/Explicit. The model is validated through finite element simulations at the single-element level and more complex models are used to simulate different specimens subjected to low-velocity impacts. The presence of pre-existing damage near the impact region results in severe changes in mechanical response, while impacts farther away from the region show similar results as those on pristine specimens.
Article
Engineering, Aerospace
Xuan Zhou, Claudio Sbarufatti, Marco Giglio, Leiting Dong, Satya N. Atluri
Article
Engineering, Mechanical
Dario Poloni, Daniele Oboe, Claudio Sbarufatti, Marco Giglio
Summary: The inverse Finite Element Method (iFEM) is used to reconstruct the full-field displacement on beam or shell structures using a network of strain sensors. A Gaussian Process is proposed as a strain pre-extrapolation and interpolation technique to provide strain values and compute the uncertainty on the reconstructed displacement field.
MECHANICAL SYSTEMS AND SIGNAL PROCESSING
(2023)
Article
Engineering, Mechanical
Xuan Zhou, Claudio Sbarufatti, Marco Giglio, Leiting Dong
Summary: Online damage quantification suffers from insufficient labeled data, but adopting domain adaptation can improve its accuracy. However, existing domain adaptation methods are not suitable for damage quantification as it is a regression problem with continuous labels. This study proposes a novel domain adaptation method, the Online Fuzzy-set-based Joint Distribution Adaptation for Regression, which converts real-valued labels into fuzzy class labels and measures distribution discrepancy to achieve accurate damage quantification. The proposed method is demonstrated to significantly improve damage quantification in a realistic environment, and it is expected to be applied to fleet-level digital twin considering individual differences.
MECHANICAL SYSTEMS AND SIGNAL PROCESSING
(2023)
Article
Chemistry, Analytical
Daniele Oboe, Dario Poloni, Claudio Sbarufatti, Marco Giglio
Summary: The inverse finite element method (iFEM) is used to compute the displacement and strain field of a structure based on strain measurements and a geometric discretization. Previous works focused on damage detection and localization, but did not estimate the damage size accurately. To address this issue, a new approach is proposed, introducing damage systematically in the iFEM model to minimize discrepancy with the physical structure. The approach was experimentally verified on an aluminum plate subjected to fatigue crack propagation.
Article
Instruments & Instrumentation
Dario Poloni, Daniele Oboe, Claudio Sbarufatti, Marco Giglio
Summary: In the past two decades, the aerospace industry has shifted to using composite materials like carbon fiber reinforced polymers (CFRP) instead of aluminum for lighter and fuel-efficient aircrafts. This shift has resulted in the use of adhesive bonding for structural connections and repairs. However, detecting and predicting the debonding of these adhesive interfaces is challenging, leading to increased maintenance costs and reduced platform availability. This paper proposes an inverse finite element method (iFEM) as a load and material independent approach to estimate debonding entity in adhesive-bonded joints, which can be considered a significant scientific advancement in this field.
SMART MATERIALS AND STRUCTURES
(2023)
Proceedings Paper
Engineering, Civil
Dario Poloni, Daniele Oboe, Claudio Sbarufatti, Marco Giglio
Summary: This paper proposes a pre-extrapolation technique based on Gaussian Process for the strain field in the Inverse Finite Element method (iFEM), aiming to improve the solution when the sensor network is sparse and measureless elements are present. The proposed technique incorporates measurement uncertainty and provides confidence intervals for the solution.
EUROPEAN WORKSHOP ON STRUCTURAL HEALTH MONITORING (EWSHM 2022), VOL 2
(2023)
Article
Energy & Fuels
Shitong Fang, Houfan Du, Tao Yan, Keyu Chen, Zhiyuan Li, Xiaoqing Ma, Zhihui Lai, Shengxi Zhou
Summary: This paper proposes a new type of nonlinear VIV energy harvester (ANVEH) that compensates for the decrease in peak energy output at low wind speeds by introducing an auxiliary structure. Theoretical and experimental results show that ANVEH performs better than traditional nonlinear VIV energy harvesters under various system parameter variations.
Article
Energy & Fuels
Wei Jiang, Shuo Zhang, Teng Wang, Yufei Zhang, Aimin Sha, Jingjing Xiao, Dongdong Yuan
Summary: A standardized method was developed to evaluate the availability of solar energy resources in road areas, which combined the Analytic Hierarchy Process (AHP) and the Geographic Information System (GIS). By analyzing critical factors and using a multi-indicator evaluation method, the method accurately evaluated the utilization of solar energy resources and guided the optimal location selection for road photovoltaic (PV) projects. The results provided guidance for the application of road PV projects and site selection for route corridors worldwide, promoting the integration of transportation and energy.
Article
Energy & Fuels
Chang Liu, Jacob A. Wrubel, Elliot Padgett, Guido Bender
Summary: The study investigates the effects of coating defects on the performance of the anode porous transport layer (PTL) in water electrolyzers. The results show that an increasing fraction of uncoated regions on the PTL leads to decreased cell performance, with continuous uncoated regions having a more severe impact compared to multiple thin uncoated strips.
Article
Energy & Fuels
Marcos Tostado-Veliz, Xiaolong Jin, Rohit Bhakar, Francisco Jurado
Summary: In this paper, a coordinated charging price mechanism for clusters of parking lots is proposed. The research shows that enabling vehicle-to-grid characteristics can bring significant economic benefits for users and the cluster coordinator, and vehicle-to-grid impacts noticeably on the risk-averse character of the uncertainty-aware strategies. The developed pricing mechanism can reduce the cost for users, avoiding to directly translate the energy cost to charging points.
Article
Energy & Fuels
Duan Kang
Summary: Building an energy superpower is a key strategy for China and a long-term goal for other countries. This study proposes an evaluation system and index for measuring energy superpower, and finds that China has significantly improved its ranking over the past 21 years, surpassing other countries.
Article
Energy & Fuels
Fucheng Deng, Yifei Wang, Xiaosen Li, Gang Li, Yi Wang, Bin Huang
Summary: This study investigated the synergistic blockage mechanism of sand and hydrate in gravel filling layer and the evolution of permeability in the layer. Experimental models and modified permeability models were established to analyze the effects of sand particles and hydrate formation on permeability. The study provided valuable insights for the safe and efficient exploitation of hydrate reservoirs.
Article
Energy & Fuels
Hao Wang, Xiwen Chen, Natan Vital, Edward Duffy, Abolfazl Razi
Summary: This study proposes a HVAC energy optimization model based on deep reinforcement learning algorithm. It achieves 37% energy savings and ensures thermal comfort for open office buildings. The model has a low complexity, uses a few controllable factors, and has a short training time with good generalizability.
Article
Energy & Fuels
Moyue Cong, Yongzhuo Gao, Weidong Wang, Long He, Xiwang Mao, Yi Long, Wei Dong
Summary: This study introduces a multi-strategy ultra-wideband energy harvesting device that achieves high power output without the need for external power input. By utilizing asymmetry, stagger array, magnetic coupling, and nonlinearity strategies, the device maintains a stable output voltage and high power density output at non-resonant frequencies. Temperature and humidity monitoring are performed using Bluetooth sensors to adaptively assess the device.
Article
Energy & Fuels
Tianshu Dong, Xiudong Duan, Yuanyuan Huang, Danji Huang, Yingdong Luo, Ziyu Liu, Xiaomeng Ai, Jiakun Fang, Chaolong Song
Summary: Electrochemical water splitting is crucial for hydrogen production, and improving the hydrogen separation rate from the electrode is essential for enhancing water electrolyzer performance. However, issues such as air bubble adhesion to the electrode plate hinder the process. Therefore, a methodology to investigate the two-phase flow within the electrolyzer is in high demand. This study proposes using a microfluidic system as a simulator for the electrolyzer and optimizing the two-phase flow by manipulating the micro-structure of the flow.
Article
Energy & Fuels
Shuo Han, Yifan Yuan, Mengjiao He, Ziwen Zhao, Beibei Xu, Diyi Chen, Jakub Jurasz
Summary: Giving full play to the flexibility of hydropower and integrating more variable renewable energy is of great significance for accelerating the transformation of China's power energy system. This study proposes a novel day-ahead scheduling model that considers the flexibility limited by irregular vibration zones (VZs) and the probability of flexibility shortage in a hydropower-variable renewable energy hybrid generation system. The model is applied to a real hydropower station and effectively improves the flexibility supply capacity of hydropower, especially during heavy load demand in flood season.
Article
Energy & Fuels
Zhen Wang, Kangqi Fan, Shizhong Zhao, Shuxin Wu, Xuan Zhang, Kangjia Zhai, Zhiqi Li, Hua He
Summary: This study developed a high-performance rotary energy harvester (AI-REH) inspired by archery, which efficiently accumulates and releases ultralow-frequency vibration energy. By utilizing a magnetic coupling strategy and an accumulator spring, the AI-REH achieves significantly accelerated rotor speeds and enhanced electric outputs.
Article
Energy & Fuels
Yi Yang, Qianyi Xing, Kang Wang, Caihong Li, Jianzhou Wang, Xiaojia Huang
Summary: In this study, a novel hybrid Quantile Regression (QR) model is proposed for Probabilistic Load Forecasting (PLF). The model integrates causal dilated convolution, residual connection, and Bidirectional Long Short-Term Memory (BiLSTM) for multi-scale feature extraction. In addition, a Combined Probabilistic Load Forecasting System (CPLFS) is proposed to overcome the inherent flaws of relying on a single model. Simulation results show that the hybrid QR outperforms traditional models and CPLFS exceeds the best benchmarks in terms of prediction accuracy and stability.
Article
Energy & Fuels
Wen-Jiang Zou, Young-Bae Kim, Seunghun Jung
Summary: This paper proposes a dynamic prediction model for capacity fade in vanadium redox flow batteries (VRFBs). The model accurately predicts changes in electrolyte volume and capacity fade, enhancing the competitiveness of VRFBs in energy storage applications.
Article
Energy & Fuels
Yuechao Ma, Shengtie Wang, Guangchen Liu, Guizhen Tian, Jianwei Zhang, Ruiming Liu
Summary: This paper focuses on the balance of state of charge (SOC) among multiple battery energy storage units (MBESUs) and bus voltage balance in an islanded bipolar DC microgrid. A SOC automatic balancing strategy is proposed considering the energy flow relationship and utilizing the adaptive virtual resistance algorithm. The simulation results demonstrate the effectiveness of the proposed strategy in achieving SOC balancing and decreasing bus voltage unbalance.
Article
Energy & Fuels
Raad Z. Homod, Basil Sh. Munahi, Hayder Ibrahim Mohammed, Musatafa Abbas Abbood Albadr, Aissa Abderrahmane, Jasim M. Mahdi, Mohamed Bechir Ben Hamida, Bilal Naji Alhasnawi, A. S. Albahri, Hussein Togun, Umar F. Alqsair, Zaher Mundher Yaseen
Summary: In this study, the control problem of the multiple-boiler system (MBS) is formulated as a dynamic Markov decision process and a deep clustering reinforcement learning approach is applied to obtain the optimal control policy. The proposed strategy, based on bang-bang action, shows superior response and achieves more than 32% energy saving compared to conventional fixed parameter controllers under dynamic indoor/outdoor actual conditions.
