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Deep neural network-enabled battery open-circuit voltage estimation based on partial charging data

Journal of Energy Chemistry 2024年第3期90卷 120-132,I0005页

作者： ziyou zhou Yonggang Liu Chengming Zhang Weixiang Shen Rui Xiong State Key Laboratory of Mechanical Transmission for Advanced Equipment&College of Mechanical and Vehicle Engineering Chongqing UniversityChongqing 400000China Department of Vehicle Engineering School of Mechanical EngineeringBeijing Institute of TechnologyBeijing 100081China Department of Electrical Engineering Harbin Institute of TechnologyHarbin 150000HeilongjiangChina School of Science Computing and Engineering TechnologiesSwinburne University of TechnologyHawthornVictoria 3122Australia

Battery management systems(BMSs) play a vital role in ensuring efficient and reliable operations of lithium-ion *** main function of the BMSs is to estimate battery states and diagnose battery health using battery open-circuit voltage(OCV).However,acquiring the complete OCV data online can be a challenging endeavor due to the time-consuming measurement process or the need for specific operating conditions required by OCV estimation *** addressing these concerns,this study introduces a deep neural network-combined framework for accurate and robust OCV estimation,utilizing partial daily charging *** incorporate a generative deep learning model to extract aging-related features from data and generate high-fidelity OCV *** analysis is employed to identify the optimal partial charging data,optimizing the OCV estimation precision while preserving exceptional *** validation results,using data from nickel-cobalt-magnesium(NCM) batteries,illustrate the accurate estimation of the complete OCV-capacity curve,with an average root mean square errors(RMSE) of less than 3 *** this level of precision for OCV estimation requires only around 50 s collection of partial charging *** validations on diverse battery types operating under various conditions confirm the effectiveness of our proposed *** cases of precise health diagnosis based on OCV highlight the significance of conducting online OCV *** method provides a flexible approach to achieve complete OCV estimation and holds promise for generalization to other tasks in BMSs.

关键词： Lithium-ion battery Open-circuit voltage Health diagnosis Deep learning

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Tunable photonic crystals based on ferroelectric and ferromagnetic materials by focused ion beam

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Chinese Optics Letters 2007年第12期5卷 693-695页

作者： ziyou zhou Xiaoyue Huang Raghav Vanga Rong Li Physics Department Michigan Technological University Houghton MI 49931 USA Material Science and Engineering Department Michigan Technological University Houghton MI 49931 USA

By making photonic crystals in ferroelectric and ferromagnetic materials, field-provoked tunability of photonic crystals is broadening the interest in new applications of on-chip photonic devices. We report a nano-precise fabrication of various designs of photonic crystals in these non-conventional materials using the focused ion beam milling technique. Standard methods are developed and parameters for different materials are calibrated. Optical responses such as bandgaps and polarization status changing from planar film waveguide svstem with these patterns have been examined on ferromagnetic materials.

关键词： Computer aided design Electron beam lithography Ion beams Photonic crystals Photonic devices Thin films

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Two-stage aging trajectory prediction of LFP lithium-ion battery based on transfer learning with the cycle life prediction

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Green Energy and Intelligent Transportation 2022年第1期1卷 104-120页

作者： ziyou zhou Yonggang Liu Mingxing You Rui Xiong Xuan zhou State Key Laboratory of Mechanical Transmission&College of Mechanical and Vehicle Engineering Chongqing UniversityChongqing400000China Department of Vehicle Engineering School of Mechanical EngineeringBeijing Institute of TechnologyBeijing 100081China Department of Electrical and Computer Engineering Kettering UniversityFlintMI-48504USA

With the wide application of the LFP lithium-ion batteries,more attention is paid to the battery life and future aging behaviors as the safety and performance of the battery are guaranteed by accurate battery aging *** recent years,long-term aging trajectory prediction of the lithium-ion battery is always a challenge due to its complex nonlinear aging behaviors especially the aging behaviors in the two aging stages are quite different when the battery experiences the two-stage aging process under fast-charging ***,it is harder to achieve accurate long-term aging trajectory prediction of the LFP lithium-ion batteries on the condition of the two-stage aging *** address it,a novel transfer learning strategy combined with the cycle life prediction technology is presented in this ***,a new cycle life prediction method is proposed based on feature extraction and deep learning technology and achieves accurate cycle life *** transfer learning is started by developing a base aging model offline to learn the information of the two-stage aging ***,taking the predicted cycle life as its prior information,the Bayesian model migration technology is employed to predict the aging trajectory accurately,and the uncertainty of the aging trajectory is *** batches of the battery datasets are used for performance evaluation and comparison with two *** is novel to combine the cycle life prediction and transfer learning technique to achieve accurate two-stage aging trajectory prediction with only a few data available(first 30%).

关键词： Battery aging trajectory prediction Data-driven method Feature engineering Cycle life prediction Transfer learning

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Vascularized lymph node transfer using axilla as recipient site restores lymphatic flow in upper limb lymphedema: Evidence from magnetic resonance lymphangiography

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Chinese Journal of Plastic and Reconstructive Surgery 2024年第1期6卷 1-7页

作者： Zhaohua Jiang ziyou Yu Shengli Li Department of Plastic and Reconstructive Surgery Shanghai Ninth People's Hospital Shanghai Jiao Tong University

Background: Lymphedema is a debilitating condition that frequently occurs after breast cancer treatment. Vascularized lymph node transfer(VLNT) is a promising approach to reduce lymphedema. This study used magnetic resonance lymphangiography(MRL) to assess lymphatic reconnections post-VLNT in patients with breast cancerrelated lymphedema(BCRL).Methods: The clinical records of six female patients with unilateral upper limb BCRL who underwent VLNT(4cases) or VLNT combined with breast reconstruction(2 cases) were retrospectively reviewed. All patients were examined using MRL preoperatively and at the 1-year follow-up. The morphological characteristics of the lymphatic network, dermal backflow patterns, and architecture of the lymph nodes were evaluated. Clinical outcomes, patient satisfaction, and complications were ***: At the 1-year follow-up, reduction in tissue edema and limb circumference was achieved in all six *** MRL, the implanted lymph nodes in the axillary region of the affected upper arm were enhanced and visualized in all six patients. Reconnected lymphatic vessels in the subcutaneous tissue associated with the implanted lymph nodes were observed in four patients. Decreased dermal backflow and lymphatic vessel dilation of the affected limbs were observed in all six patients. No disruption of the lymph flow in the donor area was ***: This is the first study to provide direct imaging evidence for the reconnection of afferent lymphatic channels between implanted lymph nodes and the recipient lymphatic system in patients with BCRL. Overall, our study demonstrates the mechanism and efficacy of VLNT in reducing lymphedema.

关键词： Magnetic resonance lymphangiography Vascularized lymph node transfer Breast cancer Lymphedema

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Battery pack capacity estimation for electric vehicles based on enhanced machine learning and field data

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Journal of Energy Chemistry 2024年第5期92卷 605-618页

作者： Qingguang Qi Wenxue Liu Zhongwei Deng Jinwen Li ziyou Song Xiaosong Hu College of Mechanical and Vehicle Engineering Chongqing UniversityChongqing 400044China School of Mechanical and Electrical Engineering University of Electronic Science and Technology of ChinaChengdu 611731SichuanChina Department of Mechanical Engineering National University of SingaporeSingapore 117575Singapore

Accurate capacity estimation is of great importance for the reliable state monitoring,timely maintenance,and second-life utilization of lithium-ion *** numerous works on battery capacity estimation using laboratory datasets,most of them are applied to battery cells and lack satisfactory fidelity when extended to real-world electric vehicle(EV)battery *** challenges intensify for large-sized EV battery packs,where unpredictable operating profiles and low-quality data acquisition hinder precise capacity *** fill the gap,this study introduces a novel data-driven battery pack capacity estimation method grounded in field *** proposed approach begins by determining labeled capacity through an innovative combination of the inverse ampere-hour integral,open circuit voltage-based,and resistance-based correction ***,multiple health features are extracted from incremental capacity curves,voltage curves,equivalent circuit model parameters,and operating temperature to thoroughly characterize battery aging behavior.A feature selection procedure is performed to determine the optimal feature set based on the Pearson correlation ***,a convolutional neural network and bidirectional gated recurrent unit,enhanced by an attention mechanism,are employed to estimate the battery pack capacity in real-world EV ***,the proposed method is validated with a field dataset from two EVs,covering approximately 35,000 *** results demonstrate that the proposed method exhibits better estimation performance with an error of less than 1.1%compared to existing *** work shows great potential for accurate large-sized EV battery pack capacity estimation based on field data,which provides significant insights into reliable labeled capacity calculation,effective features extraction,and machine learning-enabled health diagnosis.

关键词： Electricvehicle Lithium-ion battery pack Capacity estimation Machine learning Field data

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Parallel Physics-Informed Neural Networks Method with Regularization Strategies for the Forward-Inverse Problems of the Variable Coefficient Modified KdV Equation

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Journal of Systems Science & Complexity 2024年第2期37卷 511-544页

作者： zhou Huijuan School of Science Shanghai Maritime UniversityShanghai 201306China

This paper mainly introduces the parallel physics-informed neural networks(PPINNs)method with regularization strategies to solve the data-driven forward-inverse problems of the variable coefficient modified Korteweg-de Vries(VC-MKdV)*** the forward problem of the VC-MKdV equation,the authors use the traditional PINN method to obtain satisfactory data-driven soliton solutions and provide a detailed analysis of the impact of network width and depth on solving accuracy and ***,the author finds that the traditional PINN method outperforms the one with locally adaptive activation functions in solving the data-driven forward problems of the VC-MKdV *** for the data-driven inverse problem of the VC-MKdV equation,the author introduces a parallel neural networks to separately train the solution function and coefficient function,successfully addressing the function discovery problem of the VC-MKdV *** further enhance the network’s generalization ability and noise robustness,the author incorporates two regularization strategies into the *** amount of numerical experimental data in this paper demonstrates that the PPINNs method can effectively address the function discovery problem of the VC-MKdV equation,and the inclusion of appropriate regularization strategies in the PPINNs can improves its performance.

关键词： Data-driven forward-inverse problems parallel physics-informed neural networks regularization strategies variable coefficient modified KdV equation

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Deep-reinforcement-learning-based UAV autonomous navigation and collision avoidance in unknown environments

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Chinese Journal of Aeronautics 2024年第3期37卷 237-257页

作者： Fei WANG Xiaoping ZHU zhou zhou Yang TANG School of Astronautics Northwestern Polytechnical UniversityXi’an 710072China School of Aeronautics Northwestern Polytechnical UniversityXi’an 710072China

In some military application scenarios,Unmanned Aerial Vehicles(UAVs)need to perform missions with the assistance of on-board cameras when radar is not available and communication is interrupted,which brings challenges for UAV autonomous navigation and collision *** this paper,an improved deep-reinforcement-learning algorithm,Deep Q-Network with a Faster R-CNN model and a Data Deposit Mechanism(FRDDM-DQN),is proposed.A Faster R-CNN model(FR)is introduced and optimized to obtain the ability to extract obstacle information from images,and a new replay memory Data Deposit Mechanism(DDM)is designed to train an agent with a better *** training,a two-part training approach is used to reduce the time spent on training as well as retraining when the scenario *** order to verify the performance of the proposed method,a series of experiments,including training experiments,test experiments,and typical episodes experiments,is conducted in a 3D simulation *** results show that the agent trained by the proposed FRDDM-DQN has the ability to navigate autonomously and avoid collisions,and performs better compared to the FRDQN,FR-DDQN,FR-Dueling DQN,YOLO-based YDDM-DQN,and original FR outputbased FR-ODQN.

关键词： Faster R-CNN model Replay memory Data Deposit Mechanism(DDM) Two-part training approach Image-based Autonomous Navigation and Collision Avoidance(ANCA) Unmanned Aerial Vehicle(UAV)

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Oxidation of emerging organic contaminants by in-situ H_(2)O_(2) fenton system

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Green Energy & Environment 2024年第3期9卷 417-434页

作者： Yuqin Ni Chuxiang zhou Mingyang Xing Yi zhou State Environmental Protection Key Lab of Environmental Risk Assessment and Control on Chemical Processes School of Resources&Environmental EngineeringEast China University of Science and TechnologyShanghai200237China Key Laboratory for Advanced Materials and Institute of Fine Chemicals National Engineering Laboratory for Industrial Wastewater TreatmentSchool of Chemistry and Molecular EngineeringEast China University of Science and Technology130 Meilong RoadShanghai200237China Department of Applied Natural Sciences Technische Hochschule LubeckMonkhofer Weg 23923562 LubeckGermany

The existence and risk of emerging organic contaminants(EOCs)have been under consideration and paid much effort to degrade these *** system is one of the most widely used technologies to solve this *** original Fenton system relies on the hydroxyl radicals produced by Fe(Ⅱ)/H_(2)O_(2) to oxidize the organic ***,the application of the Fenton system is limited by its low iron cycling efficiency and the high risks of hydrogen peroxide transportation and *** introduction of external energy(including light and electricity etc.)can effectively promote the Fe(Ⅲ)/Fe(Ⅱ)cycle and the reduction of oxygen to produce hydrogen peroxide in *** review introduces three in-situ Fenton systems,which are electro-Fenton,Photo-Fenton,and chemical *** mechanism,influencing factors,and catalysts of these three in-situ Fenton systems in degrading EOCs are discussed *** review strengthens the understanding of Fenton and in-situ Fenton systems in degradation,offering further insight into the real application of the in-situ Fenton system in the removal of EOCs.

关键词： In-situ H_(2)O_(2)production Fenton Emerging organic contaminants Photocatalysis Electrocatalysis

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SPARSE RECOVERY BASED ON THE GENERALIZED ERROR FUNCTION

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Journal of Computational Mathematics 2024年第3期42卷 679-704页

作者： Zhiyong zhou Department of Statistics and Data Science and Institute of Digital FinanceHangzhou City UniversityHangzhou 310015China

In this paper,we offer a new sparse recovery strategy based on the generalized error *** introduced penalty function involves both the shape and the scale parameters,making it extremely *** both constrained and unconstrained models,the theoretical analysis results in terms of the null space property,the spherical section property and the restricted invertibility factor are *** practical algorithms via both the iteratively reweighted■_(1)and the difference of convex functions algorithms are *** experiments are carried out to demonstrate the benefits of the suggested approach in a variety of *** practical application in magnetic resonance imaging(MRI)reconstruction is also investigated.

关键词： Sparse recovery Generalized error function Nonconvex regularization Itera-tive reweighted Li Difference of convex functions algorithms

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Emergent Geometry of KP Hierarchy

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Acta Mathematica Sinica,English Series 2024年第1期40卷 3-25页

作者： Jian zhou Department of Mathematical Sciences Tsinghua UniversityBeijng 100084P.R.China

We explain how to construct a quantum deformation of a spectral curve associated to a tau-function of the KP *** construction is applied to Witten-Kontsevich tau-function to give a natural explanation of some earlier ... 详细信息

关键词： KP hierarchy tau-functions spectral curves

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