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Multi-model ensemble learning for battery state-of-health estimation:Recent advances and perspectives

Journal of Energy Chemistry 2025年第1期100卷 739-759页

作者： Chuanping Lin Jun Xu Delong Jiang Jiayang Hou Ying Liang Zhongyue Zou Xuesong Mei State Key Laboratory for Manufacturing Systems engineering Xi'an Jiaotong University Shaanxi Key Laboratory of intelligent Robots School of Mechanical Engineering Xi'an Jiaotong University Department of Electrical engineering and Automation Luoyang Institute of Science and Technology School of Mechanical engineering Hangzhou Dianzi University

The burgeoning market for lithium-ion batteries has stimulated a growing need for more reliable battery performance monitoring. Accurate state-of-health(SOH) estimation is critical for ensuring battery operational performance. Despite numerous data-driven methods reported in existing research for battery SOH estimation, these methods often exhibit inconsistent performance across different application scenarios. To address this issue and overcome the performance limitations of individual data-driven models,integrating multiple models for SOH estimation has received considerable attention. Ensemble learning(EL) typically leverages the strengths of multiple base models to achieve more robust and accurate outputs. However, the lack of a clear review of current research hinders the further development of ensemble methods in SOH estimation. Therefore, this paper comprehensively reviews multi-model ensemble learning methods for battery SOH estimation. First, existing ensemble methods are systematically categorized into 6 classes based on their combination strategies. Different realizations and underlying connections are meticulously analyzed for each category of EL methods, highlighting distinctions, innovations, and typical applications. Subsequently, these ensemble methods are comprehensively compared in terms of base models, combination strategies, and publication trends. Evaluations across 6 dimensions underscore the outstanding performance of stacking-based ensemble methods. Following this, these ensemble methods are further inspected from the perspectives of weighted ensemble and diversity, aiming to inspire potential approaches for enhancing ensemble performance. Moreover, addressing challenges such as base model selection, measuring model robustness and uncertainty, and interpretability of ensemble models in practical applications is emphasized. Finally, future research prospects are outlined, specifically noting that deep learning ensemble is poised to advance ens

关键词： Contrastive Learning

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Research on the balance optimization algorithm of image recognition accuracy and speed based on autocollimator measurement

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Optoelectronics Letters 2025年第2期21卷 121-128页

作者： LI Renpu MA Long CUI Jiwen GUO Junqi Andrei KULIKOV WEN Dandan Chongqing engineering Research Center of intelligent Sensing Technology and Microsystem Chongqing University of Post and Telecommunications School of Instrumentation science and engineering Harbin Institute of Technology Higher School of engineering and Technology ITMO University

The autocollimator is an important device for achieving precise,small-angle,non-contact *** primarily obtains angular parameters of a plane target mirror indirectly by detecting the position of the imaging *** is limited report on the core algorithmic techniques in current commercial products and recent scientific *** paper addresses the performance requirements of coordinate reading accuracy and operational speed in autocollimator image *** proposes a cross-image center recognition scheme based on the Hough transform and another based on Zernike moments and the least squares *** experimental evaluation of the accuracy and speed of both schemes,the optimal image recognition scheme balancing measurement accuracy and speed for the autocollimator is *** these,the center recognition method based on Zernike moments and the least squares method offers higher measurement accuracy and stability,while the Hough transform-based method provides faster measurement speed.

关键词： Least squares approximations

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Accurate manipulation of anthropomorphic robotic arm based on 3D human eye gazing with posture error compensation

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science China Technological sciences 2025年

作者： Quanlin LI Xianzong WANG Jiaji MA Qianjun HUANG Qizhi JIANG Wenbin CHEN Caihua XIONG State Key Laboratory of intelligent Manufacturing Equipment and Technology School of Mechanical Science and Engineering Huazhong University of Science and Technology institute of Medical Equipment science and engineering Huazhong University of Science and Technology

As humans and robots work closer together than ever, anthropomorphic robotic arms with intuitive human-robot interaction interfaces have drawn massive attention to improving the quality of robot-assisted manipulation. In pursuit of this,we designed a dedicated 7-degrees-of-freedom (DoF) anthropomorphic robotic arm having three compact differential joints and a head-mounted gaze tracker enabling head-pose-tracked 3D gaze estimation. Moreover, two key challenges were addressed to achieve accurate robot-assisted manipulation of the object indicated by the direction of human gaze. First, a novel predictive pupil feature was proposed for 3D gaze estimation. Differing from most existing features subjected to the common paraxial approximation assumption, the proposed novel predictive pupil feature considered the light refraction at two corneal surfaces with a more realistic eye model, significantly improving the 3D gaze estimation accuracy when the eyeball rotates at large angles. Second, a novel optimization-based approach was developed to efficiently compensate for the posture errors of the designed 7-DoF anthropomorphic robotic arm for accurate manipulation. Compared with the existing Jacobian-based or optimization-based approaches with nominal joint values as iteration initial, the proposed approach computed the optimal iteration initial and realized faster convergence for real-time posture error compensation. With the posture error compensation in real time and 3D gaze estimated accurately, the human can command accurate robot-assisted manipulation using his eyes intuitively. The proposed system was successfully tested on five healthy subjects.

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Optimizing battery deployment: Aging trajectory prediction enabling homogenous performance grouping

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Journal of Energy Chemistry 2025年第1期100卷 565-577页

作者： Shuquan Wang Feng Gao Zhan Ma Hao Tian Yusen Zhang School of Control science and engineering Shandong University Key Lab of Power System intelligent Dispatch and Control of Ministry of Education Shandong University

As battery deployments in electric vehicles and energy storage systems grow, ensuring homogeneous performance across units is crucial. We propose a multi-derivative imaging fusion(MDIF) model, employing advanced imaging and machine learning to predict battery aging trajectories from minimal initial data, thus facilitating effective performance grouping before deployment. Utilizing a derivative strategy and Gramian Angular Difference Field for dimensional enhancement, the MDIF model uncovers subtle predictive features from discharge curve data after only ten cycles. The architecture includes a parallel convolutional neural network with lateral connections to enhance feature integration and *** on a self-developed dataset, the model achieves an average root-mean-square error of 0.047 Ah and an average mean absolute percentage error of 1.60%, demonstrating high precision and *** robustness is further validated through transfer learning on two publicly available datasets, adapting with minimal retraining. This approach significantly reduces the testing cycles required, lowering both time and costs associated with battery testing. By enabling precise battery behavior predictions with limited data, the MDIF model optimizes battery utilization and deployment strategies, enhancing system efficiency and sustainability.

关键词： Lithium-ion battery Battery prognostics Fusion model Convolutional neural network Transfer learning

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High-energy-density lithium manganese iron phosphate for lithium-ion batteries: Progresses, challenges, and prospects

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Journal of Energy Chemistry 2025年第1期100卷 1-17页

作者： Bokun Zhang Xiaoyun Wang Shuai Wang Yan Li Libo Chen Handong Jiao Zhijing Yu Jiguo Tu State Key Laboratory of Advanced Metallurgy University of Science and Technology Beijing Research institute for intelligent Wearable Systems The Hong Kong Polytechnic University institute of Advanced Structure Technology Beijing Institute of Technology

The soaring demand for smart portable electronics and electric vehicles is propelling the advancements in high-energy–density lithium-ion *** manganese iron phosphate (LiMnxFe1-xPO4) has garnered significant attention as a promising positive electrode material for lithium-ion batteries due to its advantages of low cost,high safety,long cycle life,high voltage,good high-temperature performance,and high energy *** LiMnxFe1-xPO4has made significant breakthroughs in the past few decades,there are still facing great challenges in poor electronic conductivity and Li-ion diffusion,manganese dissolution affecting battery cycling performance,as well as low tap *** review systematically summarizes the reaction mechanisms,various synthesis methods,and electrochemical properties of LiMnxFe1-xPO4to analyze reaction processes accurately and guide material ***,the main challenges currently faced are concluded,and the corresponding various modification strategies are discussed to enhance the reaction kinetics and electrochemical performance of LiMnxFe1-xPO4,including multi-scale particle regulation,heteroatom doping,surface coating,as well as microscopic morphology ***,in view of the current research challenges faced by intrinsic reaction processes,kinetics,and energy storage applications,the promising research directions are *** importantly,it is expected to provide key insights into the development of high-performance and stable LiMnxFe1-xPO4materials,to achieve practical energy storage requirements.

关键词： Semiconductor doping

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Record-breaking bifunctional oxygen electrocatalyst accomplished by a data-driven approach for zinc-air batteries

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Journal of Energy Chemistry 2025年第1期100卷 144-145页

作者： Deviprasath Chinnadurai Zhi Wei Seh institute of Materials Research and engineering (IMRE) Agency for Science Technology and Research (A*STAR)

Rechargeable zinc-air batteries(ZABs) have recently drawn great attention in energy research due to their high theoretical capacity,low costs, and inherently safe nature [1–3]. However, the sluggish cathode reactions necessitate the development of bifunctional oxygen electrocatalysts with lower ΔE indicator values. The ΔE indicator is commonly employed to quantitatively evaluate the electrocatalytic activity of a bifunctional oxygen electrocatalyst,representing the overall overpotential from oxygen reduction reaction(ORR) to oxygen evolution reaction(OER).

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Unlocking the stable interface in aqueous zinc-ion battery with multifunctional xylose-based electrolyte additives

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Journal of Energy Chemistry 2025年第1期100卷 770-778页

作者： Xiaoqin Li Jian Xiang Lu Qiu Xiaohan Chen Yinkun Zhao Yujue Wang Qu Yue Taotao Gao Wenlong Liu Dan Xiao Zhaoyu Jin Panpan Li institute for Advanced Study Chengdu University School of Mechanical engineering Chengdu University College of Food and Biological engineering Chengdu University institute of Fundamental and Frontier sciences University of Electronic Science and Technology of China College of Materials science and engineering Sichuan University

The growth of dendrites and the side reactions occurring at the Zn anode pose significant challenges to the commercialization of aqueous Zn-ion batteries（AZIBs）. These challenges arise from the inherent conflict between mass transfer and electrochemical kinetics. In this study, we propose the use of a multifunctional electrolyte additive based on the xylose（Xylo） molecule to address these issues by modulating the solvation structure and electrode/electrolyte interface, thereby stabilizing the Zn anode. The introduction of the additive alters the solvation structure, creating steric hindrance that impedes charge transfer and then reduces electrochemical kinetics. Furthermore, in-situ analyses demonstrate that the reconstructed electrode/electrolyte interface facilitates stable and rapid Zn2+ion migration and suppresses corrosion and hydrogen evolution reactions. As a result, symmetric cells incorporating the Xylo additive exhibit significantly enhanced reversibility during the Zn plating/stripping process, with an impressively long lifespan of up to 1986 h, compared to cells using pure ZnSO4electrolyte. When combined with a polyaniline cathode, the full cells demonstrate improved capacity and long-term cyclic stability. This work offers an effective direction for improving the stability of Zn anode via electrolyte design, as well as highperformance AZIBs.

关键词： Solvation

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Review of precipitation strengthening in ultrahigh-strength martensitic steel

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International Journal of Minerals,Metallurgy and Materials 2025年第2期32卷 256-269页

作者： Zhihao Tian Chunlei Shang Chaolei Zhang Xiaoye Zhou Honghui Wu Shuize Wang Guilin Wu Junheng Gao Jiaming Zhu Xinping Mao institute for Carbon Neutrality University of Science and Technology BeijingBeijing 100083China institute of Steel Sustainable Technology Liaoning Academy of MaterialsShenyang 110004China Department of Materials science and engineering Shenzhen MSU-BIT UniversityShenzhen 518172China institute of Materials intelligent Technology Liaoning Academy of MaterialsShenyang 110004China School of Civil engineering Shandong UniversityJinan 250061China

Martensite is an important microstructure in ultrahigh-strength steels,and enhancing the strength of martensitic steels often involves the introduction of precipitated phases within the martensitic *** considerable research efforts devoted to this area,a systematic summary of these advancements is *** review focuses on the precipitates prevalent in ultrahigh-strength martensitic steel,primarily carbides(e.g.,MC,M_(2)C,and M_(3)C)and intermetallic compounds(e.g.,Ni Al,Ni_(3)X,and Fe_(2)Mo).The precipitation-strengthening effect of these precipitates on ultrahigh-strength martensitic steel is discussed from the aspects of heat treatment processes,microstructure of precipitate-strengthened martensite matrix,and mechanical ***,a perspective on the development of precipitation-strengthened martensitic steel is presented to contribute to the advancement of ultrahigh-strength martensitic *** review highlights significant findings,ongoing challenges,and opportunities in the development of ultrahigh-strength martensitic steel.

关键词： ultrahigh-strength martensitic steel precipitation strengthening mechanical property carbide intermetallic compound

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Targets capture by distributed active swarms via bio-inspired reinforcement learning

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science China(Physics,Mechanics & Astronomy) 2025年第1期Mechanics & Astronomy) .卷 210-221页

作者： Kun Xu Yue Li Jun Sun Shuyuan Du Xinpeng Di Yuguang Yang Bo Li institute of Biomechanics and Medical engineering Applied Mechanics Laboratory Department of Engineering MechanicsTsinghua University institute of Nuclear and New Energy Technology Tsinghua University Shanghai Aerospace Control Technology institute Shanghai Key Laboratory of Aerospace intelligent Control Technology College of Automation engineering Nanjing University of Aeronautics and Astronautics

Natural swarms arranged from cells to herds are usually decentralized but display intriguing collective intelligence in coordinating individuals across large scales to efficiently achieve their common goals. Learning from nature may provide new strategies for controlling collective dynamics of synthetic swarms to accomplish specific functions. Here, we present a bioinspired computational framework that steers distributed active swarms to collectively capture and merge targets via reinforcement learning. We exploit collective milling structures of natural herds to cage the targets, and adopt a switching control policy inspired by sperms' chiral dynamics to optimize the trajectories of individuals, through which the active swarms can selforganize to enclose single or multiple distant targets in a dynamical, adaptive and scalable manner. There exists a critical swarm size, beyond which the excessive competition between agents would generate large mechanical forces, leading to capture instability but enabling the transition from short-distance to long-distance merging capture of multiple targets. This work provides physical insights into distributed active swarms and could offer a multilevel, decentralized strategy toward controlling swarm robotics in wide applications such as bio-medical devices, machine immunity, and target clearance.

关键词： swarm intelligence robotics multi-tasks artificial intelligence bionics

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Inexact proximal gradient algorithm with random reshuffling for nonsmooth optimization

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science China(Information sciences) 2025年第1期68卷 219-237页

作者： Xia JIANG Yanyan FANG Xianlin ZENG Jian SUN Jie CHEN National Key Lab of Autonomous intelligent Unmanned Systems School of AutomationBeijing Institute of Technology Beijing institute of Technology Chongqing Innovation Center School of Electronic and Information engineering Tongji University

Proximal gradient algorithms are popularly implemented to achieve convex optimization with nonsmooth regularization. Obtaining the exact solution of the proximal operator for nonsmooth regularization is challenging because errors exist in the computation of the gradient; consequently, the design and application of inexact proximal gradient algorithms have attracted considerable attention from researchers. This paper proposes computationally efficient basic and inexact proximal gradient descent algorithms with random reshuffling. The proposed stochastic algorithms take randomly reshuffled data to perform successive gradient descents and implement only one proximal operator after all data pass through. We prove the convergence results of the proposed proximal gradient algorithms under the sampling-without-replacement reshuffling *** computational errors exist in gradients and proximal operations, the proposed inexact proximal gradient algorithms can converge to an optimal solution neighborhood. Finally, we apply the proposed algorithms to compressed sensing and compare their efficiency with some popular algorithms.

关键词： proximal operator random reshuffling inexact computation compressed sensing nonsmooth optimization

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