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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 UniversityXi’an 710049ShaanxiChina Shaanxi Key Laboratory of Intelligent Robots School of Mechanical EngineeringXi’an Jiaotong UniversityXi’an 710049ShaanxiChina Department of Electrical Engineering and Automation Luoyang Institute of Science and TechnologyLuoyang 471023HenanChina School of Mechanical Engineering Hangzhou Dianzi UniversityHangzhou 310018ZhejiangChina

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： Lithium-ion battery state-of-health estimation Data-driven Machine learning Ensemble learning Ensemble diversity

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Manipulator-based autonomous inspections at road checkpoints:Application of faster YOLO for detecting large objects

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Defence technology（防务技术） 2022年第6期18卷 937-951页

作者： Qing-xin Shi Chang-sheng Li Bao-qiao Guo Yong-gui Wang Huan-yu Tian Hao Wen Fan-sheng Meng Xing-guang Duan School of Mechatronical Engineering Beijing Institute of TechnologyBeijing100081China Beijing Advanced Innovation Center for Intelligent Robots and Systems Beijing Institute of TechnologyBeijing100081China State Key Laboratory of Explosion Science and Technology Beijing Institute of TechnologyBeijing100081China

With the increasing number of vehicles,manual security inspections are becoming more laborious at road *** address it,a specialized Road Checkpoints Robot(RCRo)system is proposed,incorporated with enhanced You Only Look Once(YOLO)and a 6-degree-of-freedom(DOF)manipulator,for autonomous identity verification and vehicle *** modified YOLO is characterized by large objects’sensitivity and faster detection speed,named“LF-YOLO”.The better sensitivity of large objects and the faster detection speed are achieved by means of the Dense module-based backbone network connecting two-scale detecting network,for object detection tasks,along with optimized anchor boxes and improved loss *** the manipulator motion,Octree-aided motion control scheme is adopted for collision-free motion through Robot Operating System(ROS).The proposed LF-YOLO which utilizes continuous optimization strategy and residual technique provides a promising detector design,which has been found to be more effective during actual object detection,in terms of decreased average detection time by 68.25%and 60.60%,and increased average Intersection over Union(Io U)by 20.74%and6.79%compared to YOLOv3 and YOLOv4 through *** comprehensive functional tests of RCRo system demonstrate the feasibility and competency of the multiple unmanned inspections in practice.

关键词： Robot applications Object detection Vehicle inspection Identity verification You only look once(YOLO)

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Research on Image Denoising and Multi-Target Tracking Algorithm in Nuclear Radiation Environment 42

Research on Image Denoising and Multi-Target Tracking Algori...

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42nd Chinese Control Conference, CCC 2023

作者： Wang, Zhiguo Peng, Zhihong Zhang, Nan School of Automation Beijing Institute of Technology Beijing100081 China National Key Lab of Autonomous Intelligent Unmanned Systems Beijing100081 China

ISBN: (纸本)9789887581543

In this paper, an image denoising and multi-target tracking algorithm suitable for nuclear radiation scenes is designed, and the complexity of the filtering algorithm is reduced through an improved adaptive median filtering algorithm. Reduce the computational load of the network, and adapt to the nuclear radiation scene by introducing the acceleration component into the Kalman filter of the DeepSORT algorithm. Experiments show that the improved adaptive median filtering algorithm can reduce the computing time by about 53% without reducing the filtering effect;the improved YOLOv5s and DeepSORT multi-target tracking algorithms do not significantly sacrifice the tracking effect, and the FPS has been effectively improved. More meet the realtime requirements. © 2023 Technical Committee on Control Theory, Chinese Association of Automation.

关键词： Target tracking

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Process planning and contour-based error compensation for precision grinding of miniature scalpels

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先进制造进展（英文版） 2024年第1期12卷 108-123页

作者： Cheng Fan Cao-Yang Xue Jun Zhao Wei Jiang Wen-Ge Han Lei Zhang Li-Ning Sun Jiangsu Provincial Key Laboratory of Advanced Robotics School of Mechanical and Electrical EngineeringSoochow UniversitySuzhou 215021JiangsuPeople's Republic of China Key Laboratory of Fluid Power and Mechatronic Systems School of Mechanical and Electrical EngineeringZhejiang UniversityHangzhou 310027People's Republic of China State Key Laboratory of Intelligent Equipment and Manufacturing School of Mechanical EngineeringZhejiang University of TechnologyHangzhou 310023People's Republic of China

Miniature scalpels are mainly used in microsur-geries such as ophthalmic and cardiovascular *** size of a miniature scalpel is only a few millimeters,and the precision of the blade shape is high,which makes pro-duction of miniature scalpels extremely *** study proposes a new sharpening process for grinding miniature scalpels on a four-axis machine tool.A post-processing algo-rithm for a four-axis grinding machine based on a kinemat-ics model is *** then propose a corresponding parameter calibration method for the parameters used in the kinematics *** of possible errors in the param-eter calibration,a contour-based error compensation method is proposed for accurate adjustments to the edge shape fol-lowing *** can solve the problem of large devia-tions between the actual edge shape after grinding and the ideal edge *** effectiveness of the proposed process planning and error compensation method is verified experi-mentally,and the grinding process parameters of the minia-ture scalpel are optimized to improve its surface processing *** sharpness of the optimized miniature scalpel is less than 0.75 N,and the blade shape is symmetrical,which meets the technical requirements of miniature scalpels.

关键词： Miniature scalpel Sharpening Kinematics Error compensation Grinding

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Cross-Domain Object Detection Based on Attention Mechanism 42

Cross-Domain Object Detection Based on Attention Mechanism

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42nd Chinese Control Conference, CCC 2023

作者： Huang, Maochen Chen, Wenjie Wu, Bing National Key Lab of Autonomous Intelligent Unmanned Systems Beijing10081 China School of Automation Beijing Institute of Technology Beijing100081 China

ISBN: (纸本)9789887581543

A sizable training dataset is always necessary to ensure the robustness of object detection algorithms. However, the number of training datasets is limited by the shortage of real sample data and the high cost of labeling, so we want to improve the cross-domain performance of object detection from virtual to real-world domains. In this work, we use Faster R-CNN as the basic model and supplement it with an attention mechanism. It then confirms the effectiveness of the attention module in cross-domain object detection tasks by comparison. Additionally, to further enhance algorithm performance, we create a new attention model based on the structure of the classical attention model, develop several optimization strategies, and evaluate each one through experiments to identify the best model. This model is preferable to the existing attention model cited in this work since it has fewer parameters, a similar computational load, and higher accuracy. It also offers a novel attention-assisted model for research on cross-domain object detection algorithms. © 2023 Technical Committee on Control Theory, Chinese Association of Automation.

关键词： Object detection

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Modeling and Prediction of Fatigue Properties of Additively Manufactured Metals

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Acta Mechanica Solida Sinica 2023年第2期36卷 181-213页

作者： Wei Tang Ziming Tang Wenjun Lu Shuai Wang Min Yi State Key Lab of Mechanics and Control of Mechanical Structures&Institute for Frontier Science&Key Lab for Intelligent Nano Materials and Devices of Ministry of Education&College of Aerospace Engineering Nanjing University of Aeronautics and Astronautics(NUAA)Nanjing 210016China Department of Mechanical and Energy Engineering Southern University of Science and TechnologyShenzhen 518055China

Additive manufacturing(AM)has emerged as an advanced technique for the fabrication of complex near-net shaped and lightweight metallic parts with acceptable mechanical *** strength of AM metals has been confirmed comparable or even superior to that of metals manufactured by conventional processes,but the fatigue performance is still a knotty issue that may hinder the substitution of currently used metallic components by AM counterparts when the cyclic loading and thus fatigue failure *** essential complements to high-cost and time-consuming experimental fatigue tests of AM metals,models for fatigue performance prediction are highly *** this review,different models for predicting the fatigue properties of AM metals are summarized in terms of fatigue life,fatigue limit and fatigue crack growth,with a focus on the incorporation of AM characteristics such as AM defect and processing parameters into the *** predicting the fatigue life of AM metals,empirical models and theoretical models(including local characteristic model,continuum damage mechanics model and probabilistic method)are *** terms of fatigue limit,the introduced models involve the Kitagawa–Takahashi model,the Murakami model,the El-Haddad model,*** modeling the fatigue crack growth of AM metals,the summarized methodologies include the Paris equation,the Hartman-Schijve equation,the NASGRO equation,the small-crack growth model,and numerical *** of these models for AM metals are similar to those for conventionally processed materials,but are modified and pay more attention to the AM ***,an outlook for possible directions of the modeling and prediction of fatigue properties of AM metals is provided.

关键词： Additive manufacturing Fatigue properties Metals Modeling and prediction Microstructure

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Multi-Stage Sensor Weapon Target Assignment Problem based on Modified MOEA/D 42

Multi-Stage Sensor Weapon Target Assignment Problem based on...

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42nd Chinese Control Conference, CCC 2023

作者： Zong, Ao Chen, Chen Meng, Kai School of Automation Beijing Institute of Technology Beijing100081 China The National Key Lab of Autonomous Intelligent Unmanned Systems Beijing100081 China

ISBN: (纸本)9789887581543

In this paper, we study a novel sensor weapon target assignment (SWTA) problem, called the multi-stage SWTA problem, and the multi-stage refers to the division of the operational process into several interception stages. The objective of the multi-stage SWTA is to maximize the total value of destroyed targets by using as few sensors and weapons as possible. To solve this problem, a modified multi-objective evolutionary algorithm based on decomposition (MOEA/D) has been designed. First, a constructive heuristic mechanism based on the marginal benefit is introduced to ensure the feasibility of the initial population. Second, a novel penalty value is added to the penalty-based boundary intersection to expand the choice of solutions, and combined with the non-dominated solutions selection to improve the convergence of the algorithm. Third, a mechanism for deleting invalid genes is designed to transform infeasible solutions into feasible ones. Experimental results show that compared with existing algorithms, the proposed method provides a high-quality multi-stage SWTA scheme in terms of solution accuracy, convergence, and diversity performance. © 2023 Technical Committee on Control Theory, Chinese Association of Automation.

关键词： Combinatorial optimization

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DA-Ada: Learning Domain-Aware Adapter for Domain Adaptive Object Detection 38

DA-Ada: Learning Domain-Aware Adapter for Domain Adaptive Ob...

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38th Conference on Neural Information Processing systems, NeurIPS 2024

作者： Li, Haochen Zhang, Rui Yao, Hantao Zhang, Xin Hao, Yifan Song, Xinkai Li, Xiaqing Zhao, Yongwei Li, Ling Chen, Yunji Intelligent Software Research Center Institute of Software CAS Beijing China State Key Lab of Processors Institute of Computing Technology CAS Beijing China State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation CAS Beijing China University of Chinese Academy of Sciences Beijing China

Domain adaptive object detection (DAOD) aims to generalize detectors trained on an annotated source domain to an unlabelled target domain. As the visual-language models (VLMs) can provide essential general knowledge on unseen images, freezing the visual encoder and inserting a domain-agnostic adapter can learn domain-invariant knowledge for DAOD. However, the domain-agnostic adapter is inevitably biased to the source domain. It discards some beneficial knowledge discriminative on the unlabelled domain, ***-specific knowledge of the target domain. To solve the issue, we propose a novel Domain-Aware Adapter (DA-Ada) tailored for the DAOD task. The key point is exploiting domain-specific knowledge between the essential general knowledge and domain-invariant knowledge. DA-Ada consists of the Domain-Invariant Adapter (DIA) for learning domain-invariant knowledge and the Domain-Specific Adapter (DSA) for injecting the domain-specific knowledge from the information discarded by the visual encoder. Comprehensive experiments over multiple DAOD tasks show that DA-Ada can efficiently infer a domain-aware visual encoder for boosting domain adaptive object detection. Our code is available at https://***/Therock90421/DA-Ada. © 2024 Neural information processing systems foundation. All rights reserved.

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Koopman Operator Learning Control of the Piezoelectric Actuator 42

Koopman Operator Learning Control of the Piezoelectric Actua...

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42nd Chinese Control Conference, CCC 2023

作者： Huang, Xiang Zhang, Hai-Tao School of Artificial Intelligence and Automation Huazhong University of Science and Technology Engineering Research Center of Autonomous Intelligent Unmanned Systems The Key Laboratory of Image Processing and Intelligent Control The State Key Laboratory of Digital Manufacturing Equipment and Technology Wuhan430074 China

ISBN: (纸本)9789887581543

The piezoelectric actuator is one kind of device that can drive nanoscale motion. However, the nonlinear hysteresis effect induced by its natural material greatly degrades its positioning accuracy. To handle this challenging issue, this work develops a Koopman model predict control (Koopman- MPC) framework for the piezoelectric actuator. Specifically, the Koopman operator theory is adapted for modeling the piezoelectric actuator dynamics. A simple yet powerful linear model spanned in a high-dimensional space is thus constructed to characterize the hysteresis dynamics. Subsequently, upon the established Koopman model, an MPC scheme is put forward for tracking control of piezoelectric actuators. Therein, by sustained optimizing a cost function containing future outputs and control increments, the control input is obtained. Moreover, extensive tracking simulations are carried out on a simulated piezoelectric actuator for verifying the feasibility and effectiveness of the Koopman- Mpc scheme. © 2023 Technical Committee on Control Theory, Chinese Association of Automation.

关键词： Model predictive control

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Dynamic Graph Dismantling with Reinforcement Supervised Learning 42

Dynamic Graph Dismantling with Reinforcement Supervised Lear...

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42nd Chinese Control Conference, CCC 2023

作者： Wang, Yumei Tang, Chuancong Zhang, Hai-Tao School of Artificial Intelligence and Automation Huazhong University of Science and Technology The Engineering Research Center of Autonomous Intelligent Unmanned Systems The Key Laboratory of Image Processing and Intelligent Control The State Key Laboratory of Digital Manufacturing Equipment and Technology Wuhan430074 China

ISBN: (纸本)9789887581543

There are always some 'key' nodes in a big complex network, which can joint the most connected subgraphs. How to identify these nodes, finding a minimum set of nodes to attack for reducing the size of residual network's Largest Connected Component(LCC) to break up the original network, has become a research hotspot. Therefore, a method for determining the 'key' nodes based on reinforcement learning framework and supervised learning model is proposed. This algorithm can not only utilize the dynamic exploration ability of reinforcement learning to collect a rich training dataset, but also take advantage of the characteristics that supervised learning is adaptive and has strong generalization ability to possess high efficiency and strong robustness. In order to further improve the algorithm's performance, ϵ-greedy mechanism is used to explore more network states. The experiment results show that given the same fraction of removed nodes, our algorithm can make the residual LCC smaller in various networks which is superior to the state-of-the-art algorithms in terms of effectiveness and generalization. © 2023 Technical Committee on Control Theory, Chinese Association of Automation.

关键词： Complex networks

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