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A comparative study of data-driven battery capacity estimation based on partial charging curves

Journal of Energy Chemistry 2024年第1期88卷 409-420,I0010页

作者： Chuanping Lin Jun Xu Delong Jiang Jiayang Hou Ying Liang Xianggong Zhang Enhu Li 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 Wuhan Institute of Marine Electric Propulsion China State Shipbuilding Corporation LimitedWuhan 430064HubeiChina Gresgying Digital Technology Ltd. Xi’an 710086ShaanxiChina

With its generality and practicality, the combination of partial charging curves and machine learning(ML) for battery capacity estimation has attracted widespread attention. However, a clear classification,fair comparison, and performance rationalization of these methods are lacking, due to the scattered existing studies. To address these issues, we develop 20 capacity estimation methods from three perspectives:charging sequence construction, input forms, and ML models. 22,582 charging curves are generated from 44 cells with different battery chemistry and operating conditions to validate the performance. Through comprehensive and unbiased comparison, the long short-term memory(LSTM) based neural network exhibits the best accuracy and robustness. Across all 6503 tested samples, the mean absolute percentage error(MAPE) for capacity estimation using LSTM is 0.61%, with a maximum error of only 3.94%. Even with the addition of 3 m V voltage noise or the extension of sampling intervals to 60 s, the average MAPE remains below 2%. Furthermore, the charging sequences are provided with physical explanations related to battery degradation to enhance confidence in their application. Recommendations for using other competitive methods are also presented. This work provides valuable insights and guidance for estimating battery capacity based on partial charging curves.

关键词： Lithium-ion battery Partial charging curves Capacity estimation Data-driven Sampling frequency

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Simulation of magnetization process and Faraday effect of magnetic bilayer films

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Chinese Physics B 2024年第9期33卷 590-597页

作者： Sheng Gao An Du Lei Zhang Tian-Guang Li Da-Cheng Ma Department of Basic and General Studies Shenyang Institute of Science and TechnologyShenyang 110167China College of Science Northeastern UniversityShenyang 110819China National Frontiers Science Center for Industrial Intelligence and Systems Optimization Northeastern UniversityShenyang 110819China Office of Academic Research Shenyang Institute of Science and TechnologyShenyang 110167China State Key Laboratory of Robotics Shenyang Institute of AutomationChinese Academy ofSciencesShenyang 110016China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of SciencesShenyang110016China

We described ferromagnetic film and bilayer films composed of two ferromagnetic layers coupled through antiferromagnetic interfacial interaction by classical Heisenberg model and simulated their magnetization state,magnetic permeability,and Faraday effect at zero and finite temperature by using the Landau–Lifshitz–Gilbert(LLG)*** results indicate that in a microwave field with positive circular polarization,the ferromagnetic film has one resonance peak while the bilayer film has two resonance ***,the resonance peak disappears in ferromagnetic film,and only one resonance peak emerges in bilayer film in the negative circularly polarized microwave *** the microwave field’s frequency exceeds the film’s resonance frequency,the Faraday rotation angle of the ferromagnetic film is the greatest,and it decreases when the thickness of the two halves of the bilayer is *** the microwave field’s frequency remains constant,the Faraday rotation angle fluctuates with temperature in the same manner as spontaneous magnetization *** a DC magnetic field is applied in the direction of the anisotropic axis of the film,the Faraday rotation angle varies with the DC magnetic field and shows a similar shape of the hysteresis loop.

关键词： magnetic bilayer films magnetic permeability hysteresis loop Faraday effect Landau-Lifshitz-Gilbert(LLG)equation

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Analysis of coupled axial-torsional vibration of drill-string with an inclination angle 41

Analysis of coupled axial-torsional vibration of drill-strin...

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第41届中国控制会议

作者： Jiahao Zhang Chengda Lu Hengyu Huang Jun Gao Min Wu School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education CCTEG Xi'an Research Institute

ISBN: (数字)9789887581536

ISBN: (纸本)9781665482561

Drill-string vibration has a great negative influence on the safety and efficiency of geological *** that the influence of drill-string inclination is not well considered in the literature,this paper develops a novel drill-string vibration model for geological drilling with inclined ***,the correlation between the inclination angle of borehole and the axial and torsional forces on the drill-string is *** on this,a drill-string motion model with the parameters of inclination angle is ***,the bit-rock interaction is considered as two processes,axial cutting and torsional friction,and an axial-torsional coupled bit-rock interaction model with state-dependent time delay is *** combining the established drill-string motion model and bit-rock interaction model,a coupled axial-torsional vibration model of drill-string in inclined borehole is obtained with the effect of borehole wall friction taken into *** drill-string vibration model proposed in this paper is more general,and includes the existing coupled drill-string vibration model of vertical borehole,which is closer to the actual situation.

关键词： drill-string vibration drill-string inclination bit-rock interaction

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Credibility Metric Weighting for Accelerating Qualitative Model Evaluation 36

Credibility Metric Weighting for Accelerating Qualitative Mo...

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36th European Modeling and Simulation Symposium, EMSS 2024, Held at the 21st International Multidisciplinary Modeling and Simulation Multiconference, I3M 2024

作者： Hu, Ke Gong, Jiabei Zhang, Jiayi Tang, Bijun Laili, Yuanjun Zhang, Lin Ren, Lei School of Automation Science and Electrical Engineering Beihang University Beijing100191 China Zhongguancun Laboratory Beijing100094 China Key Laboratory of Intelligent Manufacturing Systems Technology Beijing100854 China Beijing Institute of Astronautical Systems Engineering Beijing100076 China

ISBN: (纸本)9791281988026

Credibility evaluation of a simulation model is an important premise of the simulation, since incredible model can produce unconvincing or even wrong results. The overall credibility of a simulation model is evaluated by weighting a group of metrics. These metrics are calculated by different kinds of quantitative or qualitative methods. As the weighting criteria of different kinds of models is hard to define uniformly, existing qualitative methods depend highly on expert scoring. However, expert scoring with metric weighting process is subjective and time-consuming. It is challenging to weight the metrics appropriately and evaluate a simulation model efficiently. Therefore, this paper proposes an automated metric weighting method for accelerating the credibility evaluation. It applies the historical scoring records of similar models as the reference. Then, it introduces an evolutionary algorithm to calculate the possible weights of the metrics inversely and calculate the overall credibility value of the simulation model. Experimental results on a typical simulation model verify that the proposed method is able to weight the metrics within seconds and calculate the credibility value with high degree of alignment with expert scoring. © 2024 The Authors.

关键词： Digital elevation model

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Optimal Linear Deception Attacks on Remote State Estimation with Constrained Alarm Rates: A Low-Dimensional Case 63

Optimal Linear Deception Attacks on Remote State Estimation ...

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63rd IEEE Conference on Decision and Control, CDC 2024

作者： Shang, Jun Zhang, Hanwen Zhou, Jing Chen, Tongwen Tongji University Shanghai Research Institute for Intelligent Autonomous Systems National Key Laboratory of Autonomous Intelligent Unmanned Systems Frontiers Science Center for Intelligent Autonomous Systems Department of Control Science and Engineering Shanghai200092 China University of Science and Technology Beijing Key Laboratory of Knowledge Automation for Industrial Processes of Ministry of Education School of Automation and Electrical Engineering Beijing100083 China University of Alberta Department of Electrical and Computer Engineering EdmontonABT6G 1H9 Canada

ISBN: (纸本)9798350316339

This study addresses linear attacks on remote state estimation within the context of a constrained alarm rate. Smart sensors, which are equipped with local Kalman filters, transmit innovations instead of raw measurements through a wireless communication network. This transmission is vulnerable to malicious data interception and manipulation by attackers. The aim of this research is to identify the optimal attack strategy that degrades the system performance while adhering to stealthiness constraints. A notable innovation of this paper is the direct association of the attack's stealthiness with the alarm rate, diverging from traditional approaches that rely on the covariance of the innovation or the Kullback-Leibler divergence, which are conventional metrics that have been extensively explored in previous studies. Our findings reveal that the optimal attack strategy exhibits some structural characteristics in systems of low dimensions. The performance of the proposed attack strategy is demonstrated through numerical examples. © 2024 IEEE.

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Reinforcement Learning Applied to Agent Path Planning 41

Reinforcement Learning Applied to Agent Path Planning

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第41届中国控制会议

作者： Fenglan Sun Xiaoshuai Wu Guoxiong Long Key Laboratory of Intelligent Analysis and Decision on Complex Systems School of ScienceChongqing University of Posts and Telecommunications Key Laboratory of Intelligent Air-Ground Cooperative Control for Universities in Chongqing College of AutomationChongqing University of Posts and Telecommunications Department of Complexity Science Potsdam Institute for Climate Impact Research

ISBN: (数字)9789887581536

ISBN: (纸本)9781665482561

This paper provides an enhanced Q-learning algorithm for agent path planning in traditional grid maps,which solves the problem of agent path planning in grid *** an unknown environment,the classic Q-learning algorithm addresses the agent's path planning ***,there are several limits to this strategy in terms of path planning:the agent could only move nearby grids,and the step size is only one *** Q-learning algorithm,including changes the agent's direction and step *** action direction of agents is increased from four to *** movement step of agents is raised from one to three *** new method makes the convergence faster and proxy path ***,a set of simulation tests are presented to validate the modified Q-learning algorithm.

关键词： Agent Path planning Q-learning Grid method

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Leader-Following Adaptive Tracking Control for A Class of Uncertain Discrete-Time Nonlinear Multi-Agent systems Under Event-Triggered Communication

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Journal of systems science & complexity 2025年第2期38卷 756-781页

作者： XU Yuchun ZHANG Yanjun ZHANG Ji-Feng State Key Laboratory of Mathematical Sciences Academy of Mathematics and Systems Science Chinese Academy of Sciences School of Mathematics Sciences University of Chinese Academy of Sciences School of Automation Beijing Institute of Technology State Key Laboratory of Autonomous Intelligent Unmanned Systems Beijing Institute of Technology School of Automation and Electrical Engineering Zhongyuan University of Technology School of Mathematical Sciences University of Chinese Academy of Sciences

This paper studies the leader-following adaptive tracking control problem for multi-agent systems comprising a leader agent and N follower agents with uncertain nonlinear dynamics. Specifically, a novel event-triggered communication based adaptive distributed observer is developed to enable each follower agent to estimate the leader's information. Then, new forms of adaptive control law and parameter update law are designed with the estimated leader's signals. The developed distributed adaptive control strategy has several characteristics:(i) With the introduced time-varying observer gain, the designed adaptive distributed observer eliminates the need for global graph information but ensures convergence of the estimates;(ii) By appropriately designing the event-triggered mechanism,the communication frequency among follower agents is reduced in the sense that the communication rate decays over time;(iii) The newly designed adaptive control law ensures a linear estimation error equation, facilitating the development of a stable parameter update law without requiring prior knowledge of uncertain system parameters. The stability of closed-loop system and leader-following asymptotical tracking are achieved. Simulation study demonstrates the theoretical results.

关键词： Adaptive control event-triggered communication heterogeneous nonlinear agent leader-following tracking

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Full Attention Tracker: A Good Combination of Pixel-Level and Region-Level Cross-Correlation 42

Full Attention Tracker: A Good Combination of Pixel-Level an...

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42nd Chinese Control Conference, CCC 2023

作者： Wang, Yuxuan Yan, Liping Feng, Zihang Xia, Yuanqing Xiao, Bo School of Automation Beijing Institute of Technology Key Laboratory of Intelligent Control and Decision of Complex Systems Beijing100081 China School of Artificial Intelligence Beijing University of Posts and Telecommunications Beijing100081 China

ISBN: (纸本)9789887581543

The tracker based on Siamese neural network is currently a technical method with high accuracy in the tracking field. With the introduction of transformer in the visual tracking field, the attention mechanism has gradually emerged in tracking tasks. However, due to the characteristics of attention operation, Transformer usually has slow convergence speed, and its pixel-level correlation discrimination in tracking is more likely to lead to overfitting, which is not conducive to long-term tracking. A brand new framework FAT was designed, which is the improvement of MixFormer. The operation for simultaneous feature extraction and target information integration in MixFormer is retained, and the Mixing block is introduced to suppress the background as much as possible before the information interaction. In addition, a new operation is designed: the result of region-level cross-correlation is used as a guidance to help the learning of pixel-level cross-correlation in attention, thereby accelerating the model convergence speed and enhancing the model generalization. Finally, a joint loss function is designed to further improve the accuracy of the model. Experiments show that the presented tracker achieves excellent performance on five benchmark datasets. © 2023 Technical Committee on Control Theory, Chinese Association of automation.

关键词： Pixels

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Two-Stage Representation Refinement Based on Convex Combination for 3-D Human Poses Estimation

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第12期5卷 6500-6508页

作者： Chen, Luefeng Cao, Wei Zheng, Biao Wu, Min Pedrycz, Witold Hirota, Kaoru China University of Geosciences School of Automation Wuhan430074 China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan430074 China Ministry of Education Engineering Research Center of Intelligent Technology for Geo-Exploration Wuhan430074 China University of Alberta Department of Electrical and Computer Engineering EdmontonABT6G 2R3 Canada Macau University of Science and Technology Institute of Systems Engineering Taipa999078 China Istinye University Research Center of Performance and Productivity Analysis Istanbul34010 Turkey Tokyo Institute of Technology Yokohama226-8502 Japan

In the human pose estimation task, on the one hand, 3-D pose always has difficulty in dividing different 2-D poses if the view is limited;on the other hand, it is hard to reduce the lifting ambiguity because of the lack of depth information, it is an important and challenging problem. Therefore, two-stage representation refinement based on the convex combination for 3-D human pose estimation is proposed, in which the two-stage method includes a dense-spatial-temporal convolutional network and a local-to-refine network. The former is applied to determine the features between each video frame;the latter is used to get the different scales of pose details. It aims to address the difficulty of estimating 3-D human pose from 2-D image sequences. In such a way, it can better use the relations between every frame in the sequence of the pose video to produce more accurate results. Finally, we combine the above network with a block called convex combination to help refine the 3-D pose location. We test the proposed approach on both Human3.6m and MPII datasets. The result confirms that our method can achieve better performance than improved CNN supervision, a simple yet effective baseline, and coarse-to-fine volumetric prediction. Besides, a robustness test experiment is carried out for the proposed method while the input is interrupted. The result verifies that our method shows better robustness. © 2024 IEEE.

关键词： Convolution

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Reinforcement learning-enhanced dynamic multi-objective evolutionary algorithm for cooperative shovel scheduling in open-pit mining

Reinforcement learning-enhanced dynamic multi-objective evol...

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2024 China automation Congress, CAC 2024

作者： Zhang, Zihao Li, Changhe Yang, Shengxiang School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan430074 China School of Artificial Intelligence Anhui University of Science & Technology Hefei232001 China School of Computer Science and Informatics De Montfort University Leicester United Kingdom

ISBN: (纸本)9798350368604

Aiming at the ore blending problem in the open-pit mining scenario, the optimization objective is to minimize the grade variance of the sliding window of the ore flow, and a cooperative shovel scheduling model for real-time ore blending is established. A reinforcement learning-enhanced dynamic multi- objective evolutionary algorithm is proposed, where a Q-learning operator selection mechanism is introduced to reuse the information from the previous environment for tracking the optimal solution in the dynamic environment. Experimental results show that the proposed method can effectively control the grade fluctuation of the ore flow and dynamically respond to external events, and can find a better solution in real time compared with the traditional operator selection algorithm. © 2024 IEEE.

关键词： Open pit mining

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