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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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Distributed Adaptive Tracking Control for Fuzzy Nonlinear MASs Under Round-Robin Protocol

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IEEE Transactions on Fuzzy systems 2025年第5期33卷 1488-1498页

作者： Fan, Sha Meng, Min Fu, Yukai Deng, Chao Nanjing University of Posts and Telecommunications College of Automation College of Artificial Intelligence Nanjing210023 China Tongji University Department of Control Science and Engineering College of Electronics and Information Engineering Shanghai200070 China Chinese Academy of Sciences State Key Laboratory of Robotics Shenyang Institute of Automation Shenyang110016 China Chinese Academy of Sciences Key Laboratory of Networked Control Systems Shenyang110016 China Nanjing University of Posts and Telecommunications Institute of Advanced Technology Nanjing210049 China

In this article, the cooperative fuzzy tracking control problem for a type of high-order nonlinear multi-agent systems under the Round-Robin (RR) communication protocol is solved. To effectively reduce the communication among agents, an RR communication protocol is first proposed, where each agent can communicate with only one neighbor at any given time. Based on the developed communication protocol, a hierarchical control approach is introduced, consisting of a distributed observer layer that utilizes information from both local and neighboring agents, and a decentralized control layer that operates solely based on local agent information. Specifically, a distributed observer is designed in the distributed observer layer to achieve the distributed observation objective by introducing a time-varying gain in the observer input. Furthermore, an improved distributed observer with the upper triangular form is constructed to produce a signal with high-order derivatives. In the decentralized control layer, fuzzy adaptive controllers are designed for all agents by using the backstepping technique. By using the Lyapunov stability theory, rigorous stability analysis shows that the cooperative fuzzy tracking control problem is solved under the developed hierarchical control approach. In the end, a simulation example is presented to demonstrate the validity of our proposed method. © 1993-2012 IEEE.

关键词： Protocols Observers Stability analysis Decentralized control Backstepping Vectors Symmetric matrices Network topology Telecommunications System dynamics

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Enhanced Grid Integration of PV systems Using Double-Stage Configuration and PUC7 Inverter Controlled by MPC

Enhanced Grid Integration of PV Systems Using Double-Stage C...

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International Conference on Artificial Intelligence in Renewable Energetic systems, IC-AIRES 2024

作者： Khettab, Soufian Kheldoun, Aissa Bradai, Rafik Hattabi, Intissar SET Laboratory Blida 1 University Faculty of Technology Automation and Electrical Engineering Department Blida09000 Algeria Laboratory of Signals and Systems Institute of Electrical and Electronic Engineering University M’hamed Bougara Boumerdes35000 Algeria LATSI Blida 1 University Faculty of Technology Electronics Department Blida09000 Algeria

ISBN: (纸本)9783031803000

The paper introduces a double-stage, single-phase photovoltaic (PV) system connected to the grid using a packed U-cell seven-level (PUC7) inverter, governed by Model Predictive Control (MPC). This system aims to optimize grid integration by achieving enhanced current injection with minimal Total Harmonic Distortion (THD), reactive power compensation, and efficient extraction and injection of maximum power from the PV source. Simulation results underscore the efficacy of the proposed control strategy in enhancing power quality, responsiveness, and accuracy across varying weather conditions, thereby validating the feasibility and robustness of the proposed PV system. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Predictive control systems

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Optimizing Route Efficiency in Formula One (F1) Vehicles Using Reinforcement Learning Algorithms 4

Optimizing Route Efficiency in Formula One (F1) Vehicles Usi...

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4th IEEE International Conference on Artificial Intelligence in Cybersecurity, ICAIC 2025

作者： Narejo, Sanam Khan, Muhammad Taimoor Shaikh, Muhammad Zakir Dhirani, Lubna Luxmi Chowdhery, Bhawani Shankar Mehran Univerity of Engineering and Technology Department of Computer Systems Jamshoro Pakistan Mehran Univerity of Engineering and Technology Jamshoro National Centre for Robotics Automation and Artificial Intelligence Pakistan University of Malaga Mechanical Engineering and Energy Efficiency School of Industrial Engineering Malaga Spain University of Limerick Department of Electronic and Computer Engineering Limerick Ireland

ISBN: (纸本)9798331518882

This research explores the application of reinforcement learning (RL) to enhance route efficiency and performance of a Formula One (F1) car within a simulation environment. The simulation is implemented using Python, NEAT (NeuroEvolution of Augmenting Topologies), and PyGame to create a dynamic system where neural networks control the car's navigation. RL enables the F1 car, acting as an agent, to learn optimal decisions through a fitness-based reward mechanism by interacting with its environment. Equipped with radar sensors to detect obstacles and measure distances, the virtual car adjusts its speed and steering to avoid collisions and optimize movement. Over successive generations, the RL algorithm refines the car's driving ability, improving speed and directional control to maximize distance covered and minimize lap times. A fitness-based evaluation system tracks progress, providing metrics such as best and average fitness scores, which highlight the car's evolving performance. Results demonstrate the effectiveness of RL in enhancing autonomous driving capabilities, enabling the car to navigate complex environments and improve decision-making across generations. © 2025 IEEE.

关键词： Reinforcement learning

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Deterministic Learning-Based Tracking Control for Parabolic PDE systems With Infinite-Dimensional Nonlinear Uncertain Dynamics

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IEEE Transactions on Automatic Control 2025年第5期70卷 3272-3287页

作者： Zhang, Jingting Yuan, Chengzhi Wu, Fen Wang, Cong Cheng, Hong University of Electronic Science and Technology of China Center for Robotics School of Automation Engineering Chengdu611731 China University of Rhode Island Mechanical Industrial and Systems Engineering Department KingstonRI02881 United States North Carolina State University Mechanical and Aerospace Engineering Department RaleighNC27695 United States Shandong University School of Control Science and Engineering Jinan250061 China

In this article, we investigate the joint problem of dynamics learning and tracking control for a class of parabolic partial differential equation (PDE) systems with infinite-dimensional uncertain nonlinear dynamics. A new learning control scheme is proposed based on the deterministic learning (DL) theory. One key feature of the proposed scheme is its capability of accurately learning the system's nonlinear uncertain dynamics during real-time tracking control with provable stability and convergence of the overall PDE closed-loop system. Specifically, the Galerkin method is first employed to deal with the infinite dimensionality of the PDE system;a novel DL-based adaptive learning control scheme is then proposed using dual radial basis function neural networks (RBF NNs), in which a pair of RBF NNs are employed to address, respectively, the matched and unmatched components of uncertain nonlinear system dynamics. This control scheme is finally examined on the original PDE system, and it is rigorously proved that: first the PDE system's state tracks the prescribed reference trajectory with guaranteed closed-loop stability and tracking accuracy;and second locally accurate identification of the PDE system's dominant nonlinear uncertain dynamics can be achieved with provable convergence of associated NN weights to their optimal values, thereby the learned knowledge can be ultimately stored and represented by the convergent constant RBF NN models. Based on this, an experience-based control scheme is further proposed, which is capable of recalling the associated learned knowledge in real-time to further improve control performance and reduce computational complexity with maintained provable stabilization. It is worth stressing that although this work is focused particularly on parabolic PDE systems, it is groundbreaking with important technical breakthroughs that would facilitate a more complete extension of the DL theory from traditional ordinary differential equation syste

关键词： Galerkin methods

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Policy pathways utilizing extended producer responsibility and eco-modulation frameworks for sustainable food packaging waste management in India: A review

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Results in Engineering 2025年 26卷

作者： Dey, Amrita Ashok S, Denis Department of Design and Automation Cyber-Physical Systems Lab School of Mechanical Engineering Vellore Institute of Technology Tamilnadu Vellore632014 India

Despite the growing concerns surrounding food packaging waste, India still lacks a well-defined and effectively enforced policy framework to tackle this issue. Multilayered packaging, constituting 35 % of total packaging in India, is currently deemed unrecyclable due to its complex structure and is currently landfilled. While countries like the UK, Belgium, France, Italy, and Canada have successfully implemented measures such as landfill taxes and "pay-as-you-throw" policies, India has yet to establish strict regulations and economic incentives to promote sustainable waste management. The absence of strong Extended Producer Responsibility (EPR) enforcement, inadequate eco-modulation strategies, and limited consumer incentives such as reduced GST on recycled products or rewards for eco-conscious practices highlight the urgent need for India to bridge this policy gap and adopt a more structured approach towards a circular economy. Strengthening of regulatory frameworks and a collaborative approach involving the government, producers, informal sector, and consumers is essential for developing an efficient waste management system. Through this review, we aim to highlight successful global strategies and motivate the adoption of similar policies in India to mitigate the growing environmental threat posed by food packaging waste. © 2025

关键词： Labels

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Robust Practical Stabilization for Complex Dynamical Networks With DoS Attacks and Actuator Saturation

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IEEE Transactions on systems, Man, and Cybernetics: systems 2025年第5期55卷 3108-3118页

作者： Shi, Xueya Peng, Zhinan Yu, Junzhi Li, Hongfei Southwest University College of Electronic and Information Engineering Chongqing400715 China Sichuan Artificial Intelligence Research Institute Yibin644000 China University of Electronic Science and Technology of China School of Automation Engineering Chengdu611731 China Peking University State Key Laboratory for Turbulence and Complex Systems Department of Advanced Manufacturing and Robotics College of Engineering Beijing100871 China

This article addresses the problem of designing an attack-resilient adaptive event-triggered (AET) controller for complex dynamical networks (CDNs) under DoS attacks and actuator saturation, with a focus on robust practical stability (RPS). First, considering the impact of DoS attacks on closed-loop systems, an AET controller against DoS attacks is designed. Unlike other event-triggered controllers, the complete timeline is divided, and the AET controller is built with two switching modes based on the intervals of dormant and active periods of DoS attacks in which the system is located. Second, to reconcile AET controller with actuator saturation, a switched system modeling approach is established that explicitly incorporates saturation constraints into the coupled network dynamics. Third, a switched Lyapunov-Krasovskii functional (LKF) is proposed, with which sufficient conditions are provided to ensured the RPS, and a joint design strategy is developed for the desired triggered matrix and feedback gain using linear matrix inequalities (LMIs). Moreover, the results are generalized to the case of actuator faults, indicating that the system is able to achieve RPS with actuator faults. Finally, the proposed method is verified through an example. © 2013 IEEE.

关键词： Lyapunov functions

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Exploring Token-Level Augmentation in Vision Transformer for Semi-Supervised Semantic Segmentation

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IEEE Signal Processing Letters 2025年 32卷 1885-1889页

作者： Zhang, Dengke Tang, Quan Liu, Fagui Mei, Haiqing Chen, C. L. Philip South China University of Technology School of Computer Science and Engineering Guangzhou510006 China Pengcheng Laboratory Department of New Network Shenzhen518000 China China Tobacco Guangdong Industrial Corporation Guangzhou510385 China Chinese Academy of Sciences State Key Laboratory of Management and Control for Complex Systems Institute of Automation Beijing100080 China

Semi-supervised semantic segmentation has witnessed remarkable advancements in recent years. However, existing algorithms are based on convolutional neural networks, and directly applying them to Vision Transformers poses certain limitations due to conceptual disparities. To this end, we propose TokenSwap, a data augmentation technique designed explicitly for semi-supervised semantic segmentation with Vision Transformers. TokenSwap aligns well with the global attention mechanism by mixing images at the token level, enhancing the learning capability for contextual information among image patches and the utilization of unlabeled data. We further incorporate image augmentation and feature augmentation to promote the diversity of augmentation. Moreover, to enhance consistency regularization, we propose a dual-branch framework where each branch applies image and feature augmentation to the input image. We conduct extensive experiments across multiple benchmark datasets, including Pascal VOC 2012, Cityscapes, and COCO. Results suggest that the proposed method outperforms state-of-the-art algorithms with notably observed accuracy improvement, especially under limited fine annotations. © 1994-2012 IEEE.

关键词： Semantic Segmentation

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Linear Convergent Distributed Nash Equilibrium Seeking With Compression

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IEEE Transactions on Automatic Control 2025年第5期70卷 3316-3323页

作者： Chen, Xiaomeng Wu, Yuchi Yi, Xinlei Huang, Minyi Shi, Ling Hong Kong University of Science and Technology Department of Electronic and Computer Engineering Hong Kong Shanghai University School of Mechatronic Engineering and Automation Shanghai200444 China College of Electronics and Information Engineering Tongji University Department of Control Science and Engineering Shanghai201804 China Massachusetts Institute of Technology Lab for Information & Decision Systems CambridgeMA02139 United States Carleton University School of Mathematics and Statistics OttawaONK1S 5B6 Canada

Information compression techniques are majorly employed to reduce communication cost over peer-to-peer links. In this article, we investigate distributed Nash equilibrium (NE) seeking problems in a class of noncooperative games over directed graphs with information compression. To improve communication efficiency, a compressed distributed NE seeking (C-DNES) algorithm is proposed to obtain an NE for games, where the differences between decision vectors and their estimates are compressed. The proposed algorithm is compatible with a general class of compressors, including both unbiased and biased compressors. Moreover, our approach only requires the adjacency matrix of the directed graph to be row-stochastic, in contrast to past works that relied on balancedness or specific global network parameters. It is shown that C-DNES not only inherits the advantages of conventional distributed NE algorithms, achieving linear convergence rate for games with restricted strongly monotone mappings, but also saves communication costs in terms of transmitted bits. Finally, numerical simulations illustrate the advantages of C-DNES in saving communication cost by an order of magnitude under different compressors. © 1963-2012 IEEE.

关键词： Stochastic systems

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Analysis of Disease-induced Changes in Human Locomotor Patterns Through the Co-joint Synergistic Attention Algorithm

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IEEE Transactions on Neural systems and Rehabilitation Engineering 2025年 33卷 1695-1706页

作者： Chen, Jingyao Wang, Chen Hou, Zeng-Guang Deng, Pingye Peng, Liang Zhang, Pu Xu, Ningcun Macau University of Science and Technology Faculty of Innovation Engineering CASIA-MUST Joint Laboratory of Intelligence Science and Technology 999078 China Beijing Academy of Science and Technology Institute of Analysis and Testing Beijing100089 China Chinese Academy of Sciences State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Beijing100190 China University of Chinese Academy of Sciences School of Artificial Intelligence Beijing100049 China China Rehabilitation Research Center Department of Rehabilitation Evaluation Beijing100068 China

Objective: Aiming to quantify and analyze disease-induced alterations in human movement, we explored the co-joint synergy patterns in locomotion through a vision-based co-joint synergistic attention algorithm. Methods: We recruited 30 participants (including 15 poststroke patients and 15 healthy individuals) and extracted their 3D visual motor data for the joint feature coupling by a serial attention module. And we designed a dual-stream classification module for preclassification based on the spatio-temporal characteristics of the data. Then we extracted the important co-joint synergy patterns by a looping mask module and the co-joint synergy variability score. Results: Through the co-joint synergistic attention algorithm, we found significant differences in joint synergy patterns between post-stroke patients and healthy individuals during upper and lower limb tasks. Furthermore, we obtained quantitative results on the effect of specific diseases on cojoint synergy patterns among healthy individuals and patients. The validity of the result was verified by comparing with the commonly used Non-negative Matrix Factorization (NMF) and the Muscle Synergy Fractionation (MSF) methods. Conclusion: Specific diseases can cause changes in human movement patterns, and by the co-joint synergistic attention algorithm we can analyze the alterations in joint synergies and also quantify the importance of different synergy groups. Significance: This research proposes a new approach for identifying specific co-joint synergy patterns arising from disease-altered biomechanics, which provides a new targeted protocol for the rehabilitation process. © 2025 IEEE.

关键词： Motors Diseases Trajectory Stroke (medical condition) Muscles Motor drives Couplings Topology Thumb Three-dimensional displays

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