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Capacity Estimation of Lithium-ion Battery based on Electrochemical Model with Electrolyte Dynamics ⁎

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IFAC-PapersOnLine 2020年第2期53卷 5909-5914页

作者： Guangwei Chen Zhitao Liu Hongye Su State Key Laboratory of Industrial Control Technology Institute of Cyber-Systems and Control Zhejiang University Hangzhou 310027 China

The remaining capacity of a battery is a crucial indicator, which has significant impact on state of Charge (SoC) estimation and the safe operations of electric vehicles. In this paper, an electrochemical model considering the electrolyte dynamics is proposed to estimate the real capacity of a lithium-ion battery. The electrochemical model with electrolyte dynamics governed by several partial differential equations has the potential to accurately describe varieties of phenomenons inside the battery. Furthermore, a Pade’ one-order approximation is adopted to obtain the transfer function between the boundary lithium-ion concentration and input current, then a boundary state estimator is proposed to estimate the the boundary lithium-ion concentration. After that, the least square method is used to obtain the adaptive update law for maximum concentration estimation in anode. Finally, the correctness of the aforementioned estimation methods is verified through simulation.

关键词： lithium-ion battery electrochemical model capacity electrolyte dynamics Pade’ approximation least square method

A Spatial-Temporal Attention Model for Human Trajectory Prediction

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IEEE/CAA Journal of Automatica Sinica 2020年第4期7卷 965-974页

作者： Xiaodong Zhao Yaran Chen Jin Guo Dongbin Zhao the School of Automation and Electrical Engineering University of Science and Technology BeijingBeijing 100083 the State Key Laboratory of Management and Control for Complex Systems Institute of AutomationChinese Academy of SciencesBeijing 100190China the Key Laboratory of Knowledge Automation for Industrial Processes Ministry of EducationBeijing 100083China

Human trajectory prediction is essential and promising in many related applications. This is challenging due to the uncertainty of human behaviors, which can be influenced not only by himself, but also by the surrounding environment. Recent works based on long-short term memory(LSTM) models have brought tremendous improvements on the task of trajectory prediction. However, most of them focus on the spatial influence of humans but ignore the temporal influence. In this paper, we propose a novel spatial-temporal attention(ST-Attention) model,which studies spatial and temporal affinities jointly. Specifically,we introduce an attention mechanism to extract temporal affinity,learning the importance for historical trajectory information at different time instants. To explore spatial affinity, a deep neural network is employed to measure different importance of the neighbors. Experimental results show that our method achieves competitive performance compared with state-of-the-art methods on publicly available datasets.

关键词： Attention mechanism long-short term memory(LSTM) spatial-temporal model trajectory prediction

Domain generalization for vision-based driving trajectory generation

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arXiv 2021年

作者： Wang, Yunkai Zhang, Dongkun Cui, Yuxiang Chen, Zexi Jing, Wei Chen, Junbo Xiong, Rong Wang, Yue State Key Laboratory of Industrial Control Technology Institute of Cyber-Systems and Control Zhejiang University Hangzhou China Department of Autonomous Driving Lab Alibaba DAMO Academy Hangzhou China

One of the challenges in vision-based driving trajectory generation is dealing with out-of-distribution scenarios. In this paper, we propose a domain generalization method for vision-based driving trajectory generation for autonomous vehicles in urban environments, which can be seen as a solution to extend the Invariant Risk Minimization (IRM) method in complex problems. We leverage an adversarial learning approach to train a trajectory generator as the decoder. Based on the pre-trained decoder, we infer the latent variables corresponding to the trajectories, and pre-train the encoder by regressing the inferred latent variable. Finally, we fix the decoder but fine-tune the encoder with the final trajectory loss. We compare our proposed method with the state-of-the-art trajectory generation method and some recent domain generalization methods on both datasets and simulation, demonstrating that our method has better generalization ability. Our project is available at https://***/view/dg-traj-gen. Copyright © 2021, The Authors. All rights reserved.

关键词： Signal encoding

Lidar-Inertial and Downward Vision Based Mobile Robot SLAM in Digital Workshops

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Lidar-Inertial and Downward Vision Based Mobile Robot SLAM i...

Data Driven control and Learning systems (DDCLS)

作者： Yanchao Zhang Hao Geng Bin Liu Yinlong Zhang Xudong Yuan Sichao Zhang School of Information Science and Engineering Shenyang University of Technology Shenyang China Guangzhou Institute of Industrial Intelligence Guangzhou China State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences Shenyang China Key Laboratory of Networked Control Systems Chinese Academy of Sciences Shenyang China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang China

ISBN: (数字)9798350361674

ISBN: (纸本)9798350361681

In order to solve the problems of long-term cumulative error and the absence of absolute pose in the existing Lidar-Inertial Measurement Unit (IMU) fusion methods for Automated Guided Vehicle (AGV) Simultaneous Localization and Mapping (SLAM), we propose a new SLAM method for mobile robot, integrating Lidar, IMU, and a downward vision camera. Firstly, we tightly couple Lidar-IMU and downward vision camera using a multimodal fusion framework. Downward visual measurements are integrated as QR factors into the factor graph, providing absolute pose information to correct cumulative error in the Lidar-IMU system and enable the joint pose estimation of the AGV. Secondly, an enhanced Iterative Closest Point (ICP) point cloud matching algorithm has been developed to correct the incorrect pose. This approach achieves Scan-to-Map alignment by employing a coarse-to-fine matching strategy, thereby enhancing the accuracy of point cloud alignment. Finally, the extended Kalman filter is used to optimize the Lidar pose drift, as well as the Lidar mapping effectively by using the Lidar-IMU as the measurements and the downward visual measurements as the updates. The algorithm has been extensively evaluated on the developed AGV experimental platform. The experimental results show that the position error is less than 0.02 meters and the orientation error is less than 1°, which solves the problems of long-term error accumulation and the lack of absolute pose information for AGV navigation.

关键词： Point cloud compression Visualization Laser radar Simultaneous localization and mapping Conferences Robot vision systems Measurement uncertainty

Intention-Aware Planner for Robust and Safe Aerial Tracking

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arXiv 2023年

作者： Ren, Qiuyu Yu, Huan Dai, Jiajun Zheng, Zhi Meng, Jun Xu, Li Xu, Chao Gao, Fei Cao, Yanjun The State Key Laboratory of Industrial Control Technology College of Control Science and Engineering Zhejiang University Hangzhou310027 China Huzhou Institute Zhejiang University Huzhou313000 China Huzhou Laboratory of Autonomous Intelligent Systems Huzhou313000 China The Robotics Institute Zhejiang University Yuyao315400 China College of Electrical Engineering Zhejiang University Hangzhou310027 China

Autonomous target tracking with quadrotors has wide applications in many scenarios, such as cinematographic follow-up shooting or suspect chasing. Target motion prediction is necessary when designing the tracking planner. However, the widely used constant velocity or constant rotation assumption can not fully capture the dynamics of the target. The tracker may fail when the target happens to move aggressively, such as sudden turn or deceleration. In this paper, we propose an intention-aware planner by additionally considering the intention of the target to enhance safety and robustness in aerial tracking applications. Firstly, a designated intention prediction method is proposed, which combines a user-defined potential assessment function and a state observation function. A reachable region is generated to specifically evaluate the turning intentions. Then we design an intention-driven hybrid A* method to predict the future possible positions for the target. Finally, an intention-aware optimization approach is designed to generate a spatial-temporal optimal trajectory, allowing the tracker to perceive unexpected situations from the target. Benchmark comparisons and real-world experiments are conducted to validate the performance of our method. Copyright © 2023, The Authors. All rights reserved.

关键词： Aircraft detection

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

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arXiv 2025年

作者： Zhang, Dengke Tang, Quan Liu, Fagui Mei, Haiqing Chen, C.L. Philip The School of Computer Science and Engineering South China University of Technology Guangzhou510006 China The Department of New Network Pengcheng Laboratory Shenzhen518000 China The China Tobacco Guangdong Industrial Corporation Guangzhou510385 China State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences 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 TokenMix, a data augmentation technique specifically designed for semi-supervised semantic segmentation with Vision Transformers. TokenMix aligns well with the global attention mechanism by mixing images at the token level, enhancing learning capability for contextual information among image patches. 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. Copyright © 2025, The Authors. All rights reserved.

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Toward Consistent drift-free visual inertial localization on keyframe based map

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arXiv 2021年

作者： Zhang, Zhuqing Jiao, Yanmei Huang, Shoudong Wang, Yue Xiong, Rong State Key Laboratory of Industrial Control Technology Institute of Cyber-Systems and Control Zhejiang University Zhejiang China Centre for Autonomous Systems Faculty of Engineering and Information Technology University of Technology Sydney UltimoNSW2007 Australia

Global localization is essential for robots to perform further tasks like navigation. In this paper, we propose a new framework to perform global localization based on a filter-based visual-inertial odometry framework MSCKF. To reduce the computation and memory consumption, we only maintain the keyframe poses of the map and employ Schmidt-EKF to update the state. This global localization framework is shown to be able to maintain the consistency of the state estimator. Furthermore, we introduce a re-linearization mechanism during the updating phase. This mechanism could ease the linearization error of observation function to make the state estimation more precise. The experiments show that this mechanism is crucial for large and challenging scenes. Simulations and experiments demonstrate the effectiveness and consistency of our global localization framework. Copyright © 2021, The Authors. All rights reserved.

关键词： state estimation

SAMOC: Enabling Atomic Invocations for Cross-chain Crowdsourcing Testing DApps in industrial control Through Trusted Smart Community and Lock Mechanism

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SAMOC: Enabling Atomic Invocations for Cross-chain Crowdsour...

IEEE International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom)

作者： Weiguo Huang Yong Ding Jun Li Yujue Wang Hai Liang Changsong Yang Guangxi Key Laboratory of Cryptography and Information Security School of Computer Science and Information Security Guilin University of Electronic Technology Guilin China Guangxi Engineering Research Center of Industrial Internet Security and Blockchain Guilin University of Electronic Technology Guilin China Lion Rock Labs of Cyberspace Security Institute of Cyberspace Technology HKCT Institute for Higher Education China China Industrial Control Systems Cyber Emergency Response Team Beijing China Hangzhou Innovation Institute of Beihang University Hangzhou China

ISBN: (数字)9798331506209

ISBN: (纸本)9798331506216

Crowdsourcing testing leverages extensive user participation to identify and fix potential issues in industrial control software, ensuring its security and reliability. Decentralized applications (DApps) can utilize blockchain and smart contract technologies to ensure the decentralization, transparency, and tamper-resistance of industrial control software testing. However, testing efforts conducted via DApps are typically confined to a single blockchain. Coordinating and scheduling DApps deployed on different blockchains has become a critical challenge. Moreover, DApps need to verify the credibility of crowdsourcing participants to ensure the reliability of testing. In order to address these issues, this paper proposes SAMOC, an atomic cross-chain system based on the Atomic-Oracle chain, which aims to realize atomic cross-chain invocations for DApps. SAMOC can realize atomic cross-chain invokes, and the intelligent community deployed on the Atomic-Oracle chain calculates the reputation of users’ accounts across multiple chains through Oracle. This paper also designs a corresponding incentive mechanism to ensure that the auditing of cross-chain invokes is honest and trustworthy. Experimental results show that the cross-chain invokes of the proposed SAMOC scheme have lower latency compared to AtomCI.

关键词： Crowdsourcing Software testing Smart cities industrial control Smart contracts Decentralized applications Software Blockchains Software reliability Security

SAMOC: Enabling Atomic Invocations for Cross-chain Crowdsourcing Testing DApps in industrial control Through Trusted Smart Community and Lock Mechanism 23

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SAMOC: Enabling Atomic Invocations for Cross-chain Crowdsour...

23rd IEEE International Conference on Trust, Security and Privacy in Computing and Communications, TrustCom 2024

作者： Huang, Weiguo Ding, Yong Li, Jun Wang, Yujue Liang, Hai Yang, Changsong Guilin University of Electronic Technology Guangxi Key Laboratory of Cryptography and Information Security School of Computer Science and Information Security Guilin China Hkct Institute for Higher Education Lion Rock Labs of Cyberspace Security Institute of Cyberspace Technology 999077 China Guilin University of Electronic Technology Guangxi Engineering Research Center of Industrial Internet Security and Blockchain Guilin China China Industrial Control Systems Cyber Emergency Response Team Beijing China Hangzhou Innovation Institute of Beihang University Hangzhou China

Crowdsourcing testing leverages extensive user participation to identify and fix potential issues in industrial control software, ensuring its security and reliability. Decentralized applications (DApps) can utilize blockchain and smart contract technologies to ensure the decentralization, transparency, and tamper-resistance of industrial control software testing. However, testing efforts conducted via DApps are typically confined to a single blockchain. Coordinating and scheduling DApps deployed on different blockchains has become a critical challenge. Moreover, DApps need to verify the credibility of crowdsourcing participants to ensure the reliability of testing. In order to address these issues, this paper proposes SAMOC, an atomic cross-chain system based on the Atomic-Oracle chain, which aims to realize atomic cross-chain invocations for DApps. SAMOC can realize atomic cross-chain invokes, and the intelligent community deployed on the Atomic-Oracle chain calculates the reputation of users' accounts across multiple chains through Oracle. This paper also designs a corresponding incentive mechanism to ensure that the auditing of cross-chain invokes is honest and trustworthy. Experimental results show that the cross-chain invokes of the proposed SAMOC scheme have lower latency compared to AtomCI. © 2024 IEEE.

关键词： Software testing