Intelligent Control and Energy Systems for Modern Mobility and Industry

A special issue of World Electric Vehicle Journal (ISSN 2032-6653).

Deadline for manuscript submissions: 31 December 2024 | Viewed by 728

Special Issue Editors


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Guest Editor
Department of Automation, University of Science and Technology of China, Hefei, China
Interests: hybrid mobile robots;power systems of new energy vehicles;multi-energy complementarity and collaboration of distributed micro-grid
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Guest Editor
School of Management and Engineering, Nanjing University, Nanjing, China
Interests: intelligent control theory and methods; machine learning and data mining; pattern recognition and artificial intelligence; granular computing and knowledge discovery; image processing; industrial intelligence

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Guest Editor
School of Aeronautics and Astronautics, Sun Yat-sen University, Guangzhou, China
Interests: distributed cooperative sensing and control of vehicles; theory of complex systems in air and space; research on modelling simulation and parallel experiments; applied research on cooperative sensing of vehicles

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Guest Editor
School of Alectrical Engineering and Automation, Tianjin University of Technology, Tianjin, China
Interests: hybrid electric vehicles; energy management

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Guest Editor
Pengcheng Laboratory, Shenzhen, China
Interests: artificial intelligence applications; intelligent control and intelligent sys-tems; industrial monitoring technology; digital twin simulation modelling; industrial internet edge computing

Special Issue Information

Dear Colleagues,

In the era of rapid technological advancement, the integration of energy systems with intelligent control mechanisms is revolutionizing modern mobility and industry. This convergence encompasses a wide array of cutting-edge technologies and methodologies, including hybrid mobile robots, new energy vehicle power systems, and multi-energy complementarity within distributed micro-grids. Additionally, advanced energy integration plays a crucial role in optimizing these systems. Research in these areas necessitates the support of diverse and comprehensive fields such as artificial intelligence, machine learning, data mining, and intelligent control theories. Scholars and experts from various domains are required to collaborate, communicate, and jointly promote the intelligentization and electrification of related fields.

As we look toward the future, the convergence of energy, intelligent sensing, and control systems within a unified model is anticipated to give rise to super-intelligent entities. These entities will be capable of revolutionizing industries through their autonomous decision-making capabilities and their ability to optimize energy use on an unprecedented scale. To achieve these ambitious goals, research must be underpinned by robust fields such as artificial intelligence, machine learning, data mining, and advanced control theories. Scholars and experts in various fields are required to communicate and jointly promote the process of intelligence in related fields.  Intelligentization and electrification are crucial for ensuring that vehicles operate autonomously and are environmentally friendly.

Prof. Dr. Zonghai Chen
Prof. Dr. Huaxiong Li
Dr. Xuerong Yang
Dr. Liang Xu
Dr. Xiaojun Liang
Guest Editors

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Keywords

  • intelligent control
  • energy management
  • hybrid mobile robots
  • distributed micro-grids
  • machine learning
  • artificial intelligence
  • digital twin simulation
  • industrial internet edge computing

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Published Papers (1 paper)

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Research

20 pages, 3115 KiB  
Article
Real-Time LiDAR–Inertial Simultaneous Localization and Mesh Reconstruction
by Yunqi Cheng, Meng Xu, Kezhi Wang, Zonghai Chen and Jikai Wang
World Electr. Veh. J. 2024, 15(11), 495; https://doi.org/10.3390/wevj15110495 - 29 Oct 2024
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Abstract
In this paper, a novel LiDAR–inertial-based Simultaneous Localization and Mesh Reconstruction (LI-SLAMesh) system is proposed, which can achieve fast and robust pose tracking and online mesh reconstruction in an outdoor environment. The LI-SLAMesh system consists of two components, including LiDAR–inertial odometry and a [...] Read more.
In this paper, a novel LiDAR–inertial-based Simultaneous Localization and Mesh Reconstruction (LI-SLAMesh) system is proposed, which can achieve fast and robust pose tracking and online mesh reconstruction in an outdoor environment. The LI-SLAMesh system consists of two components, including LiDAR–inertial odometry and a Truncated Signed Distance Field (TSDF) free online reconstruction module. Firstly, to reduce the odometry drift errors we use scan-to-map matching, and inter-frame inertial information is used to generate prior relative pose estimation for later LiDAR-dominated optimization. Then, based on the motivation that the unevenly distributed residual terms tend to degrade the nonlinear optimizer, a novel residual density-driven Gauss–Newton method is proposed to obtain the optimal pose estimation. Secondly, to achieve fast and accurate 3D reconstruction, compared with TSDF-based mapping mechanism, a more compact map representation is proposed, which only maintains the occupied voxels and computes the vertices’ SDF values of each occupied voxels using an iterative Implicit Moving Least Squares (IMLS) algorithm. Then, marching cube is performed on the voxels and a dense mesh map is generated online. Extensive experiments are conducted on public datasets. The experimental results demonstrate that our method can achieve significant localization and online reconstruction performance improvements. The source code will be made public for the benefit of the robotic community. Full article
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