Material Synthesis, Manufacturing and Electrochemical Modelling for Lithium-Ion Batteries in Electric Vehicle

Special Issue Editors


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Guest Editor
Department of Engineering Science, University of Oxford, Parks Road, Oxford OX1 3PJ, UK
Interests: lithium-ion batteries; electrode manufacturing; porous materials; electrochemical analysis

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Guest Editor
School of Chemistry, University of Birmingham, Birmingham B15 2TT, UK
Interests: lithium-ion batteries; all solid state batteries; electrode synthesis; synthesis route optimisation; electrode/electrolyte characterisation

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Guest Editor
Department of Engineering Science, University of Oxford, Parks Road, Oxford OX1 3PJ, UK
Interests: lithium-ion batteries; sodium-ion batteries; electrode optimization; rate-limiting electrode; dendrite inhibition; battery degradation; battery modelling

Special Issue Information

Dear Colleagues,

Lithium-ion batteries will play a pivotal role in advancing electrification in the automotive industry and achieving our net zero goals. Nevertheless, lithium-ion batteries are facing challenges in existing electric vehicles, e.g., limited driving range, slow charging rate and unreliable temperature performance. These issues hinder the further wide adoption of electric vehicles and their complete replacement of internal combustion engine vehicles.

This Special Issue focuses on the latest progress in the development of lithium-ion batteries, including but not limited to:

  • Electrode material synthesis (new material discovery, synthesis route optimisation, etc.);
  • Electrode manufacturing and structure optimisation;
  • Battery modelling (degradation, SEI, temperature, particle cracking, etc.);
  • Testing metrology;
  • Battery characterisation;
  • Lithium-ion battery cell and pack design;
  • Life cycle assessment;
  • Lithium and beyond (various types of metal-ion batteries).

Dr. Pengcheng Zhu
Dr. Bo Dong
Dr. Yongxiu Chen
Guest Editors

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Keywords

  • batteries
  • electric Vehicle
  • degradation

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Published Papers (1 paper)

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Research

14 pages, 5532 KiB  
Article
Multi-Cell Displacement Measurement During the Assembly of Automotive Power Batteries Based on Machine Vision
by Yueda Xu, Yanfeng Xing, Hongbo Zhao, Yufang Lin, Lijia Ren and Zhihan Zhou
World Electr. Veh. J. 2025, 16(1), 27; https://doi.org/10.3390/wevj16010027 - 6 Jan 2025
Viewed by 955
Abstract
The positioning of lithium battery tabs in electric vehicles is a crucial aspect of the power battery assembly process. During the pre-tightening process of the lithium battery stack assembly, cells and foams undergo different deformations, leading to varying displacements of cells at different [...] Read more.
The positioning of lithium battery tabs in electric vehicles is a crucial aspect of the power battery assembly process. During the pre-tightening process of the lithium battery stack assembly, cells and foams undergo different deformations, leading to varying displacements of cells at different levels. Consequently, determining tab positions poses numerous challenges during the pre-tightening process of the stack assembly. To address these challenges, this paper proposes a method for detecting feature points and calculating the displacement of lithium battery stack tabs based on the MicKey method. This research focuses on the cell tab, utilizing the hue, saturation, and value (HSV) color space for image segmentation to adaptively extract the cell tab region and further obtain the ROI of the cell tab. In order to enhance the accuracy of tab displacement calculation, a novel method for feature point detection and displacement calculation of lithium battery stacks based on the MicKey (Metric Keypoints) method is introduced. MicKey can predict the coordinates of corresponding keypoints in the 3D camera space through keypoint matching based on neural networks, and it can acquire feature point pairs of the subject to be measured through its unique depth reduction characteristics. Results demonstrate that the average displacement error and root mean square error of this method are 0.03 mm and 0.04 mm, respectively. Compared to other feature matching algorithms, this method can more consistently and accurately detect feature points and calculate displacements, meeting the positioning accuracy requirements for the stack pole ear in the actual assembly process. It provides a theoretical foundation for subsequent procedures. Full article
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