A Micromesh Multi-Scaled Features Extraction Network for Li-Ion Batteries SOH Estimation

The great progress and wide application of electronic products and electric vehicles have entailed more stringent requirements for the reliability of lithium-ion batteries. The State of Health (SOH) serves as a significant indicator for evaluating the condition of such batteries. However existing methods are lack of refined modeling for sequences and unable to make precise estimation for SOH. In this paper, a micromesh multi-scaled features extraction network (MMFEN) is proposed for accurately estimating SOH. A refined representation block is developed for heterogeneous elaborate feature extraction. Then, a multi-head convolution attention block is constructed to capture multi-scaled efficient state information. To demonstrate the superiority of MMFEN, experiments are conducted on the NASA and CALCE data published online and a real-world electric vehicles (EVs) data set which is collected from existing battery management systems. Comparing with traditional methods, MMFEN achieves remarkable performance with average root mean square error of 1.21% and mean absolute percentage error of 0.99% on NASA samples, 2.78% and 2.71% on CALCE dataset, while 2.39% and 2.08% on EVs data set, respectively.

• Record URL:
  • https://doi.org/10.1109/TVT.2025.3544483
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/issn/00189545
• Supplemental Notes:
  • Copyright © 2025, IEEE.
• Authors:
  • Wang, Min
  • Chen, Yitian
  • Guo, Dongxu
  • 0000-0003-3697-6913
  • Xu, Zhiwei
• Publication Date: 2025-7

Language

• English

Media Info

• Media Type: Web
• Features: References;
• Pagination: pp 10321-10331
• Serial:
  • IEEE Transactions on Vehicular Technology
  • Volume: 74
  • Issue Number: 7
  • Publisher: Institute of Electrical and Electronics Engineers (IEEE)
  • ISSN: 0018-9545
  • Serial URL: http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=25

Subject/Index Terms

• TRT Terms: Data management; Deep learning; Electric vehicles; Lithium batteries
• Subject Areas: Data and Information Technology; Energy; Highways; Vehicles and Equipment;

Filing Info

• Accession Number: 01977142
• Record Type: Publication
• Files: TRIS
• Created Date: Jan 26 2026 8:41AM