Digital twin modeling method for lithium-ion batteries based on data-mechanism fusion driving

Lithium-ion batteries have been rapidly developed as clean energy sources in many industrial fields, such as new energy vehicles and energy storage. The core issues hindering their further promotion and application are reliability and safety. A digital twin model that maps onto the physical entity of the battery with high simulation accuracy helps to monitor internal states and improve battery safety. This work focuses on developing a digital twin model via a mechanism-data-driven parameter updating algorithm to increase the simulation accuracy of the internal and external characteristics of the full-time domain battery under complex working conditions. An electrochemical model is first developed with the consideration of how electrode particle size impacts battery characteristics. By adding the descriptions of temperature distribution and particle-level stress, a multi-particle size electrochemical-thermal-mechanical coupling model is established. Then, considering the different electrical and thermal effect among individual cells, a model for the battery pack is constructed. A digital twin model construction method is finally developed and verified with battery operating data.

• Record URL:
  • https://doi.org/10.1016/j.geits.2024.100162
• Record URL:
  • http://www.sciencedirect.com/science/article/pii/S2773153724000148
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/issn/27731537
• Supplemental Notes:
  • © 2024 The Author(s). Published by Elsevier Ltd on behalf of Beijing Institute of Technology Press Co., Ltd. Abstract reprinted with permission of Elsevier.
• Authors:
  • Lyu, Chao
  • Xu, Shaochun
  • Li, Junfu
  • Pecht, Michael
• Publication Date: 2024-10

Language

• English

Media Info

• Media Type: Web
• Features: References;
• Pagination: 100162
• Serial:
  • Green Energy and Intelligent Transportation
  • Volume: 3
  • Issue Number: 5
  • Publisher: Elsevier
  • ISSN: 2773-1537
  • Serial URL: https://www.sciencedirect.com/journal/green-energy-and-intelligent-transportation
• Publication flags:

  Open Access (libre)

Subject/Index Terms

• TRT Terms: Battery electrodes; Design; Lithium batteries; Performance; Simulation
• Subject Areas: Design; Energy; Highways; Vehicles and Equipment;

Filing Info

• Accession Number: 01935593
• Record Type: Publication
• Files: TRIS
• Created Date: Oct 31 2024 9:18AM