Remaining useful life prediction of lithium-ion batteries based on autoregression with exogenous variables model

The gradual decrease capacity serves as a pivotal health indicator, reflecting the condition of lithium-ion batteries. Accurate forecasting of capacity can ascertain the remaining lifespan of these batteries at any given cycle, which is crucial for managing batteries in electric vehicles. This paper proposes an Autoregression with Exogenous Variables (AREV) model, which continually updates itself through a sliding window, offering predictions of battery state of health and remaining useful life, which extends battery prognostics at a fixed operating condition to different operating conditions. In addition, unlike most models that require multiple battery data of the same type for training, the proposed model only requires the use of fragmented data of the target battery with length around 30-50 cycles for capacity prediction and determines battery life based on battery failure thresholds. The above two points enable this model to be updated online without the need for any offline training. Finally, four different types of battery dataset , with different chemical substances and different charge and discharge conditions (especially dataset that follows random walk discharging profile to stimulate the real power consumption process) , are applied to verify the effectiveness and robustness of proposed RUL prediction approach. It shows that the proposed model can accurately predicting future capacity values. Timely warning signals can be issued before the end of life of battery, thereby ensuring the safe driving of electric vehicles and timely battery replacement.

• Record URL:
  • https://doi.org/10.1016/j.ress.2024.110485
• Record URL:
  • http://www.sciencedirect.com/science/article/pii/S095183202400557X
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/issn/09518320
• Supplemental Notes:
  • © 2024 Elsevier Ltd. All rights are reserved, including those for text and data mining, AI training, and similar technologies. Abstract reprinted with permission of Elsevier.
• Authors:
  • Huang, Zhelin
  • 0000-0002-6152-8744
  • Ma, Zhihua
• Publication Date: 2024-12

Language

• English

Media Info

• Media Type: Web
• Features: References;
• Pagination: 110485
• Serial:
  • Reliability Engineering & System Safety
  • Volume: 252
  • Issue Number: 0
  • Publisher: Elsevier
  • ISSN: 0951-8320
  • Serial URL: https://www.sciencedirect.com/journal/reliability-engineering-and-system-safety

Subject/Index Terms

• TRT Terms: Aging (Materials); Condition surveys; Electric discharge; Lithium batteries; Predictive models; Service life
• Subject Areas: Energy; Highways; Maintenance and Preservation; Vehicles and Equipment;

Filing Info

• Accession Number: 01933845
• Record Type: Publication
• Files: TRIS
• Created Date: Oct 16 2024 11:10AM