Battery engineering safety technologies (BEST): M5 framework of mechanisms, modes, metrics, modeling, and mitigation

The increasing adoption of electric vehicles (EVs) has underscored the importance of lithium-ion batteries (LIBs), which, however, pose inherent safety risks. These issues can escalate from moderate faults to critical failures, potentially leading to thermal runaway—a dangerous chain reaction that can result in fires and explosions. Therefore, addressing and mitigating these safety hazards is crucial. This review introduces the concept of Battery Engineering Safety Technologies (BEST), summarizing recent advancements and aiming to outline a holistic and hierarchical framework for addressing real-world battery safety issues step by step: mechanisms, modes, metrics, modelling, and mitigation. Specifically, the M5 framework includes: (a) identification of mechanisms and causes, (b) failure mode and effects analysis, (c) metrics for evaluation, (d) modelling and forecasting, and (e) mitigation through material optimization, cell, and system design. Applications of the M5 hierarchical assessment, stemming from observational, empirical, statistical, and physical understanding of batteries at the materials, cell, and pack levels, not only have the potential to produce new insights but also contribute to dramatic efficiencies, more accurate predictions, and better interpretability for the evolution of electrochemical systems. It concludes with an overview of current challenges and future directions in battery safety research, emphasizing data-centered, AI-based digital solutions.

• Record URL:
  • https://doi.org/10.1016/j.etran.2024.100364
• Record URL:
  • http://www.sciencedirect.com/science/article/pii/S2590116824000547
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/issn/25901168
• Supplemental Notes:
  • © 2024 Elsevier B.V. All rights are reserved, including those for text and data mining, AI training, and similar technologies. Abstract reprinted with permission of Elsevier.
• Authors:
  • Zhao, Jingyuan
  • 0000-0002-6527-4294
  • Lv, Zhilong
  • Li, Di
  • Feng, Xuning
  • Wang, Zhenghong
  • Wu, Yuyan
  • Shi, Dapai
  • Fowler, Michael
  • Burke, Andrew F
• Publication Date: 2024-12

Language

• English

Media Info

• Media Type: Web
• Features: References;
• Pagination: 100364
• Serial:
  • eTransportation
  • Volume: 22
  • Issue Number: 0
  • Publisher: Elsevier
  • ISSN: 2590-1168
  • Serial URL: https://www.sciencedirect.com/journal/etransportation

Subject/Index Terms

• TRT Terms: Electric batteries; Electric vehicles; Lithium batteries; Mathematical models; Metrics (Quantitative assessment); Research; Vehicle safety
• Subject Areas: Energy; Highways; Vehicles and Equipment;

Filing Info

• Accession Number: 01938959
• Record Type: Publication
• Files: TRIS
• Created Date: Dec 9 2024 9:56AM