Evidence map›Paper›PMID 38746910›Full record

ReviewInnovation (Cambridge (Mass.))2024

Advances and challenges in thermal runaway modeling of lithium-ion batteries.

Gongquan Wang, Ping Ping, Depeng Kong, Rongqi Peng, Xu He, Yue Zhang, Xinyi Dai, Jennifer Wen

Abstract readReview
In one paragraph

Review in Innovation (Cambridge (Mass.)), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed.

  1. Review
  2. Review
  3. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors.

Gongquan WangCenter for Offshore Engineering and Safety Technology, China University of Petroleum (East China), Qingdao 266580, China.
Ping PingCollege of Chemical Engineering, China University of Petroleum (East China), Qingdao 266580, China.
Depeng KongCenter for Offshore Engineering and Safety Technology, China University of Petroleum (East China), Qingdao 266580, China.
Rongqi PengCenter for Offshore Engineering and Safety Technology, China University of Petroleum (East China), Qingdao 266580, China.
Xu HeCenter for Offshore Engineering and Safety Technology, China University of Petroleum (East China), Qingdao 266580, China.
Yue ZhangCenter for Offshore Engineering and Safety Technology, China University of Petroleum (East China), Qingdao 266580, China.
Xinyi DaiCenter for Offshore Engineering and Safety Technology, China University of Petroleum (East China), Qingdao 266580, China.
Jennifer WenCentre for Energy Resilience, School of Mechanical Engineering Sciences, University of Surrey, Guildford, Surrey GU2 7XH, UK.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The broader application of lithium-ion batteries (LIBs) is constrained by safety concerns arising from thermal runaway (TR). Accurate prediction of TR is essential to comprehend its underlying mechanisms, expedite battery design, and enhance safety protocols, thereby significantly promoting the safer use of LIBs. The complex, nonlinear nature of LIB systems presents substantial challenges in TR modeling, stemming from the need to address multiscale simulations, multiphysics coupling, and computing efficiency issues. This paper provides an extensive review and outlook on TR modeling technologies, focusing on recent advances, current challenges, and potential future directions. We begin with an overview of the evolutionary processes and underlying mechanisms of TR from multiscale perspectives, laying the foundation for TR modeling. Following a comprehensive understanding of TR phenomena and mechanisms, we introduce a multiphysics coupling model framework to encapsulate these aspects. Within this framework, we detail four fundamental physics modeling approaches: thermal, electrical, mechanical, and fluid dynamic models, highlighting the primary challenges in developing and integrating these models. To address the intrinsic trade-off between computational accuracy and efficiency, we discuss several promising modeling strategies to accelerate TR simulations and explore the role of AI in advancing next-generation TR models. Last, we discuss challenges related to data availability, model scalability, and safety standards and regulations.

Identifiers

PMID38746910
PMCPMC11089405

What OpenQuestion holds

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LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.