Crash Failure Prediction of Lithium-ion Batteries Based on Finite Element and Machine Learning Methods
Abstract
The aging state and operational environment of lithium-ion batteries (LIBs) in electric vehicles are highly complex and variable. To investigate LIB safety under foreign object collisions, this study develops a detailed finite element model of 18650 LIBs at different cycle counts. Following model validation, we conduct comprehensive simulation tests using indenters of varying types, sizes, intrusion angles, and loading positions. A machine learning model is subsequently developed to rapidly predict battery failure displacement and load. Results demonstrate that this approach achieves high-accuracy prediction of LIB failure behavior, providing a valuable reference for other LIB application scenarios.
Downloads
Published
Issue
Section
License
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License [CC BY] that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).