Crash Safety Design for Lithium-ion Vehicle Battery Module with Machine Learning
Lithium-ion battery systems have been used as the main power source for electric vehicles due to their lightweight and high energy density. The impact safety of these battery systems has been a primary issue. In this work, the crashworthiness design of a typical vehicle battery module is implemented through numerical (finite element) simulations integrated with machine learning algorithms (decision trees). The module with multiple layered porous cells is modeled with a simplified, homogeneous material law, and subjects to the impact of a cylindrical indenter. The main protective component on the module - cover plate is designed as an energy absorbing sandwich structure with a core of cellular solids. Large scale simulations are conducted with various design variable values for the sandwich structure, and the results form a design (simulation) dataset. Based on the dataset, machine learning is applied to the sandwich cover plate design to: (1) correlate the design variables to the response; (2) investigate the complex inter-relationship between design variables; and (3) derive decision-making rules to achieve the designs with highest energy absorbing capability.
- Record URL:
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Availability:
- Find a library where document is available. Order URL: http://worldcat.org/issn/01487191
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Supplemental Notes:
- Abstract reprinted with permission of SAE International.
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Authors:
- Zhu, Feng
- Logakannan, Krishna
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Conference:
- WCX SAE World Congress Experience
- Location: Detroit & Online Michigan, United States
- Date: 2022-4-5 to 2022-4-7
- Publication Date: 2022-3-29
Language
- English
Media Info
- Media Type: Web
- Features: References;
- Pagination: pp 1667-1677
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Serial:
- SAE Technical Paper
- Volume: 4
- Issue Number: 5
- Publisher: Society of Automotive Engineers (SAE)
- ISSN: 0148-7191
- EISSN: 2688-3627
- Serial URL: http://papers.sae.org/
Subject/Index Terms
- TRT Terms: Electric vehicles; Finite element method; Lithium batteries; Machine learning
- Subject Areas: Highways; Vehicles and Equipment;
Filing Info
- Accession Number: 01841939
- Record Type: Publication
- Source Agency: SAE International
- Report/Paper Numbers: 2022-01-0863
- Files: TRIS, SAE
- Created Date: Apr 6 2022 2:18PM