A universal anisotropic model for a lithium‐ion cylindrical cell validated under axial, lateral, and bending loads-Reference-Cited by-同舟云学术

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A universal anisotropic model for a lithium‐ion cylindrical cell validated under axial, lateral, and bending loads

Published:2022-03-13 Issue:4 Volume:10 Page:1431-1448
ISSN:2050-0505
Container-title:Energy Science & Engineering
language:en
Short-container-title:Energy Science & Engineering

Author:

Song Yihan¹,Gilaki Mehdi¹,Keshavarzi Mohammad Mehdi¹,Sahraei Elham¹^ORCID

Affiliation:

1. Electric Vehicle Safety Lab (EVSL), Department of Mechanical Engineering Temple University Philadelphia Pennsylvania USA

Funder

Massachusetts Institute of Technology

Temple University

Office of Naval Research Global

Publisher

Wiley

Subject

General Energy,Safety, Risk, Reliability and Quality

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1002/ese3.1111

Reference67 articles.

1. Electrical Energy Storage for the Grid: A Battery of Choices

2. Challenges in the development of advanced Li-ion batteries: a review

3. Effect of tariffs on the performance and economic benefits of PV-coupled battery systems

4. Internal short circuit detection for battery pack using equivalent parameter and consistency method

5. Experimental Study on the Combustion Characteristics of Primary Lithium Batteries Fire

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1. Coupling effect of state of charge and loading rate on internal short circuit of lithium-ion batteries induced by mechanical abuse;Applied Energy;2024-12

2. Dynamic response analysis of cylindrical lithium-ion battery under impact loadings: A theoretical study;Thin-Walled Structures;2024-12

3. Validation of Sahraei Failure Criterion on cylindrical and pouch Lithium-ion battery cells;Journal of Energy Storage;2024-07

4. Development of a Homogenized Finite Element Model for Pouch Lithium-Ion Battery Cells Considering Porosity and Pressure Sensitivity;Energies;2024-02-29

5. Mechanical Behavior and Failure Prediction of Cylindrical Lithium-Ion Batteries Under Mechanical Abuse Using Data-Driven Machine Learning;2024

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