Lithium-Ion Battery Classification and Detection Using an Optimal Machine Learning Algorithm

Authors

  • Vineetha . K  Department of Computer Science and Engineering, Kings Engineering College, Chennai, Tamilnadu, India
  • S. Vilma Veronica  Department of Computer Science and Engineering, Kings Engineering College, Chennai, Tamilnadu, India
  • S. Hemalatha  Department of Computer Science and Engineering, Kings Engineering College, Chennai, Tamilnadu, India
  • G. Suresh  Department of Computer Science and Engineering, Kings Engineering College, Chennai, Tamilnadu, India

DOI:

https://doi.org/10.32628/IJSRSET2310538

Keywords:

LIB, Rediction, Battery Management System, Python Programming Language, Jupyter Notebook

Abstract

In today's civilization, lithium-ion batteries (LIBs) are essential energy storage technologies. In terms of energy density, power density, cycle life, safety, etc., the performance and cost are still unsatisfactory. Traditional "trial-and-error" procedures necessitate a large number of time-consuming trials to further enhance battery performance. The End-of-life (EOL) LIBs come in a variety of shapes and sizes, which makes it difficult to automate a few unit processes (such cell-level disassembly) of the recycling process. Meanwhile, LIBs contain dangerous and combustible components, posing serious risks to human exposure. In this paper, we anticipate the various crystal system types based on the system's LIB using an optimal machine learning (OML) approach.

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International Journal of Scientific Research in Science, Engineering and Technology

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Published

2023-10-30

Issue

Volume 10, Issue 5, September-October-2023

Section

Research Articles

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This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite

[1]
Vineetha . K, S. Vilma Veronica, S. Hemalatha, G. Suresh "Lithium-Ion Battery Classification and Detection Using an Optimal Machine Learning Algorithm" International Journal of Scientific Research in Science, Engineering and Technology (IJSRSET), Print ISSN : 2395-1990, Online ISSN : 2394-4099, Volume 10, Issue 5, pp.222-228, September-October-2023. Available at doi : https://doi.org/10.32628/IJSRSET2310538