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International Journal for Research Trends and Innovation
International Peer Reviewed & Refereed Journals, Open Access Journal
ISSN Approved Journal No: 2456-3315 | Impact factor: 8.14 | ESTD Year: 2016
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Impact Factor : 8.14
Issue per Year : 12
Volume Published : 11
Issue Published : 118
Article Submitted : 21686
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Total Authors : 22487
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| Paper Title: | A Study of Different Machine Learning Algorithms for State of Charge (SOC) Estimation in Lithium-ion Battery Pack |
| Authors Name: | Keya Karkun , Akanksha Yadav , Shruti Pawar , Shubham Pal , Manisha Shitole |
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IJRTI_203886
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| Published Paper Id: | IJRTI2505208 |
| Published In: | Volume 10 Issue 5, May-2025 |
| DOI: | |
| Abstract: | Reading the state of charge( SoC) using battery control systems is laborious because of their life and responsibility. Since battery declination is generally non-direct, predicting SoC estimation with significantly lower declination is laborious. So, the estimation of SOC is an increasingly major problem in icing the effectiveness and safety of the battery. To overcome these issues in SOC estimation, we set up multitudinous styles in the scientific literature, with differing degrees of perfection and intricacy. The SOC of lithium- ion batteries can now be precisely predicted using supervised knowledge approaches. Reliable assessment of the state of charge( SoC) of a battery ensures safe operation, extends battery continuance, and optimizes system performance. This work compares and studies the performance, benefits, and downsides of six supervised knowledge ways for SOC estimates. Different SoC estimate styles are mooted, including both conventional and contemporary styles. These correspond of ways using voltage and current measures and more complex algorithms using electrochemical models, impedance spectroscopy, and machine knowledge styles, incorporating the use of artificial intelligence and machine knowledge for flexible SoC estimation. In the future, SoC estimates will be a vital element of a larger ecosystem for energy operation, allowing for the indefectible integration of energy storage into smart grids and espousing farther environmentally friendly energy habits. |
| Keywords: | lithium-ion battery, state of charge (SOC), machine learning algorithms, accurate social estimation, supervised learning. |
| Cite Article: | "A Study of Different Machine Learning Algorithms for State of Charge (SOC) Estimation in Lithium-ion Battery Pack", International Journal for Research Trends and Innovation (www.ijrti.org), ISSN:2455-2631, Vol.10, Issue 5, page no.c60-c66, May-2025, Available :http://www.ijrti.org/papers/IJRTI2505208.pdf |
| Downloads: | 000441 |
| ISSN: |
2456-3315 | IMPACT FACTOR: 8.14 Calculated By Google Scholar| ESTD YEAR: 2016 An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.14 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator |
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Published Paper ID: IJRTI2505208
Registration ID:203886
Published In: Volume 10 Issue 5, May-2025
DOI (Digital Object Identifier):
Page No: c60-c66 Country: Pune, MAHARASHTRA, India Research Area: Computer Science & Technology Publisher : IJ Publication Published Paper URL : https://www.ijrti.org/viewpaperforall?paper=IJRTI2505208 Published Paper PDF: https://www.ijrti.org/papers/IJRTI2505208 |
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ISSN: 2456-3315
Impact Factor: 8.14 and ISSN APPROVED,
Journal Starting Year (ESTD) : 2016
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