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ISSN Approved Journal No: 2456-3315 | Impact factor: 8.14 | ESTD Year: 2016
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| Paper Title: | Machine Learning Predictive Models for Lithium-Ion Battery Life Expectancy |
| Authors Name: | AK Madan , Shubh Kaushik , Tarun Chaturvedi , Vidushi Srivastava |
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IJRTI_186760
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| Published Paper Id: | IJRTI2305165 |
| Published In: | Volume 8 Issue 5, May-2023 |
| DOI: | |
| Abstract: | Automobiles play a fundamental role in our daily lives. Traditionally, widely operated automobiles use hydrocarbon fuels such as gasoline or diesel. Air pollution, climate change, and other issues have been brought up as a result of the widespread use of these vehicles. This has increased the production of green energy powered cars. These types of vehicles usually utilize lithium ion batteries (LIB) as their energy source. LIBs have a finite lifespan and cannot be reused. When a LIB is used past its expiration date, it can have disastrous results, including an explosion. Therefore, a precise estimation of the remaining lifespan of LIBs is crucial when it comes to electric vehicles. Accurate RUL prediction models can be utilized to address the aforementioned problems. To estimate the RUL of a group of LIBs, ML models including LSTM (Long Short-Term Memory) networks, SVM (Support Vector Machine), and SBM (Similarity-based Model) are applied as part of this research. The impact of battery resting (calendar ageing) is also taken into account. In comparison to SVM models, it has been found that LSTM models perform better and train much faster. SBM can be trained more quickly than SVM and LSTM models, and it outperforms both of them in terms of performance. Additionally, it has been noted that batteries' resting time affects how long they will still be functional. |
| Keywords: | SVM, RUL (Remaining Useful Life), LSTM, Lithium Ion Battery, SBM, SOH - State of Health, SOC - State of Charge, Neural Networks, Machine Learning, prediction, Regression, Battery Life |
| Cite Article: | "Machine Learning Predictive Models for Lithium-Ion Battery Life Expectancy", International Journal of Science & Engineering Development Research (www.ijrti.org), ISSN:2455-2631, Vol.8, Issue 5, page no.1052 - 1063, May-2023, Available :http://www.ijrti.org/papers/IJRTI2305165.pdf |
| Downloads: | 000205143 |
| 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: IJRTI2305165
Registration ID:186760
Published In: Volume 8 Issue 5, May-2023
DOI (Digital Object Identifier):
Page No: 1052 - 1063 Country: New Delhi , Delhi , 110042, Delhi, India Research Area: Science & Technology Publisher : IJ Publication Published Paper URL : https://www.ijrti.org/viewpaperforall?paper=IJRTI2305165 Published Paper PDF: https://www.ijrti.org/papers/IJRTI2305165 |
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ISSN: 2456-3315
Impact Factor: 8.14 and ISSN APPROVED,
Journal Starting Year (ESTD) : 2016
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