Summary of The State Of Lithium-ion Battery Health Prognostics in the Cps Era, by Gaurav Shinde et al.
The State of Lithium-Ion Battery Health Prognostics in the CPS Era
by Gaurav Shinde, Rohan Mohapatra, Pooja Krishan, Harish Garg, Srikanth Prabhu, Sanchari Das, Mohammad Masum, Saptarshi Sengupta
First submitted to arxiv on: 28 Mar 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Signal Processing (eess.SP)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper explores the integration of Prognostics and Health Management (PHM) within lithium-ion batteries. The authors examine Remaining Useful Life (RUL), a critical concept in prognostics, which enables predicting component failure before it occurs. The paper reviews various RUL prediction methods, from traditional models to data-driven techniques, highlighting the shift towards deep learning architectures in Li-ion battery health prognostics. Practical applications of PHM across industries are also explored, providing insights into real-world implementations. This comprehensive guide serves both researchers and practitioners in the field of Li-ion battery PHM. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper talks about making lithium-ion batteries better by predicting when they might fail. It’s like having a special radar system that tells us when something is going wrong before it does. The authors look at different ways to do this and how deep learning can help make predictions more accurate. They also talk about how this technology can be used in real-life situations, like making sure electric cars have enough power or keeping medical devices running smoothly. |
Keywords
* Artificial intelligence * Deep learning
