Summary of A Scientific Machine Learning Approach For Predicting and Forecasting Battery Degradation in Electric Vehicles, by Sharv Murgai et al.
A Scientific Machine Learning Approach for Predicting and Forecasting Battery Degradation in Electric Vehicles
by Sharv Murgai, Hrishikesh Bhagwat, Raj Abhijit Dandekar, Rajat Dandekar, Sreedath Panat
First submitted to arxiv on: 18 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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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 The paper presents a novel approach to predicting and forecasting battery degradation in electric vehicles using a Scientific Machine Learning framework. This hybrid method integrates domain knowledge with neural networks to capture both known and unknown degradation dynamics, improving predictive accuracy while reducing data requirements. The model achieved high precision in experimental data, demonstrating its enhanced capabilities. By enhancing battery longevity and minimizing waste, the approach contributes to the sustainability of energy systems and accelerates the global transition toward cleaner, more responsible energy solutions, aligning with the UN’s SDG agenda. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper is about a new way to predict how well electric vehicle batteries will last over time. Right now, it’s hard to know when a battery will start to wear out, which makes it difficult to make sure they’re safe and reliable. The scientists used a special kind of computer program that combines what we already know about batteries with machine learning (a type of AI) to create a more accurate prediction model. This can help us make better decisions about how to use our energy resources in the future, which is important for reducing our impact on the planet. |
Keywords
» Artificial intelligence » Machine learning » Precision
