Summary of Domain Knowledge-guided Machine Learning Framework For State Of Health Estimation in Lithium-ion Batteries, by Andrea Lanubile and Pietro Bosoni and Gabriele Pozzato and Anirudh Allam and Matteo Acquarone and Simona Onori
Domain knowledge-guided machine learning framework for state of health estimation in Lithium-ion batteries
by Andrea Lanubile, Pietro Bosoni, Gabriele Pozzato, Anirudh Allam, Matteo Acquarone, Simona Onori
First submitted to arxiv on: 22 Sep 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Systems and Control (eess.SY)
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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 proposed machine learning-based method estimates electric vehicle battery state of health by extracting five online indicators from real-world operation data. These indicators provide physical insights into energy and power fade, enabling accurate capacity estimation even with partially missing data. The method computes the indicators using experimental data from aged cells and trains linear regression models for real-time degradation estimation. Trained models achieve capacity estimation within 1.5% to 2.5% maximum absolute percentage error. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps keep electric vehicle batteries healthy by estimating their state of health. It’s like a doctor for your car battery! The researchers created special tools (indicators) that can be used while the car is being driven or charged, which gives them a good idea of how well the battery is holding up. They tested these indicators using data from five old batteries and found that they were really accurate. |
Keywords
» Artificial intelligence » Linear regression » Machine learning
