Summary of Predicting Battery Capacity Fade Using Probabilistic Machine Learning Models with and Without Pre-trained Priors, by Michael J. Kenney et al.
Predicting Battery Capacity Fade Using Probabilistic Machine Learning Models With and Without Pre-Trained Priors
by Michael J. Kenney, Katerina G. Malollari, Sergei V. Kalinin, Maxim Ziatdinov
First submitted to arxiv on: 8 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Materials Science (cond-mat.mtrl-sci)
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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 explores the efficacy of fully Bayesian machine learning in forecasting battery health with quantified uncertainty. It compares three probabilistic ML approaches: Gaussian Process (GP), Structured Gaussian Process (sGP), and Fully Bayesian Neural Network (BNN). The study evaluates their accuracy in predicting capacity degradation and estimating uncertainty, considering factors like pre-training and prior distributions of hyperparameters. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper uses machine learning to predict battery health and estimate uncertainty. It compares three approaches: GP, sGP, and BNN. Each approach has its strengths and weaknesses. The researchers want to see which one is best at predicting capacity degradation and estimating uncertainty. They look at how well each model works when it’s trained on past data. |
Keywords
» Artificial intelligence » Machine learning » Neural network
