Summary of Gaussian Process-based Online Health Monitoring and Fault Analysis Of Lithium-ion Battery Systems From Field Data, by Joachim Schaeffer et al.
Gaussian process-based online health monitoring and fault analysis of lithium-ion battery systems from field data
by Joachim Schaeffer, Eric Lenz, Duncan Gulla, Martin Z. Bazant, Richard D. Braatz, Rolf Findeisen
First submitted to arxiv on: 27 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Systems and Control (eess.SY); Applications (stat.AP)
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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 presents a novel approach to health monitoring, fault analysis, and detection for lithium iron phosphate battery systems using Gaussian process resistance models. The authors apply their method to a dataset containing 232 cells from 29 battery systems that were returned to the manufacturer due to warranty issues. They develop probabilistic fault detection rules using recursive spatiotemporal Gaussian processes, which enable fast processing of over a million data points. The results show that often, only one cell exhibits abnormal behavior or a knee point, consistent with weakest-link failure for cells connected in series. The study highlights the potential of online monitoring based on data and demonstrates how batteries degrade and fail in the field. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about making sure electric car batteries work safely and last a long time. To do this, scientists used a special kind of math called Gaussian process resistance models to look at real battery data from 29 cars. They found that even when one cell in the battery starts acting weird, it can cause problems for all the other cells. This is important because it helps us understand how batteries fail and how we can fix them before they stop working altogether. |
Keywords
* Artificial intelligence * Spatiotemporal
