Summary of Early-cycle Internal Impedance Enables Ml-based Battery Cycle Life Predictions Across Manufacturers, by Tyler Sours (1) et al.
Early-Cycle Internal Impedance Enables ML-Based Battery Cycle Life Predictions Across Manufacturers
by Tyler Sours, Shivang Agarwal, Marc Cormier, Jordan Crivelli-Decker, Steffen Ridderbusch, Stephen L. Glazier, Connor P. Aiken, Aayush R. Singh, Ang Xiao, Omar Allam
First submitted to arxiv on: 5 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 This paper introduces a methodology that combines traditional voltage-capacity features with Direct Current Internal Resistance (DCIR) measurements to predict the end-of-life (EOL) of lithium-ion batteries across different manufacturers. The approach uses early-cycle DCIR data to capture critical degradation mechanisms related to internal resistance growth, enhancing model robustness. The models successfully predict the number of cycles to EOL for unseen manufacturers with a mean absolute error (MAE) of 150 cycles, demonstrating cross-manufacturer generalizability. This reduces the need for extensive new data collection and retraining, enabling manufacturers to optimize new battery designs using existing datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study helps predict when lithium-ion batteries will stop working by combining two types of data: how much charge they hold and their internal resistance. They found that by looking at how the battery’s internal resistance changes over time, they can make more accurate predictions about when it will stop working. This is important because different manufacturers use slightly different materials and processes to make batteries, so being able to predict EOL across different types of batteries is a big deal. |
Keywords
» Artificial intelligence » Mae
