Summary of Transformer-based Capacity Prediction For Lithium-ion Batteries with Data Augmentation, by Gift Modekwe et al.
Transformer-based Capacity Prediction for Lithium-ion Batteries with Data Augmentation
by Gift Modekwe, Saif Al-Wahaibi, Qiugang Lu
First submitted to arxiv on: 22 Jul 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Signal Processing (eess.SP)
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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 proposes a novel approach to estimating the capacities of lithium-ion batteries, which are crucial for technological advancements in transportation, electronics, and clean energy storage. Current methods fail to account for long-term temporal dependencies in key variables such as voltage, current, and temperature, leading to inadequate state-of-health monitoring. To address this, the authors develop a transformer-based battery capacity prediction model that captures both short-term and long-term patterns in battery data. The proposed method uses data augmentation to increase data size, improving performance. Results validate the approach using benchmark datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper is about finding a better way to figure out how much charge is left in lithium-ion batteries. These batteries are super important for things like electric cars and smartphones. Right now, we don’t have a great way to estimate how much charge is left because our methods don’t take into account the changes that happen over time. This makes it hard to keep track of how well the battery is doing. The scientists in this paper came up with a new approach using something called transformer networks. They also found a way to make their method work better by adding more fake data. This helps them get better results. |
Keywords
* Artificial intelligence * Data augmentation * Temperature * Transformer
