Summary of Machine Learning Based Prediction Of Vanadium Redox Flow Battery Temperature Rise Under Different Charge-discharge Conditions, by Anirudh Narayan D et al.
Machine Learning based prediction of Vanadium Redox Flow Battery temperature rise under different charge-discharge conditions
by Anirudh Narayan D, Akshat Johar, Divye Kalra, Bhavya Ardeshna, Ankur Bhattacharjee
First submitted to arxiv on: 26 Apr 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 machine learning (ML) approach to predict the thermal behavior of Vanadium Redox Flow Batteries (VRFBs) during charge-discharge operations. The authors experimentally study the temperature rise of a kW-scale VRFB system under different currents and electrolyte flow rates, then train three ML algorithms – Linear Regression, Support Vector Regression, and Extreme Gradient Boosting – using a practical dataset from a 1kW/6kWh VRFB storage. A comparative analysis shows that XGBoost achieves the highest accuracy (around 99%) in predicting temperature rise. The results can be used to control VRFB temperatures during operation and inform the development of optimized thermal management systems. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper uses machine learning to predict how batteries will get hotter when they’re being charged or discharged. Scientists tested a big battery with different amounts of electricity flowing through it, then used three special computer programs (called algorithms) to see which one could best guess what would happen next. The best algorithm got almost all the predictions right! This information can be useful for people who want to make sure batteries don’t get too hot and have to be cooled down. |
Keywords
» Artificial intelligence » Extreme gradient boosting » Linear regression » Machine learning » Regression » Temperature » Xgboost
