Summary of Remaining Useful Life Prediction For Batteries Utilizing An Explainable Ai Approach with a Predictive Application For Decision-making, by Biplov Paneru et al.
Remaining Useful Life Prediction for Batteries Utilizing an Explainable AI Approach with a Predictive Application for Decision-Making
by Biplov Paneru, Bipul Thapa, Durga Prasad Mainali, Bishwash Paneru, Krishna Bikram Shah
First submitted to arxiv on: 26 Sep 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Systems and Control (eess.SY)
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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 develops machine learning models for estimating the Remaining Useful Life (RUL) of batteries, a crucial task for determining their lifespan and recharge requirements. The authors introduce a two-level ensemble learning (TLE) framework and a CNN+MLP hybrid model, comparing them to traditional, deep, and hybrid machine learning models. The TLE model outperforms baseline models in RMSE, MAE, and R squared error, demonstrating its predictive capabilities. Additionally, the XGBoost classifier achieves 99% classification accuracy, validated through cross-validation techniques. The models predict relay-based charging triggers, enabling automated and energy-efficient charging processes. This automation reduces energy consumption and enhances battery performance by optimizing charging cycles. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper predicts Remaining Useful Life (RUL) of batteries using machine learning. They make new models that are better than old ones. These new models are good at guessing how long a battery will last and what it needs to recharge. The best model is the two-level ensemble learning (TLE) model, which does well on tests. Another model, XGBoost, also does very well at classifying batteries into different groups based on their RUL. |
Keywords
» Artificial intelligence » Classification » Cnn » Machine learning » Mae » Xgboost
