Summary of Taking Second-life Batteries From Exhausted to Empowered Using Experiments, Data Analysis, and Health Estimation, by Xiaofan Cui et al.
Taking Second-life Batteries from Exhausted to Empowered using Experiments, Data Analysis, and Health Estimation
by Xiaofan Cui, Muhammad Aadil Khan, Gabriele Pozzato, Surinder Singh, Ratnesh Sharma, Simona Onori
First submitted to arxiv on: 29 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: 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 study explores the reuse of retired electric vehicle batteries in grid energy storage, presenting algorithms for health monitoring and prediction. The research implements a cycling protocol to simulate grid energy storage load profiles on a dataset of second-life batteries collected over 15 months. Four machine-learning-based models are compared, with one achieving a mean absolute percentage error below 2.3%. An adaptive online health estimation algorithm is also proposed, integrating clustering-based methods to reduce estimation errors. The results demonstrate the feasibility of repurposing retired batteries for second-life applications, showcasing potential for over a decade of grid energy storage use. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Retired electric vehicle batteries can be reused in grid energy storage, benefiting the environment and economy. This study focuses on health monitoring algorithms for these batteries used in grid storage. The research tested and analyzed a dataset of second-life batteries for 15 months, simulating real-world usage patterns. Four machine-learning models were compared to predict battery health, with one being particularly accurate. A new online algorithm was also developed to improve predictions during actual use. The results show that these old batteries can be used again in grid energy storage for over a decade. |
Keywords
* Artificial intelligence * Clustering * Machine learning
