Generative learning assisted state-of-health estimation for sustainable battery recycling with random retirement conditions
Shengyu Tao, Ruifei Ma, Zixi Zhao, Guangyuan Ma, Lin Su, Heng Chang, Yuou Chen, Haizhou Liu, Zheng Liang, Tingwei Cao, Haocheng Ji, Zhiyuan Han, Minyan Lu, Huixiong Yang, Zongguo Wen, Jianhua Yao, Rong Yu, Guodan Wei, Yang Li, Xuan Zhang, Tingyang Xu, Guangmin Zhou · 2024
Read Generative learning assisted state-of-health estimation for sustainable battery recycling with random retirement conditions by Shengyu Tao, Ruifei Ma, Zixi Zhao, Guangyuan Ma, Lin Su, Heng Chang, Yuou Chen, Haizhou Liu, Zheng Liang, Tingwei Cao, Haocheng Ji, Zhiyuan Han, Minyan Lu, Huixiong Yang, Zongguo Wen, Jianhua Yao, Rong Yu, Guodan Wei, Yang Li, Xuan Zhang, Tingyang Xu, Guangmin Zhou (2024) in the Tower of Babel, a free online library of public-domain works preserved in clean, searchable text. No sign-up. The full text is served as a plain-text file at a predictable URL, where text is present: https://antarctic-labs.com/tower-of-babel/pmc-11584641.txt.
Description
Shengyu Tao and co-authors published a 2024 study of judging retired battery health with help from generated data. Sorting used batteries for reuse or recycling needs a fast health check, but batteries retire at random charge and health levels, so training data are patchy. The team taught a generative model to fill the gaps, then trained a health estimator on the enriched data. The testbed held 2,700 retired lithium-ion samples across many chemistries, shapes, and past uses.
The estimator predicted health with errors under 6 percent even at charge levels never seen in training. Scaled to a projected 2030 wave of retirements, the authors estimate savings near 4.9 billion dollars and large avoided emissions. The broader lesson: synthetic data can stand in where real data are costly to gather.
Catalog record
| CREATOR | Shengyu Tao, Ruifei Ma, Zixi Zhao, Guangyuan Ma, Lin Su, Heng Chang, Yuou Chen, Haizhou Liu, Zheng Liang, Tingwei Cao, Haocheng Ji, Zhiyuan Han, Minyan Lu, Huixiong Yang, Zongguo Wen, Jianhua Yao, Rong Yu, Guodan Wei, Yang Li, Xuan Zhang, Tingyang Xu, Guangmin Zhou |
|---|---|
| YEAR | 2024 |
| COLLECTION | PAPERS |
| FORMAT | TXT |
| FILE SIZE | 52 KB |
| VERSION | 1.0 |
| LICENSE | CC BY 4.0 |
| RIGHTS | CC BY |
| SOURCE | Europe PMC |