Immediate remaining capacity estimation of heterogeneous second-life lithium-ion batteries <i>via</i> deep generative transfer learning
This work proposes a novel deep generative transfer learning algorithm to estimate the relative remaining capacity of second-life batteries using minimal field data, enabling safe and sustainable reuse under data scarce and heterogeneous conditions.
Authors
- Shengyu Tao (ORCID: https://orcid.org/0000-0001-5249-432X)
- Lluc Canals Casals (ORCID: https://orcid.org/0000-0002-4791-9917)
- Shida Jiang (ORCID: https://orcid.org/0000-0002-5243-0956)
- Guangmin Zhou (ORCID: https://orcid.org/0000-0002-3629-5686)
- Scott Jason Moura (ORCID: https://orcid.org/0000-0002-6393-4375)
- Jinpeng Tian (ORCID: https://orcid.org/0000-0002-2816-327X)
- Stephen J. Harris (ORCID: https://orcid.org/0000-0002-5211-3934)
- Xuan Zhang (ORCID: https://orcid.org/0000-0002-7537-4002)
- Tongda Zhang (ORCID: https://orcid.org/0000-0002-9227-7618)
- Junzhe Shi (ORCID: https://orcid.org/0000-0003-2337-231X)
- C. Y. Chung (ORCID: https://orcid.org/0000-0001-6607-2240)
- Dong Keun Lee (ORCID: https://orcid.org/0000-0002-2617-7627)
- Ruohan Guo (ORCID: https://orcid.org/0009-0004-6867-3380)
Institutions
- Hong Kong Polytechnic University (HK)
- Lawrence Berkeley National Laboratory (US)
- Applied Optimization (United States) (US)
- System Science Applications (United States) (US)
- Tsinghua–Berkeley Shenzhen Institute (CN)
- Universitat Politècnica de Catalunya (ES)
- Tsinghua University (CN)
Publication Details
- Journal
- Energy & Environmental Science
- Published
- 2025-01-01
- DOI
- https://doi.org/10.1039/d5ee02217g
- Citations
- 45
- Primary Topic
- Advanced Battery Technologies Research
- Type
- article
- Field-Weighted Citation Impact
- 30.73