Non-destructive degradation pattern decoupling for early battery trajectory prediction via physics-informed learning
Tsinghua–Berkeley Shenzhen Institute · University of California, Berkeley · +4 more institutions
Indexed incrossref
Abstract
The paper proposes a physics-informed model to predict battery lifetime trajectories by computing thermodynamic and kinetic parameters, saving costly data that has not been established for sustainable manufacturing, reuse, and recycling.
Citation impact
55
total citations
- FWCI
- 29.59
- Percentile
- 100%
- References
- 50
Citations per year
Authors
23- STShengyu Tao
Tsinghua–Berkeley Shenzhen Institute, University of California, Berkeley, Tsinghua University
- MZMengtian Zhang
Tsinghua–Berkeley Shenzhen Institute, Tsinghua University
- ZZZixi Zhao
Tsinghua–Berkeley Shenzhen Institute, Tsinghua University
- HLHaoyang Li
Tsinghua University
- RMRuifei Ma
Tsinghua–Berkeley Shenzhen Institute, Tsinghua University
Topics & keywords
Topics
Keywords
- Decoupling (probability)
- Trajectory
- Battery (electricity)
- Degradation (telecommunications)
- Artificial intelligence
- Computer science
- Physics
- Engineering
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