Predicting the state of charge and health of batteries using data-driven machine learning
Agency for Science, Technology and Research · Institute of High Performance Computing · +3 more institutions
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Abstract
No abstract available for this paper.
Citation impact
728
total citations
- FWCI
- 37.18
- Percentile
- 100%
- References
- 123
Citations per year
Authors
5- MNMan‐Fai Ng
Agency for Science, Technology and Research, Institute of High Performance Computing
- JZJin Zhao
Nanyang Technological University
- QYQingyu Yan
Nanyang Technological University
- GJG. J. ConduitCorresponding
University of Cambridge
- ZWZhi Wei SehCorresponding
Agency for Science, Technology and Research, Institute of Materials Research and Engineering
Topics & keywords
Topics
Keywords
- Battery (electricity)
- Computer science
- Throughput
- Machine learning
- State of charge
- Artificial intelligence
- Field (mathematics)
- State (computer science)
UN Sustainable Development Goals
- Affordable and clean energy
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