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State-of-charge estimation of Li-ion batteries using deep neural networks: A machine learning approach
Why is this work in the frame?
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Canadian affiliationAn author listed a Canadian institution. This is the only route the usual frame has.
Canadian funderA Canadian agency funded it. The work may carry no Canadian affiliation at all.
Machine scores (provisional)
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Opus teacher head0.016
GPT teacher head0.264
- Teacher spread
- 0.247 · how far apart the two teachers sit on this one work
- Validation status
score_only:v0-immature-baseline· verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it
Abstract
No abstract. This is not a gap in this database — OpenAlex has none either. 23.3% of the frame is in this state, and the screen finds HALF as much metaresearch here, so the absence is a measured bias rather than a missing field.
The record
- Venue
- Journal of Power Sources
- Topic
- Advanced Battery Technologies Research
- Field
- Engineering
- Canadian institutions
- McMaster University
- Funders
- Canada Research ChairsGovernment of CanadaUniversity of Wisconsin-MadisonNvidiaCanada Excellence Research Chairs, Government of CanadaU.S. Environmental Protection Agency
- Keywords
- Battery (electricity)State of chargeArtificial neural networkComputer scienceProcess (computing)Artificial intelligencePower (physics)
- Has abstract in OpenAlex
- no