Long Short-Term Memory Networks for State of Charge and Average Temperature State Estimation of SPMeT Lithium–Ion Battery Model
Bibliographic record
Abstract
Lithium–ion batteries are the dominant battery type for emerging technologies in the efforts to slow climate change. Accurate and quick estimations of state of charge (SOC) and internal cell temperature are vital to battery-management systems to enable the effective operation of portable electronics and electric vehicles. Therefore, a long short-term memory (LSTM) recurrent-neural network is proposed which completes the state estimation of SOC and internal average cell temperature (Tavg) of lithium–ion batteries under varying current loads. The network is trained and evaluated using data compiled from a newly developed extended single-particle model coupled with a thermal dynamic model. Results are promising, with root mean square values typically under 2% for SOC and 1.2 K for Tavg, while maintaining quick training and testing times. In addition, we examined a comparison of a single-feature versus multi-feature network, as well as two different approaches to data partitioning.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
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.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".