Accurate Surface Temperature Estimation of Lithium-Ion Batteries Using Feedforward and Recurrent Artificial Neural Networks
Bibliographic record
Abstract
Lithium-ion batteries are an essential component in electric vehicles. A robust battery management system (BMS) must be able to estimate the battery states including state of charge (SOC), state of health (SOH), and, ideally, battery temperature as well. The cells in the pack may experience significant temperature differences during operation, and this would typically be monitored by a multitude of temperature sensors. A surface temperature estimation model can be used to reduce the number of sensors necessary for a pack, which has the side benefit of reducing cost and potentially increasing reliability. In this paper, two data-driven models are proposed to estimate the surface temperature of Li-ion batteries. The first model is based on a feed-forward neural network (FNN), while the second model is based on a recurrent neural network (RNN) with long short-term memory (LSTM). These models are trained and tested using cylindrical cell drive cycle data at a range of temperatures. The LSTM model is shown to be capable of estimating temperature with no more than a few degrees Celsius of error, even for challenging low temperature and varying temperature conditions.
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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".