Thermal fault detection of lithium-ion battery packs through an integrated physics and deep neural network based model
Bibliographic record
Abstract
Battery packs develop faults over time, many of which are difficult to detect early. For instance, cooling system blockages raises temperatures but may not trigger alerts until protection limits are exceeded. This work presents a model-based method for early thermal fault detection and identification in battery packs. By comparing measured and estimated temperatures, the method identifies faults including failed sensors, coolant pump malfunctions, and flow blockages. The core is a high-accuracy temperature estimation model, integrating a physics-based thermal model with a neural network, achieves a root mean square error of 0.39 °C and a maximum error of 1 °C under a US06 discharge and 6C charge at 15 °C. Tested on a 72-cell air-cooled pack, the method detects faults using only eight temperature sensors within 13 to 45 minutes, with zero false detections in 11 testing cycles. This approach enables early fault alerts, enhancing reliability and safety in electric vehicles. Mina Naguib and colleagues propose an integrated physicsand machine-learning-based method for early thermal fault detection in battery packs. This approach enhances reliability and safety by identifying faults such as sensor failures and cooling system issues before they become critical.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".