Cloud-Edge Deployed Physics-Guided Bi-LSTM Framework for Real-Time Battery Core Temperature Estimation and Thermal Safety Control
Bibliographic record
Abstract
This paper presents a cloud–edge deployed, physics-guided bidirectional long short-term memory (Bi-LSTM)-based framework for real-time core temperature estimation of automotive lithium-ion batteries (LIBs), enabling enhanced thermal safety and predictive control. Unlike existing approaches that rely on surface temperature sensors or offline models, the proposed framework integrates standard BMS signals (voltage, current, surface, and ambient temperature) with physics-guided feature engineering to capture electrothermal dynamics while maintaining low computational cost. The model, trained across a wide range of C-rates, dynamic drive cycles, and ambient conditions, achieves a mean absolute error (MAE) of 0.16°C and an RMSE of 0.26°C, outperforming comparable sequence-learning architectures. Realtime validation demonstrates accurate estimation across unseen cells, achieving 0.31°C MAE and 0.40°C RMSE at the module level. Integration with CAN-based closed-loop control improves the thermal response by at least 2 minutes compared to state-of-the-art surface-temperature-based strategies. This improvement provides a critical safety margin for preventing thermal runaway. The framework is deployed on both local and cloud servers, achieving latency as low as 30 ms (local) and 85 ms (cloud), with real-time visualization through InfluxDB–Grafana, enabling remote monitoring and long-term data storage.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".