Thermal Behavior Forecasting for Battery Management Systems Using iTransformer and Kolmogorov–Arnold Network
Bibliographic record
Abstract
This paper proposes a next-generation predictive framework for thermal behavior prediction of lithium-ion batteries up to 60 seconds ahead of time, leveraging advanced deep-learning framework for time series prediction. The core of this work lies in a two-stage architecture that combines the iTransformer for accurate short-term forecasting of battery current, voltage, and temperature parameters with the Kolmogorov–Arnold Network (KAN) for core temperature estimation based on the predicted battery behavior. Experimental results using real-world drive cycles and thermal data demonstrate a high-accuracy forecasting performance, with the iTransformer achieving RMSEs as low as 0.17 A for current, 0.04 V for voltage, and 0.13°C for the surface temperature at 60 seconds ahead. The KAN achieves a core temperature estimation mean absolute error (MAE) of 0.20°C at 60 seconds ahead. An R2of 0.943 shows the system’s robustness in core temperature prediction across a full battery state-of-charge profile. The proposed framework can be directly integrated into the thermal management and charging control logic for real-time, adaptive charging and thermal management systems. This paper demonstrates the potential of advanced machine learning algorithms for predictive state estimation in lithium-ion battery management to ensure safer, more efficient, and longer-lasting energy storage systems for future e-mobility applications.
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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 | 0.000 |
| 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".