Smart sensing breaks the accuracy barrier in battery state monitoring
Bibliographic record
Abstract
Accurate state-of-charge (SOC) estimation is essential for optimizing battery performance, ensuring safety, and maximizing economic value. Conventional current and voltage measurements, however, have inherent limitations in fully inferring the multiphysics-resolved dynamics inside battery cells. This creates an accuracy barrier that constrains battery usage and reduces cost-competitiveness and sustainability, across industries dependent on battery technology. In this work, we introduce an integrated sensor framework that combines novel mechanical, thermal, gas, optical, and electrical sensors with traditional measurements to break through this barrier. We generate three unique datasets with eleven measurement types and propose an explainable machine-learning approach for SOC estimation. This approach renders the measured signals and the predictive result of machine learning physically interpretable with respect to battery SOC, offering fundamental insights into the time-varying importance of different signals. Our experimental results reveal a marked increase in SOC estimation accuracy – enhanced from 46.1% to 74.5% – compared to conventional methods. This approach not only advances SOC monitoring precision but also establishes a foundation for monitoring additional battery states to further improve safety, extend lifespan, and facilitate fast charging.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".