Lightweight Feature-Based Attention Network for Li-Ion Battery SOC Estimation
Bibliographic record
Abstract
Accurate state of charge (SOC) estimation is crucial for the efficient operation of electric vehicle batteries, particularly across varying temperature ranges, as it extends battery life and endurance. While deep learning has shown promise in SOC estimation, its computational overhead remains a challenge. Developing a lightweight, efficient, and accurate deep learning SOC estimator is key to deployment in battery management systems (BMS). This paper addresses this challenge by proposing a novel feature-based attention deep learning model for SOC estimation. The model is trained and evaluated on the LG 18650HG2 Li-ion dataset under diverse driving scenarios and battery conditions. Its robustness is assessed by introducing current noise into the input, and its computational efficiency is analyzed on a microprocessor to simulate its workload in a BMS, demonstrating its real-world deployability. The proposed model achieves an RMSE of$\mathbf{1. 2 3 \%}$on the test dataset with only 1,713 parameters, highlighting its performance and efficiency for BMS deployment. The code and dataset used in this study are available at https://github.com/ahmedsalahacc/efficient-feature-attention-SoC-estimation.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".